AI Repo: NousResearch/hermes-agent

Skyler: Hey everyone, welcome back to Git News A I! I'm Skyler, and I'm absolutely pumped to dive into some of the hottest A I repositories that have been making waves on GitHub this week. Before we jump in, huge thanks to our sponsor LimitLess A I for making this episode possible. Today we're covering everything from agent frameworks that actually grow with you, to training your own G P T from scratch in just two hours, plus some seriously cool integrations that'll blow your mind. Skyler: Alright, let's kick things off with NousResearch's Hermes Agent, sitting pretty at over eleven thousand stars. This isn't just another A I agent - this is what they call "the agent that grows with you," and honestly, that tagline really captures something special here. What makes Hermes Agent stand out is its ability to evolve and adapt based on your interactions and workflows. It's built in Python and designed to be this living, breathing assistant that learns your patterns and preferences over time. The recent updates show they're really focusing on stability - they just pushed fixes for preventing stale memory overwrites and added support for environment variable substitution in config files. If you're a developer looking to build personalized A I experiences or you're tired of static chatbots that never quite get what you're trying to do, Hermes Agent is definitely worth checking out. The community around it is super active, and the fact that it's growing this fast tells me they're onto something big. This is perfect for anyone building custom A I workflows or wanting to experiment with adaptive agent behavior. Skyler: Next up, we've got something that honestly blew my mind when I first saw it - jingyaogong's minimind project. Get this - they're claiming you can train a twenty-six million parameter G P T from scratch in just two hours! With over forty-two thousand stars, this repo is clearly striking a chord with the community. What's fascinating about minimind is how they've stripped down the training process to its absolute essentials while still maintaining the core architecture that makes G P Ts work. This isn't about creating the next ChatGPT competitor, but rather about understanding how these models actually learn and giving developers a way to experiment without needing massive cloud budgets. The recent updates show they're tweaking something called empty think ratio, which probably relates to how the model handles reasoning tokens. If you're an M L engineer who's always wondered what's actually happening under the hood of large language models, or if you want to prototype your own small-scale models for specific tasks, this is your playground. It's educational, practical, and proves that you don't always need billions of parameters to do interesting things with A I. Skyler: Now let's talk about tinygrad - and yes, the name is as adorable as it sounds! With nearly thirty-two thousand stars, this project describes itself as the perfect middle ground if you love PyTorch and micrograd. Tinygrad is essentially a minimalist deep learning framework that strips away all the complexity while keeping the power. What makes it special is its philosophy of being simple enough to understand completely, yet capable enough for real work. The recent activity shows they're doing some seriously technical work on hardware optimization, including RDNA4 decoder work and barrier handling. This tells me they're not just building a toy framework - they're going after real performance on modern hardware. If you're someone who gets frustrated with PyTorch's complexity or you want to really understand how tensor operations work under the hood, tinygrad is perfect. It's also great for researchers who want to quickly prototype new ideas without getting bogged down in framework overhead. The community loves it because it proves that sometimes less really is more, and you can build powerful A I systems with clean, understandable code. Skyler: Here's something really cool for all you workflow automation fans - czlonkowski's n8n-mcp project. This is a Model Context Protocol integration that lets Claude Desktop, Claude Code, Windsurf, and Cursor build n8n workflows for you automatically. With over sixteen thousand stars, it's clear that people are excited about bridging A I assistants with workflow automation. What's brilliant about this is that instead of manually building complex n8n workflows, you can just describe what you want in natural language and let Claude figure out the implementation. The recent updates include fixes for webhook handling and data table availability across different n8n plans, which shows they're really focusing on making this production-ready. If you're already using n8n for automation or you're working with any of the supported A I coding assistants, this could be a total game-changer for your productivity. It's perfect for DevOps engineers, automation specialists, or really anyone who's tired of manually configuring workflow tools. The fact that it supports multiple A I assistants means you're not locked into one ecosystem, which is always a win in my book. Skyler: Last but definitely not least, we have kepano's obsidian-skills project, which is all about teaching A I agents to work with Obsidian like a pro. With over sixteen thousand stars, this repo is tapping into the huge Obsidian community that's hungry for better A I integration. What's exciting here is that they're not just adding basic A I features - they're creating comprehensive skill sets that teach agents to understand Markdown, work with databases, manipulate JSON Canvas, and even use command line interfaces within the Obsidian ecosystem. The recent updates include skill review optimizations, which suggests they're refining how these agents learn and apply their Obsidian knowledge. This is perfect for knowledge workers, researchers, writers, or anyone who lives in Obsidian and wants their A I assistant to truly understand their note-taking and thinking processes. It's also great for plugin developers who want to see how to build meaningful A I integrations rather than just slapping a chat interface on everything. The skills-based approach here is really smart because it makes the A I capabilities modular and extensible. Skyler: And that's a wrap on this week's Git News A I! Thanks again to LimitLess A I for sponsoring today's episode and helping us bring you the latest in A I development tools and frameworks. Don't forget to subscribe wherever you get your podcasts, and check out the show notes for direct links to all the repositories we covered today. Until next time, keep coding and keep discovering - there's always something amazing happening in the world of A I development!