AI

AI-powered CLI that translates natural language to FFmpeg

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AI-powered CLI that translates natural language to FFmpeg

I got tired of spending 20 minutes Googling ffmpeg syntax every time I needed to process a video. So I built aiclip - an AI-powered CLI that translates plain English into perfect ffmpeg commands. Instead of this: ffmpeg -i input.mp4 -vf "scale=1280:720" -c:v libx264 -c:a aac -b:v 2000k output.mp4 Just say this: aiclip "resize video.mp4 to 720p with good quality" Key features: - Safety first: Preview every command before execution - Smart defaults: Sensible codec and quality settings - Context aware: Scans your directory for input files - Interactive mode: Iterate on commands naturally - Well-tested: 87%+ test coverage with comprehensive error handling What it can do: - Convert video formats (mov to mp4, etc.) - Resize and compress videos - Extract audio from videos - Trim and cut video segments - Create thumbnails and extract frames - Add watermarks and overlays GitHub: https://github.com/d-k-patel/ai-ffmpeg-cli PyPI: https://pypi.org/project/ai-ffmpeg-cli/ Install: pip install ai-ffmpeg-cli I'd love feedback on the UX and any features you'd find useful. What video processing tasks do you find most frustrating?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: context, tasks, code · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio, smart · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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