divyaprakash

31 ms·

1.
by divyaprakash 8mo ago
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 tra
2.
by divyaprakash 8mo ago
I was just joking about your comment on social media's 'death
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by divyaprakash 8mo ago
If social is heading that way, at least my tool saves you the manual labor of editing the funeral.
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by divyaprakash 8mo ago
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 &qu
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by divyaprakash 8mo ago
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 full
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by divyaprakash 8mo ago
Yes, more than enough. I have rtx4080 laptop gpu with 12gb vram.
7.
by divyaprakash 8mo ago
Great idea. Integrating YOLO for 'Action Following' is high on the roadmap—I'd love a PR for that if you're interested!
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by divyaprakash 8mo ago
Guilty as charged. I used Antigravity to handle the refactoring and docs so I could stay focused on the CUDA and VRAM orchestration.
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by divyaprakash 8mo ago
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
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by divyaprakash 8mo ago
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 respect
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Show HN: AutoShorts – Local, GPU-accelerated AI video pipeline for creators(github.com)
72 points by divyaprakash 8mo ago|31 comments