You've probably heard about the first METR study from July 2025 - it made the rounds at every conference and every newsletter. 16 experienced open-source developers, a proper randomized controlled trial (not a vendor survey), and the result: 19% slower with AI. In this article, Artur argues that the problem lies in the environment, not the model. Read on to find out exactly.
Monorepo keeps coming up in conversations about large-scale software architecture. For some organizations, it’s a way to bring order to a growing ecosystem of applications. For others, it raises a lot of concerns. We spoke with Bartek Sądel, an expert who works with enterprise monorepos, about how this approach works in practice, what questions companies ask, and what the real benefits and challenges are.
Welcome to GitHub All-Stars, our biweekly series where we pick a trending or freshly minted open-source project and put it under the microscope. We focus on new, relatively unknown gems - not another breakdown of the latest React release (because let's be honest, the world has enough of those). This time, we're looking at a project that lives at an unusual intersection of prepper culture, self-hosting enthusiasm, and edge computing philosophy. And it just exploded on Hacker News.
Today's subject has become one of the most talked-about open-source projects in the AI agent space, racking up over 11,000 GitHub stars in less than a month. We're looking at NanoClaw by Gavriel Cohen - a lightweight, container-isolated personal AI assistant that connects to WhatsApp (and Telegram, Discord, Slack, Signal) and runs on Anthropic's Agent SDK. Written in TypeScript, MIT-licensed, and built around one radical premise: the software that hosts powerful AI agents should be simple enough to read in eight minutes.
Most of the time, the knowledge base we want to chat and reason about with an LLM has strong inter-relations. Even the famous PageRank algorithm, which gave Google a competitive advantage and made it ahead of others, is based on the quantity and quality of links between websites. The relations within a knowledge base are crucial to fully understand it. The problem with classical RAG is that it chunks the text, discarding all internal relations. So can we do better?
Today's project dropped on Hacker News frontpage just days ago and instantly sparked one of the most interesting security discussions I've seen in a while. We're looking at Matchlock by Jingkai He - a CLI tool for running AI agents in ephemeral microVMs with network allowlisting and secret injection via MITM proxy. Built to answer a question that every developer running claude --dangerously-skip-permissions should be asking: "What's the worst that could happen?"
In the second part, Adam will focus on reinforcement learning networks by summarizing the "1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities", an article featured in NeurIPS 2025.
With the rapid growth of generative AI, we have a new great model coming out every month. It makes it hard to keep track of all the different kinds of models and choose the right one for your task. In this article, I will cover the best generative AI models at the doors of 2026. I hope to make your selection of a model at least a bit easier.
In this series, every other Wednesday, I pick one trending repo and take it apart piece by piece. This week, I pulled out one key component of the entire GasTown engine, the thing without which the agents are basically useless. That component is Beads.
Adam Kaczmarek will break down an article, "Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free," featured in NeurIPS 2025. He will also explain the background for this paper: different types of attention and gating mechanisms.
Every other Wednesday we pick one trending project and break it down into its core pieces. Not another React framework or even an agent framework - but something fresh, something that helps us understand where our industry is heading. Today’s pick: BloopAI/vibe-kanban. A tool written in Rust that tries to bring order to the chaos of working with coding agents.
I've always claimed there's no better way to learn anything than to build something with your own hands... and the second best way is to do a Code Review of someone else's code. Today, we are taking on a project that made waves on Twitter (or X) and GitHub, not so much because of the complexity of the code, but because of the philosophy behind it, and above all, because of its author, Andrej Karpathy. And in this article, we'll discuss his AI consensus mechanism called llm-council.
