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As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress

Hi Hacker News, I'm Louis. I built Screenpipe ( https://screenpipe.com ), an app that records your screen and audio locally (only!), and gives AI agents a searchable memory of what you've seen, said, and heard. This makes it easier to automate your repetitive tasks, turn them into SOPs (Standard Operating Procedure) and so on. I made a HN-style demo video at https://www.tella.tv/video/build-your-ai-second-brain-with-s... and there’s a marketing video at https://www.youtube.com/watch?v=c1jV6E9pyu

hey HN, Jonathan and Guy here, creators of OneCLI ( https://onecli.sh/ ). OneCLI is an open source vault for AI Agents. Traditional vaults are used to store your secrets and, on demand, provide them to you all in a secure way, trusting the person to keep them safe. We figured that in the agent's world, this is not the case, as you don't know what happens with the secret after it's delivered to the agent, or where it was saved. Or maybe someone even manipulated them to hand them over... From that

https://littletech.org/ https://static.politico.com/4a/bf/9c4021d8404386b0a311dcccf0...

If not, why not? That way, people can choose whether they want to install various software betas based on how much SSD wear they are willing to accept. And since this data would be made public and available in one place, software developers would be more careful about excessive logging in their betas.