Originally published at ffmpeg-micro.com You need a thumbnail from a video file. Maybe you're building a video gallery, generating preview images for a CMS, or creating social media cards from uploaded content. The usual advice is to install FFmpeg on your server and write extraction scripts. That works until you need it in production. FFmpeg can extract a single frame from any video using two fla
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
Originally published at ffmpeg-micro.com Zapier doesn't support FFmpeg. You can't install binaries, run shell commands, or execute video processing natively in a Zap. If you've tried, you've probably hit the same wall everyone else does. But Zapier can make HTTP requests. And that's all you need. By calling FFmpeg Micro's REST API from a Zapier webhook action, you can transcode, compress, convert,
If you've ever tried to download a video from Reddit, you've probably ended up with a silent MP4 file. No audio. No error. Just a video that should have sound but doesn't. This isn't a bug in your downloader. It's how Reddit stores videos. Most video platforms (YouTube, Twitter, etc.) serve videos as a single muxed file — video and audio combined in one stream. Easy to download, plays anywhere. Re
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E