Show HN: AutoShorts – Local, GPU-accelerated AI video pipeline for creators(github.com)
github.com
Show HN: AutoShorts – Local, GPU-accelerated AI video pipeline for creators
https://github.com/divyaprakash0426/autoshorts
36 comments
If social is heading that way, at least my tool saves you the manual labor of editing the funeral.
I watched a video[1] recently that posited the idea of AI slop farms making large, auto-moderated spaces impossible to find meaningful human content in. With the idea that it'll lead to a renaissance for smaller, more personal websites like forums or other niche places to flourish.
I think that sounds a little too convenient and idealistic to be what really happens, but I did find the concept to be a potential positive to what's happening around it. Facebook is already a good portion of the way there, being stuffed with bots consuming stolen or AI content from other bots, with confused elderly people in the middle.
[1] https://youtu.be/_QlsGkDvVHU
I think that sounds a little too convenient and idealistic to be what really happens, but I did find the concept to be a potential positive to what's happening around it. Facebook is already a good portion of the way there, being stuffed with bots consuming stolen or AI content from other bots, with confused elderly people in the middle.
[1] https://youtu.be/_QlsGkDvVHU
What's the intended use case for this? It seems like you'd create slop videos for social media. I'd love to see more AI use cases that aren't: uninteresting content people would prefer to avoid.
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.
Personal consumption is an interesting angle. I'm starting to think AI content is only desirable to the creator, but no one else wants to see the slop.
It’s like dreams.
big fan of the 'respects my hardware' philosophy. i feel like 90% of ai tools right now are just expensive middleware for openai, so seeing something that actually leverages local compute (and doesn't leak data) is refreshing
How much memory do you need locally? Is a rtx 3090 with 24gb enough?
Yes, more than enough. I have rtx4080 laptop gpu with 12gb vram.
Yash16(1)
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:
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.
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.
> Multi-Provider Support: Choose between OpenAI (GPT-5-mini, GPT-4o) or Google Gemini for scene analysis
This is the first sentence in your features section, so it is not strange if users don't understand if this tool is running locally or not.
This is the first sentence in your features section, so it is not strange if users don't understand if this tool is running locally or not.
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.
I don't get this reasoning. You were tired of LLM wrappers, but what is your tool? These two requirements (felt like a CLI and respects your hardware) do not line up.
Still a cool tool though! Although it seems partly AI generated.
Still a cool tool though! Although it seems partly AI generated.
Seems like the post you're replying to has since been edited to clarify that he's referring to the wrappers that rely on third party AI APIs over the internet rather than running locally.
rustyhancock(2)
It looks like it’s written by a LLM
Guilty as charged. I used Antigravity to handle the refactoring and docs so I could stay focused on the CUDA and VRAM orchestration.
This isn’t a job interview, drop the corpo speak. What’s going on with Cuda and vram? We are all friends here.
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.
Wow, great job.
I did smth similar 4 years ago with YOLO ultralytics.
Back then I used chat messsges spike as one of several variables to detect highs and fails moments. It needed a lot a human validation but was so fun.
Keep going
I did smth similar 4 years ago with YOLO ultralytics.
Back then I used chat messsges spike as one of several variables to detect highs and fails moments. It needed a lot a human validation but was so fun.
Keep going
Great idea. Integrating YOLO for 'Action Following' is high on the roadmap—I'd love a PR for that if you're interested!
Regardless, we need more tools like this to speed social media towards death.