divyaprakash

Haha fair enough.The actual internals are basically just one big fight with VRAM. I'm using decord to dump frames straight into GPU memory so the CPU doesn't bottleneck the pipeline. From there, everything—scene detection, hsv transforms, action scoring—is vectorized in torch (mostly fp16 to avoid ooming). I also had to chunk the audio stft/flux math because long files were just eating the card alive. The tts model stays cached as a singleton so it's snappy after the first run, and I'm manually tracking 'Allocated vs Reserved' memory to keep it from choking. Still plenty of refinement left on the roadmap, but it's a fun weekend project to mess around with.

I was just joking about your comment on social media's 'death

If social is heading that way, at least my tool saves you the manual labor of editing the funeral.

It’s actually designed for your own gameplay—it scans hours long raw session to find the best highlights and clips them into shorts. It's more about automating the tedious editing process for your own content rather than generating "slop" from scratch.

Fair point. I used SOTA models for the analysis to prioritize quality, but since the heavy media processing is local, API costs stay negligible (or free). The architecture is modular, though—you can definitely swap in a local LLM for a fully air-gapped setup.

Yes, more than enough. I have rtx4080 laptop gpu with 12gb vram.

Great idea. Integrating YOLO for 'Action Following' is high on the roadmap—I'd love a PR for that if you're interested!

Guilty as charged. I used Antigravity to handle the refactoring and docs so I could stay focused on the CUDA and VRAM orchestration.

Definitely. The architecture is modular—just swap the LLM prompts for 'cinematic' styles. It's headless and dockerized, so it fits well as a SaaS backend worker

Fair points all around. To be transparent: yes, I used an AI coding assistant (Antigravity) to help with the heavy lifting of refactoring the original legacy code and drafting the README. I’m with @rustyhancock on this—I’d rather focus my brainpower on the pipeline logic and hardware integration than on writing boilerplate and Markdown.

However, orchestrating things like decord with CUDA kernels, managing VRAM across parallel processes, and getting audio sync right with local TTS requires a deep understanding of the stack. An LLM can help write a function, but it won't solve the architectural 'glue' needed to make it a reliable CLI tool.

The project is open-source precisely because it’s a work in progress. It needs the 'human touch' for things like the RT-DETR auto-zoom and more nuanced video editing logic. PRs are more than welcome—I'd love to see where the community can push this beyond its current state.

I built this because I was tired of "AI tools" that were just wrappers around expensive APIs with high latency. As a developer who lives in the terminal (Arch/Nushell), I wanted something that felt like a CLI tool and respected my hardware.

The Tech:

    GPU Heavy: It uses decord and PyTorch for scene analysis. I’m calculating action density and spectral flux locally to find hooks before hitting an LLM.

    Local Audio: I’m using ChatterBox locally for TTS to avoid recurring costs and privacy leaks.

    Rendering: Final assembly is offloaded to NVENC.

Looking for Collaborators: I’m currently looking for PRs specifically around:

    Intelligent Auto-Zoom: Using YOLO/RT-DETR to follow the action in a 9:16 crop.

    Voice Engine Upgrades: Moving toward ChatterBoxTurbo or NVIDIA's latest TTS.

It's fully dockerized, and also has a makefile. Would love some feedback on the pipeline architecture!