diff --git a/.github/instructions/python script.instructions.md b/.github/instructions/python script.instructions.md
new file mode 100644
index 0000000..902d591
--- /dev/null
+++ b/.github/instructions/python script.instructions.md	
@@ -0,0 +1,32 @@
+---
+applyTo: "*.py"
+---
+
+- Do not be chatty, do not summarize the code afterwards.
+- Use `#!/usr/bin/env -S uv run --script` as the shebang line.
+- Add dependencies under the shebang line as
+
+```
+# /// script
+# dependencies = [list of external dependencies as json array]
+# ///
+```
+
+and keep them updated as the code changes.
+
+- Keep comments to minimum
+- Use `uv` for package management.
+- Use logging instead of print statements. Use `logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")` for configuration.
+- Use argparse for command-line arguments, keep argument parsing in a func called `parse_args() -> argparse.Namespace`, which gets called from `main`. Use `argparse.ArgumentDefaultsHelpFormatter` for the formatter class.
+- Use `pathlib.Path` for file operations. When the CLI args include a path, set its `type=Path`
+- Place imports at the top of the file.
+- Use httpx for HTTP requests. Do not use async.
+- Always add type annotations to function signatures, prefer built-in types, e.g. `list[str]` over `typing` module types when possible.
+- Prefer kwargs calling style for function calls, e.g. `func(arg1=value1, arg2=value2)` instead of `func(value1, value2)`.
+- Use `Path.cwd()` for the current working directory.
+- Prefer `os.getenv` over `os.environ.get` for environment variables.
+- Use `subprocess.run` for running shell commands, prefer `check=True` to raise an error on failure.
+- Use `tempfile.TemporaryDirectory()` for temporary directories.
+- Do not shorten parameter names unnecessarily, unless it's an idiomatic abbreviation.
+- Use f-strings for string formatting.
+- When calling an external command, use the long form of the arguments, e.g. `--output` instead of `-o`.
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..3a4f41e
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
\ No newline at end of file
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 321f0d3..1bafd7b 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -7,9 +7,10 @@
         {
             "name": "Python: Current File",
             "type": "python",
-            "args": ["2022-12-06"],
+            "env": {"MISTRAL_API_KEY":"8J5hUwxVMCiQjhXxElefDqU7XJYAvVXn", "OPENROUTER_API_KEY": "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"},
             "request": "launch",
             "program": "${file}",
+            "cwd": "${workspaceFolder}",
             "console": "integratedTerminal",
             "justMyCode": true
         }
diff --git a/ai_imagez.py b/ai_imagez.py
new file mode 100755
index 0000000..7159a18
--- /dev/null
+++ b/ai_imagez.py
@@ -0,0 +1,363 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["click"]
+# ///
+
+import logging
+import random
+import subprocess
+from pathlib import Path
+
+import click
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s: %(message)s",
+)
+logger = logging.getLogger(name=__name__)
+
+VALID_EXTENSIONS = {".jpeg", ".jpg", ".png", ".webm", ".webp"}
+
+
+def is_valid_image(file_path: Path) -> bool:
+    """Check if file is a valid image type."""
+    return file_path.suffix.lower() in VALID_EXTENSIONS
+
+
+def move_to_trash(file_paths: list[Path]) -> None:
+    """Move files to Trash using trash CLI (single batched call)."""
+    resolved: list[str] = []
+    for file_path in file_paths:
+        if not file_path.is_file():
+            raise FileNotFoundError(f"File not found: {file_path}")
+        resolved.append(str(file_path.resolve()))
+
+    if resolved:
+        subprocess.run(["trash", *resolved], check=True)
+
+
+def build_target_file_index(target_dirs: list[Path]) -> dict[str, Path]:
+    """
+    Build a mapping of file stems to paths from target directories.
+
+    Scans target directories once and returns a dict for efficient lookups.
+    """
+    index: dict[str, Path] = {}
+
+    for target_dir in target_dirs:
+        if not target_dir.is_dir():
+            continue
+
+        for candidate in target_dir.iterdir():
+            if not candidate.is_file():
+                continue
+
+            if not is_valid_image(file_path=candidate):
+                continue
+
+            index[candidate.stem] = candidate
+
+    return index
+
+
+def find_edited_version(
+    image_path: Path,
+    target_index: dict[str, Path],
+) -> Path | None:
+    """
+    Search target file index for edited version of image.
+
+    An image is considered edited if its stem starts with the original stem
+    followed by '-edit'. Matches by stem only, ignoring extension.
+
+    Returns the path to the edited version or None if not found.
+    """
+    original_stem = image_path.stem
+    search_pattern = f"{original_stem}-edit"
+
+    for indexed_stem, indexed_path in target_index.items():
+        if indexed_stem.startswith(search_pattern):
+            return indexed_path
+
+    return None
+
+
+def build_directory_index(directory: Path) -> set[str]:
+    """
+    Build a set of all valid image stems in a directory.
+
+    Args:
+        directory: Path to the directory to scan
+
+    Returns:
+        Set of file stems that are valid images
+    """
+    stems: set[str] = set()
+
+    if not directory.is_dir():
+        return stems
+
+    for candidate in directory.iterdir():
+        if not candidate.is_file():
+            continue
+
+        if not is_valid_image(file_path=candidate):
+            continue
+
+        stems.add(candidate.stem)
+
+    return stems
+
+
+def collect_images(paths: list[Path]) -> list[Path]:
+    images: list[Path] = []
+    for path in paths:
+        if path.is_file():
+            if is_valid_image(path):
+                images.append(path)
+        elif path.is_dir():
+            for candidate in sorted(path.iterdir()):
+                if candidate.is_file() and is_valid_image(candidate):
+                    images.append(candidate)
+    return images
+
+
+@click.group()
+def cli() -> None:
+    """Manage and process image files."""
+    pass
+
+
+@cli.command()
+@click.argument(
+    "image_paths",
+    nargs=-1,
+    required=True,
+    type=click.Path(exists=True, path_type=Path),
+)
+@click.option(
+    "--target-dir",
+    multiple=True,
+    required=True,
+    type=click.Path(exists=True, path_type=Path),
+    help="Directory to search for edited versions",
+)
+@click.option(
+    "--dry-run",
+    is_flag=True,
+    default=False,
+    help="Preview matches without moving to trash",
+)
+def dedupe(
+    image_paths: tuple[Path, ...],
+    target_dir: tuple[Path, ...],
+    dry_run: bool,
+) -> None:
+    """
+    Remove original images when edited versions exist in target directories.
+
+    IMAGE_PATHS: One or more image files to check (jpeg, jpg, png, webm, webp)
+    """
+    target_dirs = list(target_dir)
+    valid_images = [path for path in image_paths if is_valid_image(file_path=path)]
+
+    if not valid_images:
+        logger.warning("No valid image files found")
+        return
+
+    target_index = build_target_file_index(target_dirs=target_dirs)
+    matches: list[tuple[Path, Path]] = []
+
+    for image_path in valid_images:
+        edited_version = find_edited_version(
+            image_path=image_path,
+            target_index=target_index,
+        )
+
+        if edited_version:
+            matches.append((image_path, edited_version))
+            logger.info(f"Found edited version: {image_path} -> {edited_version}")
+
+    if not matches:
+        logger.info("No edited versions found")
+        return
+
+    if dry_run:
+        click.echo(
+            click.style(
+                text="DRY RUN MODE - No files will be moved",
+                fg="yellow",
+            )
+        )
+        click.echo(f"Would move {len(matches)} image(s) to trash:")
+        for original, edited in matches:
+            click.echo(f"  {original}")
+        return
+
+    originals = [original for original, _ in matches]
+    try:
+        move_to_trash(file_paths=originals)
+        for original in originals:
+            logger.info(f"Moved to trash: {original}")
+    except (FileNotFoundError, subprocess.CalledProcessError) as e:
+        logger.error(f"Failed to move files to trash: {e}")
+
+    click.echo(
+        click.style(
+            text=f"Moved {len(matches)} image(s) to trash",
+            fg="green",
+        )
+    )
+
+
+@cli.command()
+@click.argument(
+    "paths",
+    nargs=-1,
+    required=True,
+    type=click.Path(exists=True, path_type=Path),
+)
+@click.option(
+    "--dry-run",
+    is_flag=True,
+    default=False,
+    help="Preview matches without moving to trash",
+)
+def clean(paths: tuple[Path, ...], dry_run: bool) -> None:
+    """
+    Remove originals when upscaled versions exist in the same basket.
+
+    PATHS: Image files and/or directories to scan (jpeg, jpg, png, webm, webp)
+
+    Scans all provided paths into a collection. If a file and its -upscaled
+    counterpart (stem starts with '{original}-upscaled', any extension)
+    are both in the collection, the original is moved to trash.
+    """
+    all_images = collect_images(list(paths))
+
+    if not all_images:
+        logger.warning("No valid image files found")
+        return
+
+    image_index: dict[str, Path] = {img.stem: img for img in all_images}
+    trashables: set[Path] = set()
+
+    markers = ["-upscaled", "-edit"]
+    for image in all_images:
+        if any(marker in image.stem for marker in markers):
+            continue
+
+        upscaleds = [
+            p
+            for s, p in image_index.items()
+            if s.startswith(image.stem) and "-upscaled" in s
+        ]
+        editeds = [
+            p
+            for s, p in image_index.items()
+            if s.startswith(image.stem) and "-edit" in s
+        ]
+        if editeds or upscaleds:
+            logger.info(f"Will delete {image}")
+            trashables.add(image)
+            for upscaled in upscaleds:
+                low_res_edit = upscaled.with_stem(
+                    upscaled.stem.removesuffix("-upscaled")
+                )
+                if low_res_edit in editeds:
+                    logger.info(f"Will also delete low-res edit {low_res_edit}")
+                    trashables.add(low_res_edit)
+
+    if not trashables:
+        logger.info("No upscaled or edited versions found")
+        return
+
+    if dry_run:
+        click.echo(click.style("DRY RUN MODE - No files will be moved", fg="yellow"))
+        click.echo(f"Would move {len(trashables)} image(s) to trash:")
+        for original in trashables:
+            click.echo(f"  {original}")
+        return
+
+    try:
+        move_to_trash(file_paths=list(trashables))
+        for original in trashables:
+            logger.info(f"Moved to trash: {original}")
+    except (FileNotFoundError, subprocess.CalledProcessError) as e:
+        logger.error(f"Failed to move files to trash: {e}")
+
+    click.echo(click.style(f"Moved {len(trashables)} image(s) to trash", fg="green"))
+
+
+@cli.command(name="list-editable")
+@click.argument(
+    "image_paths",
+    nargs=-1,
+    required=True,
+    type=click.Path(exists=True, path_type=Path),
+)
+@click.option(
+    "--shuffle",
+    is_flag=True,
+    default=False,
+    help="Shuffle the output filenames",
+)
+@click.option(
+    "--null",
+    is_flag=True,
+    default=False,
+    help="Output null-terminated filenames for xargs -0",
+)
+def list_editable(image_paths: tuple[Path, ...], null: bool, shuffle: bool) -> None:
+    """
+    List image files that don't have edited versions.
+
+    IMAGE_PATHS: One or more image files to check (jpeg, jpg, png, webm, webp)
+
+    Skips files with '-edit' in their stem and lists those without
+    matching edited versions (stem + '-edit' + optional suffix) in
+    the same directory.
+    """
+    valid_images = [path for path in image_paths if is_valid_image(file_path=path)]
+
+    if not valid_images:
+        logger.warning("No valid image files found")
+        return
+
+    dir_indices: dict[Path, set[str]] = {}
+    editable_files: list[Path] = []
+
+    for image_path in valid_images:
+        if "-edit" in image_path.stem:
+            continue
+
+        directory = image_path.parent
+        if directory not in dir_indices:
+            dir_indices[directory] = build_directory_index(directory=directory)
+
+        dir_stems = dir_indices[directory]
+        original_stem = image_path.stem
+        search_pattern = f"{original_stem}-edit"
+
+        has_edited = any(stem.startswith(search_pattern) for stem in dir_stems)
+
+        if not has_edited:
+            editable_files.append(image_path)
+
+    if not editable_files:
+        logger.info("No editable files found")
+        return
+
+    if shuffle:
+        random.shuffle(editable_files)
+
+    for file_path in editable_files:
+        if null:
+            click.echo(str(file_path), nl=False)
+            click.echo("\0", nl=False)
+        else:
+            click.echo(str(file_path))
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/ai_photorealistic.py b/ai_photorealistic.py
new file mode 100755
index 0000000..e864b48
--- /dev/null
+++ b/ai_photorealistic.py
@@ -0,0 +1,418 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["httpx", "pillow"]
+# ///
+import argparse
+import base64
+import json
+import logging
+import os
+import subprocess
+import tempfile
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass
+from io import BytesIO
+from pathlib import Path
+from typing import Any
+
+import httpx
+from PIL import Image, ImageOps
+
+from sort_images import read_dims
+
+MODEL_ID = "flux-2-klein-9b"
+# LOCAL_MODEL_ID = "black-forest-labs/FLUX.2-klein-9B"
+# MODEL_ID = "z-image-turbo"
+# MODEL_ID = "qwen-image"
+
+
+SYSTEM_PROMPT = """Make this incredibly photorealistic.
+Highly stylized, striking, highly detailed photo of a young, lithe woman.
+Age her by 5 years and make her a 20 year old, taller, slimmer, more slender, fitter version of herself.
+Make her 6ft tall.
+
+Give her realistic spotless skin texture.
+Her face skin must also be realistic, without any makeup.
+Make the background realistic and consistent with the lighting on her.
+
+Give her an angular, not round, narrow, elongated neck and long face shape defined by a structured, clean jawline that tapers toward a firm, slightly rounded chin.
+Do not make the head larger than the body, keep them in perfect proportion.
+Give her high, prominent cheekbones giving her sculpted, chieseled face that taper down to a more narrow, delicate chin.
+Make her really pretty and seductive.
+Preserve unnatural lip colors and wetness.
+
+Do not change the facial expression, emotion, hand and body pose, mouth, tongue and eyelid position.
+Lower her eyes slightly to give her an alluring look.
+Mouth slightly agape giving her a seductive look.
+Give her a sharp, cunning gaze, a very slight pleasant smirk without changing her head pose.
+
+Remove the drool.
+Keep the pose and don't turn people around."""
+
+SYSTEM_PROMPT = """
+Turn this into a heavily stylized real life photo of a woman.
+Age her by 5 years and make her a 20 year old, taller, slimmer, more slender, fitter version of herself.
+
+Apply realistic textures without grain or noise.
+Skin should be smooth, flawless, without pores, clean and without blemishes.
+
+Keep the shine.
+Make her taller, slimmer, slender. Don't make the head shorter.
+
+Do not change the pose, gaze, emotion.
+
+Her lips are parted, creating a soft, seductive expression.
+"""
+
+SYSTEM_PROMPT = """
+restyle this as an edited, finished photo taken by sony a7 iv.
+Photorealistic textures.
+same color grading, same colors and lighting.
+Realistic, flawless skin.
+White people should have a pale skin.
+
+keep the same ethnicity and facial features.
+
+Make the children taller and more slender with slightly elongated feminine neck and higher cheekbones, give them smaller, slimmer more feminine head and tapered face; round, smooth chin.
+age them by 5 years and make them look like 20 year old adults.
+
+keep everything else about her the same.
+same facial + eye expression (keep them as open as in the original), same pose and emotion, same tongue position.
+same head and eye angle.
+
+do not turn her face around.
+same hair style, color, length.
+same composition and crop.
+keep the same makeup, do not change the colors of the lips, keep them glossy and wet.
+"""
+SYSTEM_PROMPT = """
+Artistic super photorealistic conversion.
+extend the background horizontally but keep the people.
+# Subtle chiaroscuro lighting, not too dark.
+85mm telephoto lens, f/2.8, shallow depth of field.
+Soft-focus highlights, atmospheric bloom.
+Kodak Portra 400 cool color palette.
+#Preserve the original lighting and slight moody lighting. Not underexposed, not overexposed.
+High dynamic range with a focus on rich textures.
+balanced exposure.
+same color grading and LUT.
+
+remove compression artifacts.
+she has spotless, supple, shiny skin without splotches.
+#pale skin, goth make-up.
+
+#reduce musculature.
+#keep the original facial expression, emotion, hand and body pose.
+
+make her prettier, give her hourglass figure, a satisfied look, voluminus hair.
+
+parted lips, seductive gaze, subtly lowered eyelids.
+make her face look like a 25 year old, raised cheekbones, tapered face shape, pointy chin.
+#avoid making her look like a child and maintain adult proportions.
+#give her a subtle mischievous, confident smile, avoid neutral face.
+#make her face slimmer.
+preserve the original skin color.
+
+do not change the colors of the lips, keep them wet.
+remove all logos and watermarks.
+#reflective outfit,
+#glossy, reflective outfit,
+#clingy, skin-tight clothes.
+"""
+
+
+def without_comments(s: str) -> str:
+    return "\n".join(
+        line for line in s.splitlines() if not line.strip().startswith("#")
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Send one or more images to NanoGPT for photorealistic processing."
+    )
+    parser.add_argument(
+        "image_paths",
+        nargs="+",
+        help="One or more local image paths to send",
+    )
+    parser.add_argument(
+        "--extra-prompt",
+        default="",
+        help="Extra prompt text appended to the system prompt",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="",
+        help="Replace the system prompt with this custom prompt (instead of appending to it)",
+    )
+    parser.add_argument(
+        "--provider",
+        choices=("nanogpt", "local"),
+        default=os.getenv("IMAGE_PROVIDER", "nanogpt"),
+        help="Image provider backend (default from IMAGE_PROVIDER or nanogpt)",
+    )
+    parser.add_argument(
+        "--base-url",
+        default=os.getenv("LOCAL_IMAGE_API_BASE", "http://127.0.0.1:6006"),
+        help="Local provider base URL (default from LOCAL_IMAGE_API_BASE)",
+    )
+    return parser.parse_args()
+
+
+@dataclass(frozen=True)
+class TransformResult:
+    image_path: Path
+    output_path: Path | None = None
+    remaining: float | None = None
+
+
+class NanoGPT:
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+        self._client = httpx.Client(
+            timeout=180.0,
+            headers={
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json",
+            },
+        )
+
+    def transform(
+        self, image_path: Path, output_path: Path, prompt: str
+    ) -> TransformResult:
+        if not image_path.exists():
+            raise FileNotFoundError(f"Image not found: {image_path}")
+
+        image_w, image_h = read_dims(image_path)
+        aspect_ratio = image_w / image_h
+        aspect_ratio = max(aspect_ratio, 2 / 3)
+        target_w, target_h = 1024, int(1024 / aspect_ratio)
+        size = f"{target_w}x{target_h}"
+
+        image_data_url = _build_data_url(image_path)
+        payload = {
+            "model": MODEL_ID,
+            "prompt": prompt,
+            "imageDataUrl": image_data_url,
+            "response_format": "b64_json",
+            "n": 1,
+            "seed": int(time.time()),
+            "size": size,
+        }
+        response = self._client.post(
+            "https://nano-gpt.com/v1/images/generations", json=payload
+        )
+        if not response.is_success:
+            raise RuntimeError(
+                f"API request failed with status {response.status_code}: {response.text}"
+            )
+        result = response.json()
+
+        data = result.get("data", [])
+        if not data:
+            raise RuntimeError(f"No data in response for {image_path}")
+
+        item = data[0]
+
+        if "b64_json" in item:
+            _save_b64_image(output_path, item["b64_json"])
+        elif "url" in item:
+            _download_image(self._client, output_path, item["url"])
+        else:
+            raise RuntimeError(f"Unknown response format for {image_path}")
+
+        return TransformResult(
+            image_path=image_path,
+            output_path=output_path,
+            remaining=result.get("remainingBalance"),
+        )
+
+
+class LocalFlux:
+    def __init__(self, base_url: str) -> None:
+        self.base_url = base_url.rstrip("/")
+        self._client = httpx.Client(timeout=240.0)
+
+    def transform(
+        self, image_path: Path, output_path: Path, prompt: str
+    ) -> TransformResult:
+        if not image_path.exists():
+            raise FileNotFoundError(f"Image not found: {image_path}")
+
+        image_w, image_h = read_dims(image_path)
+        aspect_ratio = image_w / image_h
+        target_w, target_h = 1024, int(1024 / aspect_ratio)
+        size = f"{target_w}x{target_h}"
+
+        image_data_url = _build_data_url(image_path)
+        payload = {
+            "model": LOCAL_MODEL_ID,
+            "prompt": prompt,
+            "imageDataUrl": image_data_url,
+            "response_format": "b64_json",
+            "guidance_scale": 1.0,
+            "n": 1,
+            "seed": int(time.time()),
+            "size": size,
+        }
+        response = self._client.post(
+            f"{self.base_url}/v1/images/generations", json=payload
+        )
+        if not response.is_success:
+            raise RuntimeError(
+                f"Local API request failed with status {response.status_code}: {response.text}"
+            )
+        result = response.json()
+
+        data = result.get("data", [])
+        if not data:
+            raise RuntimeError(f"No data in local response for {image_path}")
+
+        item = data[0]
+        if "b64_json" in item:
+            _save_b64_image(output_path, item["b64_json"])
+        elif "url" in item:
+            _download_image(self._client, output_path, item["url"])
+        else:
+            raise RuntimeError(f"Unknown local response format for {image_path}")
+
+        return TransformResult(
+            image_path=image_path,
+            output_path=output_path,
+            remaining=result.get("remainingBalance"),
+        )
+
+
+def _build_data_url(image_path: Path) -> str:
+    with Image.open(image_path) as image:
+        image = ImageOps.exif_transpose(image)
+        image.thumbnail((3000, 3000), Image.Resampling.LANCZOS)
+        if image.mode not in {"RGB", "L"}:
+            image = image.convert("RGB")
+        buffer = BytesIO()
+        image.save(buffer, format="JPEG", quality=95, optimize=True)
+    encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
+    return f"data:image/jpeg;base64,{encoded}"
+
+
+def _save_b64_image(out_path: Path, b64_data: str) -> None:
+    out_path.write_bytes(base64.b64decode(b64_data))
+
+
+def _download_image(client: httpx.Client, out_path: Path, url: str) -> None:
+    response = client.get(url, follow_redirects=True)
+    response.raise_for_status()
+    out_path.write_bytes(response.content)
+
+
+def _next_output_path(image_path: Path) -> Path:
+    base = image_path.with_name(f"{image_path.stem}-edit.jpg")
+    if not base.exists():
+        return base
+    counter = 2
+    while True:
+        candidate = image_path.with_name(f"{image_path.stem}-edit{counter}.jpg")
+        if not candidate.exists():
+            return candidate
+        counter += 1
+
+
+def find_custom_prompts(file_paths: list[Path]) -> dict[Path, str]:
+    custom_prompt_path = Path(r"~/Downloads/prompts.jsonl").expanduser()
+    if not custom_prompt_path.exists():
+        return {}
+
+    stem_to_path: dict[str, Path] = {p.stem: p for p in file_paths}
+    prompts: dict[Path, str] = {}
+    with custom_prompt_path.open() as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                entry = json.loads(line)
+                filename = entry.get("filename")
+                prompt = entry.get("prompt")
+                if filename and prompt and filename in stem_to_path:
+                    prompts[stem_to_path[filename]] = prompt
+            except json.JSONDecodeError:
+                pass
+
+    return prompts
+
+
+def main() -> int:
+    args = parse_args()
+    provider = args.provider
+
+    extra_prompt = args.extra_prompt.strip()
+    if extra_prompt:
+        prompt = f"{SYSTEM_PROMPT}\n\n{extra_prompt}"
+    elif args.prompt.strip():
+        prompt = args.prompt.strip()
+    else:
+        prompt = SYSTEM_PROMPT
+
+    if provider == "nanogpt":
+        api_key = os.getenv("NANOGPT_API_KEY")
+        if not api_key:
+            raise SystemExit("NANOGPT_API_KEY is not set")
+        client = NanoGPT(api_key)
+    else:
+        client = LocalFlux(args.base_url)
+    image_paths = [Path(p) for p in args.image_paths]
+    custom_prompts = find_custom_prompts(image_paths)
+    got_error = False
+    with ThreadPoolExecutor() as executor:
+
+        def _submit(image_path: Path) -> TransformResult:
+            effective_prompt = without_comments(prompt)
+
+            custom_prompt = custom_prompts.get(image_path)
+            if custom_prompt:
+                print(f"Using custom prompt for {image_path}")
+                effective_prompt = custom_prompt
+
+            with tempfile.TemporaryDirectory() as temp_dir:
+                temp_path = Path(temp_dir) / image_path.name
+                result = client.transform(
+                    image_path,
+                    temp_path,
+                    prompt=effective_prompt,
+                )
+
+                output_path = _next_output_path(image_path)
+                os.replace(temp_path, output_path)
+                return TransformResult(
+                    image_path=result.image_path,
+                    output_path=output_path,
+                    remaining=result.remaining,
+                )
+
+        futures = {
+            executor.submit(_submit, image_path): image_path
+            for image_path in image_paths
+        }
+        for future in as_completed(futures):
+            try:
+                outcome = future.result()
+            except Exception as exc:
+                image_path = futures[future]
+                print(f"[error] {image_path} ({exc})")
+                got_error = True
+                continue
+
+            image_path = outcome.image_path
+            output_path = outcome.output_path
+            print(f"[ok] {image_path} -> {output_path}")
+            remaining = outcome.remaining
+            if remaining is not None:
+                print(f"[balance] {remaining}")
+
+    return 1 if got_error else 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/ai_runpod.py b/ai_runpod.py
new file mode 100755
index 0000000..6baa556
--- /dev/null
+++ b/ai_runpod.py
@@ -0,0 +1,324 @@
+#!/usr/bin/env uv run
+# /// script
+# dependencies = ["httpx", "pillow"]
+# ///
+
+import argparse
+import io
+import json
+import logging
+import math
+import random
+import subprocess
+import time
+import uuid
+from pathlib import Path
+
+import httpx
+from PIL import Image
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+logging.getLogger("httpx").setLevel(logging.WARNING)  # Suppress httpx debug logs
+logger = logging.getLogger(__name__)
+
+COMFY_URL = "https://randy-agents-mediterranean-herself.trycloudflare.com/"
+COMFY_COOKIES = {
+    "C.35620185_auth_token": "027e0e069f66d46e6d84802b7d49076e6ebab8621491d8694992f16727f99b79",
+}
+WORKFLOW_FILE = Path("/Users/abdus/Downloads/klein_fixed.json")
+IMAGE_OUTPUT_MEGAPIXELS = 2
+
+
+SYSTEM_PROMPT = """
+restyle this as an edited, finished photo taken by sony a7 iv.
+Photorealistic textures.
+same color grading, same colors and lighting.
+Realistic, flawless skin.
+# White people should have a pale skin.
+age everyone by 5 years and make them look like 25 year old adults.
+
+keep the same ethnicity and facial features.
+
+Make the children taller and more slender with slightly elongated feminine neck and higher cheekbones, give them smaller, slimmer more feminine head and tapered face; round, smooth chin.
+
+keep everything else about her the same.
+same facial + eye expression (keep them as open as in the original), same pose and emotion, same tongue position.
+same head and eye angle.
+
+do not turn her face around.
+same hair style, color, length.
+same composition and crop.
+keep the same makeup, do not change the colors of the lips, keep them glossy and wet.
+long nipples. large areolas.
+
+"""
+
+
+def preprocess_image(image_path: Path) -> bytes:
+    with Image.open(image_path) as img:
+        if img.mode in ("RGBA", "P"):
+            img = img.convert("RGB")
+        max_size = 2500  # Slightly larger to support 3MP render
+        if max(img.size) > max_size:
+            img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+        buffer = io.BytesIO()
+        img.save(buffer, format="JPEG", quality=85, optimize=True)
+        return buffer.getvalue()
+
+
+def _next_output_path(image_path: Path) -> Path:
+    base = image_path.with_name(f"{image_path.stem}-edit.jpg")
+    if not base.exists():
+        return base
+    counter = 2
+    while True:
+        candidate = image_path.with_name(f"{image_path.stem}-edit{counter}.jpg")
+        if not candidate.exists():
+            return candidate
+        counter += 1
+
+
+def tag_file(path: Path, tag: str = "Blue") -> None:
+    if not path.exists():
+        raise FileNotFoundError(f"Target path does not exist: {path}")
+
+    abs_path = str(path.resolve())
+
+    script = f'tell application "Finder" to set tags of (POSIX file "{abs_path}" as alias) to {{"{tag}"}}'
+
+    try:
+        subprocess.run(
+            ["osascript", "-e", script], check=True, capture_output=True, text=True
+        )
+    except subprocess.CalledProcessError as e:
+        # Finder may fail if the file is moved during execution or if permissions are insufficient
+        logger.warning(f"Failed to tag file: {e.stderr}")
+
+
+class Stats:
+    def __init__(self) -> None:
+        self.run_times: list[float] = []
+
+    def add(self, elapsed: float) -> None:
+        self.run_times.append(elapsed)
+
+    def format_time(self, seconds: float) -> str:
+        if seconds < 60:
+            return f"{seconds:.1f}s"
+        minutes = seconds / 60
+        return f"{minutes:.1f}m"
+
+    def print_summary(self) -> None:
+        if not self.run_times:
+            return
+
+        if len(self.run_times) == 1:
+            return
+
+        shortest = min(self.run_times)
+        longest = max(self.run_times)
+        average = sum(self.run_times) / len(self.run_times)
+        total = sum(self.run_times)
+
+        print("=" * 20)
+        print("Run Statistics")
+        print("=" * 20)
+        print(f"Total runs: {len(self.run_times)}")
+        print(f"Shortest:   {self.format_time(shortest)}")
+        print(f"Longest:    {self.format_time(longest)}")
+        print(f"Average:    {self.format_time(average)}")
+        print(f"Total time: {self.format_time(total)}")
+        print("=" * 20)
+
+
+class ComfyUI:
+    PROMPT_NODE_ID = "75:74"
+    NEGATIVE_NODE_ID = "75:67"
+    IMAGE_LOAD_NODE_ID = "76"
+    SAVE_NODE_ID = "9"
+    UPSCALE_NODE_ID = "75:80"
+    MODEL_LOAD_NODE_ID = "75:70"
+    CLIP_LOAD_NODE_ID = "75:71"
+    VAE_LOAD_NODE_ID = "75:72"
+    SEED_NODE_ID = "75:73"
+
+    def __init__(self, url: str, cookies: dict) -> None:
+        self.url = url.rstrip("/")
+        self.client = httpx.Client(timeout=None, cookies=cookies, follow_redirects=True)
+
+    def _post(self, path: str, **kwargs) -> httpx.Response:
+        """Try RunPod /api routes first, then legacy Comfy routes."""
+        candidates = [f"{self.url}/api{path}", f"{self.url}{path}"]
+        last_res: httpx.Response | None = None
+        for endpoint in candidates:
+            res = self.client.post(endpoint, **kwargs)
+            if res.status_code != 404:
+                return res
+            last_res = res
+        assert last_res is not None
+        return last_res
+
+    def _get(self, path: str, **kwargs) -> httpx.Response:
+        """Try RunPod /api routes first, then legacy Comfy routes."""
+        candidates = [f"{self.url}/api{path}", f"{self.url}{path}"]
+        last_res: httpx.Response | None = None
+        for endpoint in candidates:
+            res = self.client.get(endpoint, **kwargs)
+            if res.status_code != 404:
+                return res
+            last_res = res
+        assert last_res is not None
+        return last_res
+
+    def transform(
+        self,
+        image_path: Path,
+        save_path: Path,
+        prompt: str,
+        negative_prompt: str = "",
+    ) -> None:
+        seed = random.randint(0, 2**31 - 1)
+        client_id = str(uuid.uuid4())
+
+        # 1. Upload
+        processed_img_bytes = preprocess_image(image_path)
+        files = {"image": (f"upload_{uuid.uuid4()}.jpg", processed_img_bytes)}
+        res = self._post("/upload/image", files=files)
+        if res.is_error:
+            logger.error(f"Upload error: {res.text}")
+            res.raise_for_status()
+        filename = res.json()["name"]
+
+        # 2. Load Workflow
+        with open(WORKFLOW_FILE, "r") as f:
+            workflow = json.load(f)
+
+        # Get dimensions from the actual file
+        with Image.open(image_path) as img:
+            orig_width, orig_height = img.size
+            aspect_ratio = orig_width / orig_height
+
+        # Calculate target resolution for 3 Megapixels (approx 3,000,000 pixels)
+        # Formula: Width * (Width / Aspect) = TotalPixels
+        target_pixels = IMAGE_OUTPUT_MEGAPIXELS * 1024 * 1024
+        new_width = int(math.sqrt(target_pixels * aspect_ratio))
+        new_height = int(new_width / aspect_ratio)
+
+        # Force these into the Empty Latent node (Node 75:66)
+        # This ensures the "canvas" matches your photo's shape
+        workflow["75:66"]["inputs"]["width"] = (
+            new_width // 8
+        ) * 8  # Must be multiple of 8
+        workflow["75:66"]["inputs"]["height"] = (new_height // 8) * 8
+
+        # Also update the Scheduler (Node 75:62) so Flux knows the scale
+        workflow["75:62"]["inputs"]["width"] = workflow["75:66"]["inputs"]["width"]
+        workflow["75:62"]["inputs"]["height"] = workflow["75:66"]["inputs"]["height"]
+
+        # Inject Advanced Data
+        workflow[self.PROMPT_NODE_ID]["inputs"]["text"] = prompt
+        workflow[self.NEGATIVE_NODE_ID]["inputs"]["text"] = negative_prompt
+        workflow[self.IMAGE_LOAD_NODE_ID]["inputs"]["image"] = filename
+        workflow[self.SEED_NODE_ID]["inputs"]["noise_seed"] = seed
+
+        workflow[self.UPSCALE_NODE_ID]["inputs"]["megapixels"] = IMAGE_OUTPUT_MEGAPIXELS
+        workflow[self.UPSCALE_NODE_ID]["inputs"]["upscale_method"] = "lanczos"
+
+        # 3. Queue
+        payload = {"prompt": workflow, "client_id": client_id}
+        res = self._post("/prompt", json=payload)
+
+        if res.is_error:
+            logger.error(f"Error: {res.text}")
+            res.raise_for_status()
+
+        prompt_id = res.json()["prompt_id"]
+
+        # 4. Wait
+        preview_output = None
+        while True:
+            jobs_res = self._get(
+                "/jobs",
+                params={
+                    "status": "completed,failed,cancelled",
+                    "limit": 64,
+                    "offset": 0,
+                },
+            )
+            if jobs_res.is_error:
+                logger.error(f"Jobs poll error: {jobs_res.text}")
+                jobs_res.raise_for_status()
+
+            jobs = jobs_res.json().get("jobs", [])
+            match = next((job for job in jobs if job.get("id") == prompt_id), None)
+            if match:
+                if match.get("status") != "completed":
+                    raise RuntimeError(
+                        f"Job {prompt_id} ended with status: {match.get('status')}"
+                    )
+                preview_output = match.get("preview_output")
+                break
+            time.sleep(0.25)
+
+        # 5. Download
+        if not preview_output:
+            raise RuntimeError(f"Completed job {prompt_id} has no preview_output")
+
+        view_params = {
+            "filename": preview_output["filename"],
+            "subfolder": preview_output.get("subfolder", ""),
+            "type": preview_output.get("type", "output"),
+        }
+        img_res = self._get("/view", params=view_params)
+        if img_res.is_error:
+            logger.error(f"View error: {img_res.text}")
+            img_res.raise_for_status()
+
+        # Convert to JPEG with quality=75
+        with Image.open(io.BytesIO(img_res.content)) as img:
+            if img.mode == "RGBA":
+                img = img.convert("RGB")
+            img.save(save_path, format="JPEG", quality=75)
+
+def without_comments(s: str) -> str:
+    return "\n".join(
+        line for line in s.splitlines() if not line.strip().startswith("#")
+    )
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("images", nargs="+", type=Path)
+    args = parser.parse_args()
+
+
+    comfy = ComfyUI(COMFY_URL, COMFY_COOKIES)
+    stats = Stats()
+
+    for i, image_path in enumerate(args.images, start=1):
+        prompt = SYSTEM_PROMPT
+        replace_prompt_path = Path(__file__).parent / "flux_klein_prompt.txt"
+        if replace_prompt_path.is_file():
+            prompt = without_comments(replace_prompt_path.read_text())
+
+        save_path = _next_output_path(image_path)
+        start_time = time.time()
+        logger.info(f"Uploading ({i}/{len(args.images)}): {image_path.name}")
+        try:
+            comfy.transform(image_path, save_path, prompt)
+        except Exception as e:
+            logger.error(f"Error processing {image_path.name}: {e}")
+            continue
+        elapsed = time.time() - start_time
+        stats.add(elapsed)
+
+    stats.print_summary()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/backup-site.service b/backup-site.service
new file mode 100644
index 0000000..e69de29
diff --git a/backup-site.timer b/backup-site.timer
new file mode 100644
index 0000000..e69de29
diff --git a/backup_site.py b/backup_site.py
new file mode 100644
index 0000000..80f8299
--- /dev/null
+++ b/backup_site.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = []
+# ///
+
+import argparse
+import logging
+import os
+import shutil
+import subprocess
+import zipfile
+from datetime import datetime
+from pathlib import Path
+from urllib.parse import urlparse
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Backup MariaDB database and website files", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("--db-url", default=os.getenv("DB_URL"), help="Database URL (default: from DB_URL env var)")
+    parser.add_argument("--site-dir", type=Path, required=True, help="Website directory to backup")
+    parser.add_argument("--backup-dir", type=Path, default="/mnt/box/backup/droplet", help="Backup destination directory")
+    return parser.parse_args()
+
+
+def parse_db_url(db_url: str) -> dict:
+    if not db_url:
+        raise ValueError("Database URL is required")
+
+    parsed = urlparse(db_url)
+
+    if parsed.scheme != "mysql":
+        raise ValueError("Unsupported database URL format")
+
+    return {
+        "host": parsed.hostname or "localhost",
+        "port": str(parsed.port or 3306),
+        "user": parsed.username or "root",
+        "password": parsed.password or "",
+        "database": parsed.path.lstrip("/") if parsed.path else "",
+    }
+
+
+def dump_database(db_params: dict, dump_path: Path) -> None:
+    logger.info(f"Dumping database to {dump_path}")
+
+    cmd = [
+        "mysqldump",
+        "--host",
+        db_params["host"],
+        "--port",
+        db_params["port"],
+        "--user",
+        db_params["user"],
+        "--single-transaction",
+        "--routines",
+        "--triggers",
+        db_params["database"],
+    ]
+
+    if db_params["password"]:
+        cmd.append(f"--password={db_params['password']}")
+
+    try:
+        with dump_path.open("w") as f:
+            subprocess.run(cmd, stdout=f, stderr=subprocess.PIPE, text=True, check=True)
+        logger.info("Database dump completed successfully")
+    except subprocess.CalledProcessError as e:
+        logger.error(f"Database dump failed: {e.stderr}")
+        raise
+
+
+def zip_directory(source_dir: Path, zip_path: Path) -> None:
+    """Zip a directory"""
+    logger.info(f"Zipping directory {source_dir} to {zip_path}")
+
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipf:
+        for file_path in source_dir.rglob("*"):
+            if file_path.is_file():
+                # Use relative path within the zip
+                arcname = file_path.relative_to(source_dir.parent)
+                zipf.write(file_path, arcname)
+
+    logger.info(f"Directory zipped successfully: {zip_path}")
+
+
+def create_final_backup(temp_dir: Path, backup_dir: Path, timestamp: str) -> Path:
+    final_backup_name = f"{timestamp}_ucsuzkalem.zip"
+    final_backup_path = backup_dir / final_backup_name
+
+    logger.info(f"Creating final backup: {final_backup_path}")
+    zip_directory(temp_dir, final_backup_path)
+
+    return final_backup_path
+
+
+def main():
+    args = parse_args()
+
+    if not args.db_url:
+        raise ValueError("Database URL is required (set DB_URL env var or use --db-url)")
+
+    # Parse database URL
+    db_params = parse_db_url(args.db_url)
+    logger.info(f"Connecting to database: {db_params['host']}:{db_params['port']}/{db_params['database']}")
+
+    # Create timestamp
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+    # Setup paths
+    site_dir = Path(args.site_dir)
+    backup_dir = Path(args.backup_dir)
+    temp_dir = Path("/tmp") / f"backup_{timestamp}"
+
+    if not site_dir.exists():
+        raise FileNotFoundError(f"Site directory does not exist: {site_dir}")
+
+    # Create directories
+    backup_dir.mkdir(parents=True, exist_ok=True)
+    temp_dir.mkdir(parents=True, exist_ok=True)
+
+    # Dump database
+    db_dump_path = temp_dir / "database.sql"
+    dump_database(db_params, db_dump_path)
+
+    # Zip website files
+
+    files_zip_path = temp_dir / "files.zip"
+    zip_directory(site_dir, files_zip_path)
+
+    # Create final backup
+    final_backup_path = create_final_backup(temp_dir, backup_dir, timestamp)
+
+    # Cleanup temp directory
+    shutil.rmtree(temp_dir)
+
+    logger.info(f"Backup completed successfully: {final_backup_path}")
+    logger.info(f"Backup size: {final_backup_path.stat().st_size / (1024 * 1024):.1f} MB")
+
+
+if __name__ == "__main__":
+    exit(main())
diff --git a/classify_image.py b/classify_image.py
new file mode 100755
index 0000000..11f754e
--- /dev/null
+++ b/classify_image.py
@@ -0,0 +1,738 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["ultralytics", "torch", "numpy", "pillow", "bottle", "mediapipe"]
+# ///
+import argparse
+import json
+import logging
+import sys
+import tempfile
+import threading
+import urllib.request
+import webbrowser
+from dataclasses import dataclass
+from functools import cache
+from pathlib import Path
+from typing import NamedTuple, Optional
+
+try:
+    import bottle
+except Exception:
+    bottle = None
+
+try:
+    from ultralytics import YOLO
+    import torch
+    import numpy as np
+except Exception:
+    YOLO = None
+    torch = None
+    np = None
+
+try:
+    from PIL import Image
+except Exception:
+    Image = None
+
+try:
+    import mediapipe as mp
+except Exception:
+    mp = None
+
+VISIBILITY_THRESH = 0.1
+FACE_LANDMARK_VISIBILITY_THRESH = 0.5
+FACING_DIRECTION_THRESH = 0.03
+LOOKING_AT_CAMERA_THRESH = 0.015  # max |nose.x - eye_midpoint.x| for frontal face
+HEAD_CROP_Y_THRESH = 0.35  # shoulders must be in top 35% of frame to classify as head-cropped
+NOSE_TO_NOSE_THRESH = 0.15  # normalized image distance
+BODY_INTERSECT_MARGIN = 0.05  # expand each person's bbox by this before overlap test
+EYE_BLINK_THRESH = 0.40  # blendshape score above this → eye closed
+
+
+class Coords(NamedTuple):
+    """Represents normalized coordinates (0.0 to 1.0) and visibility for a single point."""
+
+    x: float
+    y: float
+    is_visible: bool
+
+
+@dataclass
+class PoseKeypoints:
+    """Holds structured, normalized keypoint data for all 17 COCO points as direct fields."""
+
+    # 0
+    nose: Coords
+    # 1-4
+    left_eye: Coords
+    right_eye: Coords
+    left_ear: Coords
+    right_ear: Coords
+    # 5-6
+    left_shoulder: Coords
+    right_shoulder: Coords
+    # 7-10
+    left_elbow: Coords
+    right_elbow: Coords
+    left_wrist: Coords
+    right_wrist: Coords
+    # 11-12
+    left_hip: Coords
+    right_hip: Coords
+    # 13-16
+    left_knee: Coords
+    right_knee: Coords
+    left_ankle: Coords
+    right_ankle: Coords
+
+    def shoulder_midpoint(self) -> Coords:
+        l = self.left_shoulder
+        r = self.right_shoulder
+        if l.is_visible and r.is_visible:
+            return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=True)
+        return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=False)
+
+
+@dataclass
+class FaceLandmarks:
+    """5-point face landmarks from the derronqi yolov8-face model."""
+
+    left_eye: Coords
+    right_eye: Coords
+    nose: Coords
+    left_mouth: Coords
+    right_mouth: Coords
+
+
+def _get_coords(kp_xyc: "np.ndarray", idx: int) -> Coords:
+    """Helper to safely extract Coords from the raw numpy array."""
+    x, y, conf = kp_xyc[idx]
+    is_visible = conf > VISIBILITY_THRESH
+    return Coords(x=x, y=y, is_visible=is_visible)
+
+
+def _extract_keypoints(kp_xyc: "np.ndarray") -> PoseKeypoints:
+    """Extracts all 17 COCO normalized keypoints and populates the PoseKeypoints dataclass directly."""
+    return PoseKeypoints(
+        nose=_get_coords(kp_xyc, 0),
+        left_eye=_get_coords(kp_xyc, 1),
+        right_eye=_get_coords(kp_xyc, 2),
+        left_ear=_get_coords(kp_xyc, 3),
+        right_ear=_get_coords(kp_xyc, 4),
+        left_shoulder=_get_coords(kp_xyc, 5),
+        right_shoulder=_get_coords(kp_xyc, 6),
+        left_elbow=_get_coords(kp_xyc, 7),
+        right_elbow=_get_coords(kp_xyc, 8),
+        left_wrist=_get_coords(kp_xyc, 9),
+        right_wrist=_get_coords(kp_xyc, 10),
+        left_hip=_get_coords(kp_xyc, 11),
+        right_hip=_get_coords(kp_xyc, 12),
+        left_knee=_get_coords(kp_xyc, 13),
+        right_knee=_get_coords(kp_xyc, 14),
+        left_ankle=_get_coords(kp_xyc, 15),
+        right_ankle=_get_coords(kp_xyc, 16),
+    )
+
+
+_MODEL_URLS = {
+    "yolov11n-face.pt": "https://huggingface.co/AdamCodd/YOLOv11n-face-detection/resolve/main/model.pt",
+    "yolo11s-pose.pt": "https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt",
+    "yolov8n-face-derronqi.pt": "https://huggingface.co/junjiang/GestureFace/resolve/main/yolov8n-face.pt",
+    "face_landmarker.task": "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task",
+}
+
+
+def _ensure_model(filename: str) -> Path:
+    dest = Path(__file__).parent / filename
+    if not dest.exists():
+        url = _MODEL_URLS[filename]
+        logging.warning(f"Downloading {filename} from {url} ...")
+        urllib.request.urlretrieve(url, dest)
+        logging.warning(f"Saved {filename}")
+    return dest
+
+
+def _best_device() -> str:
+    try:
+        if torch.cuda.is_available():
+            return "cuda"
+        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            return "mps"
+    except Exception:
+        pass
+    return "cpu"
+
+
+@cache
+def _face_detector():
+    if YOLO is None or torch is None:
+        logging.error("YOLO/Torch dependencies are missing.")
+        return None
+    try:
+        model = YOLO(_ensure_model("yolov11n-face.pt"))
+        model.to(_best_device())
+        return model
+    except Exception:
+        logging.exception("Face detector model initialization failed.")
+        return None
+
+
+@cache
+def _pose_detector():
+    if YOLO is None or torch is None:
+        logging.error("YOLO/Torch dependencies are missing.")
+        return None
+    try:
+        model = YOLO(_ensure_model("yolo11s-pose.pt"))
+        model.to(_best_device())
+        return model
+    except Exception:
+        logging.exception("Pose detector model initialization failed.")
+        return None
+
+
+@cache
+def _face_landmark_detector():
+    if YOLO is None or torch is None:
+        logging.error("YOLO/Torch dependencies are missing.")
+        return None
+    try:
+        model = YOLO(_ensure_model("yolov8n-face-derronqi.pt"))
+        model.to(_best_device())
+        return model
+    except Exception:
+        logging.exception("Face landmark detector model initialization failed.")
+        return None
+
+
+@cache
+def _face_landmarker():
+    if mp is None:
+        logging.error("mediapipe dependency is missing.")
+        return None
+    try:
+        from mediapipe.tasks import python as mp_python
+        from mediapipe.tasks.python import vision as mp_vision
+        base_options = mp_python.BaseOptions(
+            model_asset_path=str(_ensure_model("face_landmarker.task"))
+        )
+        options = mp_vision.FaceLandmarkerOptions(
+            base_options=base_options,
+            output_face_blendshapes=True,
+            running_mode=mp_vision.RunningMode.IMAGE,
+            num_faces=10,
+        )
+        return mp_vision.FaceLandmarker.create_from_options(options)
+    except Exception:
+        logging.exception("FaceLandmarker initialization failed.")
+        return None
+
+
+def _run_face_landmarker_model(image_path: Path):
+    detector = _face_landmarker()
+    if detector is None or Image is None:
+        return None
+    try:
+        img = Image.open(image_path).convert("RGB")
+        arr = np.asarray(img)
+        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=arr)
+        return detector.detect(mp_image)
+    except Exception:
+        logging.exception(f"FaceLandmarker failed for {image_path}.")
+        return None
+
+
+def check_eyes_closed(face_landmarker_result) -> bool:
+    if face_landmarker_result is None:
+        return False
+    if not face_landmarker_result.face_blendshapes:
+        return False
+    for face_blendshapes in face_landmarker_result.face_blendshapes:
+        scores = {b.category_name: b.score for b in face_blendshapes}
+        if scores.get("eyeBlinkLeft", 0.0) > EYE_BLINK_THRESH or \
+           scores.get("eyeBlinkRight", 0.0) > EYE_BLINK_THRESH:
+            return True
+    return False
+
+
+def _run_face_model(image_path: Path) -> list:
+    model = _face_detector()
+    if model is None:
+        return []
+    try:
+        return model(str(image_path), conf=0.5, iou=0.5, verbose=False)
+    except Exception:
+        logging.exception(f"Face detection failed for {image_path}.")
+        return []
+
+
+def _run_face_landmark_model(image_path: Path) -> list:
+    model = _face_landmark_detector()
+    if model is None:
+        return []
+    try:
+        return model(str(image_path), conf=0.25, iou=0.5, verbose=False)
+    except Exception:
+        logging.exception(f"Face landmark detection failed for {image_path}.")
+        return []
+
+
+def _run_pose_model(image_path: Path) -> list:
+    model = _pose_detector()
+    if model is None:
+        return []
+    try:
+        return model(str(image_path), conf=0.35, iou=0.5, verbose=False)
+    except Exception:
+        logging.exception(f"Pose detection failed for {image_path}.")
+        return []
+
+
+def count_faces(results: list) -> int:
+    try:
+        return len(results[0].boxes)
+    except Exception:
+        return 0
+
+
+def detect_poses(results: list) -> list[PoseKeypoints]:
+    try:
+        if not results or results[0].keypoints is None:
+            return []
+        kps_norm_xy = results[0].keypoints.xyn.cpu().numpy()
+        kps_conf = results[0].keypoints.conf.cpu().numpy()
+        all_keypoints_xyc = np.concatenate([kps_norm_xy, np.expand_dims(kps_conf, axis=2)], axis=2)
+        return [_extract_keypoints(kp_xyc) for kp_xyc in all_keypoints_xyc]
+    except Exception:
+        logging.exception("Pose keypoint extraction failed.")
+        return []
+
+
+def detect_face_landmarks(results: list) -> list[FaceLandmarks]:
+    try:
+        if not results or results[0].keypoints is None:
+            return []
+        kps_norm_xy = results[0].keypoints.xyn.cpu().numpy()
+        kps_conf = results[0].keypoints.conf.cpu().numpy()
+        all_keypoints_xyc = np.concatenate([kps_norm_xy, np.expand_dims(kps_conf, axis=2)], axis=2)
+        landmarks = []
+        for kp_xyc in all_keypoints_xyc:
+            def _lm(idx, arr=kp_xyc):
+                x, y, conf = arr[idx]
+                return Coords(x=x, y=y, is_visible=conf > FACE_LANDMARK_VISIBILITY_THRESH)
+            landmarks.append(FaceLandmarks(
+                left_eye=_lm(0),
+                right_eye=_lm(1),
+                nose=_lm(2),
+                left_mouth=_lm(3),
+                right_mouth=_lm(4),
+            ))
+        return landmarks
+    except Exception:
+        logging.exception("Face landmark extraction failed.")
+        return []
+
+
+def _face_all_invisible(kps: PoseKeypoints) -> bool:
+    return (
+        not kps.nose.is_visible
+        and not kps.left_eye.is_visible
+        and not kps.right_eye.is_visible
+    )
+
+
+def is_turned_back(kps: PoseKeypoints) -> bool:
+    shoulder_visible = kps.left_shoulder.is_visible or kps.right_shoulder.is_visible
+    return _face_all_invisible(kps) and shoulder_visible and not is_eyes_cropped_out(kps)
+
+
+def is_eyes_cropped_out(kps: PoseKeypoints) -> bool:
+    """True when face is not visible but shoulders are near the top of the frame,
+    indicating the head is above the image boundary."""
+    shoulder_visible = kps.left_shoulder.is_visible or kps.right_shoulder.is_visible
+    if not _face_all_invisible(kps) or not shoulder_visible:
+        return False
+    # Use the topmost (lowest y) visible shoulder
+    ys = [kp.y for kp in (kps.left_shoulder, kps.right_shoulder) if kp.is_visible]
+    return min(ys) < HEAD_CROP_Y_THRESH
+
+
+def get_facing_x_direction(kps: PoseKeypoints) -> Optional[float]:
+    shoulder_mid = kps.shoulder_midpoint()
+    if not kps.nose.is_visible or not shoulder_mid.is_visible:
+        return None
+    return kps.nose.x - shoulder_mid.x
+
+
+def get_face_yaw(kps: FaceLandmarks) -> Optional[float]:
+    """Nose x offset from eye midpoint. ~0 = frontal, positive = turned right, negative = turned left."""
+    if not kps.left_eye.is_visible or not kps.right_eye.is_visible or not kps.nose.is_visible:
+        return None
+    eye_mid_x = (kps.left_eye.x + kps.right_eye.x) / 2.0
+    return kps.nose.x - eye_mid_x
+
+
+def check_looking_at_camera(all_landmarks: list[FaceLandmarks]) -> bool:
+    return any(
+        (yaw := get_face_yaw(lm)) is not None and abs(yaw) < LOOKING_AT_CAMERA_THRESH
+        for lm in all_landmarks
+    )
+
+
+def check_facing_each_other(all_kps: list[PoseKeypoints]) -> bool:
+    classifiable = []
+    for kps in all_kps:
+        delta = get_facing_x_direction(kps)
+        if delta is not None:
+            shoulder_mid = kps.shoulder_midpoint()
+            classifiable.append((shoulder_mid.x, delta))
+
+    if len(classifiable) < 2:
+        return False
+
+    classifiable.sort(key=lambda t: t[0])
+
+    for i in range(len(classifiable)):
+        for j in range(i + 1, len(classifiable)):
+            left_delta = classifiable[i][1]
+            right_delta = classifiable[j][1]
+            if left_delta > FACING_DIRECTION_THRESH and right_delta < -FACING_DIRECTION_THRESH:
+                return True
+
+    return False
+
+
+def detect_person_boxes(results: list) -> list[tuple[float, float, float, float]]:
+    try:
+        if not results or results[0].boxes is None:
+            return []
+        return [tuple(box) for box in results[0].boxes.xyxyn.cpu().tolist()]
+    except Exception:
+        logging.exception("Person box extraction failed.")
+        return []
+
+
+def _face_boxes_to_body_boxes(
+    face_boxes: list[tuple[float, float, float, float]]
+) -> list[tuple[float, float, float, float]]:
+    out = []
+    for x1, y1, x2, y2 in face_boxes:
+        fw = x2 - x1
+        fh = y2 - y1
+        bx1 = max(0.0, x1 - 0.3 * fw)
+        bx2 = min(1.0, x2 + 0.3 * fw)
+        by1 = y1
+        by2 = min(1.0, y2 + 2.5 * fh)  # extend ~2.5 face-heights downward
+        out.append((bx1, by1, bx2, by2))
+    return out
+
+
+def check_bodies_intersecting(person_boxes: list[tuple[float, float, float, float]]) -> bool:
+    m = BODY_INTERSECT_MARGIN
+    for i in range(len(person_boxes)):
+        for j in range(i + 1, len(person_boxes)):
+            ax1, ay1, ax2, ay2 = person_boxes[i]
+            bx1, by1, bx2, by2 = person_boxes[j]
+            if ax1 - m < bx2 and ax2 + m > bx1 and ay1 - m < by2 and ay2 + m > by1:
+                return True
+    return False
+
+
+def check_nose_to_nose(all_kps: list[PoseKeypoints]) -> bool:
+    visible = [(kps.nose.x, kps.nose.y) for kps in all_kps if kps.nose.is_visible]
+    for i in range(len(visible)):
+        for j in range(i + 1, len(visible)):
+            dx = visible[i][0] - visible[j][0]
+            dy = visible[i][1] - visible[j][1]
+            if (dx*dx + dy*dy) ** 0.5 < NOSE_TO_NOSE_THRESH:
+                return True
+    return False
+
+
+def classify_image(image_path: Path, debug_dir: Optional[Path] = None) -> dict:
+    face_results = _run_face_model(image_path)
+    pose_results = _run_pose_model(image_path)
+    face_landmark_results = _run_face_landmark_model(image_path)
+    face_mesh_result = _run_face_landmarker_model(image_path)
+
+    total_faces = count_faces(face_results)
+    all_kps = detect_poses(pose_results)
+    face_landmarks = detect_face_landmarks(face_landmark_results)
+    turned_back = any(is_turned_back(kps) for kps in all_kps)
+    facing_each_other = check_facing_each_other(all_kps)
+    eyes_cropped_out = any(is_eyes_cropped_out(kps) for kps in all_kps)
+    nose_to_nose = check_nose_to_nose(all_kps)
+    person_boxes = detect_person_boxes(pose_results)
+    if len(person_boxes) < 2:
+        raw_face_boxes = []
+        try:
+            if face_results and face_results[0].boxes is not None:
+                raw_face_boxes = [tuple(b) for b in face_results[0].boxes.xyxyn.cpu().tolist()]
+        except Exception:
+            pass
+        person_boxes = _face_boxes_to_body_boxes(raw_face_boxes)
+    bodies_intersecting = check_bodies_intersecting(person_boxes)
+    looking_at_camera = check_looking_at_camera(face_landmarks)
+
+    if debug_dir:
+        debug_dir.mkdir(parents=True, exist_ok=True)
+        for label, results in [("face", face_results), ("pose", pose_results)]:
+            if results and results[0].boxes is not None:
+                annotated = results[0].plot()
+                img = Image.fromarray(annotated[..., ::-1])
+                img.save(debug_dir / f"{image_path.stem}_{label}.jpg")
+
+    return {
+        "image_path": str(image_path),
+        "total_faces": total_faces,
+        "facing_each_other": facing_each_other,
+        "turned_back": turned_back,
+        "eyes_cropped_out": eyes_cropped_out,
+        "nose_to_nose": nose_to_nose,
+        "bodies_intersecting": bodies_intersecting,
+        "looking_at_camera": looking_at_camera,
+        "eyes_closed": check_eyes_closed(face_mesh_result),
+    }
+
+
+HTML_PAGE = """<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<title>classify_image debug</title>
+<style>
+  body { background: #111; color: #eee; font-family: monospace; margin: 0; padding: 16px; transition: outline 0.1s; }
+  body.over { outline: 3px dashed #4f4; outline-offset: -6px; }
+  #drop {
+    border: 2px dashed #555; border-radius: 8px; padding: 40px;
+    text-align: center; color: #888; margin-bottom: 20px;
+    transition: border-color 0.1s, color 0.1s;
+  }
+  body.over #drop { border-color: #4f4; color: #4f4; }
+  #results { display: flex; flex-wrap: wrap; gap: 20px; }
+  .card {
+    display: flex; flex-direction: row; align-items: flex-start;
+    max-width: 960px; background: #1a1a1a; border-radius: 8px; padding: 12px; gap: 16px;
+  }
+  .img-wrap { position: relative; display: inline-block; flex-shrink: 0; }
+  .img-wrap img { display: block; max-width: 560px; max-height: 600px; border-radius: 4px; }
+  canvas { position: absolute; top: 0; left: 0; pointer-events: none; }
+  pre.data { font-size: 12px; margin: 0; overflow: auto; white-space: pre-wrap; color: #afa; }
+  .spinner { color: #888; padding: 20px; }
+</style>
+</head>
+<body>
+<div id="drop">Drop images here to classify</div>
+<div id="results"></div>
+<script>
+const EDGES = [
+  [0,1],[0,2],[1,3],[2,4],
+  [5,7],[7,9],[6,8],[8,10],
+  [5,6],[5,11],[6,12],[11,12],
+  [11,13],[13,15],[12,14],[14,16]
+];
+const COLORS = ["#f55","#5af","#ff5","#5f5","#f5f","#fa5","#5ff"];
+
+const drop = document.getElementById("drop");
+const results = document.getElementById("results");
+
+let dragCounter = 0;
+document.addEventListener("dragenter", e => { e.preventDefault(); if (++dragCounter === 1) document.body.classList.add("over"); });
+document.addEventListener("dragleave", () => { if (--dragCounter === 0) document.body.classList.remove("over"); });
+document.addEventListener("dragover", e => e.preventDefault());
+document.addEventListener("drop", e => {
+  e.preventDefault();
+  dragCounter = 0;
+  document.body.classList.remove("over");
+  for (const file of e.dataTransfer.files) processFile(file);
+});
+
+function processFile(file) {
+  const card = document.createElement("div");
+  card.className = "card";
+  card.innerHTML = '<div class="spinner">Processing...</div>';
+  results.prepend(card);
+
+  const fd = new FormData();
+  fd.append("image", file, file.name);
+
+  fetch("/classify", { method: "POST", body: fd })
+    .then(r => r.json())
+    .then(data => {
+      const wrap = document.createElement("div");
+      wrap.className = "img-wrap";
+      const img = document.createElement("img");
+      const canvas = document.createElement("canvas");
+      wrap.appendChild(img);
+      wrap.appendChild(canvas);
+
+      const pre = document.createElement("pre");
+      pre.className = "data";
+      pre.textContent = JSON.stringify(data.classification, null, 2);
+
+      card.innerHTML = "";
+      card.appendChild(wrap);
+      card.appendChild(pre);
+
+      img.onload = () => {
+        canvas.width = img.offsetWidth;
+        canvas.height = img.offsetHeight;
+        const ctx = canvas.getContext("2d");
+        drawOverlays(ctx, data.detections, img.offsetWidth, img.offsetHeight);
+      };
+      img.src = URL.createObjectURL(file);
+    })
+    .catch(err => { card.innerHTML = '<div class="spinner">Error: ' + err + '</div>'; });
+}
+
+function drawOverlays(ctx, detections, W, H) {
+  ctx.lineWidth = 2;
+  ctx.strokeStyle = "#0f0";
+  for (const [x1n, y1n, x2n, y2n] of detections.faces) {
+    ctx.strokeRect(x1n * W, y1n * H, (x2n - x1n) * W, (y2n - y1n) * H);
+  }
+  detections.poses.forEach((kps, pi) => {
+    const col = COLORS[pi % COLORS.length];
+    ctx.strokeStyle = col;
+    ctx.fillStyle = col;
+    for (const [a, b] of EDGES) {
+      const ka = kps[a], kb = kps[b];
+      if (ka.v && kb.v) {
+        ctx.beginPath();
+        ctx.moveTo(ka.x * W, ka.y * H);
+        ctx.lineTo(kb.x * W, kb.y * H);
+        ctx.stroke();
+      }
+    }
+    for (const kp of kps) {
+      if (kp.v) {
+        ctx.beginPath();
+        ctx.arc(kp.x * W, kp.y * H, 4, 0, 2 * Math.PI);
+        ctx.fill();
+      }
+    }
+  });
+}
+</script>
+</body>
+</html>"""
+
+
+def _extract_web_data(face_results: list, pose_results: list) -> dict:
+    faces = []
+    try:
+        if face_results and face_results[0].boxes is not None:
+            faces = face_results[0].boxes.xyxyn.cpu().tolist()
+    except Exception:
+        pass
+
+    poses = []
+    try:
+        if pose_results and pose_results[0].keypoints is not None:
+            kps_norm_xy = pose_results[0].keypoints.xyn.cpu().numpy()
+            kps_conf = pose_results[0].keypoints.conf.cpu().numpy()
+            all_keypoints_xyc = np.concatenate([kps_norm_xy, np.expand_dims(kps_conf, axis=2)], axis=2)
+            for person_kps in all_keypoints_xyc:
+                poses.append([
+                    {"x": float(kp[0]), "y": float(kp[1]), "v": bool(kp[2] > VISIBILITY_THRESH)}
+                    for kp in person_kps
+                ])
+    except Exception:
+        pass
+
+    return {"faces": faces, "poses": poses}
+
+
+def _classify_route():
+    upload = bottle.request.files.get("image")
+    suffix = Path(upload.filename).suffix or ".jpg"
+    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as f:
+        tmp = Path(f.name)
+        upload.save(f)
+    try:
+        face_results = _run_face_model(tmp)
+        pose_results = _run_pose_model(tmp)
+        face_landmark_results = _run_face_landmark_model(tmp)
+        face_mesh_result = _run_face_landmarker_model(tmp)
+        all_kps = detect_poses(pose_results)
+        face_landmarks = detect_face_landmarks(face_landmark_results)
+        person_boxes = detect_person_boxes(pose_results)
+        if len(person_boxes) < 2:
+            raw_face_boxes = []
+            try:
+                if face_results and face_results[0].boxes is not None:
+                    raw_face_boxes = [tuple(b) for b in face_results[0].boxes.xyxyn.cpu().tolist()]
+            except Exception:
+                pass
+            person_boxes = _face_boxes_to_body_boxes(raw_face_boxes)
+        classification = {
+            "image": upload.filename,
+            "total_faces": count_faces(face_results),
+            "facing_each_other": check_facing_each_other(all_kps),
+            "turned_back": any(is_turned_back(kps) for kps in all_kps),
+            "eyes_cropped_out": any(is_eyes_cropped_out(kps) for kps in all_kps),
+            "nose_to_nose": check_nose_to_nose(all_kps),
+            "bodies_intersecting": check_bodies_intersecting(person_boxes),
+            "looking_at_camera": check_looking_at_camera(face_landmarks),
+            "eyes_closed": check_eyes_closed(face_mesh_result),
+        }
+        detections = _extract_web_data(face_results, pose_results)
+        return bottle.HTTPResponse(
+            json.dumps({"classification": classification, "detections": detections}),
+            content_type="application/json",
+        )
+    finally:
+        tmp.unlink(missing_ok=True)
+
+
+def web_main():
+    if bottle is None:
+        print("bottle not installed. Run: pip install bottle", file=sys.stderr)
+        sys.exit(1)
+
+    app = bottle.Bottle()
+
+    @app.get("/")
+    def index():
+        return HTML_PAGE
+
+    @app.post("/classify")
+    def classify_route():
+        return _classify_route()
+
+    port = 7777
+    threading.Timer(0.5, lambda: webbrowser.open(f"http://localhost:{port}")).start()
+    bottle.run(app, host="localhost", port=port, quiet=True)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Classify images for face-related attributes.")
+    parser.add_argument("image_paths", nargs="*", type=Path, help="Path(s) to image(s)")
+    parser.add_argument("--debug", action="store_true", help="Save annotated debug images to _debug/ subdirectory")
+    parser.add_argument("--web", action="store_true", help="Start debug web server")
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.WARNING, stream=sys.stderr)
+
+    if args.web:
+        web_main()
+        return
+
+    if not args.image_paths:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+
+    for image_path in args.image_paths:
+        try:
+            debug_dir = (image_path.parent / "_debug") if args.debug else None
+            result = classify_image(image_path, debug_dir=debug_dir)
+            print(json.dumps(result), flush=True)
+        except KeyboardInterrupt:
+            raise
+        except Exception:
+            logging.exception(f"Error processing {image_path}.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/cluster_images.py b/cluster_images.py
new file mode 100755
index 0000000..d86d6f0
--- /dev/null
+++ b/cluster_images.py
@@ -0,0 +1,163 @@
+#!/usr/bin/env -S uv run
+# /// script
+# dependencies = [
+#   "torch",
+#   "torchvision",
+#   "timm",
+#   "scikit-learn",
+#   "pillow",
+#   "imagehash",
+# ]
+# ///
+"""Group similar large images using ResNet embeddings or perceptual hashing."""
+
+import torch
+import timm
+import numpy as np
+from PIL import Image
+from pathlib import Path
+from sklearn.cluster import KMeans, DBSCAN
+from sklearn.preprocessing import StandardScaler
+import imagehash
+import argparse
+
+# Load pretrained ResNet50 on M1
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+model = timm.create_model("resnet50", pretrained=True, num_classes=0)
+model = model.to(device)
+model.eval()
+
+
+def get_embedding(img_path, size=224):
+    """Extract embedding from image."""
+    try:
+        img = Image.open(img_path).convert("RGB")
+        img.thumbnail((size, size), Image.Resampling.LANCZOS)
+
+        canvas = Image.new("RGB", (size, size), (128, 128, 128))
+        offset = ((size - img.width) // 2, (size - img.height) // 2)
+        canvas.paste(img, offset)
+
+        x = torch.tensor(np.array(canvas), dtype=torch.float32)
+        x = x.permute(2, 0, 1) / 255.0
+        x = (x - torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)) / torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
+
+        with torch.no_grad():
+            embedding = model(x.unsqueeze(0).to(device)).squeeze().cpu().numpy()
+        return embedding
+    except Exception as e:
+        print(f"Error processing {img_path}: {e}")
+        return None
+
+
+def get_phash(img_path):
+    """Extract perceptual hash from image."""
+    try:
+        img = Image.open(img_path).convert("RGB")
+        return imagehash.phash(img)
+    except Exception as e:
+        print(f"Error processing {img_path}: {e}")
+        return None
+
+
+def hash_distance(h1, h2):
+    """Hamming distance between two hashes."""
+    return h1 - h2
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Group similar images using embeddings or perceptual hashing.")
+    parser.add_argument("paths", nargs="+", type=Path, help="Image file(s) or directory")
+    parser.add_argument("--cluster", action="store_true", help="Use ResNet embedding clustering")
+    parser.add_argument("--perceptual-hash", action="store_true", help="Use perceptual hashing")
+    parser.add_argument("--clusters", type=int, help="Number of clusters for embedding mode (auto if not specified)")
+    parser.add_argument("--hash-threshold", type=int, default=5, help="Hamming distance threshold for perceptual hash")
+    args = parser.parse_args()
+
+    if not args.cluster and not args.perceptual_hash:
+        parser.error("Either --cluster or --perceptual-hash must be specified")
+    if args.cluster and args.perceptual_hash:
+        parser.error("Cannot specify both --cluster and --perceptual-hash")
+
+    return args
+
+
+def group_by_hash(valid_files, hashes, threshold):
+    """Group images by perceptual hash similarity."""
+    labels = [-1] * len(valid_files)
+    cluster_id = 0
+
+    for i in range(len(valid_files)):
+        if labels[i] != -1:
+            continue
+        labels[i] = cluster_id
+        for j in range(i + 1, len(valid_files)):
+            if labels[j] == -1 and hash_distance(hashes[i], hashes[j]) <= threshold:
+                labels[j] = cluster_id
+        cluster_id += 1
+
+    return np.array(labels)
+
+
+def main():
+    args = parse_args()
+
+    # Collect image files
+    img_files = []
+    for path in args.paths:
+        if path.is_dir():
+            img_files.extend(path.glob("*.[jJ][pP][gG]"))
+            img_files.extend(path.glob("*.[pP][nN][gG]"))
+        else:
+            img_files.append(path)
+
+    if not img_files:
+        print("No images found.")
+        return
+
+    if args.perceptual_hash:
+        # Perceptual hash mode
+        hashes = []
+        valid_files = []
+
+        for i, img_path in enumerate(img_files):
+            print(f"Processing {i + 1}/{len(img_files)}: {img_path.name}")
+            h = get_phash(img_path)
+            if h is not None:
+                hashes.append(h)
+                valid_files.append(img_path)
+
+        labels = group_by_hash(valid_files, hashes, args.hash_threshold)
+        n_clusters = len(np.unique(labels))
+
+    else:
+        # Embedding clustering mode
+        embeddings = []
+        valid_files = []
+
+        for i, img_path in enumerate(img_files):
+            print(f"Processing {i + 1}/{len(img_files)}: {img_path.name}")
+            emb = get_embedding(img_path)
+            if emb is not None:
+                embeddings.append(emb)
+                valid_files.append(img_path)
+
+        embeddings = np.array(embeddings)
+        scaler = StandardScaler()
+        embeddings = scaler.fit_transform(embeddings)
+
+        n_clusters = args.clusters or max(2, int(np.sqrt(len(embeddings) / 2)))
+        kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+        labels = kmeans.fit_predict(embeddings)
+
+    # Save clustered images
+    for img_path, cluster_label in zip(valid_files, labels):
+        new_name = f"group{cluster_label}_{img_path.name}"
+        dest = Path.cwd() / new_name
+        img_path.rename(dest) if img_path.parent == Path.cwd() else dest.write_bytes(img_path.read_bytes())
+
+    print(f"Clustered {len(valid_files)} images into {n_clusters} clusters in {Path.cwd()}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/cull_edits.py b/cull_edits.py
new file mode 100755
index 0000000..3b6569e
--- /dev/null
+++ b/cull_edits.py
@@ -0,0 +1,939 @@
+#!/usr/bin/env -S uv run
+# /// script
+# requires-python = ">=3.14"
+# dependencies = [
+#     "bottle>=0.13.0",
+# ]
+# ///
+
+import argparse
+import functools
+import os
+import re
+import socket
+import sys
+import threading
+import time
+import webbrowser
+from pathlib import Path
+from datetime import datetime
+from typing import TypedDict
+import bottle
+
+IMAGE_EXTENSIONS: set[str] = {".jpg", ".jpeg", ".png", ".webp", ".webm"}
+
+
+class ImageGroup(TypedDict):
+    """Type definition for grouped images."""
+
+    base: str
+    original: Path
+    edits: list[Path]
+
+
+def find_available_port() -> int:
+    """Find an available random port."""
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("127.0.0.1", 0))
+        s.listen(1)
+        port = s.getsockname()[1]
+    return port
+
+
+def is_image(path: str | Path) -> bool:
+    """Check if file is a supported image."""
+    return Path(path).suffix.lower() in IMAGE_EXTENSIONS
+
+
+def discover_images(paths: list[str]) -> list[Path]:
+    """Discover all images from mixed list of files and folders."""
+    images = []
+    for path_str in paths:
+        path = Path(path_str)
+        if path.is_file() and is_image(path):
+            images.append(path.resolve())
+        elif path.is_dir():
+            for item in path.iterdir():
+                if item.is_file() and is_image(item):
+                    images.append(item.resolve())
+    return sorted(set(images))
+
+
+def group_images(images: list[Path]) -> list[ImageGroup]:
+    """
+    Group images by original + edits.
+    Original: image.jpg
+    Edits: image-edit1.jpg, image-edit2.jpg, etc.
+    Returns list of dicts: {original: Path, edits: [Path, ...]}
+    """
+    groups = {}
+
+    for img in images:
+        stem = img.stem
+
+        # Check if this is an edit
+        match = re.match(r"^(.+?)-edit", stem)
+        if match:
+            base = match.group(1)
+            if base not in groups:
+                groups[base] = {"original": None, "edits": []}
+            groups[base]["edits"].append(img)
+        else:
+            # This is an original
+            if stem not in groups:
+                groups[stem] = {"original": None, "edits": []}
+            groups[stem]["original"] = img
+
+    # Filter out groups without originals, convert to list
+    result = []
+    for base, group in groups.items():
+        if group["original"]:
+            result.append(ImageGroup(base=base, original=group["original"], edits=sorted(group["edits"])))
+
+    return sorted(result, key=lambda g: g["original"].name)
+
+
+def safe_image_path(filepath: str) -> Path:
+    """Verify image path is safe (prevent directory traversal)."""
+    # Search for the image in all_images by filename
+    for img in all_images:
+        if img.name == filepath:
+            return img
+    raise ValueError(f"Unauthorized path: {filepath}")
+
+
+def hardlink_images(src_paths: list[Path | str], dest_dir: str | Path) -> None:
+    """Hard link multiple images to destination directory."""
+    dest_path = Path(dest_dir)
+    make_dirs(dest_path)
+
+    for src in src_paths:
+        src = Path(src)
+        dest = dest_path / src.name
+        try:
+            # Remove existing file if present
+            if dest.exists():
+                dest.unlink()
+            os.link(src, dest)
+        except Exception as e:
+            print(f"Error hard linking {src} to {dest}: {e}", file=sys.stderr)
+            raise
+
+
+# Global state
+app = bottle.Bottle()
+all_images = set()
+image_groups = []
+current_group_idx = 0
+last_heartbeat = datetime.now()
+should_exit = False
+picks_dir = Path()
+flags = []
+flag_dirs = {}
+
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Image Culler</title>
+    <script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.x.x/dist/cdn.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+            background: #1e1e1e;
+            color: #e0e0e0;
+            overflow: hidden;
+            height: 100vh;
+        }
+        .container {
+            display: flex;
+            height: calc(100vh - 46px);
+            gap: 20px;
+            padding: 20px;
+        }
+        .panel {
+            display: flex;
+            flex-direction: column;
+            gap: 15px;
+        }
+        .left-panel {
+            flex: 0 0 40%;
+        }
+        .right-panel {
+            flex: 0 0 60%;
+            overflow-y: auto;
+        }
+        .image-display {
+            width: 100%;
+            height: 100%;
+            background: #2a2a2a;
+            border-radius: 8px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            overflow: hidden;
+            border: 2px solid transparent;
+            transition: border-color 0.2s;
+        }
+        .image-display:hover {
+            border-color: #4a9eff;
+        }
+        .image-display.picked {
+            border-color: #4ade80;
+            background: rgba(74, 222, 128, 0.1);
+        }
+        .image-display img {
+            max-width: 100%;
+            max-height: 100%;
+            object-fit: contain;
+        }
+        .image-info {
+            background: #2a2a2a;
+            padding: 12px;
+            border-radius: 8px;
+            font-size: 14px;
+        }
+        dialog {
+            background: #2a2a2a;
+            color: #e0e0e0;
+            border: 1px solid #404040;
+            border-radius: 8px;
+            padding: 20px;
+            max-width: 400px;
+        }
+        dialog::backdrop {
+            background: rgba(0, 0, 0, 0.5);
+        }
+        .grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 10px;
+        }
+        .grid[data-image-count="1"] {
+            grid-template-columns: 1fr;
+        }
+        .grid-item {
+            position: relative;
+            background: #2a2a2a;
+            border-radius: 8px;
+            overflow: hidden;
+            cursor: pointer;
+            border: 2px solid transparent;
+            transition: all 0.2s;
+        }
+        .grid-item:hover {
+            border-color: #4a9eff;
+        }
+        .grid-item img {
+            width: 100%;
+            height: auto;
+            display: block;
+            object-fit: contain;
+        }
+        .grid[data-image-count="1"] .grid-item img {
+            max-height: 90vh;
+        }
+        .grid-item.picked {
+            border-color: #4ade80;
+            background: rgba(74, 222, 128, 0.1);
+        }
+        .flag-pill {
+            background: #404040;
+            color: #e0e0e0;
+            border: 1px solid #555;
+        }
+        .flag-pill:hover {
+            background: #505050;
+            border-color: #666;
+        }
+        .flag-pill-active {
+            background: #4a9eff;
+            color: #000;
+            border: 1px solid #4a9eff;
+            font-weight: 500;
+        }
+        .flag-pill-active:hover {
+            background: #6ab0ff;
+            border-color: #6ab0ff;
+        }
+    </style>
+</head>
+<body>
+    <div x-data="app()" x-init="init()" @keydown.window="handleKeydown($event)">
+        <div style="background: #2a2a2a; padding: 12px 20px; border-bottom: 1px solid #404040;">
+            <div style="font-size: 16px; font-weight: 500; color: #e0e0e0; word-break: break-all;">
+                <span x-text="currentGroup ? currentGroup.original.name : 'No images'"></span>
+            </div>
+        </div>
+        <div class="container">
+            <!-- Left Panel: Original Image -->
+            <div class="panel left-panel">
+                <div class="image-display" :class="{ picked: originalPicked }" style="position: relative;">
+                    <template x-if="currentGroup">
+                        <img :src="`/image/${currentGroup.original.name}`" :alt="currentGroup.original.name">
+                        <template x-if="imageFlagAssignments[currentGroup.original.name]">
+                            <div style="position: absolute; bottom: 12px; left: 12px; background: #4a9eff; color: #000; padding: 4px 8px; border-radius: 4px; font-size: 12px; font-weight: bold;">
+                                <span x-text="imageFlagAssignments[currentGroup.original.name]"></span>
+                            </div>
+                        </template>
+                    </template>
+                    <template x-if="!currentGroup">
+                        <div style="text-align: center; color: #888;">No images</div>
+                    </template>
+                </div>
+                <div class="image-info">
+                    <template x-if="currentGroup">
+                        <div>
+                            <div style="display: flex; align-items: center; gap: 8px;">
+                                <strong>Position:</strong>
+                                <input 
+                                    type="number" 
+                                    :value="currentGroupIdx + 1"
+                                    @keyup.enter="jumpToIndex($event)"
+                                    @change="jumpToIndex($event)"
+                                    min="1" 
+                                    :max="imageGroups.length"
+                                    style="width: 90px; background: #404040; color: #e0e0e0; border: 1px solid #555; padding: 4px 8px; border-radius: 4px; font-size: 14px;"
+                                >
+                                <span style="color: #888;">/ <span x-text="imageGroups.length"></span></span>
+                            </div>
+                            <template x-if="availableFlags.length > 0">
+                                <div style="margin-top: 10px;">
+                                    <strong style="font-size: 13px;">Flags:</strong>
+                                    <div style="display: flex; flex-wrap: wrap; gap: 6px; margin-top: 6px;">
+                                        <template x-for="(flag, idx) in availableFlags" :key="idx">
+                                            <div 
+                                                @click="handleFlagAssignment(flag)"
+                                                :class="imageFlagAssignments[currentGroup.original.name] === flag ? 'flag-pill-active' : 'flag-pill'"
+                                                style="cursor: pointer; padding: 4px 8px; border-radius: 4px; font-size: 12px; white-space: nowrap; transition: all 0.2s;"
+                                            >
+                                                <span x-text="`${flag} (${idx + 1})`"></span>
+                                            </div>
+                                        </template>
+                                    </div>
+                                </div>
+                            </template>
+                        </div>
+                    </template>
+                </div>
+            </div>
+
+            <!-- Right Panel: Edit Variations Grid -->
+            <div class="panel right-panel">
+                <template x-if="currentGroup && currentGroup.edits.length > 0">
+                    <div class="grid" :data-image-count="currentGroup.edits.length">
+                        <template x-for="(edit, idx) in currentGroup.edits" :key="idx">
+                            <div 
+                                class="grid-item"
+                                :class="{ picked: pickedEdits.includes(idx) }"
+                                @click="toggleEditPick(idx)"
+                            >
+                                <img :src="`/image/${edit.name}`" :alt="`Edit ${idx + 1}`">
+                            </div>
+                        </template>
+                    </div>
+                </template>
+                <template x-if="!currentGroup || currentGroup.edits.length === 0">
+                    <div style="color: #888; text-align: center; padding: 40px;">No variations</div>
+                </template>
+            </div>
+        </div>
+
+        <!-- Help Dialog -->
+        <dialog id="helpDialog">
+            <h2>Keyboard Shortcuts</h2>
+            <div style="margin-top: 15px; font-size: 14px; line-height: 1.8;">
+                <div style="margin-bottom: 10px;">
+                    <span style="background: #404040; padding: 2px 6px; border-radius: 3px; font-weight: bold;">P</span>
+                    Toggle pick (all edits or hover one)
+                </div>
+                <div style="margin-bottom: 10px;">
+                    <span style="background: #404040; padding: 2px 6px; border-radius: 3px; font-weight: bold;">←→</span>
+                    Navigate any image
+                </div>
+                <div style="margin-bottom: 10px;">
+                    <span style="background: #404040; padding: 2px 6px; border-radius: 3px; font-weight: bold;">K</span>
+                    Skip to next pending
+                </div>
+                <div style="margin-top: 15px; font-size: 13px; color: #aaa;">
+                    <strong>Flags:</strong>
+                </div>
+                <div>
+                    <span style="background: #404040; padding: 2px 6px; border-radius: 3px; font-weight: bold;">1-9</span>
+                    Assign/unassign to flag
+                </div>
+                <div>
+                    <span style="background: #404040; padding: 2px 6px; border-radius: 3px; font-weight: bold;">/</span>
+                    Toggle reject flag
+                </div>
+            </div>
+            <div style="margin-top: 20px; text-align: right;">
+                <button @click="closeHelp()" style="background: #404040; color: #e0e0e0; border: none; padding: 8px 16px; border-radius: 4px; cursor: pointer;">Close</button>
+            </div>
+        </dialog>
+    </div>
+
+    <script>
+        function app() {
+            return {
+                imageGroups: [],
+                currentGroupIdx: 0,
+                currentGroup: null,
+                pickedEdits: [],
+                originalPicked: false,
+                groupStatus: {},
+                availableFlags: [],
+                imageFlagAssignments: {},
+
+                async init() {
+                    // Start heartbeat
+                    this.startHeartbeat();
+
+                    // Track mouse position globally for hover detection
+                    document.addEventListener('mousemove', (e) => {
+                        window.lastMouseEvent = e;
+                    });
+
+                    // Notify server when tab/window is closed
+                    window.addEventListener('beforeunload', () => {
+                        fetch('/api/shutdown', { method: 'POST' }).catch(() => {});
+                    });
+
+                    // Load images
+                    await this.loadImages();
+
+                    // Load first group
+                    this.loadGroup(0);
+                },
+
+                async loadImages() {
+                    try {
+                        const resp = await fetch('/api/images');
+                        const data = await resp.json();
+                        this.imageGroups = data.groups;
+                        this.availableFlags = data.flags_list || [];
+                        this.groupStatus = {};
+                        this.imageFlagAssignments = data.flag_assignments || {};
+                        this.imageGroups.forEach((_, idx) => {
+                            this.groupStatus[idx] = 'pending';
+                        });
+                    } catch (err) {
+                        console.error('Error loading images:', err.message);
+                    }
+                },
+
+                loadGroup(idx) {
+                    if (idx < 0 || idx >= this.imageGroups.length) return;
+                    this.currentGroupIdx = idx;
+                    this.currentGroup = this.imageGroups[idx];
+                    this.pickedEdits = [];
+                    this.originalPicked = false;
+                },
+
+                getGroupStatus(idx) {
+                    return this.groupStatus[idx] || 'pending';
+                },
+
+                jumpToIndex(event) {
+                    const value = parseInt(event.target.value);
+                    if (!isNaN(value) && value >= 1 && value <= this.imageGroups.length) {
+                        this.loadGroup(value - 1);
+                    }
+                },
+
+                toggleEditPick(idx) {
+                    if (this.pickedEdits.includes(idx)) {
+                        this.pickedEdits = this.pickedEdits.filter(i => i !== idx);
+                    } else {
+                        this.pickedEdits.push(idx);
+                    }
+                },
+
+                getHoveredElement() {
+                    // Find the element currently under the mouse pointer
+                    const mouseEvent = window.lastMouseEvent;
+                    if (!mouseEvent) return null;
+                    return document.elementFromPoint(mouseEvent.clientX, mouseEvent.clientY);
+                },
+
+                async handleKeydown(e) {
+                    // Skip keybindings if focused on input/textarea
+                    const target = e.target;
+                    const isInput = target.tagName === 'INPUT' || target.tagName === 'TEXTAREA' || target.contentEditable === 'true';
+                    if (isInput) {
+                        return;
+                    }
+
+                    const key = e.key.toLowerCase();
+
+                    if (key === 'p') {
+                        e.preventDefault();
+                        await this.handlePick();
+                    } else if (key === '?') {
+                        e.preventDefault();
+                        this.toggleHelp();
+                    } else if (key === '/') {
+                        e.preventDefault();
+                        await this.handleFlagAssignment('reject');
+                    } else if (key === 'arrowleft') {
+                        e.preventDefault();
+                        let nextIdx = this.currentGroupIdx - 1;
+                        if (nextIdx < 0) nextIdx = this.imageGroups.length - 1;
+                        this.loadGroup(nextIdx);
+                    } else if (key === 'arrowright') {
+                        e.preventDefault();
+                        let nextIdx = this.currentGroupIdx + 1;
+                        if (nextIdx >= this.imageGroups.length) nextIdx = 0;
+                        this.loadGroup(nextIdx);
+                    } else if (key === 'k') {
+                        e.preventDefault();
+                        for (let i = this.currentGroupIdx + 1; i < this.imageGroups.length; i++) {
+                            if (this.getGroupStatus(i) === 'pending') {
+                                this.loadGroup(i);
+                                return;
+                            }
+                        }
+                    } else if (key >= '1' && key <= '9') {
+                        e.preventDefault();
+                        const flagIdx = parseInt(key) - 1;
+                        if (flagIdx < this.availableFlags.length) {
+                            const flagName = this.availableFlags[flagIdx];
+                            await this.handleFlagAssignment(flagName);
+                        }
+                    }
+                },
+
+                async handlePick() {
+                    if (!this.currentGroup) return;
+
+                    const currentStatus = this.getGroupStatus(this.currentGroupIdx);
+                    
+                    // If already picked, unpick it
+                    if (currentStatus === 'picked') {
+                        try {
+                            const resp = await fetch('/api/unpick', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify({
+                                    original: this.currentGroup.original.name
+                                })
+                            });
+                            if (resp.ok) {
+                                this.groupStatus[this.currentGroupIdx] = 'pending';
+                                this.originalPicked = false;
+                                this.pickedEdits = [];
+                            }
+                        } catch (err) {
+                            console.error('Error unpicking:', err);
+                        }
+                        return;
+                    }
+
+                    const hoveredEl = this.getHoveredElement();
+                    const isHoveringOriginal = hoveredEl?.closest('.image-display:not(.right-panel *)');
+
+                    // If hovering over original, pick just the original
+                    if (isHoveringOriginal) {
+                        try {
+                            const resp = await fetch('/api/pick', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify({
+                                    original: this.currentGroup.original.name,
+                                    edits: [],
+                                    includeOriginal: true
+                                })
+                            });
+                            if (resp.ok) {
+                                this.originalPicked = true;
+                            }
+                        } catch (err) {
+                            console.error('Error picking original:', err);
+                        }
+                        return;
+                    }
+
+                    // If hovering on edit, pick just that edit
+                    const hoveredGridItem = hoveredEl?.closest('.grid-item');
+                    if (hoveredGridItem && this.currentGroup.edits.length > 0) {
+                        const idx = Array.from(hoveredGridItem.parentElement.querySelectorAll('.grid-item')).indexOf(hoveredGridItem);
+                        if (idx !== -1) {
+                            const editName = this.currentGroup.edits[idx].name;
+                        try {
+                            const resp = await fetch('/api/pick', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify({
+                                    original: this.currentGroup.original.name,
+                                    edits: [editName],
+                                    includeOriginal: false
+                                })
+                            });
+                            if (resp.ok) {
+                                this.pickedEdits.push(idx);
+                            }
+                        } catch (err) {
+                            console.error('Error picking edit:', err);
+                        }
+                        return;
+                        }
+                    }
+
+                    // If not hovering, pick all selected edits and advance
+                    let editsToPick = this.pickedEdits.map(i => this.currentGroup.edits[i].name);
+
+                    // If no edits selected but there's exactly 1 edit, pick it automatically
+                    if (editsToPick.length === 0 && this.currentGroup.edits.length === 1) {
+                        editsToPick = [this.currentGroup.edits[0].name];
+                    }
+
+                    // Only proceed if there are edits to pick
+                    if (editsToPick.length === 0) return;
+
+                    try {
+                        const resp = await fetch('/api/pick', {
+                            method: 'POST',
+                            headers: { 'Content-Type': 'application/json' },
+                            body: JSON.stringify({
+                                original: this.currentGroup.original.name,
+                                edits: editsToPick,
+                                includeOriginal: false
+                            })
+                        });
+                        if (resp.ok) {
+                            this.groupStatus[this.currentGroupIdx] = 'picked';
+                            this.loadGroup(this.currentGroupIdx + 1);
+                        }
+                    } catch (err) {
+                        console.error('Error picking edits:', err);
+                    }
+                },
+
+                startHeartbeat() {
+                    setInterval(async () => {
+                        try {
+                            await fetch('/api/heartbeat', { method: 'POST' });
+                        } catch (err) {
+                            console.error('Heartbeat failed:', err);
+                        }
+                    }, 1000);
+                },
+
+                toggleHelp() {
+                    const dialog = document.getElementById('helpDialog');
+                    if (dialog.open) {
+                        dialog.close();
+                    } else {
+                        dialog.showModal();
+                    }
+                },
+
+                closeHelp() {
+                    document.getElementById('helpDialog').close();
+                },
+
+                async handleFlagAssignment(flagName) {
+                    if (!this.currentGroup) return;
+
+                    const imageKey = this.currentGroup.original.name;
+                    
+                    // Toggle: if already assigned to this flag, unassign
+                    if (this.imageFlagAssignments[imageKey] === flagName) {
+                        try {
+                            await fetch('/api/unassign-flag', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify({
+                                    original: imageKey,
+                                    flag: flagName
+                                })
+                            });
+                            delete this.imageFlagAssignments[imageKey];
+                        } catch (err) {
+                            console.error('Error unassigning flag:', err);
+                        }
+                    } else {
+                        // Assign to new flag
+                        try {
+                            const resp = await fetch('/api/assign-flag', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify({
+                                    original: imageKey,
+                                    flag: flagName
+                                })
+                            });
+                            if (resp.ok) {
+                                this.imageFlagAssignments[imageKey] = flagName;
+                            }
+                        } catch (err) {
+                            console.error('Error assigning flag:', err);
+                        }
+                    }
+                }
+            };
+        }
+    </script>
+</body>
+</html>
+"""
+
+
+@app.get("/")
+def index() -> str:
+    return HTML_TEMPLATE
+
+
+@app.get("/api/images")
+def api_images():
+    """Return grouped images and current flag assignments."""
+    global image_groups, flags, flag_dirs
+    result = []
+    for group in image_groups:
+        result.append(
+            {
+                "base": group["base"],
+                "original": {"name": group["original"].name, "path": str(group["original"])},
+                "edits": [{"name": e.name, "path": str(e)} for e in group["edits"]],
+            }
+        )
+
+    # Build flag assignments by checking which flag directories contain which original images
+    flag_assignments = {}
+    for flag, flag_dir in flag_dirs.items():
+        if flag_dir.exists():
+            for item in flag_dir.iterdir():
+                if item.is_file():
+                    # Map the file name to its flag
+                    flag_assignments[item.name] = flag
+
+    return {"groups": result, "flags_list": flags, "flag_assignments": flag_assignments}
+
+
+@app.get("/image/<filename>")
+def serve_image(filename: str):
+    """Serve an image file safely."""
+    try:
+        target = safe_image_path(filename)
+        return bottle.static_file(target.name, root=str(target.parent), mimetype="image/jpeg")
+    except ValueError:
+        bottle.response.status = 403
+        return {"error": "Unauthorized path"}
+
+
+@app.post("/api/pick")
+def api_pick():
+    """Hard link picked images to _picks directory."""
+    global image_groups, current_group_idx, picks_dir
+
+    # Create directory before writing
+    make_dirs(picks_dir)
+
+    data = bottle.request.json
+    original_name = data.get("original")
+    edits_names = data.get("edits", [])
+    include_original = data.get("includeOriginal", False)
+
+    files_to_pick = []
+
+    # Add original if requested
+    if include_original:
+        for img in all_images:
+            if img.name == original_name:
+                files_to_pick.append(img)
+                break
+
+    # Add edits by finding them in all_images
+    for edit_name in edits_names:
+        for img in all_images:
+            if img.name == edit_name:
+                files_to_pick.append(img)
+                break
+
+    try:
+        hardlink_images(files_to_pick, picks_dir)
+        return {"status": "ok"}
+    except Exception as e:
+        bottle.response.status = 500
+        return {"error": str(e)}
+
+
+@app.post("/api/unpick")
+def api_unpick():
+    """Remove picked images from _picks directory."""
+    global picks_dir
+
+    data = bottle.request.json
+    original_name = data.get("original")
+
+    try:
+        # Find and remove the original and any edits from picks directory
+        if picks_dir.exists():
+            for item in picks_dir.iterdir():
+                if item.is_file() and (item.name == original_name or item.stem.startswith(original_name.rsplit(".", 1)[0] + "-edit")):
+                    item.unlink()
+        return {"status": "ok"}
+    except Exception as e:
+        bottle.response.status = 500
+        return {"error": str(e)}
+
+
+@app.post("/api/assign-flag")
+def api_assign_flag():
+    """Assign an image to a flag."""
+    global flag_dirs, all_images
+
+    data = bottle.request.json
+    original_name = data.get("original")
+    flag_name = data.get("flag")
+
+    # Find original file
+    original_path = None
+    for img in all_images:
+        if img.name == original_name:
+            original_path = img
+            break
+
+    if not original_path or flag_name not in flag_dirs:
+        bottle.response.status = 400
+        return {"error": "Invalid image or flag"}
+
+    # Create directory before writing
+    make_dirs(flag_dirs[flag_name])
+
+    try:
+        hardlink_images([original_path], flag_dirs[flag_name])
+        return {"status": "ok"}
+    except Exception as e:
+        bottle.response.status = 500
+        return {"error": str(e)}
+
+
+@app.post("/api/unassign-flag")
+def api_unassign_flag():
+    """Remove an image from a flag."""
+    global flag_dirs
+
+    data = bottle.request.json
+    original_name = data.get("original")
+    flag_name = data.get("flag")
+
+    if flag_name not in flag_dirs:
+        bottle.response.status = 400
+        return {"error": "Invalid flag"}
+
+    try:
+        target_file = flag_dirs[flag_name] / original_name
+        if target_file.exists():
+            target_file.unlink()
+        return {"status": "ok"}
+    except Exception as e:
+        bottle.response.status = 500
+        return {"error": str(e)}
+
+
+@app.post("/api/heartbeat")
+def api_heartbeat():
+    """Update last heartbeat timestamp."""
+    global last_heartbeat
+    last_heartbeat = datetime.now()
+    return {"status": "ok"}
+
+
+@app.post("/api/shutdown")
+def api_shutdown():
+    """Shutdown the server."""
+    global should_exit
+    should_exit = True
+    return {"status": "ok"}
+
+
+@functools.cache
+def make_dirs(path: Path) -> None:
+    """Create directory if it doesn't exist."""
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def heartbeat_monitor() -> None:
+    """Monitor for shutdown signal."""
+    global should_exit
+
+    while not should_exit:
+        time.sleep(0.5)
+
+    print("\nShutting down server.", file=sys.stderr)
+    os._exit(0)
+
+
+def main() -> None:
+    global all_images, image_groups, last_heartbeat, picks_dir, flags, flag_dirs
+
+    parser = argparse.ArgumentParser(description="Image culling app for AI image2image transforms")
+    parser.add_argument("paths", nargs="+", help="Image files or folders to cull")
+    parser.add_argument("--picks-dir", default=None, help="Directory for picked images (default: $cwd/_picks)")
+    parser.add_argument("--rejects-dir", default=None, help="Directory for rejected images (default: $cwd/_rejects)")
+    parser.add_argument("--flags", default=None, help="Comma-separated flag names (e.g., 'a,b,c,d')")
+    parser.add_argument("--flags-dir", default=None, help="Directory for flag subdirectories (default: $cwd)")
+
+    args = parser.parse_args()
+
+    # Set directories
+    cwd = Path.cwd()
+    picks_dir = Path(args.picks_dir or (cwd / "_picks"))
+    rejects_dir = Path(args.rejects_dir or (cwd / "_rejects"))
+    flags_dir = Path(args.flags_dir or cwd)
+
+    # Set up flags
+    if args.flags:
+        flags = [f.strip() for f in args.flags.split(",")]
+        for flag in flags:
+            flag_dir = flags_dir / f"_picks_flag_{flag}"
+            flag_dirs[flag] = flag_dir
+
+    # Always add reject as a flag at the end
+    flags.append("reject")
+    flag_dirs["reject"] = rejects_dir
+
+    # Discover images
+    images = discover_images(args.paths)
+    all_images = set(images)
+
+    if not images:
+        print("No images found in provided paths.", file=sys.stderr)
+        sys.exit(1)
+
+    # Group images
+    image_groups = group_images(images)
+
+    if not image_groups:
+        print("No original images found (looking for files matching naming convention).", file=sys.stderr)
+        sys.exit(1)
+
+    print(f"Found {len(image_groups)} original image(s) with variations", file=sys.stderr)
+
+    # Find available port
+    port = find_available_port()
+    url = f"http://127.0.0.1:{port}"
+
+    # Start heartbeat monitor
+    monitor_thread = threading.Thread(target=heartbeat_monitor, daemon=True)
+    monitor_thread.start()
+
+    # Open browser
+    print(f"Opening browser at {url}", file=sys.stderr)
+    webbrowser.open(url)
+
+    # Start Flask server
+    last_heartbeat = datetime.now()
+    bottle.run(app, host="127.0.0.1", port=port, quiet=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/date_images.py b/date_images.py
new file mode 100755
index 0000000..c7e34c3
--- /dev/null
+++ b/date_images.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env -S uv run
+# /// script
+# dependencies = ["piexif"]
+# ///
+
+import argparse
+from concurrent.futures import ThreadPoolExecutor
+import logging
+import re
+import subprocess
+from datetime import datetime
+from pathlib import Path
+
+import piexif
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Set creation time to date in filename and mod time to now for image files.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "paths",
+        nargs="+",
+        type=Path,
+        help="Paths to image files or folders containing images.",
+    )
+    return parser.parse_args()
+
+
+def update_image_times(path: Path) -> None:
+    match = re.search(r"\D(\d{4}-\d{2}-\d{2})\D?", path.name)
+    if not match:
+        logging.debug(f"No date found in {path.name}")
+        return
+    date_str = match.group(1)
+    try:
+        dt = datetime.fromisoformat(date_str)
+        formatted_date = dt.strftime("%m/%d/%Y %H:%M:%S")
+        today = datetime.now().strftime("%m/%d/%Y %H:%M:%S")
+        subprocess.run(["SetFile", "-d", formatted_date, str(path)], check=True)
+        subprocess.run(["SetFile", "-m", today, str(path)], check=True)
+        # Update EXIF DateTimeOriginal
+        exif_dict = piexif.load(str(path))
+        exif_dict["Exif"][piexif.ExifIFD.DateTimeOriginal] = dt.strftime("%Y:%m:%d %H:%M:%S").encode()
+        exif_bytes = piexif.dump(exif_dict)
+        piexif.insert(exif_bytes, str(path))
+        logging.info(f"Updated times and EXIF for {path}")
+    except ValueError as e:
+        logging.error(f"Invalid date {date_str} in {path.name}: {e}")
+    except subprocess.CalledProcessError as e:
+        logging.error(f"Failed to update {path}: {e}")
+    except Exception as e:
+        logging.error(f"Failed to update EXIF for {path}: {e}")
+
+
+def main() -> None:
+    args = parse_args()
+
+    files = []
+    for it in args.paths:
+        if it.is_dir():
+            files.extend(it.glob("*.jpg"))
+            files.extend(it.glob("*.jpeg"))
+        elif it.is_file() and it.suffix.lower() in {".jpg", ".jpeg"}:
+            files.append(it)
+        else:
+            logging.warning(f"Path {it} is neither an image nor a directory, skipping.")
+
+    pool = ThreadPoolExecutor(max_workers=5)
+    for path in files:
+        pool.submit(update_image_times, path)
+    pool.shutdown(wait=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/delete_downloaded_videos.py b/delete_downloaded_videos.py
new file mode 100755
index 0000000..14683d6
--- /dev/null
+++ b/delete_downloaded_videos.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["psycopg[binary]"]
+# ///
+import dataclasses
+import datetime
+import hashlib
+from pathlib import Path
+import subprocess
+import typing
+import psycopg
+import contextlib
+
+
+@contextlib.contextmanager
+def connect_db() -> typing.Generator[psycopg.Connection, typing.Any, typing.Any]:
+    with psycopg.connect("postgres://abdus:abdus@db.abdus.dev:5444/smut?sslmode=disable") as conn:
+        conn.row_factory = psycopg.rows.dict_row
+        yield conn
+
+
+def get_video_duration(video_path: Path) -> datetime.timedelta:
+    # fmt: off
+    args = [
+        'ffprobe',
+        '-v', 'error',
+        '-show_entries', 'format=duration',
+        '-of', 'default=noprint_wrappers=1:nokey=1',
+        video_path,
+    ]
+    # fmt: on
+    p = subprocess.run(args, stdout=subprocess.PIPE, check=True)
+    return datetime.timedelta(seconds=round(float(p.stdout), 1))
+
+
+def hash_partial(f: Path) -> str:
+    sha1 = hashlib.sha1()
+    chunk_size = 1024 * 1024 * 10  # 10MB chunk size
+
+    total_read = 0
+    with f.open("rb") as file:
+        while chunk := file.read(chunk_size):
+            total_read += len(chunk)
+            sha1.update(chunk)
+            break  # Only reads the first 10MB
+
+    return f"sha1:{total_read}:{sha1.hexdigest()}"
+
+
+@dataclasses.dataclass
+class SavedVideo:
+    id: int
+    remote_path: Path
+    local_path: Path
+
+    def __hash__(self):
+        return hash(self.remote_path)
+
+
+# smut=# \d videos;
+#                                                                  Table "public.videos"
+#          Column         |            Type             | Collation | Nullable |                                 Default
+# ------------------------+-----------------------------+-----------+----------+--------------------------------------------------------------------------
+#  id                     | integer                     |           | not null | nextval('videos_id_seq'::regclass)
+#  category               | ltree                       |           | not null |
+#  file_path              | text                        |           | not null |
+#  created_at             | timestamp without time zone |           |          | now()
+#  hash_partial           | text                        |           | not null |
+#  ffprobe                | jsonb                       |           | not null |
+#  size_bytes             | bigint                      |           | not null | generated always as ((ffprobe ->> 'size_bytes'::text)::bigint) stored
+#  duration_sec           | numeric                     |           | not null | generated always as ((ffprobe ->> 'duration_sec'::text)::numeric) stored
+#  suggested_filename     | text                        |           |          |
+#  marked_for_deletion_at | timestamp with time zone    |           |          |
+#  duration_human         | text                        |           |          | generated always as (ffprobe ->> 'duration_human'::text) stored
+#  last_seen_at           | timestamp with time zone    |           |          |
+# Indexes:
+#     "videos_pkey" PRIMARY KEY, btree (id)
+#     "videos_file_path_idx" gist (file_path gist_trgm_ops)
+#     "videos_file_path_uniq" UNIQUE CONSTRAINT, btree (file_path)
+def find_videos_by_hash(conn: psycopg.Connection, video_paths: list[Path]) -> list[SavedVideo]:
+    file_to_hash = {p: hash_partial(p) for p in video_paths}
+    if not file_to_hash:
+        return []
+
+    placeholders = ",".join(["%s"] * len(file_to_hash))
+    sql = f"""
+    SELECT id, file_path AS remote_path, file_path AS local_path
+    FROM videos
+    WHERE hash_partial IN ({placeholders})
+    """
+    with conn.cursor() as cur:
+        rows = cur.execute(sql, list(file_to_hash.values())).fetchall()
+        return [SavedVideo(**row) for row in rows]
+
+
+def find_videos_by_duration(conn: psycopg.Connection, video_paths: list[Path]) -> list[SavedVideo]:
+    if not video_paths:
+        return []
+    file_to_duration = {p: get_video_duration(p).total_seconds() for p in video_paths}
+    file_to_size = {p: p.stat().st_size for p in video_paths}
+
+    sql = f"""
+    SELECT id, file_path AS remote_path, file_path AS local_path
+    FROM videos
+    WHERE abs(duration_sec - %s) < 0.1 AND abs(size_bytes - %s) < 1048576
+    """
+    with conn.cursor() as cur:
+        out = []
+        for file_path in video_paths:
+            size = file_to_size[file_path]
+            duration = file_to_duration[file_path]
+            for row in cur.execute(sql, (duration, size)):
+                out.append(SavedVideo(**row))
+        return out
+
+
+def delete_videos(con: psycopg.Connection, file_paths: list[str]) -> bool:
+    with con.cursor() as cur:
+        placeholders = ",".join(["%s"] * len(file_paths))
+        sql = f"DELETE FROM videos WHERE file_path IN ({placeholders})"
+        cur.execute(sql, file_paths)
+
+
+def main():
+    videos = list(Path(r"/Users/abdus/Downloads/temp").glob("*.mp4"))
+    with connect_db() as conn:
+        found_videos = set(find_videos_by_hash(conn, videos))
+        found_videos.update(find_videos_by_duration(conn, videos))
+        found_videos = {video for video in found_videos if "/mnt/box/files/_raw/prt" in video.remote_path}
+        for video in found_videos:
+            print(f"{video.remote_path}")
+        if not found_videos:
+            print("No videos found for deletion.")
+            return
+        input("Press Enter to continue...")
+
+        print(f"Deleting {len(found_videos)} videos from database...")
+        delete_videos(conn, [video.remote_path for video in found_videos])
+
+
+if __name__ == "__main__":
+    main()
diff --git a/face_landmarker.task b/face_landmarker.task
new file mode 100644
index 0000000..c50c845
Binary files /dev/null and b/face_landmarker.task differ
diff --git a/ffmpeg-thumbnail-tile.md b/ffmpeg-thumbnail-tile.md
new file mode 100644
index 0000000..b07c202
--- /dev/null
+++ b/ffmpeg-thumbnail-tile.md
@@ -0,0 +1,29 @@
+# Generating thumbnail tiles using ffmpeg
+> ffmpeg can unsurprisingly build contact sheets, too. Is there anything ffmpeg can't do?
+
+I've been organizing my movie collection lately, and I needed a way to figure out what a video is all about without having to 
+view & seek it.
+Building a contact sheet is a simple solution for this. It places frames with from the video in a grid separated by 
+an interval (like every minute) and you can quickly see what a video contains.
+
+## ffmpeg tile filter
+
+With anything media-related, I checked to see if ffmpeg supports this: it turns out, it has a `tile` filter[^tile][tile], which gives
+us some primitives to work with.
+
+## Covering the whole video
+
+```shell
+ffmpeg -skip_frame nokey -i video.mp4 -vf 'scale=320:-1,tile=8x8' -an -vsync 0 keyframes%03d.png
+```
+
+This creates n files, each containing 8x8 grid of keyframes from the video.
+It's close, but I need a single file with tiles spanning the video from start to finish.
+
+## 
+
+```shell
+
+```
+
+[tile]: https://ffmpeg.org/ffmpeg-filters.html#tile-1 
diff --git a/flux2_private_server.py b/flux2_private_server.py
new file mode 100644
index 0000000..c93aa10
--- /dev/null
+++ b/flux2_private_server.py
@@ -0,0 +1,461 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.10,<3.11"
+# dependencies = [
+#   "torch==2.9.1+cu128",
+#   "torchvision==0.24.1+cu128",
+#   "torchaudio==2.9.1+cu128",
+#   "diffusers @ git+https://github.com/huggingface/diffusers.git",
+#   "transformers>=4.57.0",
+#   "accelerate>=1.0.0",
+#   "safetensors>=0.4.0",
+#   "huggingface_hub>=0.25.0",
+#   "numpy>=1.26.0",
+#   "pillow>=10.0.0",
+#   "fastapi>=0.115.0",
+#   "uvicorn>=0.29.0",
+# ]
+# [tool.uv]
+# extra-index-url = ["https://download.pytorch.org/whl/cu128"]
+# ///
+
+import argparse
+import base64
+import binascii
+import io
+import os
+import random
+import threading
+import time
+from typing import Any
+
+os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+
+import torch
+from diffusers import Flux2KleinPipeline
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import HTMLResponse, Response
+from pydantic import BaseModel, Field
+from PIL import Image
+
+
+def _resolve_model_size() -> str:
+    parser = argparse.ArgumentParser(add_help=False)
+    parser.add_argument("--model", choices=["4b", "9b"], default="9b")
+    args, _ = parser.parse_known_args()
+    return args.model
+
+
+def _checkpoint_for(model_size: str) -> str:
+    if model_size == "4b":
+        return "black-forest-labs/FLUX.2-klein-4B"
+    return "black-forest-labs/FLUX.2-klein-9B"
+
+
+MODEL_SIZE = _resolve_model_size()
+CHECKPOINT = _checkpoint_for(MODEL_SIZE)
+USE_COMPILE = os.environ.get("FLUX2_COMPILE", "1") != "0"
+WARMUP_RUNS = int(os.environ.get("FLUX2_WARMUP_RUNS", "2"))
+WARMUP_STEPS = int(os.environ.get("FLUX2_WARMUP_STEPS", "4"))
+WARMUP_PROMPT = "A high-resolution photo of a red fox in natural daylight"
+MAX_SEED = (2**31) - 1
+MAX_PIXELS = 4 * 1024 * 1024
+
+DEFAULT_STEPS = 4
+DEFAULT_GUIDANCE = 1.0
+
+app = FastAPI(title="FLUX2 Klein Private Server")
+PIPE: Flux2KleinPipeline | None = None
+PIPE_LOCK = threading.Lock()
+
+
+class GenerateRequest(BaseModel):
+    prompt: str = Field(..., min_length=1)
+    width: int = 1024
+    height: int = 1024
+    num_inference_steps: int = DEFAULT_STEPS
+    guidance_scale: float = DEFAULT_GUIDANCE
+    seed: int = 0
+    randomize_seed: bool = False
+    image_data_url: str | None = None
+
+
+class CompatRequest(BaseModel):
+    model: str | None = None
+    prompt: str = Field(..., min_length=1)
+    imageDataUrl: str | None = None
+    size: str | None = None
+    width: int | None = None
+    height: int | None = None
+    num_inference_steps: int | None = None
+    guidance_scale: float | None = None
+    seed: int | None = None
+    randomize_seed: bool = False
+    response_format: str = "b64_json"
+    n: int = 1
+    negative_prompt: str | None = None
+    negativePrompt: str | None = None
+
+
+def _ensure_cuda() -> None:
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required for this server")
+
+
+def _compile_pipe(pipe: Flux2KleinPipeline) -> bool:
+    if not USE_COMPILE:
+        return False
+    print("Compiling transformer and VAE decoder...")
+    start = time.perf_counter()
+    pipe.transformer = torch.compile(
+        pipe.transformer,
+        mode="max-autotune",
+        dynamic=False,
+        fullgraph=True,
+    )
+    pipe.vae.decode = torch.compile(
+        pipe.vae.decode,
+        mode="max-autotune",
+        dynamic=False,
+        fullgraph=True,
+    )
+    print(f"Compile finished in {time.perf_counter() - start:.2f}s")
+    return True
+
+
+def _warmup_pipe(pipe: Flux2KleinPipeline) -> None:
+    if WARMUP_RUNS <= 0:
+        return
+    print(f"Warmup started ({WARMUP_RUNS} runs)...")
+    start = time.perf_counter()
+    for run_idx in range(WARMUP_RUNS):
+        generator = torch.Generator(device="cuda").manual_seed(run_idx)
+        pipe(
+            prompt=WARMUP_PROMPT,
+            height=1024,
+            width=1024,
+            num_inference_steps=WARMUP_STEPS,
+            guidance_scale=1.0,
+            generator=generator,
+        ).images[0]
+    torch.cuda.synchronize()
+    print(f"Warmup finished in {time.perf_counter() - start:.2f}s")
+
+
+def _load_pipe(ckpt: str) -> Flux2KleinPipeline:
+    print(f"Loading checkpoint: {ckpt}")
+    pipe = Flux2KleinPipeline.from_pretrained(ckpt, torch_dtype=torch.bfloat16)
+    pipe = pipe.to("cuda")
+    pipe.transformer.fuse_qkv_projections()
+    pipe.vae.fuse_qkv_projections()
+    pipe.vae.to(memory_format=torch.channels_last)
+    pipe.transformer.set_attention_backend("_native_flash")
+    if _compile_pipe(pipe):
+        _warmup_pipe(pipe)
+    return pipe
+
+
+def _normalize_dims(width: int, height: int) -> tuple[int, int]:
+    if width <= 0 or height <= 0:
+        raise HTTPException(status_code=422, detail="width and height must be > 0")
+    if width % 16 != 0 or height % 16 != 0:
+        raise HTTPException(
+            status_code=422, detail="width and height must be multiples of 16"
+        )
+    if width * height > MAX_PIXELS:
+        raise HTTPException(status_code=422, detail="max resolution is 4 megapixels")
+    return width, height
+
+
+def _parse_size(
+    size: str | None, width: int | None, height: int | None
+) -> tuple[int, int]:
+    if width and height:
+        return _normalize_dims(width, height)
+    if not size:
+        return _normalize_dims(1024, 1024)
+    parts = size.lower().split("x")
+    if len(parts) != 2:
+        raise HTTPException(status_code=422, detail="size must look like 1024x1024")
+    try:
+        parsed_w = int(parts[0])
+        parsed_h = int(parts[1])
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=422, detail="size values must be integers"
+        ) from exc
+    return _normalize_dims(parsed_w, parsed_h)
+
+
+def _decode_image_data_url(image_data_url: str | None) -> Image.Image | None:
+    if not image_data_url:
+        return None
+    if "," not in image_data_url:
+        raise HTTPException(status_code=422, detail="imageDataUrl must be a data URL")
+    _, payload = image_data_url.split(",", 1)
+    try:
+        raw = base64.b64decode(payload)
+    except (ValueError, binascii.Error) as exc:
+        raise HTTPException(
+            status_code=422, detail="invalid base64 in imageDataUrl"
+        ) from exc
+    with Image.open(io.BytesIO(raw)) as image:
+        return image.convert("RGB")
+
+
+def _generate_png_bytes(
+    prompt: str,
+    width: int,
+    height: int,
+    num_inference_steps: int,
+    guidance_scale: float,
+    seed: int,
+    image: Image.Image | None,
+) -> tuple[bytes, int]:
+    if PIPE is None:
+        raise HTTPException(status_code=503, detail="model not loaded yet")
+
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    kwargs: dict[str, Any] = {
+        "prompt": prompt,
+        "height": height,
+        "width": width,
+        "num_inference_steps": num_inference_steps,
+        "guidance_scale": guidance_scale,
+        "generator": generator,
+    }
+
+    with PIPE_LOCK:
+        try:
+            if image is not None:
+                try:
+                    output = PIPE(image=image, **kwargs)
+                except TypeError:
+                    output = PIPE(images=[image], **kwargs)
+            else:
+                output = PIPE(**kwargs)
+        except Exception as exc:
+            raise HTTPException(
+                status_code=500, detail=f"generation failed: {exc}"
+            ) from exc
+
+    result_image = output.images[0]
+    buf = io.BytesIO()
+    result_image.save(buf, format="PNG")
+    return buf.getvalue(), seed
+
+
+@app.on_event("startup")
+def _startup() -> None:
+    global PIPE
+    _ensure_cuda()
+    PIPE = _load_pipe(CHECKPOINT)
+    print("Server ready")
+
+
+@app.get("/", response_class=HTMLResponse)
+def index() -> str:
+    return """<!doctype html>
+<html lang=\"en\">
+<head>
+  <meta charset=\"utf-8\" />
+  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />
+  <title>FLUX.2 Klein Playground</title>
+  <style>
+    :root { --bg:#f4f0e8; --card:#fff9ee; --text:#1f1f1f; --accent:#125b50; --muted:#6b6b6b; }
+    * { box-sizing: border-box; }
+    body { margin:0; font-family: ui-sans-serif, -apple-system, Segoe UI, sans-serif; background: radial-gradient(circle at top left,#fff6df,var(--bg)); color:var(--text); }
+    .wrap { max-width: 980px; margin: 1.5rem auto; padding: 1rem; }
+    .card { background: var(--card); border: 1px solid #eadfc8; border-radius: 14px; padding: 1rem; box-shadow: 0 8px 20px rgba(0,0,0,.06); }
+    h1 { margin: 0 0 .8rem 0; font-size: 1.4rem; }
+    .grid { display:grid; gap:.8rem; grid-template-columns: 1fr 1fr; }
+    .full { grid-column: 1 / -1; }
+    label { display:block; font-weight:600; margin-bottom:.3rem; }
+    input, textarea, button { width:100%; border-radius:10px; border:1px solid #d8ccb7; padding:.65rem; font-size:.95rem; }
+    textarea { min-height: 110px; resize: vertical; }
+    button { background: var(--accent); color:white; border:none; font-weight:700; cursor:pointer; }
+    button:disabled { opacity:.6; cursor:default; }
+    .muted { color: var(--muted); font-size:.88rem; }
+    .out img { width:100%; border-radius:12px; border:1px solid #e4d8c3; background:white; }
+    @media (max-width: 760px) { .grid { grid-template-columns: 1fr; } }
+  </style>
+</head>
+<body>
+  <div class=\"wrap\">
+    <div class=\"card\">
+      <h1>FLUX.2 Klein Playground</h1>
+      <p class=\"muted\">Private single-user UI at <code>/</code>. Distilled defaults: 4 steps, guidance 1.0.</p>
+      <form id=\"form\" class=\"grid\">
+        <div class=\"full\">
+          <label for=\"prompt\">Prompt</label>
+          <textarea id=\"prompt\" required>A cinematic photo portrait, natural skin texture, realistic lighting, high detail.</textarea>
+        </div>
+        <div>
+          <label for=\"width\">Width (multiple of 16)</label>
+          <input id=\"width\" type=\"number\" value=\"1024\" step=\"16\" min=\"16\" />
+        </div>
+        <div>
+          <label for=\"height\">Height (multiple of 16)</label>
+          <input id=\"height\" type=\"number\" value=\"1024\" step=\"16\" min=\"16\" />
+        </div>
+        <div>
+          <label for=\"steps\">Inference steps</label>
+          <input id=\"steps\" type=\"number\" value=\"4\" min=\"1\" max=\"100\" />
+        </div>
+        <div>
+          <label for=\"guidance\">Guidance scale</label>
+          <input id=\"guidance\" type=\"number\" value=\"1.0\" step=\"0.1\" />
+        </div>
+        <div>
+          <label for=\"seed\">Seed</label>
+          <input id=\"seed\" type=\"number\" value=\"0\" min=\"0\" />
+        </div>
+        <div>
+          <label for=\"edit\">Edit image (optional)</label>
+          <input id=\"edit\" type=\"file\" accept=\"image/*\" />
+        </div>
+        <div class=\"full\">
+          <button id=\"submit\" type=\"submit\">Generate</button>
+          <p id=\"status\" class=\"muted\">Idle</p>
+        </div>
+      </form>
+    </div>
+
+    <div class=\"card out\" style=\"margin-top:1rem\">
+      <h1>Output</h1>
+      <img id=\"preview\" alt=\"Generated image preview\" />
+      <p class=\"muted\">Tip: right-click image to save.</p>
+    </div>
+  </div>
+
+  <script>
+    const form = document.getElementById('form');
+    const statusEl = document.getElementById('status');
+    const preview = document.getElementById('preview');
+    const button = document.getElementById('submit');
+
+    async function fileToDataUrl(file) {
+      return await new Promise((resolve, reject) => {
+        const reader = new FileReader();
+        reader.onload = () => resolve(reader.result);
+        reader.onerror = reject;
+        reader.readAsDataURL(file);
+      });
+    }
+
+    form.addEventListener('submit', async (event) => {
+      event.preventDefault();
+      button.disabled = true;
+      statusEl.textContent = 'Generating...';
+      try {
+        const prompt = document.getElementById('prompt').value;
+        const width = Number(document.getElementById('width').value);
+        const height = Number(document.getElementById('height').value);
+        const steps = Number(document.getElementById('steps').value);
+        const guidance = Number(document.getElementById('guidance').value);
+        const seed = Number(document.getElementById('seed').value);
+        const file = document.getElementById('edit').files[0];
+        const imageDataUrl = file ? await fileToDataUrl(file) : null;
+
+        const payload = {
+          model: 'black-forest-labs/FLUX.2-klein-9B',
+          prompt,
+          size: `${width}x${height}`,
+          num_inference_steps: steps,
+          guidance_scale: guidance,
+          seed,
+          response_format: 'b64_json',
+          imageDataUrl,
+        };
+
+        const resp = await fetch('/v1/images/generations', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify(payload),
+        });
+
+        if (!resp.ok) {
+          const text = await resp.text();
+          throw new Error(text || `HTTP ${resp.status}`);
+        }
+
+        const data = await resp.json();
+        const b64 = data?.data?.[0]?.b64_json;
+        if (!b64) throw new Error('No image returned');
+        preview.src = `data:image/png;base64,${b64}`;
+        statusEl.textContent = `Done. Seed: ${data.seed ?? seed}`;
+      } catch (error) {
+        statusEl.textContent = `Error: ${error.message}`;
+      } finally {
+        button.disabled = false;
+      }
+    });
+  </script>
+</body>
+</html>
+"""
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    return {"status": "ok", "model": CHECKPOINT}
+
+
+@app.post("/generate")
+def generate(req: GenerateRequest) -> Response:
+    width, height = _normalize_dims(req.width, req.height)
+    seed = req.seed if not req.randomize_seed else random.randint(0, MAX_SEED)
+    image = _decode_image_data_url(req.image_data_url)
+    png_bytes, used_seed = _generate_png_bytes(
+        prompt=req.prompt,
+        width=width,
+        height=height,
+        num_inference_steps=req.num_inference_steps,
+        guidance_scale=req.guidance_scale,
+        seed=seed,
+        image=image,
+    )
+    return Response(
+        content=png_bytes,
+        media_type="image/png",
+        headers={"X-Seed": str(used_seed)},
+    )
+
+
+@app.post("/v1/images/generations")
+def compat_generate(req: CompatRequest) -> dict[str, Any]:
+    width, height = _parse_size(req.size, req.width, req.height)
+    seed_value = req.seed if req.seed is not None else 0
+    seed = seed_value if not req.randomize_seed else random.randint(0, MAX_SEED)
+    image = _decode_image_data_url(req.imageDataUrl)
+
+    png_bytes, used_seed = _generate_png_bytes(
+        prompt=req.prompt,
+        width=width,
+        height=height,
+        num_inference_steps=req.num_inference_steps or DEFAULT_STEPS,
+        guidance_scale=req.guidance_scale or DEFAULT_GUIDANCE,
+        seed=seed,
+        image=image,
+    )
+    b64_png = base64.b64encode(png_bytes).decode("ascii")
+    return {
+        "created": int(time.time()),
+        "model": CHECKPOINT,
+        "seed": used_seed,
+        "data": [{"b64_json": b64_png}],
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    parser = argparse.ArgumentParser(description="Private FLUX.2 Klein FastAPI server")
+    parser.add_argument("--model", choices=["4b", "9b"], default=MODEL_SIZE)
+    parser.add_argument("--host", default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=6006)
+    args = parser.parse_args()
+
+    MODEL_SIZE = args.model
+    CHECKPOINT = _checkpoint_for(MODEL_SIZE)
+    uvicorn.run(app, host=args.host, port=args.port)
diff --git a/flux_klein_prompt.txt b/flux_klein_prompt.txt
new file mode 100644
index 0000000..93e5997
--- /dev/null
+++ b/flux_klein_prompt.txt
@@ -0,0 +1,30 @@
+Artistic photorealistic conversion.
+Subtle chiaroscuro lighting, not too dark.
+120mm telephoto lens, f/2.8, shallow depth of field.
+Soft-focus highlights, atmospheric bloom.
+Kodak Ektachrome E100 color palette.
+Preserve the original lighting and moody lighting.
+High dynamic range with a focus on rich textures.
+# subtle dramatic lighting with rich lights and shadows, but not too dark.
+balanced exposure
+
+# looking at the camera
+# looking away from the camera
+
+she has flawless, spotless, tight skin. never change the skin color.
+# porcelain gothic pale skin
+raised cheekbones, tapered face, seductive look
+make her prettier without changing the skin color
+age them by 5 years and make them look like 25 year old adults.
+preserve height and proportion while maintaining the same facial features.
+# make her head 10% slimmer.
+
+depict cartoon characters as japanese
+
+slightly parted lips.
+slightly lowered or closed eyelids.
+# shiny outfit unless naked.
+# shiny, glossy outfit.
+keep the heavy makeup.
+
+make it sharp throughout. remove all text.
diff --git a/grammar.html b/grammar.html
new file mode 100644
index 0000000..19a73d0
--- /dev/null
+++ b/grammar.html
@@ -0,0 +1,251 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Text Corrector App</title>
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/jsdiff/5.1.0/diff.min.js"></script>
+        <style>
+            body {
+                font-family: "JetBrains Mono", Menlo, Consolas, monospace;
+                font-optical-sizing: auto;
+                font-size: 16px;
+                padding: 2rem;
+                margin: 0;
+                line-height: 1.5;
+            }
+
+            h2 {
+                font-size: 1rem;
+                font-weight: bold;
+                margin-bottom: 1rem;
+                line-height: 1.2;
+            }
+
+            details {
+                margin-bottom: 1rem;
+            }
+
+            summary > h2 {
+                display: inline;
+            }
+
+            button {
+                padding: 0.75rem 1.5rem;
+                -webkit-appearance: none;
+                appearance: none;
+                background-color: #222;
+                color: #f9f9f9;
+                border: none;
+                border-radius: 4px;
+                cursor: pointer;
+                font-family: inherit;
+                font-size: inherit;
+            }
+
+            /* Minimal styling for diff readability */
+            #diffOutput ins {
+                background-color: #d4ffd4;
+                text-decoration: none;
+                color: #22863a;
+            }
+            #diffOutput del {
+                background-color: #ffd4d4;
+                text-decoration: none;
+                color: #cb2431;
+            }
+            .text-container {
+                border: 1px solid #eee;
+                margin-bottom: 1rem;
+                white-space: pre-wrap;
+                word-wrap: break-word;
+            }
+            #loadingMessage,
+            #errorMessage {
+                margin-bottom: 15px;
+            }
+        </style>
+    </head>
+    <body>
+        <div>
+            <details>
+                <summary><h2>Original Text</h2></summary>
+                <div id="originalText" class="text-container"></div>
+            </details>
+        </div>
+
+        <div id="loadingMessage">Loading suggestion...</div>
+        <div id="errorMessage" style="color: red; display: none"></div>
+
+        <div id="suggestionArea" style="display: none">
+            <div>
+                <h2>Suggestion (Diff View):</h2>
+                <div id="diffOutput" class="text-container"></div>
+            </div>
+
+            <div>
+                <details>
+                    <summary><h2>Suggested Text (Clean)</h2></summary>
+                    <div id="cleanSuggestionOutput" class="text-container"></div>
+                </details>
+            </div>
+
+            <button id="acceptButton" disabled>Accept</button>
+        </div>
+
+        <script>
+            // DOM Elements
+            const originalTextEl = document.getElementById("originalText");
+            const loadingMessageEl = document.getElementById("loadingMessage");
+            const errorMessageEl = document.getElementById("errorMessage");
+            const suggestionAreaEl = document.getElementById("suggestionArea");
+            const diffOutputEl = document.getElementById("diffOutput");
+            const cleanSuggestionOutputEl = document.getElementById("cleanSuggestionOutput");
+            const acceptButtonEl = document.getElementById("acceptButton");
+
+            let currentSuggestedText = null;
+
+            async function getChatGPTSuggestion(textToReview, apiKey) {
+                // System prompt: Instructions for the AI model
+                const systemPrompt = `You are an English language assistant. Your task is to help a user who is learning English.
+When presented with their text, please:
+1. Identify and correct grammatical errors. Pay specific attention to:
+    - Missing or incorrect articles (a, an, the).
+    - Incorrect preposition usage.
+    - Errors in subject-verb agreement.
+    - Past tense and past perfect tense mistakes.
+    - Awkward or unnatural phrasing.
+2. Rephrase sentences to be simpler, clearer, and more natural, while preserving the original meaning.
+3. Preserve the original formatting (new lines, punctuation, etc.) as much as possible, unless a change is necessary for clarity or correctness.
+4. Provide ONLY the fully revised text. Do not include any explanations, apologies, preambles, or surrounding quotes like """ or \`\`\`. Just the corrected and improved plain text.
+5. If the text is already grammatically perfect and clearly phrased according to these instructions, return the original text without any changes.`;
+
+                try {
+                    const response = await fetch("https://api.openai.com/v1/chat/completions", {
+                        method: "POST",
+                        headers: {
+                            "Content-Type": "application/json",
+                            Authorization: `Bearer ${apiKey}`,
+                        },
+                        body: JSON.stringify({
+                            // Assuming window.env.OPENAI_MODEL is available as in your snippet
+                            // If not, replace with a hardcoded model or pass it as an argument
+                            model: window.env && window.env.OPENAI_MODEL ? window.env.OPENAI_MODEL : "gpt-5.1-nano",
+                            messages: [
+                                {
+                                    role: "system",
+                                    content: systemPrompt,
+                                },
+                                {
+                                    role: "user",
+                                    content: textToReview,
+                                },
+                            ],
+                            temperature: 0.8,
+                            max_tokens: Math.max(250, textToReview.length * 2 + 50),
+                        }),
+                    });
+
+                    if (!response.ok) {
+                        const errorData = await response.json();
+                        console.error("ChatGPT API Error:", errorData);
+                        return { error: `API Error: ${errorData.error?.message || response.statusText}` };
+                    }
+
+                    const data = await response.json();
+                    if (data.choices && data.choices.length > 0 && data.choices[0].message) {
+                        return { suggestion: data.choices[0].message.content.trim() };
+                    } else {
+                        console.error("ChatGPT API Error: No choices in response", data);
+                        return { error: "No suggestion received from API (empty response)." };
+                    }
+                } catch (error) {
+                    console.error("Error fetching ChatGPT suggestion:", error);
+                    return { error: `Network/Request Error: ${error.message}` };
+                }
+            }
+
+            function displayDiff(original, revised, diffContainer) {
+                if (typeof Diff === "undefined" || !Diff.diffWordsWithSpace) {
+                    diffContainer.textContent = "jsdiff library not loaded or diffWordsWithSpace not working.";
+                    console.error("jsdiff (Diff.diffWordsWithSpace) is not available.");
+                    return;
+                }
+                const diffs = Diff.diffWordsWithSpace(original, revised, { ignoreCase: true, ignoreWhitespace: true });
+                const fragment = document.createDocumentFragment();
+                diffs.forEach(function (part) {
+                    const textNode = document.createTextNode(part.value);
+                    if (part.added) {
+                        const ins = document.createElement("ins");
+                        ins.appendChild(textNode);
+                        fragment.appendChild(ins);
+                    } else if (part.removed) {
+                        const del = document.createElement("del");
+                        del.appendChild(textNode);
+                        fragment.appendChild(del);
+                    } else {
+                        fragment.appendChild(textNode);
+                    }
+                });
+                diffContainer.innerHTML = "";
+                diffContainer.appendChild(fragment);
+            }
+
+            function showError(message) {
+                loadingMessageEl.style.display = "none";
+                errorMessageEl.textContent = message;
+                errorMessageEl.style.display = "block";
+                suggestionAreaEl.style.display = "none";
+            }
+
+            async function initializeApp() {
+                if (typeof window.env.USER_INPUT === "undefined" || window.env.USER_INPUT === null) {
+                    showError("Error: window.USER_INPUT is not defined. Please set it before loading the app.");
+                    return;
+                }
+                if (typeof window.env.OPENAI_TOKEN === "undefined" || !window.env.OPENAI_TOKEN) {
+                    showError("Error: window.OPENAI_TOKEN is not defined. Please set your OpenAI API key.");
+                    return;
+                }
+                if (typeof Diff === "undefined") {
+                    showError("Error: jsdiff library could not be loaded. Check your internet connection or the CDN link.");
+                    return;
+                }
+
+                const originalText = window.env.USER_INPUT;
+                originalTextEl.textContent = originalText;
+
+                const result = await getChatGPTSuggestion(originalText, window.env.OPENAI_TOKEN);
+
+                loadingMessageEl.style.display = "none";
+
+                if (result.error) {
+                    showError(result.error);
+                } else if (result.suggestion) {
+                    currentSuggestedText = result.suggestion;
+                    suggestionAreaEl.style.display = "block";
+                    displayDiff(originalText, currentSuggestedText, diffOutputEl);
+                    cleanSuggestionOutputEl.textContent = currentSuggestedText;
+                    acceptButtonEl.disabled = false;
+                }
+            }
+
+            acceptButtonEl.addEventListener("click", () => {
+                if (currentSuggestedText !== null) {
+                    if (window.app && typeof window.app.finish === "function") {
+                        window.app.finish(currentSuggestedText);
+                        // Optionally, provide feedback or close the "app" view
+                        acceptButtonEl.textContent = "Accepted!";
+                        acceptButtonEl.disabled = true;
+                    } else {
+                        alert("Error: window.app.finish is not defined. Cannot complete action.");
+                        console.error("window.app.finish is not defined.");
+                    }
+                }
+            });
+
+            // Initialize the app when the DOM is ready
+            document.addEventListener("DOMContentLoaded", initializeApp);
+        </script>
+    </body>
+</html>
diff --git a/image_dates.py b/image_dates.py
new file mode 100644
index 0000000..56562d8
--- /dev/null
+++ b/image_dates.py
@@ -0,0 +1,279 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["Pillow"]
+# ///
+
+import argparse
+import collections
+import datetime as dt
+import logging
+import os
+import re
+import shutil
+import subprocess
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable
+
+from PIL import Image, UnidentifiedImageError
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+
+
+DATE_PATTERNS = (
+    re.compile(r"^(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})"),
+    re.compile(r"^(?P<year>\d{4})\.(?P<month>\d{2})\.(?P<day>\d{2})"),
+    re.compile(r"^(?P<year>\d{4})(?P<month>\d{2})(?P<day>\d{2})"),
+)
+
+EXIF_DATETIME_TAGS = (36867, 36868, 306)
+
+
+@dataclass
+class ImageEntry:
+    path: Path
+    group_key: str
+    filename_date: dt.datetime | None
+    exif_date: dt.datetime | None = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Update file timestamps for images grouped by filename pattern",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "directory",
+        nargs="?",
+        default=Path.cwd(),
+        type=Path,
+        help="Root directory to scan for image files",
+    )
+    parser.add_argument(
+        "--recursive",
+        action="store_true",
+        help="Recurse into subdirectories",
+    )
+    parser.add_argument(
+        "--extensions",
+        default=".jpg,.jpeg,.png,.tif,.tiff,.heic,.heif",
+        help="Comma-separated list of extensions to include",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Show planned changes without touching the filesystem",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Enable debug logging",
+    )
+    return parser.parse_args()
+
+
+def configure_logging(verbose: bool) -> None:
+    if verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+
+
+def collect_image_paths(directory: Path, extensions: set[str], recursive: bool) -> Iterable[Path]:
+    iterator = directory.rglob("*") if recursive else directory.glob("*")
+    for candidate in iterator:
+        if candidate.is_file() and candidate.suffix.lower() in extensions:
+            yield candidate
+
+
+def parse_group_and_date(path: Path) -> tuple[str, dt.datetime | None] | None:
+    name = path.stem
+    if " -- " not in name:
+        logging.debug("Skipping %s: missing group delimiter", path)
+        return None
+    try:
+        group_part, rest = name.rsplit(" -- ", 1)
+    except ValueError:
+        logging.debug("Skipping %s: unable to split group and tail", path)
+        return None
+    if not group_part:
+        logging.debug("Skipping %s: empty group part", path)
+        return None
+    date_candidate = extract_date_from_tail(rest)
+    if " -- " not in group_part and "__" in rest:
+        album_part, _, _ = rest.partition("__")
+        album_part = album_part.strip()
+        if album_part:
+            group_part = f"{group_part} -- {album_part}"
+    return group_part, date_candidate
+
+
+def extract_date_from_tail(text: str) -> dt.datetime | None:
+    for pattern in DATE_PATTERNS:
+        match = pattern.match(text)
+        if match:
+            try:
+                return dt.datetime(
+                    year=int(match.group("year")),
+                    month=int(match.group("month")),
+                    day=int(match.group("day")),
+                )
+            except ValueError:
+                return None
+    return None
+
+
+def read_exif_datetime(path: Path) -> dt.datetime | None:
+    try:
+        with Image.open(path) as image:
+            exif = image.getexif()
+    except (UnidentifiedImageError, OSError) as error:
+        logging.debug("Could not read EXIF from %s: %s", path, error)
+        return None
+    if not exif:
+        return None
+    for tag in EXIF_DATETIME_TAGS:
+        value = exif.get(tag)
+        if not value:
+            continue
+        if isinstance(value, bytes):
+            value = value.decode(errors="ignore")
+        if not isinstance(value, str):
+            continue
+        value = value.strip()
+        for fmt in ("%Y:%m:%d %H:%M:%S", "%Y-%m-%d %H:%M:%S"):
+            try:
+                return dt.datetime.strptime(value, fmt)
+            except ValueError:
+                continue
+    return None
+
+
+def most_common_datetime(candidates: list[dt.datetime]) -> dt.datetime | None:
+    if not candidates:
+        return None
+    counter = collections.Counter(candidates)
+    most_common = counter.most_common()
+    top_count = most_common[0][1]
+    top_values = [item for item, count in most_common if count == top_count]
+    return min(top_values)
+
+
+def determine_group_date(entries: list[ImageEntry]) -> dt.datetime | None:
+    name_dates = [entry.filename_date for entry in entries if entry.filename_date]
+    if name_dates:
+        logging.debug("Using filename-derived date for group %s", entries[0].group_key)
+        return most_common_datetime(name_dates)
+    exif_dates: list[dt.datetime] = []
+    for entry in entries:
+        if entry.exif_date is None:
+            entry.exif_date = read_exif_datetime(path=entry.path)
+        if entry.exif_date:
+            exif_dates.append(entry.exif_date)
+    if exif_dates:
+        logging.debug("Using EXIF-derived date for group %s", entries[0].group_key)
+    return most_common_datetime(exif_dates)
+
+
+def apply_timestamp(path: Path, target: dt.datetime, dry_run: bool) -> None:
+    timestamp = target.timestamp()
+    if dry_run:
+        logging.debug("DRY-RUN %s -> %s", path, target.isoformat(sep=" "))
+        return
+    os.utime(path, times=(timestamp, timestamp))
+
+
+def chunked(items: list[str], size: int) -> Iterable[list[str]]:
+    for index in range(0, len(items), size):
+        yield items[index : index + size]
+
+
+def apply_setfile_batch(entries: list[ImageEntry], target: dt.datetime, dry_run: bool) -> None:
+    if sys.platform != "darwin":
+        return
+    setfile = shutil.which("SetFile")
+    if not setfile:
+        return
+    formatted = target.strftime("%m/%d/%Y %H:%M:%S")
+    paths = [str(entry.path) for entry in entries]
+    for batch in chunked(items=paths, size=64):
+        if dry_run:
+            logging.debug("DRY-RUN SetFile %s files -> %s", len(batch), formatted)
+            continue
+        try:
+            subprocess.run([setfile, "-d", formatted, *batch], check=True)
+            subprocess.run([setfile, "-m", formatted, *batch], check=True)
+        except subprocess.CalledProcessError as error:
+            logging.debug("SetFile failed for %s files: %s", len(batch), error)
+
+
+def update_group(entries: list[ImageEntry], dry_run: bool) -> bool:
+    target = determine_group_date(entries=entries)
+    if target is None:
+        logging.warning("No date found for group %s", entries[0].group_key)
+        return False
+    for entry in entries:
+        apply_timestamp(path=entry.path, target=target, dry_run=dry_run)
+    apply_setfile_batch(entries=entries, target=target, dry_run=dry_run)
+    action = "DRY-RUN" if dry_run else "UPDATED"
+    logging.info(
+        "%s %s (%s files) -> %s",
+        action,
+        entries[0].group_key,
+        len(entries),
+        target.isoformat(sep=" "),
+    )
+    return True
+
+
+def process_directory(directory: Path, extensions: set[str], recursive: bool, dry_run: bool) -> None:
+    groups: dict[str, list[ImageEntry]] = collections.defaultdict(list)
+    for path in collect_image_paths(directory=directory, extensions=extensions, recursive=recursive):
+        parsed = parse_group_and_date(path=path)
+        if parsed is None:
+            continue
+        group_key, filename_date = parsed
+        groups[group_key].append(ImageEntry(path=path, group_key=group_key, filename_date=filename_date))
+    updated = 0
+    skipped = 0
+    for entries in groups.values():
+        if update_group(entries=entries, dry_run=dry_run):
+            updated += len(entries)
+        else:
+            skipped += len(entries)
+    logging.info("Updated %s files; skipped %s files", updated, skipped)
+
+
+def normalize_extensions(raw: str) -> set[str]:
+    pieces = re.split(r"[;,]", raw)
+    results: set[str] = set()
+    for piece in pieces:
+        trimmed = piece.strip().lower()
+        if not trimmed:
+            continue
+        if not trimmed.startswith("."):
+            trimmed = f".{trimmed}"
+        results.add(trimmed)
+    return results
+
+
+def main() -> None:
+    args = parse_args()
+    configure_logging(verbose=args.verbose)
+    extensions = normalize_extensions(raw=args.extensions)
+    if not extensions:
+        logging.error("No valid extensions provided")
+        raise SystemExit(1)
+    if not args.directory.exists():
+        logging.error("Directory %s does not exist", args.directory)
+        raise SystemExit(1)
+    process_directory(
+        directory=args.directory,
+        extensions=extensions,
+        recursive=args.recursive,
+        dry_run=args.dry_run,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/immich_dedupe_albums.py b/immich_dedupe_albums.py
new file mode 100644
index 0000000..a347d98
--- /dev/null
+++ b/immich_dedupe_albums.py
@@ -0,0 +1,105 @@
+import httpx
+import os
+from datetime import datetime
+from collections import defaultdict
+from typing import List, Dict, Any
+
+# Get environment variables
+IMMICH_API_KEY = os.environ.get("IMMICH_API_KEY")
+IMMICH_SERVER_URL = os.environ.get("IMMICH_SERVER_URL")
+
+if not IMMICH_API_KEY or not IMMICH_SERVER_URL:
+    raise ValueError("IMMICH_API_KEY and IMMICH_SERVER_URL environment variables must be set")
+
+# Setup HTTP client with auth headers
+headers = {"Content-Type": "application/json", "Accept": "application/json", "x-api-key": IMMICH_API_KEY}
+
+# Global HTTP client
+client = httpx.Client(headers=headers)
+
+
+def get_albums() -> List[Dict[str, Any]]:
+    """Fetch all albums from Immich API"""
+    response = client.get(f"{IMMICH_SERVER_URL}/api/albums")
+    response.raise_for_status()
+    return response.json()
+
+
+def get_album_assets(album_id: str) -> List[str]:
+    """Fetch all asset IDs from a specific album"""
+    response = client.get(f"{IMMICH_SERVER_URL}/api/albums/{album_id}?withoutAssets=false")
+    response.raise_for_status()
+    album_data = response.json()
+    return [asset["id"] for asset in album_data.get("assets", [])]
+
+
+def add_assets_to_album(album_id: str, asset_ids: List[str]) -> None:
+    """Add assets to an album"""
+    if not asset_ids:
+        return
+
+    response = client.put(f"{IMMICH_SERVER_URL}/api/albums/{album_id}/assets", json={"ids": asset_ids})
+    response.raise_for_status()
+
+
+def delete_album(album_id: str) -> None:
+    """Delete an album"""
+    response = client.delete(f"{IMMICH_SERVER_URL}/api/albums/{album_id}")
+    response.raise_for_status()
+
+
+def deduplicate_albums():
+    """Main function to deduplicate albums"""
+    print("Fetching albums...")
+    albums = get_albums()
+
+    # Group albums by name (case-insensitive)
+    album_groups = defaultdict(list)
+    for album in albums:
+        album_name = album["albumName"].lower().strip()
+        album_groups[album_name].append(album)
+
+    # Process groups with duplicates
+    for album_name, album_list in album_groups.items():
+        if len(album_list) <= 1:
+            continue  # Skip groups with only one album
+
+        print(f"\nProcessing duplicate albums for: '{album_name}' ({len(album_list)} albums)")
+
+        # Sort by creation date to find the oldest
+        album_list.sort(key=lambda x: datetime.fromisoformat(x["createdAt"].replace("Z", "+00:00")))
+        oldest_album = album_list[0]
+        duplicate_albums = album_list[1:]
+
+        print(f"  Oldest album: {oldest_album['id']} (created: {oldest_album['createdAt']})")
+
+        # Collect assets from duplicate albums
+        all_asset_ids = []
+        for dup_album in duplicate_albums:
+            print(f"  Processing duplicate: {dup_album['id']} (created: {dup_album['createdAt']})")
+            asset_ids = get_album_assets(dup_album["id"])
+            all_asset_ids.extend(asset_ids)
+            print(f"    Found {len(asset_ids)} assets")
+
+        # Add assets to oldest album
+        if all_asset_ids:
+            print(f"  Adding {len(all_asset_ids)} assets to oldest album...")
+            add_assets_to_album(oldest_album["id"], all_asset_ids)
+
+        # Delete duplicate albums
+        for dup_album in duplicate_albums:
+            print(f"  Deleting duplicate album: {dup_album['id']}")
+            delete_album(dup_album["id"])
+
+        print(f"  ✓ Merged {len(duplicate_albums)} duplicate albums into {oldest_album['id']}")
+
+
+if __name__ == "__main__":
+    try:
+        deduplicate_albums()
+        print("\n✓ Album deduplication completed successfully!")
+    except Exception as e:
+        print(f"\n✗ Error: {e}")
+        raise
+    finally:
+        client.close()
diff --git a/install_backup_service.py b/install_backup_service.py
new file mode 100644
index 0000000..cf8c9a0
--- /dev/null
+++ b/install_backup_service.py
@@ -0,0 +1,78 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = []
+# ///
+
+import argparse
+import logging
+import subprocess
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Install cron job")
+    parser.add_argument("--name", required=True, help="Name for the cron job")
+    parser.add_argument("command", nargs=argparse.REMAINDER, help="Command to run")
+    return parser.parse_args()
+
+
+def get_current_crontab() -> str:
+    try:
+        result = subprocess.run(["crontab", "-l"], capture_output=True, text=True, check=True)
+        return result.stdout
+    except subprocess.CalledProcessError:
+        return ""
+
+
+def create_cron_entry(name: str, command: list) -> str:
+    command_str = " ".join(command)
+    return f"0 0 * * * {command_str}"
+
+
+def job_exists(name: str, current_crontab: str) -> bool:
+    comment = f"# {name}"
+    return comment in current_crontab
+
+
+def install_cron_job(name: str, command: list) -> None:
+    current_crontab = get_current_crontab()
+
+    if job_exists(name, current_crontab):
+        logger.info(f"Cron job '{name}' already exists")
+        return
+
+    cron_entry = create_cron_entry(name, command)
+    job_comment = f"# {name}"
+    new_crontab = current_crontab.rstrip()
+
+    if new_crontab and not new_crontab.endswith("\n"):
+        new_crontab += "\n"
+
+    new_crontab += f"{job_comment}\n{cron_entry}\n"
+
+    process = subprocess.Popen(["crontab", "-"], stdin=subprocess.PIPE, text=True)
+    process.communicate(input=new_crontab)
+
+    if process.returncode != 0:
+        raise RuntimeError("Failed to install cron job")
+
+    logger.info(f"Cron job '{name}' installed successfully")
+
+
+def main():
+    args = parse_args()
+
+    if not args.command:
+        raise ValueError("Command is required")
+
+    logger.info(f"Installing cron job '{args.name}' with command: {' '.join(args.command)}")
+    install_cron_job(args.name, args.command)
+
+    logger.info("Installation completed successfully")
+    logger.info("Check cron jobs with: crontab -l")
+
+
+if __name__ == "__main__":
+    exit(main())
diff --git a/prompter/prompter.html b/prompter/prompter.html
new file mode 100644
index 0000000..d0e9c9a
--- /dev/null
+++ b/prompter/prompter.html
@@ -0,0 +1,523 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Prompter</title>
+  <!-- Persist plugin must load before Alpine core -->
+  <script defer src="https://cdn.jsdelivr.net/npm/@alpinejs/persist@3.x.x/dist/cdn.min.js"></script>
+  <script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.x.x/dist/cdn.min.js"></script>
+  <style>
+    *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+
+    body {
+      display: flex;
+      height: 100vh;
+      overflow: hidden;
+      font-family: system-ui, -apple-system, sans-serif;
+      background: #0f0f0f;
+      color: #e0e0e0;
+    }
+
+    /* ── Image panel (left 50%) ──────────────────────────────────────────── */
+    .image-panel {
+      width: 50%;
+      height: 100vh;
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      justify-content: center;
+      background: #111;
+      padding: 20px;
+      gap: 10px;
+      border-right: 1px solid #1e1e1e;
+    }
+
+    .image-panel img {
+      max-width: 100%;
+      max-height: calc(100vh - 72px);
+      object-fit: contain;
+    }
+
+    .image-meta {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      gap: 4px;
+    }
+
+    .image-filename {
+      font-size: 12px;
+      color: #666;
+      word-break: break-all;
+      text-align: center;
+      max-width: 100%;
+    }
+
+    .image-counter {
+      display: flex;
+      align-items: center;
+      gap: 4px;
+      font-size: 12px;
+      color: #555;
+    }
+
+    .image-counter input[type="number"] {
+      background: transparent;
+      border: none;
+      border-bottom: 1px solid #444;
+      color: #999;
+      font-family: inherit;
+      font-size: 12px;
+      outline: none;
+      padding: 0 2px;
+      text-align: right;
+      width: 3.5ch;
+      -moz-appearance: textfield;
+    }
+
+    .image-counter input[type="number"]:focus {
+      border-bottom-color: #4a6cf7;
+      color: #e0e0e0;
+    }
+
+    .image-counter input::-webkit-outer-spin-button,
+    .image-counter input::-webkit-inner-spin-button { -webkit-appearance: none; }
+
+    /* ── Options panel (right 50%) ───────────────────────────────────────── */
+    .options-panel {
+      width: 50%;
+      height: 100vh;
+      display: flex;
+      flex-direction: column;
+      overflow: hidden;
+    }
+
+    .panel-top,
+    .panel-bottom {
+      flex: 1 1 0;
+      min-height: 0;
+      overflow-y: auto;
+      padding: 24px;
+      display: flex;
+      flex-direction: column;
+      gap: 8px;
+    }
+
+    .panel-bottom {
+      border-top: 1px solid #1e1e1e;
+    }
+
+    .section-title {
+      font-size: 11px;
+      color: #555;
+      text-transform: uppercase;
+      letter-spacing: 0.08em;
+      margin-bottom: 4px;
+    }
+
+    .option {
+      border: 1px solid #2a2a2a;
+      border-radius: 8px;
+      padding: 12px 14px;
+      cursor: pointer;
+      transition: border-color 0.1s;
+    }
+
+    .option:hover { border-color: #3a3a3a; }
+    .option.active { border-color: #4a6cf7; background: rgba(74, 108, 247, 0.06); }
+
+    .option-meta {
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      margin-bottom: 10px;
+      font-size: 12px;
+      color: #666;
+    }
+
+    .saved-badge { color: #4caf50; margin-left: auto; font-size: 11px; }
+
+    .kbd {
+      display: inline-block;
+      background: #1e1e1e;
+      border: 1px solid #333;
+      border-radius: 3px;
+      padding: 1px 6px;
+      font-size: 11px;
+      font-family: monospace;
+      color: #888;
+      white-space: nowrap;
+    }
+
+    textarea {
+      width: 100%;
+      background: #1a1a1a;
+      border: 1px solid #333;
+      border-radius: 6px;
+      color: #e0e0e0;
+      font-size: 14px;
+      line-height: 1.5;
+      padding: 10px;
+      resize: vertical;
+      min-height: 90px;
+      font-family: inherit;
+      outline: none;
+      transition: border-color 0.1s;
+    }
+
+    textarea:focus { border-color: #4a6cf7; }
+
+    .btn-save {
+      margin-top: 10px;
+      padding: 8px 18px;
+      background: #4a6cf7;
+      color: #fff;
+      border: none;
+      border-radius: 6px;
+      font-size: 14px;
+      cursor: pointer;
+      transition: background 0.1s;
+    }
+
+    .btn-save:hover { background: #3b5de8; }
+
+    .history-section { margin-top: 8px; }
+    .history-item { margin-top: 8px; }
+
+    .history-text {
+      font-size: 13px;
+      color: #bbb;
+      white-space: pre-wrap;
+      word-break: break-word;
+      line-height: 1.4;
+    }
+
+    .blocks-grid {
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 6px;
+      margin-top: 4px;
+    }
+
+    .block-pill {
+      background: #1e1e1e;
+      border: 1px solid #333;
+      border-radius: 8px;
+      color: #ccc;
+      cursor: pointer;
+      font-family: inherit;
+      font-size: 12px;
+      line-height: 1.4;
+      padding: 6px 10px;
+      text-align: left;
+      transition: border-color 0.1s, background 0.1s;
+      white-space: pre-wrap;
+      word-break: break-word;
+    }
+
+    .block-pill:hover {
+      background: #2a2a2a;
+      border-color: #4a6cf7;
+      color: #e0e0e0;
+    }
+  </style>
+</head>
+<body x-data="prompter" @keydown.window="handleKey($event)">
+
+  <!-- ── Left: image ───────────────────────────────────────────────────── -->
+  <div class="image-panel">
+    <img :src="`/image/${currentIndex}`" :alt="currentImage" />
+    <div class="image-meta">
+      <div class="image-filename" x-text="currentImage.split('/').pop()"></div>
+      <div class="image-counter">
+        <input
+          type="number"
+          min="1"
+          :max="images.length"
+          :value="currentIndex + 1"
+          @change="jumpTo($event.target.valueAsNumber - 1)"
+          @keydown.stop
+          @click.stop
+        />
+        <span>/ <span x-text="images.length"></span></span>
+      </div>
+    </div>
+  </div>
+
+  <!-- ── Right: options ────────────────────────────────────────────────── -->
+  <div class="options-panel">
+
+    <!-- ── Top half: prompt + history ──────────────────────────────────── -->
+    <div class="panel-top">
+      <div class="section-title">Prompt</div>
+
+      <!-- Custom prompt -->
+      <div class="option" :class="{ active: selectedOption === 'custom' }" @click="selectCustom()">
+        <div class="option-meta">
+          <span class="kbd">alt+1</span>
+          <span>Custom prompt</span>
+          <span class="saved-badge" x-show="selections[currentImage]">&#10003;&nbsp;saved</span>
+        </div>
+        <textarea
+          x-ref="textarea"
+          x-model="customPrompt"
+          @focus="selectedOption = 'custom'"
+          placeholder="Enter a prompt&#8230;"
+          rows="5"
+        ></textarea>
+        <button class="btn-save" @click.stop="saveAndNext()">Save &amp; Next</button>
+      </div>
+
+      <!-- History -->
+      <div class="history-section" x-show="promptHistory.length > 0">
+        <div class="section-title">Previously used</div>
+        <template x-for="(prompt, i) in promptHistory" :key="prompt">
+          <div
+            class="option history-item"
+            :class="{ active: selectedOption === i }"
+            @click="selectHistory(i)"
+          >
+            <div class="option-meta">
+              <span class="kbd" x-text="`alt+${i + 2}`"></span>
+            </div>
+            <div class="history-text" x-text="prompt"></div>
+          </div>
+        </template>
+      </div>
+    </div>
+
+    <!-- ── Bottom half: building blocks ────────────────────────────────── -->
+    <div class="panel-bottom" x-show="buildingBlocks.length > 0">
+      <div class="section-title">Building blocks</div>
+      <div class="blocks-grid">
+        <template x-for="block in buildingBlocks" :key="block">
+          <button class="block-pill" @click="appendBlock(block)" x-text="block"></button>
+        </template>
+      </div>
+    </div>
+
+  </div>
+
+  <script>
+    /**
+     * @typedef {{ images: string[], canned_prompts: string[], building_blocks: string[], existing_prompts: Record<string, string> }} ApiImages
+     */
+
+    document.addEventListener('alpine:init', () => {
+      Alpine.data('prompter', () => ({
+
+        /** @type {string[]} Ordered list of absolute image paths from the server */
+        images: [],
+
+        /** @type {number} Zero-based index of the currently displayed image */
+        currentIndex: 0,
+
+        /** @type {Record<string, string>} Absolute image path → saved prompt text */
+        selections: {},
+
+        /** @type {string} Current value of the custom-prompt textarea */
+        customPrompt: '',
+
+        /** @type {'custom' | number | null} Which option row is highlighted */
+        selectedOption: null,
+
+        /** @type {string[]} Reusable text fragments loaded from the server */
+        buildingBlocks: [],
+
+        /**
+         * Prompt history persisted to localStorage via Alpine Persist.
+         * Ordered most-recent-first; no duplicates.
+         * @type {string[]}
+         */
+        promptHistory: Alpine.$persist([]).as('prompter_history'),
+
+        /**
+         * Absolute path of the image currently on screen.
+         * @returns {string}
+         */
+        get currentImage() {
+          return this.images[this.currentIndex] ?? '';
+        },
+
+        /**
+         * Fetch the image list from the server, merge canned prompts into
+         * history, and restore any previously saved prompt for the first image.
+         */
+        async init() {
+          /** @type {ApiImages} */
+          const data = await fetch('/api/images').then(r => r.json());
+
+          this.images = data.images;
+          this.selections = { ...data.existing_prompts };
+          this.buildingBlocks = data.building_blocks ?? [];
+
+          // Append canned prompts not already present in the persisted history.
+          const inHistory = new Set(this.promptHistory);
+          for (const p of data.canned_prompts) {
+            if (!inHistory.has(p)) {
+              this.promptHistory.push(p);
+            }
+          }
+
+          // Restore a previously saved prompt for the first image (if any).
+          const saved = this.selections[this.currentImage];
+          if (saved) {
+            this.customPrompt = saved;
+            this.selectedOption = 'custom';
+          }
+        },
+
+        /**
+         * Append a building block to the textarea, separated by a blank line.
+         * If the textarea is empty the block becomes the entire content.
+         * @param {string} block
+         */
+        appendBlock(block) {
+          const cur = this.customPrompt.trimEnd();
+          this.customPrompt = cur ? cur + '\n' + block : block;
+          this.selectedOption = 'custom';
+          this.$nextTick(() => this.$refs.textarea.focus());
+        },
+
+        /** Highlight the custom-prompt option and focus the textarea. */
+        selectCustom() {
+          this.selectedOption = 'custom';
+          this.$nextTick(() => this.$refs.textarea.focus());
+        },
+
+        /**
+         * Copy a history entry into the textarea and focus it.
+         * @param {number} i - Index into promptHistory
+         */
+        selectHistory(i) {
+          this.customPrompt = this.promptHistory[i];
+          this.selectedOption = 'custom';
+          this.$nextTick(() => this.$refs.textarea.focus());
+        },
+
+        /**
+         * POST the current textarea content to the server and update local
+         * state. Does nothing if the textarea is blank.
+         */
+        async saveCurrentIfNeeded() {
+          const prompt = this.customPrompt.trim();
+          if (!prompt) return;
+
+          const filePath = this.currentImage;
+          this.selections[filePath] = prompt;
+          this._addToHistory(prompt);
+
+          await fetch('/api/save', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ file_path: filePath, prompt }),
+          });
+        },
+
+        /**
+         * Prepend a prompt to history, removing any earlier duplicate entry.
+         * @param {string} prompt
+         */
+        _addToHistory(prompt) {
+          this.promptHistory = [
+            prompt,
+            ...this.promptHistory.filter(p => p !== prompt),
+          ];
+        },
+
+        /**
+         * Populate the textarea with the saved prompt for the current image,
+         * or clear it if no prompt has been saved yet.
+         */
+        _loadCurrentSelection() {
+          const saved = this.selections[this.currentImage];
+          this.customPrompt = saved ?? '';
+          this.selectedOption = saved ? 'custom' : null;
+        },
+
+        /** Save the current prompt (if any) then advance to the next image. */
+        async navigateNext() {
+          if (this.currentIndex >= this.images.length - 1) return;
+          await this.saveCurrentIfNeeded();
+          this.currentIndex++;
+          this._loadCurrentSelection();
+        },
+
+        /** Save the current prompt (if any) then go back to the previous image. */
+        async navigatePrev() {
+          if (this.currentIndex <= 0) return;
+          await this.saveCurrentIfNeeded();
+          this.currentIndex--;
+          this._loadCurrentSelection();
+        },
+
+        /** Save the current prompt (if any) and advance to the next image. */
+        async saveAndNext() {
+          await this.saveCurrentIfNeeded();
+          if (this.currentIndex < this.images.length - 1) {
+            this.currentIndex++;
+            this._loadCurrentSelection();
+          }
+        },
+
+        /**
+         * Save the current prompt (if any) then jump to a specific index.
+         * Out-of-range values are clamped silently.
+         * @param {number} idx - Zero-based target index
+         */
+        async jumpTo(idx) {
+          const target = Math.max(0, Math.min(idx, this.images.length - 1));
+          if (target === this.currentIndex) return;
+          await this.saveCurrentIfNeeded();
+          this.currentIndex = target;
+          this._loadCurrentSelection();
+        },
+
+        /**
+         * Global keyboard handler (attached to window via @keydown.window).
+         *
+         * Shortcuts:
+         *   Alt+1        → focus the custom-prompt textarea
+         *   Alt+N (N≥2)  → load history[N-2] into the textarea and focus it
+         *   → / k        → next image  (k ignored while textarea is focused)
+         *   ← / j        → prev image  (j ignored while textarea is focused)
+         *
+         * Arrow keys navigate from anywhere, including inside the textarea,
+         * and auto-save the current prompt if non-empty.
+         *
+         * @param {KeyboardEvent} e
+         */
+        handleKey(e) {
+          const inTextarea =
+            this.$refs.textarea &&
+            document.activeElement === this.$refs.textarea;
+          if (inTextarea) return;
+
+          if (e.altKey) {
+            const m = e.code.match(/^Digit(\d)$/);
+            if (m) {
+              const n = parseInt(m[1], 10);
+              e.preventDefault();
+              if (n === 1) {
+                this.selectCustom();
+              } else if (n >= 2 && n - 2 < this.promptHistory.length) {
+                this.selectHistory(n - 2);
+              }
+              return;
+            }
+          }
+
+          if (e.key === 'ArrowRight' || e.key === 'k') {
+            e.preventDefault();
+            this.navigateNext();
+          } else if (e.key === 'ArrowLeft' || e.key === 'j') {
+            e.preventDefault();
+            this.navigatePrev();
+          }
+        },
+      }));
+    });
+  </script>
+</body>
+</html>
diff --git a/prompter/prompter.py b/prompter/prompter.py
new file mode 100755
index 0000000..a04d9b3
--- /dev/null
+++ b/prompter/prompter.py
@@ -0,0 +1,205 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "bottle",
+# ]
+# ///
+"""Browse images one-by-one and assign text prompts via a local web UI.
+
+Saves results to a JSONL file: {"filename": "...", "prompt": "..."}
+Images navigated past without a prompt produce no output line.
+"""
+
+import argparse
+import json
+import socket
+import sys
+import threading
+import time
+import webbrowser
+from pathlib import Path
+
+import bottle
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+CANNED_PROMPTS: list[str] = [
+    "photorealistic. skin must be flawless",
+]
+"""Pre-populated prompt history shown in the UI on first launch."""
+
+BUILDING_BLOCKS: list[str] = [
+    "make her prettier",
+    "age her by 5 years and make her look like a 25 year old",
+    "keep the dark skin",
+    "wet skin",
+    "skin covered with baby oil, shiny skin",
+    "soaking wet skin and hair, wet clothes clinging to her body",
+    "long, flowy hair",
+    "japanese",
+    "greek",
+    "no extra or missing fingers. each hand must have 5 fingers with the same hand pose as the original image",
+    # expression
+    "direct her gaze at the camera",
+    "give her a serious and seductive look",
+    "give her a playful expression",
+    "slightly parted lips",
+    "french kiss, eyes closed, tongue out",
+    "closed eyes",
+    "lower her eyelids and slightly part her lips in a seductive fashion",
+    "exaggerated expression",
+    "soft dramatic lighting",
+    # outfit
+    "replace the outfit with glossy latex",
+    "remove the tan lines, she doesn't wear white bra",
+]
+"""Palette of text snippets the user can append to any prompt."""
+
+VALID_EXTENSIONS: frozenset[str] = frozenset({".webp", ".jpg", ".jpeg", ".png"})
+
+HTML: str = (Path(__file__).parent / "prompter.html").read_text(encoding="utf-8")
+
+# ---------------------------------------------------------------------------
+# App
+# ---------------------------------------------------------------------------
+
+
+def find_free_port() -> int:
+    """Bind to port 0 and return the OS-assigned port number."""
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("127.0.0.1", 0))
+        return s.getsockname()[1]
+
+
+class PrompterApp:
+    def __init__(self, images: list[str], output_path: Path) -> None:
+        self.images = images
+        self.output_path = output_path
+        self.app = bottle.Bottle()
+        self.app.route("/")(self.index)
+        self.app.route("/api/images")(self.api_images)
+        self.app.route("/image/<idx:int>")(self.serve_image)
+        self.app.route("/api/save", method="POST")(self.api_save)
+
+    def index(self) -> str:
+        return HTML
+
+    def api_images(self) -> str:
+        """Return image list, canned prompts, building blocks, and existing prompts."""
+        stem_to_path: dict[str, str] = {Path(p).stem: p for p in self.images}
+        existing_by_stem = self._load_existing_prompts()
+        existing_prompts = {
+            stem_to_path[stem]: prompt
+            for stem, prompt in existing_by_stem.items()
+            if stem in stem_to_path
+        }
+        bottle.response.content_type = "application/json"
+        return json.dumps(
+            {
+                "images": self.images,
+                "canned_prompts": CANNED_PROMPTS,
+                "building_blocks": BUILDING_BLOCKS,
+                "existing_prompts": existing_prompts,
+            }
+        )
+
+    def serve_image(self, idx: int) -> bottle.HTTPResponse:
+        if idx < 0 or idx >= len(self.images):
+            bottle.abort(404, "Image not found")
+        path = Path(self.images[idx])
+        return bottle.static_file(path.name, root=str(path.parent))
+
+    def api_save(self) -> str:
+        data: dict = bottle.request.json or {}
+        file_path: str = data.get("file_path", "")
+        prompt: str = data.get("prompt", "")
+        if file_path and prompt:
+            self._save_entry(file_path, prompt)
+        bottle.response.content_type = "application/json"
+        return json.dumps({"ok": True})
+
+    def _load_existing_prompts(self) -> dict[str, str]:
+        """Read the output JSONL and return a mapping of stem → prompt.
+
+        When a filename appears multiple times the last entry wins, so re-running
+        the tool and overwriting a previous prompt works naturally.
+        """
+        if not self.output_path.exists():
+            return {}
+        prompts: dict[str, str] = {}
+        with open(self.output_path, encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    entry = json.loads(line)
+                    if "filename" in entry and "prompt" in entry:
+                        prompts[entry["filename"]] = entry["prompt"]
+                except json.JSONDecodeError:
+                    pass
+        return prompts
+
+    def _save_entry(self, file_path: str, prompt: str) -> None:
+        """Append one {filename, prompt} record to the JSONL output file."""
+        with open(self.output_path, "a", encoding="utf-8") as f:
+            f.write(
+                json.dumps({"filename": Path(file_path).stem, "prompt": prompt}) + "\n"
+            )
+
+    def run(self, port: int) -> None:
+        bottle.run(self.app, host="localhost", port=port, quiet=True)
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Browse images and assign text prompts via a web UI.",
+    )
+    parser.add_argument("images", nargs="+", help="Image paths to browse")
+    parser.add_argument(
+        "-o",
+        "--output-path",
+        default="prompts.jsonl",
+        metavar="FILE",
+        help="Output JSONL file path (default: prompts.jsonl)",
+    )
+    args = parser.parse_args()
+
+    images = [
+        str(Path(p).resolve())
+        for p in args.images
+        if Path(p).suffix.lower() in VALID_EXTENSIONS
+    ]
+
+    if not images:
+        exts = ", ".join(sorted(VALID_EXTENSIONS))
+        print(
+            f"Error: no valid images found. Expected extensions: {exts}",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    print(f"Loaded {len(images)} image(s).")
+
+    port = find_free_port()
+    url = f"http://localhost:{port}"
+    print(f"Starting server at {url}")
+
+    threading.Thread(
+        target=lambda: (time.sleep(0.8), webbrowser.open(url)),
+        daemon=True,
+    ).start()
+
+    PrompterApp(images, Path(args.output_path)).run(port)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pullio.py b/pullio.py
new file mode 100644
index 0000000..bd41f40
--- /dev/null
+++ b/pullio.py
@@ -0,0 +1,681 @@
+#!/usr/bin/env python3
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import logging
+import os
+import shlex
+import shutil
+import subprocess
+import sys
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from urllib import error, request
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+LOGGER = logging.getLogger("pullio")
+LABEL_PREFIX = "org.hotio.pullio"
+
+
+@dataclass(frozen=True)
+class Config:
+    compose_binary: str
+    docker_binary: str
+    cache_location: Path
+    tag: str
+    parallel: int
+    compose_type: str
+    script_hash: str
+    telegram_bot_token: str
+    telegram_chat_id: str
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Pull and optionally update Docker Compose containers based on labels.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--tag", default="")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--parallel", type=int, default=1)
+    args = parser.parse_args()
+    if args.parallel < 1:
+        parser.error("--parallel must be >= 1")
+    return args
+
+
+def run_command(
+    command: list[str],
+    *,
+    check: bool = True,
+    env: dict[str, str] | None = None,
+    input_text: str | None = None,
+) -> str:
+    completed = subprocess.run(
+        command,
+        check=check,
+        capture_output=True,
+        text=True,
+        env=env,
+        input=input_text,
+    )
+    return completed.stdout.strip()
+
+
+def detect_compose_type(compose_binary: str, docker_binary: str) -> str:
+    if compose_binary:
+        return "V1"
+    if docker_binary:
+        try:
+            run_command([docker_binary, "compose", "version"])
+            return "V2"
+        except subprocess.CalledProcessError:
+            return "NONE"
+    return "NONE"
+
+
+def docker_inspect_value(docker_binary: str, name: str, template: str) -> str:
+    try:
+        value = run_command([docker_binary, "inspect", f"--format={template}", name])
+    except subprocess.CalledProcessError:
+        return ""
+    if value == "<no value>":
+        return ""
+    return value
+
+
+def docker_image_inspect_value(docker_binary: str, image: str, template: str) -> str:
+    try:
+        value = run_command(
+            [docker_binary, "image", "inspect", f"--format={template}", image]
+        )
+    except subprocess.CalledProcessError:
+        return ""
+    if value == "<no value>":
+        return ""
+    return value
+
+
+def compose_pull(config: Config, workdir: str, service: str) -> bool:
+    if config.compose_type == "V1":
+        cmd = [config.compose_binary, "pull", service]
+    elif config.compose_type == "V2":
+        cmd = [config.docker_binary, "compose", "pull", service]
+    elif config.docker_binary:
+        cmd = [
+            config.docker_binary,
+            "run",
+            "--rm",
+            "-v",
+            "/var/run/docker.sock:/var/run/docker.sock",
+            "-v",
+            f"{workdir}:{workdir}",
+            f"-w={workdir}",
+            "linuxserver/docker-compose",
+            "pull",
+            service,
+        ]
+    else:
+        LOGGER.error(
+            "Neither Docker Compose nor Docker binary is available. Cannot pull."
+        )
+        return False
+
+    try:
+        run_command(cmd)
+        return True
+    except subprocess.CalledProcessError:
+        return False
+
+
+def compose_up(config: Config, workdir: str, service: str) -> bool:
+    if config.compose_type == "V1":
+        cmd = [config.compose_binary, "up", "-d", "--always-recreate-deps", service]
+    elif config.compose_type == "V2":
+        cmd = [
+            config.docker_binary,
+            "compose",
+            "up",
+            "-d",
+            "--always-recreate-deps",
+            service,
+        ]
+    elif config.docker_binary:
+        cmd = [
+            config.docker_binary,
+            "run",
+            "--rm",
+            "-v",
+            "/var/run/docker.sock:/var/run/docker.sock",
+            "-v",
+            f"{workdir}:{workdir}",
+            f"-w={workdir}",
+            "linuxserver/docker-compose",
+            "up",
+            "-d",
+            "--always-recreate-deps",
+            service,
+        ]
+    else:
+        LOGGER.error(
+            "Neither Docker Compose nor Docker binary is available. Cannot bring up services."
+        )
+        return False
+
+    try:
+        run_command(cmd)
+        return True
+    except subprocess.CalledProcessError:
+        return False
+
+
+def post_json(url: str, payload: dict[str, object]) -> None:
+    body = json.dumps(payload).encode("utf-8")
+    req = request.Request(
+        url=url,
+        data=body,
+        headers={
+            "User-Agent": "Pullio",
+            "Content-Type": "application/json",
+        },
+        method="POST",
+    )
+    try:
+        with request.urlopen(req) as response:
+            response.read()
+    except (error.HTTPError, error.URLError) as exc:
+        LOGGER.warning("Webhook request failed: %s", exc)
+
+
+def now_iso_utc() -> str:
+    return (
+        datetime.now(timezone.utc)
+        .isoformat(timespec="milliseconds")
+        .replace("+00:00", "Z")
+    )
+
+
+def parse_script_command(value: str) -> list[str]:
+    return shlex.split(value) if value else []
+
+
+def prepare_script_env(
+    *,
+    container: str,
+    image: str,
+    avatar: str,
+    old_image_id: str,
+    new_image_id: str,
+    old_version: str,
+    new_version: str,
+    old_revision: str,
+    new_revision: str,
+    compose_service: str,
+    compose_workdir: str,
+    author_url: str,
+) -> dict[str, str]:
+    env = os.environ.copy()
+    env.update(
+        {
+            "PULLIO_CONTAINER": container,
+            "PULLIO_IMAGE": image,
+            "PULLIO_AVATAR": avatar,
+            "PULLIO_OLD_IMAGE_ID": old_image_id,
+            "PULLIO_NEW_IMAGE_ID": new_image_id,
+            "PULLIO_OLD_VERSION": old_version,
+            "PULLIO_NEW_VERSION": new_version,
+            "PULLIO_OLD_REVISION": old_revision,
+            "PULLIO_NEW_REVISION": new_revision,
+            "PULLIO_COMPOSE_SERVICE": compose_service,
+            "PULLIO_COMPOSE_WORKDIR": compose_workdir,
+            "PULLIO_AUTHOR_URL": author_url,
+        }
+    )
+    return env
+
+
+def send_telegram_notification(
+    *,
+    status: str,
+    container_name: str,
+    old_version: str,
+    new_version: str,
+    image_name: str,
+    bot_token: str,
+    chat_id: str,
+    old_revision: str,
+    new_revision: str,
+    old_image_id: str,
+    new_image_id: str,
+    color: int,
+    author_avatar: str,
+    author_url: str,
+) -> None:
+    version_indicator = "=" if old_version == new_version else ">"
+    revision_indicator = "=" if old_revision == new_revision else ">"
+    digest_indicator = "=" if old_image_id == new_image_id else ">"
+
+    lines = [
+        f"{container_name}",
+        status.replace("\\n", " "),
+        f"Image: {image_name}",
+        f"Image ID: {old_image_id[:11]} {digest_indicator} {new_image_id[:11]}",
+    ]
+    if old_version and new_version:
+        lines.append(f"Version: {old_version} {version_indicator} {new_version}")
+    if old_revision and new_revision:
+        lines.append(
+            f"Revision: {old_revision[:6]} {revision_indicator} {new_revision[:6]}"
+        )
+    if author_url:
+        lines.append(f"URL: {author_url}")
+    if author_avatar:
+        lines.append(f"Avatar: {author_avatar}")
+    lines.append(f"Color: {color}")
+    lines.append(f"Time: {now_iso_utc()}")
+
+    payload = {
+        "chat_id": chat_id,
+        "text": "\n".join(lines),
+        "disable_web_page_preview": True,
+    }
+    post_json(f"https://api.telegram.org/bot{bot_token}/sendMessage", payload)
+
+
+def send_generic_webhook(
+    *,
+    status_generic: str,
+    container_name: str,
+    old_version: str,
+    new_version: str,
+    image_name: str,
+    webhook: str,
+    old_revision: str,
+    new_revision: str,
+    old_image_id: str,
+    new_image_id: str,
+    avatar: str,
+    author_url: str,
+) -> None:
+    payload = {
+        "container": container_name,
+        "image": image_name,
+        "avatar": avatar,
+        "old_image_id": old_image_id,
+        "new_image_id": new_image_id,
+        "old_version": old_version,
+        "new_version": new_version,
+        "old_revision": old_revision,
+        "new_revision": new_revision,
+        "type": status_generic,
+        "url": author_url,
+        "timestamp": now_iso_utc(),
+    }
+    post_json(webhook, payload)
+
+
+def process_container(config: Config, container_name: str) -> None:
+    LOGGER.info("%s: Checking...", container_name)
+
+    image_name = docker_inspect_value(
+        config.docker_binary, container_name, "{{.Config.Image}}"
+    )
+    container_image_digest = docker_inspect_value(
+        config.docker_binary, container_name, "{{.Image}}"
+    )
+
+    docker_compose_service = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        '{{ index .Config.Labels "com.docker.compose.service" }}',
+    )
+    docker_compose_version = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        '{{ index .Config.Labels "com.docker.compose.version" }}',
+    )
+    docker_compose_workdir = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        '{{ index .Config.Labels "com.docker.compose.project.working_dir" }}',
+    )
+
+    old_version = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        '{{ index .Config.Labels "org.opencontainers.image.version" }}',
+    )
+    old_revision = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        '{{ index .Config.Labels "org.opencontainers.image.revision" }}',
+    )
+
+    pullio_update = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.update" }}}}',
+    )
+    pullio_notify = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.notify" }}}}',
+    )
+    pullio_telegram_bot_token = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.telegram.bot_token" }}}}',
+    )
+    pullio_telegram_chat_id = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.telegram.chat_id" }}}}',
+    )
+    pullio_generic_webhook = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.generic.webhook" }}}}',
+    )
+    pullio_script_update = parse_script_command(
+        docker_inspect_value(
+            config.docker_binary,
+            container_name,
+            f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.script.update" }}}}',
+        )
+    )
+    pullio_script_notify = parse_script_command(
+        docker_inspect_value(
+            config.docker_binary,
+            container_name,
+            f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.script.notify" }}}}',
+        )
+    )
+    pullio_registry_authfile = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.registry.authfile" }}}}',
+    )
+    pullio_author_avatar = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.author.avatar" }}}}',
+    )
+    pullio_author_url = docker_inspect_value(
+        config.docker_binary,
+        container_name,
+        f'{{{{ index .Config.Labels "{LABEL_PREFIX}{config.tag}.author.url" }}}}',
+    )
+
+    if not docker_compose_version or (
+        pullio_update != "true" and pullio_notify != "true"
+    ):
+        return
+
+    if pullio_registry_authfile and Path(pullio_registry_authfile).is_file():
+        LOGGER.info("%s: Registry login...", container_name)
+        try:
+            auth = json.loads(
+                Path(pullio_registry_authfile).read_text(encoding="utf-8")
+            )
+            run_command(
+                [
+                    config.docker_binary,
+                    "login",
+                    "--username",
+                    str(auth.get("username", "")),
+                    "--password-stdin",
+                    str(auth.get("registry", "")),
+                ],
+                input_text=str(auth.get("password", "")),
+            )
+        except (json.JSONDecodeError, OSError, subprocess.CalledProcessError) as exc:
+            LOGGER.warning("%s: Registry login failed: %s", container_name, exc)
+
+    LOGGER.info("%s: Pulling image...", container_name)
+    if not compose_pull(config, docker_compose_workdir, docker_compose_service):
+        LOGGER.error("%s: Pulling failed!", container_name)
+
+    image_digest = docker_image_inspect_value(config.docker_binary, image_name, "{{.Id}}")
+    new_version = docker_image_inspect_value(
+        config.docker_binary,
+        image_name,
+        '{{ index .Config.Labels "org.opencontainers.image.version" }}',
+    )
+    new_revision = docker_image_inspect_value(
+        config.docker_binary,
+        image_name,
+        '{{ index .Config.Labels "org.opencontainers.image.revision" }}',
+    )
+
+    status = "I've got an update waiting for me.\nGive it to me, please."
+    status_generic = "update_available"
+    color = 768753
+
+    if image_digest != container_image_digest and pullio_update == "true":
+        script_env = prepare_script_env(
+            container=container_name,
+            image=image_name,
+            avatar=pullio_author_avatar,
+            old_image_id=container_image_digest.removeprefix("sha256:"),
+            new_image_id=image_digest.removeprefix("sha256:"),
+            old_version=old_version,
+            new_version=new_version,
+            old_revision=old_revision,
+            new_revision=new_revision,
+            compose_service=docker_compose_service,
+            compose_workdir=docker_compose_workdir,
+            author_url=pullio_author_url,
+        )
+        if pullio_script_update:
+            LOGGER.info("%s: Stopping container...", container_name)
+            try:
+                run_command([config.docker_binary, "stop", container_name])
+            except subprocess.CalledProcessError:
+                LOGGER.warning(
+                    "%s: Failed to stop container before update script.", container_name
+                )
+            LOGGER.info("%s: Executing update script...", container_name)
+            try:
+                subprocess.run(pullio_script_update, env=script_env, check=False)
+            except OSError as exc:
+                LOGGER.warning(
+                    "%s: Update script failed to start: %s", container_name, exc
+                )
+
+        LOGGER.info("%s: Updating container...", container_name)
+        if compose_up(config, docker_compose_workdir, docker_compose_service):
+            status = "I just updated myself.\nFeeling brand spanking new again!"
+            status_generic = "update_success"
+            color = 3066993
+        else:
+            LOGGER.error("%s: Updating container failed!", container_name)
+            status = (
+                "I tried to update myself.\nIt didn't work out, I might need some help."
+            )
+            status_generic = "update_failure"
+            color = 15158332
+
+        notified_path = (
+            config.cache_location / f"{config.script_hash}-{container_name}.notified"
+        )
+        try:
+            notified_path.unlink(missing_ok=True)
+        except OSError:
+            LOGGER.warning("%s: Failed to clear notify cache file.", container_name)
+
+    if image_digest != container_image_digest and pullio_notify == "true":
+        notified_path = (
+            config.cache_location / f"{config.script_hash}-{container_name}.notified"
+        )
+        try:
+            notified_path.touch(exist_ok=True)
+            notified_digest = notified_path.read_text(encoding="utf-8").strip()
+        except OSError:
+            notified_digest = ""
+
+        if notified_digest != image_digest:
+            script_env = prepare_script_env(
+                container=container_name,
+                image=image_name,
+                avatar=pullio_author_avatar,
+                old_image_id=container_image_digest.removeprefix("sha256:"),
+                new_image_id=image_digest.removeprefix("sha256:"),
+                old_version=old_version,
+                new_version=new_version,
+                old_revision=old_revision,
+                new_revision=new_revision,
+                compose_service=docker_compose_service,
+                compose_workdir=docker_compose_workdir,
+                author_url=pullio_author_url,
+            )
+            if pullio_script_notify:
+                LOGGER.info("%s: Executing notify script...", container_name)
+                try:
+                    subprocess.run(pullio_script_notify, env=script_env, check=False)
+                except OSError as exc:
+                    LOGGER.warning(
+                        "%s: Notify script failed to start: %s", container_name, exc
+                    )
+
+            old_digest_short = container_image_digest.removeprefix("sha256:")
+            new_digest_short = image_digest.removeprefix("sha256:")
+
+            effective_telegram_bot_token = (
+                pullio_telegram_bot_token or config.telegram_bot_token
+            )
+            effective_telegram_chat_id = (
+                pullio_telegram_chat_id or config.telegram_chat_id
+            )
+
+            if effective_telegram_bot_token and effective_telegram_chat_id:
+                LOGGER.info("%s: Sending telegram notification...", container_name)
+                send_telegram_notification(
+                    status=status,
+                    container_name=container_name,
+                    old_version=old_version,
+                    new_version=new_version,
+                    image_name=image_name,
+                    bot_token=effective_telegram_bot_token,
+                    chat_id=effective_telegram_chat_id,
+                    old_revision=old_revision,
+                    new_revision=new_revision,
+                    old_image_id=old_digest_short,
+                    new_image_id=new_digest_short,
+                    color=color,
+                    author_avatar=pullio_author_avatar,
+                    author_url=pullio_author_url,
+                )
+
+            if pullio_generic_webhook:
+                LOGGER.info("%s: Sending generic webhook...", container_name)
+                send_generic_webhook(
+                    status_generic=status_generic,
+                    container_name=container_name,
+                    old_version=old_version,
+                    new_version=new_version,
+                    image_name=image_name,
+                    webhook=pullio_generic_webhook,
+                    old_revision=old_revision,
+                    new_revision=new_revision,
+                    old_image_id=old_digest_short,
+                    new_image_id=new_digest_short,
+                    avatar=pullio_author_avatar,
+                    author_url=pullio_author_url,
+                )
+
+            try:
+                notified_path.write_text(image_digest, encoding="utf-8")
+            except OSError:
+                LOGGER.warning("%s: Failed to write notify cache file.", container_name)
+
+
+def main() -> int:
+    args = parse_args()
+
+    if args.debug:
+        logging.getLogger().setLevel(logging.DEBUG)
+
+    compose_binary = os.getenv("COMPOSE_BINARY") or (
+        shutil.which("docker-compose") or ""
+    )
+    docker_binary = os.getenv("DOCKER_BINARY") or (shutil.which("docker") or "")
+    telegram_bot_token = os.getenv("TELEGRAM_BOT_TOKEN", "")
+    telegram_chat_id = os.getenv("TELEGRAM_CHAT_ID", "")
+    cache_location = Path("/tmp")
+
+    tag = f".{args.tag}" if args.tag else ""
+    compose_type = detect_compose_type(compose_binary, docker_binary)
+
+    if not docker_binary:
+        LOGGER.error("Docker binary not found.")
+        return 1
+
+    try:
+        script_hash = hashlib.sha1(Path(__file__).read_bytes()).hexdigest()
+    except OSError:
+        script_hash = hashlib.sha1(
+            str(Path(__file__).resolve()).encode("utf-8")
+        ).hexdigest()
+
+    config = Config(
+        compose_binary=compose_binary,
+        docker_binary=docker_binary,
+        cache_location=cache_location,
+        tag=tag,
+        parallel=args.parallel,
+        compose_type=compose_type,
+        script_hash=script_hash,
+        telegram_bot_token=telegram_bot_token,
+        telegram_chat_id=telegram_chat_id,
+    )
+
+    LOGGER.info(
+        'Running with "DEBUG=%s", "TAG=%s", and "PARALLEL=%s".',
+        args.debug,
+        tag,
+        args.parallel,
+    )
+
+    try:
+        raw_containers = run_command([docker_binary, "ps", "--format", "{{.Names}}"])
+    except subprocess.CalledProcessError as exc:
+        LOGGER.error("Failed to list running containers: %s", exc)
+        return 1
+
+    containers = sorted([line for line in raw_containers.splitlines() if line])
+    LOGGER.info(
+        "Processing %s containers with parallelism of %s",
+        len(containers),
+        args.parallel,
+    )
+
+    try:
+        if args.parallel > 1:
+            with ThreadPoolExecutor(max_workers=args.parallel) as executor:
+                list(
+                    executor.map(
+                        lambda name: process_container(config, name), containers
+                    )
+                )
+        else:
+            for container_name in containers:
+                process_container(config, container_name)
+    except KeyboardInterrupt:
+        return 130
+
+    LOGGER.info("Pruning docker images...")
+    try:
+        run_command([docker_binary, "image", "prune", "--force"])
+    except subprocess.CalledProcessError as exc:
+        LOGGER.warning("Image prune failed: %s", exc)
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3001401
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,24 @@
+[project]
+name = "playground"
+version = "0.0.1"
+requires-python = ">=3.13"
+dependencies = [
+    "browser-cookie3>=0.20.1",
+    "bs4>=0.0.2",
+    "click>=8.3.0",
+    "cryptography>=46.0.3",
+    "debugpy>=1.8.15",
+    "httpx>=0.28.1",
+    "keyring>=25.6.0",
+    "mistralai>=1.5.1",
+    "openai>=1.98.0",
+    "pdf2image>=1.17.0",
+    "piexif>=1.1.3",
+    "playwright-stealth>=2.0.0",
+    "playwright[chromium]>=1.55.0",
+    "psycopg[binary]>=3.3.4",
+    "pytest>=9.1.1",
+    "srt>=3.5.3",
+    "typer-slim>=0.19.2",
+    "watchdog>=6.0.0",
+]
diff --git a/reset_router.py b/reset_router.py
new file mode 100755
index 0000000..d063af1
--- /dev/null
+++ b/reset_router.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = [
+#   "playwright>=1.45.0",
+# ]
+# ///
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+
+from playwright.sync_api import Playwright, TimeoutError as PlaywrightTimeoutError, sync_playwright
+
+ROUTER_URL = "http://192.168.1.1"
+ROUTER_USERNAME = "abdus"
+ROUTER_PASSWORD = "xAsametk50"
+
+
+def reboot_router(playwright: Playwright, headed: bool, timeout_ms: int) -> None:
+    browser = playwright.chromium.launch(headless=not headed)
+    context = browser.new_context(ignore_https_errors=True)
+    page = context.new_page()
+    page.set_default_timeout(timeout_ms)
+
+    try:
+        page.goto(f"{ROUTER_URL}/login", wait_until="domcontentloaded")
+
+        username_box = page.get_by_role("textbox", name="User Name")
+        password_box = page.get_by_role("textbox", name="Password")
+
+        username_box.click()
+        username_box.press("ControlOrMeta+a")
+        username_box.press("Backspace")
+        username_box.type(ROUTER_USERNAME, delay=80)
+
+        password_box.click()
+        password_box.press("ControlOrMeta+a")
+        password_box.press("Backspace")
+        password_box.type(ROUTER_PASSWORD, delay=140)
+        page.get_by_role("textbox", name="Password").press("Enter")
+
+        page.wait_for_load_state("domcontentloaded")
+
+        try:
+            page.get_by_text("The username or password is not correct", exact=False).wait_for(
+                state="visible", timeout=1500
+            )
+            raise RuntimeError("Router rejected configured credentials")
+        except PlaywrightTimeoutError:
+            pass
+
+        menu_trigger = page.locator("#h_menu_list").first
+        if menu_trigger.count() and menu_trigger.is_visible():
+            menu_trigger.click()
+
+        restart_icon = page.locator("#navbar_reboot .icon-menu-restart, #navbar_reboot").first
+        restart_icon.wait_for(state="visible")
+
+        with page.expect_response(lambda r: "/cgi-bin/Reboot" in r.url, timeout=timeout_ms):
+            restart_icon.click()
+            page.get_by_role("button", name="OK").click()
+
+        time.sleep(5)
+
+        print("Router reboot request sent.")
+    finally:
+        context.close()
+        browser.close()
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Log in and reboot router")
+    parser.add_argument("--headed", action="store_true", help="Run with visible browser")
+    parser.add_argument(
+        "--timeout",
+        type=int,
+        default=60000,
+        help="Timeout in milliseconds (default: 60000)",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    try:
+        with sync_playwright() as playwright:
+            reboot_router(playwright, headed=args.headed, timeout_ms=args.timeout)
+        return 0
+    except Exception as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/reset_router_http.py b/reset_router_http.py
new file mode 100644
index 0000000..d04fb55
--- /dev/null
+++ b/reset_router_http.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = [
+#   "httpx>=0.27.0",
+# ]
+# ///
+
+import argparse
+import time
+
+import httpx
+
+ROUTER_BASE_URL = "http://192.168.1.1"
+
+# Captured from successful login flow for abdus:xAsametk50.
+# Firmware expects encrypted payload at /UserLogin.
+USER_LOGIN_PAYLOAD = {
+    "content": "z7oqX7AWMrPjJdAEgrXCZY9q/+mrsyodlD53apbPmPXBkuB1SJXdoXr8+NcUbuRwn/jyc3HJlyKcMrAHDl3aFbN8/Gnyy8TzRKQkCAoSdVEVifzmWBPgpoDKvvT/p6n4TrAi2+b1eJ18Sr4ab1MWrI0XKNbBLEU+0UJlAd/UOHo=",
+    "key": "UwLW2sfQZ5S5XegolmSX0kqpxwzeJEIXq8lp3gLkNpNX+qiBAC4E2ColaigMtsMPbVEEFGk+RiPixF9L4xI4licoguc71zEfG9U3YUvNZZeBpV3JKMLLDzhs5aFDIb9M4k/zQ623zByOWDKjnUhHqqRK1I8hEyhThbbhKUkvo67/6vcRHHE9c0Lcxc2SrIGeyRUvPe5F+zXH8PbUn4aPq6fVTPmECRbiZOOm6zoy99PvyRVIYiCOkWqeXHhGIdMl2YZhA2UeM+1lwWtFQjSgefPeCfdhtIuEfFfUUaj3DyPJfD/+CyUT0QGUH6SD5sf1M8DySqOglgH1dIXEPLyIwA==",
+    "iv": "u+z76g62wnZYAehSfgffrPzpstykp6E891Z9CrDLfO0=",
+}
+
+
+def _extract_session_key(resp: httpx.Response) -> int:
+    data = resp.json()
+    if isinstance(data, list) and data:
+        return int(data[0]["SessionKey"])
+    if isinstance(data, dict) and "SessionKey" in data:
+        return int(data["SessionKey"])
+    raise RuntimeError(f"Could not parse SessionKey from /changeSessionKey response: {data!r}")
+
+
+def _wait_for_router(client: httpx.Client, max_wait: float) -> None:
+    deadline = time.time() + max_wait
+    last_error = None
+    while time.time() < deadline:
+        try:
+            resp = client.get("/login")
+            resp.raise_for_status()
+            return
+        except (httpx.ConnectTimeout, httpx.ReadTimeout, httpx.ConnectError, httpx.NetworkError) as exc:
+            last_error = exc
+            time.sleep(2)
+    raise RuntimeError(f"Router not reachable within {max_wait:.0f}s") from last_error
+
+
+def reboot_router(timeout: float, post_confirm_sleep: float) -> None:
+    with httpx.Client(base_url=ROUTER_BASE_URL, timeout=timeout, follow_redirects=True) as client:
+        _wait_for_router(client, max_wait=90)
+
+        login = client.post("/UserLogin", json=USER_LOGIN_PAYLOAD)
+        login.raise_for_status()
+
+        check = client.get("/cgi-bin/UserLoginCheck")
+        check.raise_for_status()
+        if "ZCFG_SUCCESS" not in check.text:
+            raise RuntimeError(f"Login check failed: {check.text}")
+
+        key_resp = client.get("/changeSessionKey")
+        key_resp.raise_for_status()
+        session_key = _extract_session_key(key_resp)
+
+        reboot = client.post("/cgi-bin/Reboot", params={"sessionkey": session_key})
+        reboot.raise_for_status()
+
+        time.sleep(post_confirm_sleep)
+        print("Router reboot request sent.")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Reboot router via HTTP API (no Playwright)")
+    parser.add_argument("--timeout", type=float, default=20.0, help="HTTP timeout in seconds")
+    parser.add_argument(
+        "--post-confirm-sleep",
+        type=float,
+        default=5.0,
+        help="Seconds to wait after reboot request",
+    )
+    args = parser.parse_args()
+    reboot_router(timeout=args.timeout, post_confirm_sleep=args.post_confirm_sleep)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/rg_download.py b/rg_download.py
new file mode 100755
index 0000000..b3c6fee
--- /dev/null
+++ b/rg_download.py
@@ -0,0 +1,565 @@
+#!/usr/bin/env -S uv run
+# /// script
+# requires-python = ">=3.12"
+# dependencies = ["httpx"]
+# ///
+"""
+RapidGator (rapidgator.net) downloader — reverse-engineered from JDownloader's RapidGatorNet plugin.
+
+Usage:
+  ./rg_download.py <url> [--user EMAIL] [--pass PASSWORD] [--out DIR] [--2captcha KEY]
+
+Premium accounts use the clean v2 API path (no captcha, full speed).
+Free/anonymous downloads scrape the website; reCAPTCHA v2 or Cloudflare Turnstile is
+required — a 2captcha API key is strongly recommended (manual solving isn't feasible for
+these captcha types).
+"""
+
+import re
+import sys
+import os
+import time
+import argparse
+import getpass
+from pathlib import Path
+from urllib.parse import urlparse, unquote
+
+import httpx
+
+# ── constants ────────────────────────────────────────────────────────────────
+
+API_BASE = "https://rapidgator.net/api/v2/"
+SITE_BASE = "https://rapidgator.net"
+
+SUPPORTED_DOMAINS = {"rapidgator.net", "rapidgator.asia", "rg.to"}
+
+# JD plugin regex: (?i)/file/([a-z0-9]{32}|\d+)
+FILE_ID_RE = re.compile(r"(?i)/file/([a-z0-9]{32}|\d+)")
+
+HEADERS = {
+    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+                  "(KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36",
+    "Accept-Language": "en-US,en;q=0.8",
+}
+
+
+# ── helpers ──────────────────────────────────────────────────────────────────
+
+def extract_file_id(url: str) -> str | None:
+    m = FILE_ID_RE.search(url)
+    return m.group(1) if m else None
+
+
+def check_domain(url: str):
+    host = urlparse(url).hostname or ""
+    host = host.removeprefix("www.")
+    if host not in SUPPORTED_DOMAINS:
+        sys.exit(f"Unsupported domain '{host}'. Supported: {', '.join(sorted(SUPPORTED_DOMAINS))}")
+
+
+def _api_get(session: httpx.Client, endpoint: str, params: dict) -> dict:
+    """GET request to the v2 API; raises on HTTP error, returns parsed JSON."""
+    r = session.get(f"{API_BASE}{endpoint}", params=params, headers=HEADERS, timeout=30)
+    try:
+        data = r.json()
+    except Exception:
+        r.raise_for_status()
+        raise
+    return data
+
+
+def _api_check(data: dict, context: str = "") -> dict:
+    """Raise a RuntimeError if the API returned an error status."""
+    status = data.get("status", "")
+    if status != "success":
+        err = data.get("details") or data.get("error") or data
+        raise RuntimeError(f"API error{' (' + context + ')' if context else ''}: {err}")
+    return data.get("details", {})
+
+
+# ── API calls (mirroring JD's loginAPI / requestFileInformationAPI / handlePremium_api) ──
+
+def api_login(session: httpx.Client, email: str, password: str) -> str:
+    """GET /user/login → returns session_id (used as ?token= in all other calls)."""
+    data = _api_get(session, "user/login", {"login": email, "password": password})
+    details = _api_check(data, "login")
+    # JD stores the field named "session_id" (PROPERTY_sessionid constant)
+    sid = details.get("session_id") or details.get("token")
+    if not sid:
+        raise RuntimeError(f"Login succeeded but no session_id in response: {details}")
+    return sid
+
+
+def api_user_info(session: httpx.Client, token: str) -> dict:
+    """GET /user/info?token= → account details (is_premium, premium_end_time, …)."""
+    data = _api_get(session, "user/info", {"token": token})
+    return _api_check(data, "user/info")
+
+
+def api_file_info(session: httpx.Client, token: str, file_id: str) -> dict:
+    """GET /file/info?token=&file_id= → file metadata (name, size, hash, …)."""
+    data = _api_get(session, "file/info", {"token": token, "file_id": file_id})
+    details = _api_check(data, "file/info")
+    # Nested under "file" key
+    return details.get("file") or details
+
+
+def api_file_download(session: httpx.Client, token: str, file_id: str) -> str:
+    """GET /file/download?token=&file_id= → direct download_url."""
+    data = _api_get(session, "file/download", {"token": token, "file_id": file_id})
+    details = _api_check(data, "file/download")
+    url = details.get("download_url")
+    if not url:
+        raise RuntimeError(f"No download_url in response: {details}")
+    return url
+
+
+# ── 2captcha ─────────────────────────────────────────────────────────────────
+
+class TwoCaptcha:
+    """
+    Thin wrapper around the 2captcha API v2.
+    Supports reCAPTCHA v2 (RecaptchaV2TaskProxyless) and
+    Cloudflare Turnstile (TurnstileTaskProxyless) — the two captcha types
+    used by RapidGator's free download page.
+    """
+
+    API = "https://api.2captcha.com"
+    POLL_INTERVAL = 5
+    POLL_TIMEOUT = 180
+
+    def __init__(self, api_key: str, client: httpx.Client):
+        self.api_key = api_key
+        self.client = client
+
+    def _call(self, endpoint: str, payload: dict) -> dict:
+        r = self.client.post(
+            f"{self.API}{endpoint}",
+            json={"clientKey": self.api_key, **payload},
+            headers={"Content-Type": "application/json"},
+            timeout=30,
+        )
+        r.raise_for_status()
+        data = r.json()
+        err_id = data.get("errorId") or 0
+        if err_id and err_id != 0:
+            raise RuntimeError(
+                f"2captcha error: {data.get('errorCode') or data.get('errorDescription') or data}"
+            )
+        return data
+
+    def _poll(self, task_id: int) -> str:
+        """Poll until ready, return solution token."""
+        print(f"  2captcha task {task_id} submitted, polling…", flush=True)
+        deadline = time.monotonic() + self.POLL_TIMEOUT
+        while time.monotonic() < deadline:
+            time.sleep(self.POLL_INTERVAL)
+            result = self._call("/getTaskResult", {"taskId": task_id})
+            if result.get("status") == "ready":
+                token = result["solution"]["token"]
+                print(f"  2captcha solved (token length: {len(token)})")
+                return token
+            print("  …waiting for solution")
+        raise RuntimeError(f"2captcha timed out after {self.POLL_TIMEOUT}s")
+
+    def solve_recaptcha_v2(self, site_key: str, page_url: str) -> str:
+        """Submit a RecaptchaV2TaskProxyless and return the g-recaptcha-response token."""
+        task_id = self._call("/createTask", {
+            "task": {
+                "type": "RecaptchaV2TaskProxyless",
+                "websiteURL": page_url,
+                "websiteKey": site_key,
+            }
+        })["taskId"]
+        return self._poll(task_id)
+
+    def solve_turnstile(self, site_key: str, page_url: str) -> str:
+        """Submit a TurnstileTaskProxyless and return the cf-turnstile-response token."""
+        task_id = self._call("/createTask", {
+            "task": {
+                "type": "TurnstileTaskProxyless",
+                "websiteURL": page_url,
+                "websiteKey": site_key,
+            }
+        })["taskId"]
+        return self._poll(task_id)
+
+
+# ── free download (website scrape) ────────────────────────────────────────────
+
+_RECAPTCHA_RE = re.compile(
+    r'class=["\']g-recaptcha["\'][^>]*data-sitekey=["\']([^"\']+)["\']'
+    r'|data-sitekey=["\']([^"\']+)["\'][^>]*class=["\']g-recaptcha["\']'
+)
+_TURNSTILE_RE = re.compile(
+    r'class=["\']cf-turnstile["\'][^>]*data-sitekey=["\']([^"\']+)["\']'
+    r'|data-sitekey=["\']([^"\']+)["\'][^>]*class=["\']cf-turnstile["\']'
+)
+_DL_URL_RE = re.compile(
+    r"'(https?://[A-Za-z0-9\-_]+\.[^/]+//\?r=download/index&session_id=[A-Za-z0-9]+)'"
+    r"|\"(https?://[^/]+/download/[^<>\"']+)\""
+)
+
+# X-Requested-With is required by the AJAX endpoints — they return empty without it
+_XHR_HEADERS = {"X-Requested-With": "XMLHttpRequest"}
+
+
+def _first_group(m: re.Match | None) -> str | None:
+    if not m:
+        return None
+    return next((g for g in m.groups() if g), None)
+
+
+def free_download(
+    session: httpx.Client,
+    file_id: str,
+    file_name: str,
+    solver: TwoCaptcha | None,
+    out_dir: Path,
+):
+    """
+    Website-based free download flow (mirrors JD handleDownloadWebsite / free path).
+
+    Actual page flow discovered by inspection:
+      1. GET /file/<hex_id>  → server sets PHPSESSID + file_id cookies;
+                               page JS has: var fid = <numeric>; var secs = 180;
+                               var startTimerUrl = '/download/AjaxStartTimer';
+      2. GET /download/AjaxStartTimer?fid=<numeric>  (XHR)
+                             → {"state":"started", "sid":"<token>"}
+      3. Wait `secs` seconds (free-tier countdown)
+      4. GET /download/AjaxGetDownloadLink?sid=<token>  (XHR)
+                             → poll until {"state":"done"}
+      5. GET /download/captcha  → HTML with reCAPTCHA v2 or Turnstile widget
+      6. POST /download/captcha with solved token
+         → redirect to the actual download URL  OR
+           response body contains it
+    """
+    file_url = f"{SITE_BASE}/file/{file_id}"
+
+    # ── step 1: load file page ────────────────────────────────────────────────
+    print(f"Loading file page: {file_url}")
+    r = session.get(file_url, headers=HEADERS, timeout=30)
+    r.raise_for_status()
+    page_html = r.text
+
+    # Check for premium-only direct link already embedded (logged-in premium path)
+    pm = re.search(r"var\s+premium_download_link\s*=\s*'(https?://[^']+)'", page_html)
+    if pm and pm.group(1):
+        stream_download(session, pm.group(1), out_dir, file_name)
+        return
+
+    # Extract numeric fid (set by server in page JS and cookie, distinct from hex URL ID)
+    fid_m = re.search(r"var\s+fid\s*=\s*(\d+)", page_html)
+    if not fid_m:
+        sys.exit("Could not find numeric fid in page JS — page layout may have changed.")
+    numeric_fid = fid_m.group(1)
+
+    # Extract client-side wait timer (default 180 for free users)
+    secs_m = re.search(r"var\s+secs\s*=\s*(\d+)", page_html)
+    wait_secs = int(secs_m.group(1)) if secs_m else 180
+
+    print(f"  Numeric fid={numeric_fid}, wait={wait_secs}s")
+
+    if wait_secs > 600:
+        sys.exit(f"Server wait is {wait_secs}s — IP appears rate-limited. Try again later.")
+
+    # ── step 2: start the server-side timer ──────────────────────────────────
+    print("Starting download timer…")
+    r2 = session.get(
+        f"{SITE_BASE}/download/AjaxStartTimer",
+        params={"fid": numeric_fid},
+        headers={**HEADERS, **_XHR_HEADERS, "Referer": file_url},
+        timeout=30,
+    )
+    r2.raise_for_status()
+    try:
+        timer_data = r2.json()
+    except Exception:
+        sys.exit(f"AjaxStartTimer returned non-JSON: {r2.text[:300]!r}")
+
+    if timer_data.get("state") == "error":
+        sys.exit(f"AjaxStartTimer error: {timer_data.get('code') or timer_data}")
+    if timer_data.get("state") != "started":
+        sys.exit(f"Unexpected AjaxStartTimer state: {timer_data}")
+
+    sid = timer_data.get("sid")
+    if not sid:
+        sys.exit(f"AjaxStartTimer response missing sid: {timer_data}")
+    print(f"  sid={sid}")
+
+    # ── step 3: wait for the server countdown ────────────────────────────────
+    print(f"  Waiting {wait_secs}s…", flush=True)
+    for remaining in range(wait_secs, 0, -5):
+        time.sleep(min(5, remaining))
+        print(f"\r  {remaining - min(5, remaining)}s remaining…  ", end="", flush=True)
+    print()
+
+    # ── step 4: confirm server is ready ──────────────────────────────────────
+    print("Confirming download ready…")
+    for attempt in range(10):
+        r3 = session.get(
+            f"{SITE_BASE}/download/AjaxGetDownloadLink",
+            params={"sid": sid},
+            headers={**HEADERS, **_XHR_HEADERS, "Referer": file_url},
+            timeout=30,
+        )
+        r3.raise_for_status()
+        try:
+            link_data = r3.json()
+        except Exception:
+            sys.exit(f"AjaxGetDownloadLink returned non-JSON: {r3.text[:300]!r}")
+
+        dl_state = link_data.get("state")
+        if dl_state == "done":
+            break
+        if dl_state == "error":
+            code = link_data.get("code", "")
+            if "wait" in str(code).lower():
+                print(f"  Server not ready yet, retrying in 5s… ({code})")
+                time.sleep(5)
+                continue
+            sys.exit(f"AjaxGetDownloadLink error: {link_data}")
+        print(f"  Unexpected state '{dl_state}', retrying…")
+        time.sleep(5)
+    else:
+        sys.exit(f"AjaxGetDownloadLink never returned done. Last response: {link_data}")
+
+    # ── step 5: load captcha page ─────────────────────────────────────────────
+    captcha_url = f"{SITE_BASE}/download/captcha"
+    print("Loading captcha page…")
+    r4 = session.get(captcha_url, headers={**HEADERS, "Referer": file_url}, timeout=30)
+    r4.raise_for_status()
+    captcha_html = r4.text
+
+    if not captcha_html.strip():
+        sys.exit("Captcha page returned empty — session may have expired. Try again.")
+
+    # Detect captcha type and extract site key
+    recaptcha_key = _first_group(_RECAPTCHA_RE.search(captcha_html))
+    turnstile_key = _first_group(_TURNSTILE_RE.search(captcha_html))
+
+    # Also extract CSRF token from <meta name="__token"> if present
+    csrf_m = re.search(r'<meta\s+name=["\']__token["\']\s+content=["\']([^"\']+)["\']', captcha_html)
+    csrf_token = csrf_m.group(1) if csrf_m else None
+
+    # Extract any hidden form fields
+    hidden_fields: dict[str, str] = {}
+    for hm in re.finditer(r'<input[^>]+type=["\']hidden["\'][^>]*name=["\']([^"\']+)["\'][^>]*value=["\']([^"\']*)["\']', captcha_html, re.I):
+        hidden_fields[hm.group(1)] = hm.group(2)
+    for hm in re.finditer(r'<input[^>]+name=["\']([^"\']+)["\'][^>]+type=["\']hidden["\'][^>]*value=["\']([^"\']*)["\']', captcha_html, re.I):
+        hidden_fields[hm.group(1)] = hm.group(2)
+
+    if recaptcha_key:
+        captcha_type = "reCAPTCHA v2"
+        captcha_field = "g-recaptcha-response"
+        site_key = recaptcha_key
+    elif turnstile_key:
+        captcha_type = "Cloudflare Turnstile"
+        captcha_field = "cf-turnstile-response"
+        site_key = turnstile_key
+    else:
+        sys.exit(
+            "No reCAPTCHA v2 or Turnstile found on captcha page.\n"
+            f"Page excerpt: {captcha_html[:500]}"
+        )
+
+    print(f"  Detected {captcha_type} (sitekey: {site_key[:24]}…)")
+
+    if not solver:
+        sys.exit(
+            f"{captcha_type} detected — a --2captcha key is required for free downloads.\n"
+            "Alternatively use a premium account (--user / --pass)."
+        )
+
+    if recaptcha_key:
+        captcha_token = solver.solve_recaptcha_v2(site_key, file_url)
+    else:
+        captcha_token = solver.solve_turnstile(site_key, file_url)
+
+    # ── step 6: submit captcha form ───────────────────────────────────────────
+    print("Submitting captcha…")
+    form_data: dict = {
+        **hidden_fields,
+        captcha_field: captcha_token,
+        "DownloadCaptchaForm[verifyCode]": captcha_token,
+    }
+    if csrf_token:
+        form_data["_csrf"] = csrf_token
+
+    r5 = session.post(
+        captcha_url,
+        data=form_data,
+        headers={**HEADERS, "Referer": captcha_url},
+        timeout=30,
+    )
+    r5.raise_for_status()
+
+    # The final URL may come from a redirect the client followed, or be in the body
+    final_url = str(r5.url)
+    if "/download/" in final_url or final_url.startswith("http") and "rapidgator" not in final_url:
+        stream_download(session, final_url, out_dir, file_name)
+        return
+
+    # Search for download URL in response body
+    dl_m = _DL_URL_RE.search(r5.text)
+    if dl_m:
+        stream_download(session, _first_group(dl_m), out_dir, file_name)
+        return
+
+    # Last-resort: look for any https link pointing outside rapidgator (CDN URL)
+    cdn_m = re.search(r"https?://[a-z0-9\-]+\.rapidgator\.net/[^\s\"'<>]+", r5.text)
+    if cdn_m:
+        stream_download(session, cdn_m.group(0), out_dir, file_name)
+        return
+
+    sys.exit(
+        f"Could not extract download URL after captcha submission.\n"
+        f"Response URL: {final_url}\n"
+        f"Body excerpt: {r5.text[:500]}"
+    )
+
+
+# ── download ──────────────────────────────────────────────────────────────────
+
+def _filename_from_response(r: httpx.Response, url: str, fallback: str) -> str:
+    cd = r.headers.get("content-disposition", "")
+    if cd:
+        m = re.search(r"filename\*=UTF-8''([^;\r\n]+)", cd, re.I)
+        if m:
+            return unquote(m.group(1).strip().strip('"'))
+        m = re.search(r'filename="?([^";\r\n]+)"?', cd, re.I)
+        if m:
+            return m.group(1).strip()
+    path = urlparse(url).path
+    name = Path(path).name
+    return unquote(name) if name else (fallback or "download")
+
+
+def stream_download(session: httpx.Client, url: str, dest: Path, fallback_name: str = ""):
+    # Enforce HTTPS (JD plugin does this explicitly)
+    if url.startswith("http://"):
+        url = "https://" + url[7:]
+
+    dest.mkdir(parents=True, exist_ok=True)
+    with session.stream("GET", url, headers=HEADERS, timeout=None, follow_redirects=True) as r:
+        r.raise_for_status()
+        filename = _filename_from_response(r, url, fallback_name)
+        out_path = dest / filename
+        print(f"Downloading → {out_path}")
+        total = int(r.headers.get("content-length", 0))
+        done = 0
+        with open(out_path, "wb") as f:
+            for chunk in r.iter_bytes(chunk_size=65536):
+                f.write(chunk)
+                done += len(chunk)
+                if total:
+                    pct = done * 100 // total
+                    print(f"\r  {pct}%  {done // 1024} / {total // 1024} KB", end="", flush=True)
+    print(f"\nDone: {out_path}")
+
+
+# ── main flow ─────────────────────────────────────────────────────────────────
+
+def download(
+    url: str,
+    username: str | None,
+    password: str | None,
+    out_dir: Path,
+    twocaptcha_key: str | None = None,
+):
+    check_domain(url)
+
+    file_id = extract_file_id(url)
+    if not file_id:
+        sys.exit(f"Could not extract file ID from: {url}")
+
+    session = httpx.Client(follow_redirects=True)
+    token: str | None = None
+    solver = TwoCaptcha(twocaptcha_key, session) if twocaptcha_key else None
+
+    # ── 1. authenticate (premium path) ────────────────────────────────────────
+    if username and password:
+        print(f"Logging in as {username} …")
+        try:
+            token = api_login(session, username, password)
+            print("Logged in OK.")
+        except RuntimeError as e:
+            msg = str(e).lower()
+            if "wrong" in msg or "password" in msg or "login" in msg:
+                sys.exit(f"Login failed: {e}")
+            raise
+
+    # ── 2. check file ─────────────────────────────────────────────────────────
+    if token:
+        print(f"Checking file {file_id} via API…")
+        try:
+            info = api_file_info(session, token, file_id)
+        except RuntimeError as e:
+            sys.exit(f"File info failed: {e}")
+
+        name = info.get("name") or file_id
+        size = info.get("size") or 0
+        hash_ = info.get("hash") or info.get("md5") or ""
+        print(f"  Name : {name}")
+        print(f"  Size : {size // 1024 // 1024} MB" if size else "  Size : unknown")
+        if hash_:
+            print(f"  MD5  : {hash_}")
+
+        # ── 3. get download URL (premium API) ─────────────────────────────────
+        print("Requesting download URL…")
+        try:
+            direct_url = api_file_download(session, token, file_id)
+        except RuntimeError as e:
+            msg = str(e).lower()
+            if "daily" in msg or "limit" in msg:
+                sys.exit(f"Download limit reached: {e}")
+            sys.exit(f"Could not get download URL: {e}")
+
+        # ── 4. download ───────────────────────────────────────────────────────
+        stream_download(session, direct_url, out_dir, name)
+
+    else:
+        # ── free / anonymous path — website scrape + captcha ──────────────────
+        print("No credentials provided — attempting free download (captcha required).")
+        # Get file name from page if possible (best effort)
+        name = file_id
+        try:
+            r0 = session.get(f"{SITE_BASE}/file/{file_id}", headers=HEADERS, timeout=15)
+            title_m = re.search(r"<title>\s*Download file\s*([^<>\"]+)</title>", r0.text, re.I)
+            if title_m:
+                name = title_m.group(1).strip()
+        except Exception:
+            pass
+
+        free_download(session, file_id, name, solver, out_dir)
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="RapidGator downloader")
+    parser.add_argument("url", help="rapidgator.net / rg.to file URL")
+    parser.add_argument("--user", "-u", default=os.environ.get("RG_USER"), help="Account email")
+    parser.add_argument("--pass", "-p", dest="password",
+                        default=os.environ.get("RG_PASS"), help="Account password")
+    parser.add_argument("--out", "-o", default=None, help="Output directory (default: current directory)")
+    parser.add_argument("--2captcha", dest="twocaptcha_key",
+                        default=os.environ.get("TWOCAPTCHA_API_KEY"),
+                        help="2captcha API key (or set TWOCAPTCHA_API_KEY env var)")
+    args = parser.parse_args()
+
+    password = args.password
+    if args.user and not password:
+        password = getpass.getpass(f"Password for {args.user}: ")
+
+    download(
+        url=args.url,
+        username=args.user,
+        password=password,
+        out_dir=Path(args.out) if args.out else Path.cwd(),
+        twocaptcha_key=args.twocaptcha_key,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sort_images_yolo.py b/sort_images_yolo.py
new file mode 100755
index 0000000..8808409
--- /dev/null
+++ b/sort_images_yolo.py
@@ -0,0 +1,618 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["ultralytics", "torch", "opencv-python", "numpy", "pillow"]
+# ///
+import argparse
+from functools import cache
+import logging
+from pathlib import Path
+from typing import List, Tuple, NamedTuple, Optional
+from dataclasses import dataclass
+import math
+from itertools import combinations
+
+# --- Heavyweight imports for detection ---
+try:
+    from ultralytics import YOLO
+    import torch
+    import numpy as np
+except Exception:  # pragma: no cover - optional
+    YOLO = None
+    torch = None
+    np = None
+
+try:
+    import cv2
+except Exception:  # pragma: no cover - optional
+    cv2 = None
+
+try:
+    from PIL import Image
+except Exception:
+    Image = None
+
+# --- Constants ---
+# Tolerance for centering/pose checking (Normalized 0-1 space)
+# Minimum confidence/visibility score for a keypoint to be used
+VISIBILITY_THRESH = 0.1
+
+# --- Structured Coordinate and Keypoint Types ---
+
+
+class Coords(NamedTuple):
+    """Represents normalized coordinates (0.0 to 1.0) and visibility for a single point."""
+
+    x: float
+    y: float
+    is_visible: bool
+
+
+@dataclass
+class PoseKeypoints:
+    """Holds structured, normalized keypoint data for all 17 COCO points as direct fields."""
+
+    # 0
+    nose: Coords
+    # 1-4
+    left_eye: Coords
+    right_eye: Coords
+    left_ear: Coords
+    right_ear: Coords
+    # 5-6
+    left_shoulder: Coords
+    right_shoulder: Coords
+    # 7-10
+    left_elbow: Coords
+    right_elbow: Coords
+    left_wrist: Coords
+    right_wrist: Coords
+    # 11-12
+    left_hip: Coords
+    right_hip: Coords
+    # 13-16
+    left_knee: Coords
+    right_knee: Coords
+    left_ankle: Coords
+    right_ankle: Coords
+
+    def shoulder_midpoint(self) -> Coords:
+        """Return midpoint between left and right shoulder if available."""
+        l = self.left_shoulder
+        r = self.right_shoulder
+        if l.is_visible and r.is_visible:
+            return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=True)
+        return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=False)
+
+    def hip_midpoint(self) -> Coords:
+        """Return midpoint between left and right hip if available."""
+        l = self.left_hip
+        r = self.right_hip
+        if l.is_visible and r.is_visible:
+            return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=True)
+        return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=False)
+
+    def eye_midpoint(self) -> Coords:
+        """Return midpoint between left and right eye if available."""
+        l = self.left_eye
+        r = self.right_eye
+        if l.is_visible and r.is_visible:
+            return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=True)
+        return Coords(x=(l.x + r.x) / 2.0, y=(l.y + r.y) / 2.0, is_visible=False)
+
+
+# --- Dataclasses for Structured Output ---
+
+
+@dataclass
+class PoseDetectionResult:
+    """Encapsulates results from the pose detection strategy."""
+
+    boxes: np.ndarray
+    keypoints_xyc: np.ndarray
+    # List of all detected people's keypoints
+    all_pose_kps: List[PoseKeypoints]
+
+    # --- Centering logic moved into a method (CLEANED) ---
+
+    @dataclass
+    class CenterResult:
+        is_centered: bool
+        reason: str
+        coords: Optional[Coords]
+        threshold: float
+
+    def is_centered(self, center_threshold: float) -> "PoseDetectionResult.CenterResult":
+        """
+        Checks if any person's core (nose, shoulder midpoint, or hip midpoint)
+        is horizontally centered within the image based on the threshold.
+
+        Returns: (is_centered, centered_by_point_name, centering_point_coords)
+        """
+        cx = 0.5
+        band_min = cx - center_threshold
+        band_max = cx + center_threshold
+
+        def _is_in_band(c: Coords) -> bool:
+            return c.is_visible and (band_min <= c.x <= band_max)
+
+        for kps in self.all_pose_kps:
+            nose = kps.nose
+            shoulder_mid = kps.shoulder_midpoint()
+            hip_mid = kps.hip_midpoint()
+            eye_mid = kps.eye_midpoint()
+
+            important_features = [nose, shoulder_mid, eye_mid]
+            visible_features = [f for f in important_features if f.is_visible]
+
+            if not visible_features:
+                continue
+
+            all_centered = all(_is_in_band(f) for f in visible_features)
+            if all_centered:
+                # Representative point: prefer shoulder, then nose, then hip, then eyes
+                pref = None
+                for f in (shoulder_mid, nose, hip_mid, eye_mid):
+                    if f.is_visible:
+                        pref = f
+                        break
+                if pref is None:
+                    pref = visible_features[0]
+                return PoseDetectionResult.CenterResult(is_centered=True, reason="multiple", coords=pref, threshold=center_threshold)
+
+        return PoseDetectionResult.CenterResult(is_centered=False, reason="none", coords=None, threshold=center_threshold)
+
+    def is_torso_centered(self, center_threshold: float) -> Tuple[bool, str, Optional[Coords]]:
+        """
+        Determines if the torso (the line passing through the shoulder midpoint
+        and the hip midpoint) crosses the central vertical band of the image.
+
+        Returns: (is_centered, "torso_line" or "none", Coords of intersection/midpoint)
+        """
+        band_min = 0.5 - center_threshold
+        band_max = 0.5 + center_threshold
+
+        for kps in self.all_pose_kps:
+            l_sh = kps.left_shoulder
+            r_sh = kps.right_shoulder
+            l_hp = kps.left_hip
+            r_hp = kps.right_hip
+
+            # Need visibility for both shoulders and both hips to form the line
+            if not (l_sh.is_visible and r_sh.is_visible and l_hp.is_visible and r_hp.is_visible):
+                continue
+
+            sx = (l_sh.x + r_sh.x) / 2.0
+            sy = (l_sh.y + r_sh.y) / 2.0
+            hx = (l_hp.x + r_hp.x) / 2.0
+            hy = (l_hp.y + r_hp.y) / 2.0
+
+            seg_min_x = min(sx, hx)
+            seg_max_x = max(sx, hx)
+
+            # Quick reject: if the x-range of the segment doesn't touch the band
+            if seg_max_x < band_min or seg_min_x > band_max:
+                continue
+
+            # If either endpoint is already inside the band, return that endpoint/midpoint
+            if band_min <= sx <= band_max:
+                return True, "torso_line", Coords(x=sx, y=sy, is_visible=True)
+            if band_min <= hx <= band_max:
+                return True, "torso_line", Coords(x=hx, y=hy, is_visible=True)
+
+            # Otherwise the segment crosses the band somewhere between the endpoints.
+            # Compute intersection with the central vertical line x=0.5 when possible.
+            dx = hx - sx
+            dy = hy - sy
+            if abs(dx) < 1e-6:
+                # Vertical segment (x nearly constant) and we already know it intersects band
+                mid_x = sx
+                mid_y = (sy + hy) / 2.0
+                return True, "torso_line", Coords(x=mid_x, y=mid_y, is_visible=True)
+
+            # param t where x(t) = sx + t*dx == 0.5
+            t = (0.5 - sx) / dx
+            if 0.0 <= t <= 1.0:
+                inter_y = sy + t * dy
+                return True, "torso_line", Coords(x=0.5, y=inter_y, is_visible=True)
+
+            # Fallback: return midpoint of segment if we reach here (shouldn't normally)
+            mid_x = (sx + hx) / 2.0
+            mid_y = (sy + hy) / 2.0
+            return True, "torso_line", Coords(x=mid_x, y=mid_y, is_visible=True)
+
+        return False, "none", None
+
+    def is_upright(self, angle_threshold_degrees: float = 20.0) -> bool:
+        """Return True if the torso (or any pair of important features) is approximately vertical.
+
+        Logic: consider important features (eye_mid, shoulder_mid, hip_mid, nose). If at least two
+        visible features form a vector whose angle to vertical is within `angle_threshold_degrees`,
+        consider the person upright.
+        """
+
+        def angle_from_vertical(p1: Coords, p2: Coords) -> float:
+            vx = p2.x - p1.x
+            vy = p2.y - p1.y
+            if abs(vx) < 1e-9 and abs(vy) < 1e-9:
+                return 90.0
+            # angle between (vx, vy) and vertical (0,1): use atan2(|vx|, |vy|)
+            ang_rad = math.atan2(abs(vx), abs(vy))
+            return math.degrees(ang_rad)
+
+        for kps in self.all_pose_kps:
+            eye = kps.eye_midpoint()
+            shoulder = kps.shoulder_midpoint()
+            hip = kps.hip_midpoint()
+            nose = kps.nose
+
+            features = [f for f in (shoulder, hip) if f.is_visible]
+            if len(features) < 2:
+                continue
+
+            # Check all pairs; if any pair is near-vertical, return True
+            for a_f, b_f in combinations(features, 2):
+                a = angle_from_vertical(a_f, b_f)
+                if a <= angle_threshold_degrees:
+                    return True
+
+        return False
+
+    def is_laying(self, angle_threshold_degrees: float = 20.0) -> bool:
+        """Return True if the torso (or any pair of important features) is approximately horizontal.
+
+        Logic: consider important features (eye_mid, shoulder_mid, hip_mid, nose). If at least two
+        visible features form a vector whose angle to horizontal is within `angle_threshold_degrees`,
+        consider the person laying down.
+        """
+
+        def angle_from_horizontal(p1: Coords, p2: Coords) -> float:
+            vx = p2.x - p1.x
+            vy = p2.y - p1.y
+            if abs(vx) < 1e-9 and abs(vy) < 1e-9:
+                return 90.0
+            # angle between (vx, vy) and horizontal (1,0): use atan2(|vy|, |vx|)
+            ang_rad = math.atan2(abs(vy), abs(vx))
+            return math.degrees(ang_rad)
+
+        for kps in self.all_pose_kps:
+            eye = kps.eye_midpoint()
+            shoulder = kps.shoulder_midpoint()
+            hip = kps.hip_midpoint()
+            nose = kps.nose
+
+            features = [f for f in (shoulder, hip) if f.is_visible]
+            if len(features) < 2:
+                continue
+
+            # Check all pairs; if any pair is near-horizontal, return True
+            for a_f, b_f in combinations(features, 2):
+                a = angle_from_horizontal(a_f, b_f)
+                if a <= angle_threshold_degrees:
+                    return True
+
+        return False
+
+
+# --- Utility Functions ---
+
+
+def read_dims(image_path: Path) -> tuple[int, int]:
+    # ... (read_dims implementation remains UNCHANGED) ...
+    with image_path.open("rb") as file:
+        if file.read(2) != b"\xff\xd8":
+            raise ValueError(f"{image_path} is not a valid JPEG file")
+
+        file.seek(0)
+        try:
+            img = Image.open(file)
+            width, height = img.size
+            return width, height
+        except Exception:
+            file.seek(0)
+            while True:
+                marker = file.read(1)
+                if not marker or marker != b"\xff":
+                    raise ValueError(f"Invalid JPEG format in {image_path}")
+                marker_type = int.from_bytes(file.read(1), byteorder="big")
+                length = int.from_bytes(file.read(2), byteorder="big") - 2
+                is_sof = 0xC0 <= marker_type <= 0xCF and marker_type not in (0xC4, 0xC8, 0xCC)
+                if is_sof:
+                    file.seek(1, 1)
+                    height = int.from_bytes(file.read(2), byteorder="big")
+                    width = int.from_bytes(file.read(2), byteorder="big")
+                    return width, height
+                file.seek(length, 1)
+
+
+def calc_threshold(width: int, height: int, target_aspect: float = 1080 / 2400) -> float:
+    """Compute a per-image center threshold using its dimensions.
+
+    For tall images the center area will be wider than the image so we return 0.5 (full width). For
+    wide images the square center is narrower and the returned threshold < 0.5.
+    """
+    img_aspect = width / height
+
+    if target_aspect >= img_aspect:
+        return 0.5
+
+    # center_width = target_aspect * height (in pixels)
+    center_width = target_aspect * height
+    half_width_norm = (center_width / 2.0) / width
+    return min(max(half_width_norm, 0.0), 0.5)
+
+
+@cache
+def make_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+@cache
+def _yolov8_detector(model_type: str):
+    if YOLO is None or torch is None:
+        logging.error("YOLO/Torch dependencies are missing.")
+        return None
+    try:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    except Exception:
+        device = "cpu"
+    try:
+        if model_type == "pose":
+            model = YOLO(Path(__file__).parent / "yolov8n-pose.pt")
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+        model.to(device)
+        return model
+    except Exception:
+        logging.exception(f"YOLOv8-{model_type} model initialization failed.")
+        return None
+
+
+def _get_coords(kp_xyc: np.ndarray, idx: int) -> Coords:
+    """Helper to safely extract Coords from the raw numpy array."""
+    x, y, conf = kp_xyc[idx]
+    is_visible = conf > VISIBILITY_THRESH
+    return Coords(x=x, y=y, is_visible=is_visible)
+
+
+def _extract_keypoints(kp_xyc: np.ndarray) -> PoseKeypoints:
+    """Extracts all 17 COCO normalized keypoints and populates the PoseKeypoints dataclass directly."""
+
+    return PoseKeypoints(
+        nose=_get_coords(kp_xyc, 0),
+        left_eye=_get_coords(kp_xyc, 1),
+        right_eye=_get_coords(kp_xyc, 2),
+        left_ear=_get_coords(kp_xyc, 3),
+        right_ear=_get_coords(kp_xyc, 4),
+        left_shoulder=_get_coords(kp_xyc, 5),
+        right_shoulder=_get_coords(kp_xyc, 6),
+        left_elbow=_get_coords(kp_xyc, 7),
+        right_elbow=_get_coords(kp_xyc, 8),
+        left_wrist=_get_coords(kp_xyc, 9),
+        right_wrist=_get_coords(kp_xyc, 10),
+        left_hip=_get_coords(kp_xyc, 11),
+        right_hip=_get_coords(kp_xyc, 12),
+        left_knee=_get_coords(kp_xyc, 13),
+        right_knee=_get_coords(kp_xyc, 14),
+        left_ankle=_get_coords(kp_xyc, 15),
+        right_ankle=_get_coords(kp_xyc, 16),
+    )
+
+
+# --- Core Detection Function (UNCHANGED) ---
+
+
+def detect_pose(image_path: Path) -> PoseDetectionResult:
+    """
+    Performs pose detection and returns a structured result object.
+    """
+    yolo_model = _yolov8_detector("pose")
+    all_person_boxes = np.array([])
+    all_keypoints_xyc = np.array([])
+    all_pose_kps = []
+
+    if yolo_model is None:
+        return PoseDetectionResult(boxes=all_person_boxes, keypoints_xyc=all_keypoints_xyc, all_pose_kps=all_pose_kps)
+
+    try:
+        results = yolo_model(str(image_path), conf=0.65, iou=0.5, verbose=False)
+
+        if results and results[0].boxes and results[0].keypoints:
+            all_person_boxes = results[0].boxes.xyxy.cpu().numpy()
+            kps_norm_xy = results[0].keypoints.xyn.cpu().numpy()
+            kps_conf = results[0].keypoints.conf.cpu().numpy()
+            # Combine into an N_person x 17 x 3 array (x, y, confidence)
+            all_keypoints_xyc = np.concatenate([kps_norm_xy, np.expand_dims(kps_conf, axis=2)], axis=2)
+
+            if all_person_boxes.size > 0:
+                for kp_xyc in all_keypoints_xyc:
+                    # Encapsulate the raw keypoint data for clean access
+                    all_pose_kps.append(_extract_keypoints(kp_xyc))
+
+        return PoseDetectionResult(boxes=all_person_boxes, keypoints_xyc=all_keypoints_xyc, all_pose_kps=all_pose_kps)
+
+    except Exception:
+        logging.exception("YOLOv8-Pose detection failed.")
+        return PoseDetectionResult(boxes=all_person_boxes, keypoints_xyc=all_keypoints_xyc, all_pose_kps=all_pose_kps)
+
+
+# --- Debug Drawing Function (UNCHANGED) ---
+
+
+def draw_debug_image(
+    image_path: Path,
+    width: int,
+    height: int,
+    result: PoseDetectionResult,
+    center_result: PoseDetectionResult.CenterResult,
+    save_path: Path,
+) -> None:
+    """
+    Draws the centering zone, bounding boxes, and highlights the successful centering point
+    using the coordinates provided by the is_centered method.
+    """
+    if cv2 is None or np is None:
+        logging.error("OpenCV/Numpy is required for debug but is not available.")
+        return
+
+    # Check for centering first
+    is_centered = center_result.is_centered
+    centered_by = center_result.reason
+    centering_point_coords = center_result.coords
+
+    img = cv2.imdecode(np.fromfile(str(image_path), dtype=np.uint8), cv2.IMREAD_COLOR)
+    if img is None:
+        logging.error(f"Could not load image for debugging: {image_path}")
+        return
+
+    # Draw Centering Zone
+    cx = width / 2.0
+    thresh_px_x = int(width * center_result.threshold)
+    x_mid_start = int(cx - thresh_px_x)
+    x_mid_end = int(cx + thresh_px_x)
+    y_mid_start, y_mid_end = 0, height
+
+    BOX_THICKNESS = 10
+    KP_RADIUS = 10
+    KP_THICKNESS = -1
+    # VISIBILITY_THRESH is not strictly needed here but kept for clarity
+
+    overlay = img.copy()
+    zone_color = (0, 255, 0) if is_centered else (0, 0, 255)  # Green if centered, Red otherwise
+    cv2.rectangle(overlay, (x_mid_start, y_mid_start), (x_mid_end, y_mid_end), zone_color, -1)
+    alpha = 0.2
+    img = cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0)
+
+    # Draw Detections
+    for box_idx in range(len(result.boxes)):
+        box = result.boxes[box_idx]
+        kp_xyc = result.keypoints_xyc[box_idx]
+        kp_px = (kp_xyc[:, :2] * np.array([width, height])).astype(int)
+        kps_conf = kp_xyc[:, 2]
+
+        # The bounding box color is based on the global centering status
+        person_color = (0, 255, 0) if is_centered else (255, 0, 0)
+
+        x1, y1, x2, y2 = map(int, box)
+        cv2.rectangle(img, (x1, y1), (x2, y2), person_color, BOX_THICKNESS)
+
+        # Highlight the calculated centering point (Assumes the first person detected is the one that triggered the center check)
+        if box_idx == 0 and is_centered and centering_point_coords:
+            norm_coords = centering_point_coords
+            centering_point_px = (int(norm_coords.x * width), int(norm_coords.y * height))
+
+            # Highlight the calculated centering point
+            cv2.circle(img, centering_point_px, 12, person_color, -1)
+            cv2.circle(img, centering_point_px, 6, (255, 255, 255), -1)
+
+        # Draw all visible keypoints (for context)
+        for i in range(len(kp_px)):
+            if kps_conf[i] > VISIBILITY_THRESH:
+                cv2.circle(img, tuple(kp_px[i]), KP_RADIUS, (255, 255, 0), KP_THICKNESS)
+
+    # Save the debug image
+
+    _, buffer = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 60])
+    save_path.write_bytes(buffer.tobytes())
+    logging.info(f"Saved debug image to {save_path}. Centered by: {centered_by}")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Sort images based on pose centering.")
+    parser.add_argument("image_paths", nargs="+", type=Path, help="Path(s) to JPEG image(s)")
+
+    parser.add_argument(
+        "--by-pose",
+        action="store_true",
+        default=True,
+        help="Sort images based on horizontal centering of the person's core (nose/shoulders/hips).",
+    )
+
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Saves a debug image showing the centering zone and detected points, but does NOT move the original file.",
+    )
+
+    parser.add_argument(
+        "--is-upright",
+        action="store_true",
+        help="Also detect upright posture and move upright images to `_pose_upright`.",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    """Main function to process images."""
+    args = parse_args()
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+
+    for image_path in args.image_paths:
+        if not image_path.is_file():
+            logging.warning(f"{image_path} is not a file. Skipping.")
+            continue
+
+        if image_path.suffix.lower() not in [".jpg", ".jpeg"]:
+            logging.warning(f"{image_path} is not a JPEG file. Skipping.")
+            continue
+
+        try:
+            width, height = read_dims(image_path)
+
+            # 1. Run detection (only data extraction)
+            detection_result = detect_pose(image_path)
+
+            # Compute per-image threshold and run centering logic
+            threshold = calc_threshold(width=width, height=height)
+            center_res = detection_result.is_centered(center_threshold=threshold)
+            is_centered = center_res.is_centered
+            centered_by = center_res.reason
+
+            # Define target path/directory based on centering result
+            target_dir = image_path.parent / ("_pose_centered" if is_centered else "_pose_other")
+            # If user requested upright detection and the person is upright, override target
+            if args.is_upright:
+                try:
+                    if detection_result.is_upright(angle_threshold_degrees=20):
+                        target_dir = image_path.parent / "_pose_upright"
+                    elif detection_result.is_laying(angle_threshold_degrees=40):
+                        target_dir = image_path.parent / "_pose_laying"
+                except Exception:
+                    # If upright detection fails, fall back to normal behavior
+                    pass
+            make_dir(target_dir)
+            target_path = (target_dir / image_path.name).with_suffix(".jpg")
+
+            has_detections = detection_result.boxes.size > 0
+
+            # --- DEBUG LOGIC (Only output image, no move) ---
+            if args.debug:
+                if has_detections:
+                    debug_filename = image_path.stem + "_debug" + image_path.suffix
+                    debug_path = target_dir / debug_filename
+                    draw_debug_image(
+                        image_path=image_path,
+                        width=width,
+                        height=height,
+                        result=detection_result,
+                        center_result=center_res,
+                        save_path=debug_path,
+                    )
+                    logging.info(f"Processed {image_path} (Debug mode active). File was NOT moved.")
+                else:
+                    logging.info(f"Processed {image_path} (Debug mode active). No person detected, skipping debug output.")
+            # --- NON-DEBUG LOGIC (Move file) ---
+            else:
+                image_path.rename(target_path)
+                logging.info(f"Moved {image_path} to {target_path}. Centered by: {centered_by}")
+
+        except KeyboardInterrupt:
+            raise
+        except Exception as e:
+            logging.exception(f"Error processing {image_path}. Skipping.")
+            continue
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/youtube_subbed.py b/youtube_subbed.py
new file mode 100755
index 0000000..25e55b0
--- /dev/null
+++ b/youtube_subbed.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["httpx", "srt"]
+# ///
+
+import argparse
+import dataclasses
+import logging
+import subprocess
+import os
+from pathlib import Path
+import httpx
+import srt
+import subtitle_translator
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser()
+    p.add_argument("url", help="YouTube video URL")
+    p.add_argument("--lang", help="Target language code (e.g. de, fr, es)")
+    p.add_argument("--quality", choices=["720p", "1080p"], default="1080p", help="Video quality to download")
+    p.add_argument("--hardcode", action="store_true", help="Burn subtitles into video")
+    return p.parse_args()
+
+
+@dataclasses.dataclass
+class Video:
+    title: str
+    video_path: Path
+    subtitle_path: Path
+
+
+def download_video(url: str, save_dir: Path, quality: str = "1080p") -> Video:
+    result = subprocess.run(["yt-dlp", "--get-title", url], capture_output=True, text=True, check=True)
+    title = result.stdout.strip()
+    safe_title = "".join(c if c.isalnum() or c in " .-_" else "_" for c in title)
+    height = quality.rstrip("p")
+    fmt = f"bestvideo[height={height}]+bestaudio/best[height={height}]/best"
+    # Check if subtitles already exist
+    sub_path = None
+    for pat in (f"{safe_title}.en.srt", f"{safe_title}.en.*.srt"):
+        found = list(save_dir.glob(pat))
+        if found:
+            sub_path = found[0]
+            break
+    if not sub_path:
+        # Download only subtitles first
+        sub_cmd = [
+            "yt-dlp",
+            "--skip-download",
+            "--write-auto-sub",
+            "--write-subs",
+            "--sub-lang",
+            "en",
+            "--convert-subs",
+            "srt",
+            "--output",
+            str(save_dir / f"{safe_title}.%(ext)s"),
+            url,
+        ]
+        logging.info(f"Downloading subtitles: {url}")
+        subprocess.run(sub_cmd, check=True)
+        sub_path = save_dir / f"{safe_title}.en.srt"
+    # Check if video already exists
+    for ext in ("mp4", "mkv", "webm"):
+        video_path = save_dir / f"{safe_title}.{ext}"
+        if video_path.exists():
+            logging.info(f"Video already exists: {video_path.name}, skipping download.")
+            return Video(title=safe_title, video_path=video_path, subtitle_path=sub_path)
+    outtmpl = str(save_dir / f"{safe_title}.%(ext)s")
+    cmd = [
+        "yt-dlp",
+        "-f",
+        fmt,
+        "--output",
+        outtmpl,
+        url,
+    ]
+    logging.info(f"Downloading video: {url} at {quality}")
+    subprocess.run(cmd, check=True)
+    for ext in ("mp4", "mkv", "webm"):
+        video_path = save_dir / f"{safe_title}.{ext}"
+        if video_path.exists():
+            return Video(title=safe_title, video_path=video_path, subtitle_path=sub_path)
+    raise RuntimeError("Video not downloaded")
+
+
+def hardcode_subs(video: Path, srt: Path, lang: str) -> Path:
+    out = video.with_name(f"{video.stem}.{lang}.hardcoded.mp4")
+    cmd = [
+        "ffmpeg",
+        "-y",
+        "-hwaccel",
+        "videotoolbox",
+        "-i",
+        str(video),
+        "-vf",
+        f"subtitles='{srt}'",
+        "-c:v",
+        "h264_videotoolbox",
+        "-crf",
+        "30",
+        "-c:a",
+        "copy",
+        str(out),
+    ]
+    logging.info(f"Hardcoding subtitles into video: {out.name}")
+    subprocess.run(cmd, check=True)
+    return out
+
+
+type Subs = dict[str, Path]
+
+
+def embed_subs(video_path: Path, subs: dict[str, Path]) -> Path:
+    # subs: {lang: srt_path}
+    out = video_path.with_name(f"{video_path.stem}.embedded.mp4")
+    cmd = [
+        "ffmpeg",
+        "-y",
+        "-i",
+        str(video_path),
+    ]
+    # Add each subtitle as an input
+    for srt_path in subs.values():
+        cmd.extend(["-i", str(srt_path)])
+    # Copy video and audio streams (no re-encoding)
+    cmd.extend(
+        [
+            "-c:v",
+            "copy",
+            "-c:a",
+            "copy",
+        ]
+    )
+    # Add subtitle codecs and metadata for each sub
+    for i, lang in enumerate(subs.keys()):
+        cmd.extend([f"-c:s:{i}", "mov_text"])
+        cmd.extend([f"-metadata:s:s:{i}", f"language={lang}"])
+    # Map video, audio, and all subtitle streams
+    cmd.extend(["-map", "0:v", "-map", "0:a"])
+    for i in range(len(subs)):
+        cmd.extend(["-map", f"{i + 1}:s"])
+    cmd.append(str(out))
+    logging.info(f"Embedding {len(subs)} subtitles into video (no re-encoding): {out.name}")
+    subprocess.run(cmd, check=True)
+    return out
+
+
+def main():
+    logging.basicConfig(level=logging.INFO, format="%(name)s: %(asctime)s %(levelname)s: %(message)s")
+    logging.getLogger("httpx").setLevel(logging.WARNING)  # Suppress httpx debug logs
+    args = parse_args()
+    save_dir = Path.cwd()
+    vid = download_video(args.url, save_dir=save_dir, quality=args.quality)
+    subs = {"en": vid.subtitle_path}
+    if args.lang:
+        translated_path = vid.subtitle_path.with_name(f"{vid.subtitle_path.name}.{args.lang}.srt")
+        subtitle_translator.translate(
+            subtitle_path=vid.subtitle_path,
+            lang=args.lang,
+            save_path=translated_path,
+            condense=2,
+        )
+        subs[args.lang] = translated_path
+
+    if args.hardcode:
+        out = hardcode_subs(vid.video_path, subs[args.lang], args.lang)
+    else:
+        out = embed_subs(video_path=vid.video_path, subs=subs)
+    logging.info(f"Saved: {vid.title} to {out.name}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/youtube_transcript_v2.py b/youtube_transcript_v2.py
new file mode 100755
index 0000000..1469de7
--- /dev/null
+++ b/youtube_transcript_v2.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = [
+#   "youtube-transcript-api",
+# ]
+# ///
+
+import argparse
+import logging
+import sys
+from pathlib import Path
+from urllib.parse import urlparse, parse_qs
+
+from youtube_transcript_api import YouTubeTranscriptApi
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+
+
+def extract_video_id(url: str) -> str:
+    parsed = urlparse(url)
+
+    if parsed.hostname in ("youtu.be", "www.youtu.be"):
+        return parsed.path.lstrip("/")
+
+    if parsed.hostname in ("youtube.com", "www.youtube.com", "m.youtube.com"):
+        if parsed.path == "/watch":
+            return parse_qs(parsed.query)["v"][0]
+        elif parsed.path.startswith("/embed/"):
+            return parsed.path.split("/")[2]
+        elif parsed.path.startswith("/v/"):
+            return parsed.path.split("/")[2]
+
+    raise ValueError(f"Could not extract video ID from URL: {url}")
+
+
+def format_timestamp(seconds: float) -> str:
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    millis = int((seconds % 1) * 1000)
+    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"
+
+
+def transcript_to_srt(transcript: list[dict]) -> str:
+    srt_lines = []
+    for i, entry in enumerate(transcript, start=1):
+        start_time = format_timestamp(entry["start"])
+        end_time = format_timestamp(entry["start"] + entry["duration"])
+        text = entry["text"]
+
+        srt_lines.append(f"{i}")
+        srt_lines.append(f"{start_time} --> {end_time}")
+        srt_lines.append(text)
+        srt_lines.append("")
+
+    return "\n".join(srt_lines)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Fetch YouTube video transcripts",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("url", help="YouTube video URL")
+    parser.add_argument(
+        "--save-srt",
+        type=Path,
+        metavar="FILE_PATH",
+        help="Save transcript as SRT file to the specified path",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    try:
+        video_id = extract_video_id(args.url)
+        logging.info(f"Fetching transcript for video ID: {video_id}")
+
+        ytt_api = YouTubeTranscriptApi()
+        transcript = ytt_api.fetch(video_id)
+
+        if args.save_srt:
+            srt_content = transcript_to_srt(transcript.to_raw_data())
+            args.save_srt.write_text(srt_content)
+            logging.info(f"SRT file saved to: {args.save_srt}")
+        else:
+            text = "\n".join(snippet.text for snippet in transcript)
+            print(text)
+
+    except Exception as e:
+        logging.error(f"Failed to fetch transcript: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/yt-history-remover.user.js b/yt-history-remover.user.js
new file mode 100644
index 0000000..384e02f
--- /dev/null
+++ b/yt-history-remover.user.js
@@ -0,0 +1,182 @@
+// ==UserScript==
+// @name         YouTube History Backspace Remover
+// @namespace    http://tampermonkey.net/
+// @version      2.0
+// @description  Hover over a video in YouTube watch history and press Backspace to remove it
+// @author       You
+// @match        https://www.youtube.com/feed/history
+// @match        https://www.youtube.com/feed/history/*
+// @grant        none
+// ==/UserScript==
+
+(function() {
+    'use strict';
+
+    // videoId → feedbackToken, populated from ytInitialData
+    let tokenMap = {};
+    let hoveredLink = null;
+    let isProcessing = false;
+
+    // --- Token extraction from ytInitialData ---
+
+    function extractTokens(data) {
+        const tokens = {};
+        (function walk(obj) {
+            if (!obj || typeof obj !== 'object') return;
+            if (Array.isArray(obj)) { obj.forEach(walk); return; }
+            if (obj.listItemViewModel) {
+                const lv = obj.listItemViewModel;
+                const title = lv.title?.content || '';
+                if (title.toLowerCase().includes('remove from watch history')) {
+                    const ep = lv.rendererContext?.commandContext?.onTap?.innertubeCommand?.feedbackEndpoint;
+                    if (ep?.feedbackToken && ep?.contentId) {
+                        tokens[ep.contentId] = ep.feedbackToken;
+                    }
+                }
+            }
+            for (const v of Object.values(obj)) walk(v);
+        })(data);
+        return tokens;
+    }
+
+    function refreshTokens() {
+        if (!window.ytInitialData) return;
+        const found = extractTokens(window.ytInitialData);
+        const n = Object.keys(found).length;
+        if (n > 0) {
+            tokenMap = Object.assign(tokenMap, found);
+            console.log(`[YT History Remover] ${n} tokens loaded (total: ${Object.keys(tokenMap).length})`);
+        }
+    }
+
+    // --- SAPISIDHASH for Authorization header ---
+
+    async function buildAuthHeader() {
+        const sapisid = document.cookie.match(/(?:^|;\s*)SAPISID=([^;]+)/)?.[1];
+        if (!sapisid) return null;
+        const ts = Math.floor(Date.now() / 1000);
+        const buf = await crypto.subtle.digest('SHA-1', new TextEncoder().encode(`${ts} ${sapisid} https://www.youtube.com`));
+        const hex = [...new Uint8Array(buf)].map(b => b.toString(16).padStart(2, '0')).join('');
+        return `SAPISIDHASH ${ts}_${hex}`;
+    }
+
+    // --- API call to remove from history ---
+
+    async function callFeedbackApi(feedbackToken) {
+        const ctx = window.ytcfg?.data_?.INNERTUBE_CONTEXT;
+        if (!ctx) { console.log('[YT History Remover] No INNERTUBE_CONTEXT'); return false; }
+
+        const auth = await buildAuthHeader();
+        const headers = {
+            'Content-Type': 'application/json',
+            'X-YouTube-Client-Name': '1',
+            'X-YouTube-Client-Version': window.ytcfg?.data_?.INNERTUBE_CLIENT_VERSION || '',
+            'X-Origin': 'https://www.youtube.com',
+        };
+        if (auth) headers['Authorization'] = auth;
+
+        const res = await fetch('/youtubei/v1/feedback?prettyPrint=false', {
+            method: 'POST',
+            headers,
+            credentials: 'include',
+            body: JSON.stringify({ context: ctx, feedbackTokens: [feedbackToken] }),
+        });
+        if (!res.ok) { console.log(`[YT History Remover] HTTP ${res.status}`); return false; }
+        const json = await res.json();
+        return json.feedbackResponses?.[0]?.isProcessed === true;
+    }
+
+    // --- DOM helpers ---
+
+    function getVideoId(href) {
+        try {
+            const url = new URL(href);
+            if (url.pathname.startsWith('/shorts/')) return url.pathname.split('/')[2] || null;
+            return url.searchParams.get('v');
+        } catch { return null; }
+    }
+
+    function findVideoLink(el) {
+        while (el && el !== document.body) {
+            if (el.tagName === 'A' && el.href && (el.href.includes('/watch?') || el.href.includes('/shorts/')))
+                return el;
+            el = el.parentElement;
+        }
+        return null;
+    }
+
+    function hideInDOM(videoId) {
+        for (const sel of [`a[href*="v=${videoId}"]`, `a[href*="/shorts/${videoId}"]`]) {
+            const link = document.querySelector(sel);
+            if (!link) continue;
+            const row = link.closest('yt-lockup-view-model')
+                || link.closest('ytm-shorts-lockup-view-model')
+                || link.closest('ytd-item-section-renderer');
+            if (row) { row.style.display = 'none'; return; }
+        }
+    }
+
+    // --- Main remove action ---
+
+    async function tryRemove() {
+        if (isProcessing || !hoveredLink) return;
+        isProcessing = true;
+        try {
+            const videoId = getVideoId(hoveredLink.href);
+            if (!videoId) return;
+            const token = tokenMap[videoId];
+            if (!token) {
+                console.log(`[YT History Remover] No token for ${videoId}. Tokens available: ${Object.keys(tokenMap).length}`);
+                return;
+            }
+            const ok = await callFeedbackApi(token);
+            if (ok) {
+                hideInDOM(videoId);
+                delete tokenMap[videoId];
+                hoveredLink = null;
+                console.log(`[YT History Remover] Removed ${videoId}`);
+            } else {
+                console.log(`[YT History Remover] API returned not-processed for ${videoId}`);
+            }
+        } catch (e) {
+            console.error('[YT History Remover]', e);
+        } finally {
+            isProcessing = false;
+        }
+    }
+
+    // --- Event listeners ---
+
+    document.addEventListener('mouseover', e => {
+        if (isProcessing) return;
+        const link = findVideoLink(e.target);
+        if (link) hoveredLink = link;
+    });
+
+    document.addEventListener('keydown', e => {
+        if (e.key !== 'Backspace' || !hoveredLink || isProcessing) return;
+        const ae = document.activeElement;
+        if (ae && (ae.tagName === 'INPUT' || ae.tagName === 'TEXTAREA' || ae.isContentEditable)) return;
+        e.preventDefault();
+        tryRemove();
+    });
+
+    document.addEventListener('mouseout', e => {
+        if (isProcessing || !hoveredLink) return;
+        const rt = e.relatedTarget;
+        if (!rt || !hoveredLink.contains(rt)) hoveredLink = null;
+    });
+
+    // Re-extract when YouTube finishes a SPA navigation
+    document.addEventListener('yt-navigate-finish', () => setTimeout(refreshTokens, 500));
+
+    // Polling: re-read ytInitialData (updated by YouTube on SPA nav) and clean stale refs
+    setInterval(() => {
+        refreshTokens();
+        if (hoveredLink && !document.contains(hoveredLink)) hoveredLink = null;
+    }, 2000);
+
+    // Initial extraction
+    refreshTokens();
+    console.log(`[YT History Remover] Ready. Tokens loaded: ${Object.keys(tokenMap).length}`);
+})();