I Built an Open-Source AI Video Clipping Pipeline

Sreegeethesh — Sat, 08 Aug 2026 06:46:05 +0000

I Built an Open-Source AI Video Clipping Pipeline

I wanted a tool that could take a long video and turn it into social-ready clips without uploading the entire video to some SaaS platform.

So I built AutoClip.

The idea is simple:

YouTube URL / Video File
          ↓
      Transcription
          ↓
   LLM Clip Selection
          ↓
   Vertical Reframing
          ↓
    Speaker Tracking
          ↓
   Animated Captions
          ↓
     MP4 Export
          ↓
    Buffer Scheduling

The interesting part is that the video processing happens locally.

AutoClip uses an LLM to identify moments worth clipping, but only the transcript text is sent to the LLM. The actual video stays on the user's machine.

It supports multiple caption styles:

Word Pop
Karaoke Fill
Clean Lower Third
Boxed

The final clips are rendered as 9:16 MP4s ready for social platforms.

Publishing Architecture

I also wanted publishing to follow the same privacy model.

Instead of building a cloud scheduler, AutoClip uses a BYOK approach.

The stack is:

Your PC
  ↓
AutoClip processes video
  ↓
Cloudflare R2
  ↓
Buffer
  ↓
Social Platform

The R2 bucket acts as temporary media storage so Buffer can access the video.

Once the job is handed off, the user's computer doesn't need to stay online.

No central media storage.

No SaaS layer.

No subscription.

No watermark.

No telemetry.

Just your infrastructure and your API keys.

Why I Built It

There are plenty of AI video tools already.

I wanted one where I could actually understand and control what happens to my content.

The goal was:

Your machine edits.
Your Buffer publishes.
Your content stays yours.

AutoClip is open source.

GitHub:

SREEGEETHES / Autoclip

AutoClip

The local-first AI clipper that finishes the job. Drop in a long video → get ranked, caption-burned, speaker-tracked 9:16 clips out — and schedule them to post while your PC is off.

Paste a YouTube link or drop a file. AutoClip transcribes it, uses an LLM to find the moments actually worth clipping, reframes them to vertical while tracking whoever is speaking, burns in animated captions, exports platform-ready MP4s — then hands the finished clips to Buffer to publish on schedule. No accounts with us. No uploads to our servers. No watermarks. No subscription.

Your machine edits. Your Buffer publishes. Your R2 bridges the media. Nobody owns your content but you.

What it does

Ingest anything — a YouTube URL or a local file, hours long or minutes.
Find the real hooks — an LLM reads the transcript and ranks the moments worth clipping, down to the word. Timing…

View on GitHub

I'd love feedback from anyone building content automation, video tooling, or local-first AI applications.

How I built an offline semantic photo search app (Python + Vector DB)

Sreegeethesh — Mon, 03 Aug 2026 10:51:14 +0000

Show DEV 🚀

I built OmniCard, a local-first AI media manager.

Features:

• Semantic image search

• AI captions

• Smart tags

• Duplicate detection

• SQLite + LanceDB

• Desktop application with pywebview

The goal was simple:

Search photos by what's inside them instead of by filename.

Example:

"Drone over mountains"

instead of

IMG_4829.jpg

The app stores everything locally, so your media never leaves your computer.

Live demo:
https://sreegeethes.github.io/Omnicard/

GitHub:
https://github.com/SREEGEETHES/Omnicard

Release:
https://github.com/SREEGEETHES/Omnicard/releases/tag/v1.0.0

Would love feedback from other Python and AI developers.

DEV Community: Sreegeethesh

I Built an Open-Source AI Video Clipping Pipeline

I Built an Open-Source AI Video Clipping Pipeline

Publishing Architecture

Why I Built It

SREEGEETHES / Autoclip

AutoClip

What it does

How I built an offline semantic photo search app (Python + Vector DB)

Show DEV 🚀