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.
Every other Wednesday, we’ll pick one trending repository from the previous week and give it some focused attention by preparing a tutorial, article, or code review – learning from its creators in the process. Today, we’re taking a look at a project that tackles one of the most embarrassing yet universal problems in our industry: f/git-rewrite-commits.
Welcome to the fourth article in the This Month We AIed series. In this edition, we will demonstrate how a simple CLAUDE.md file can transform chaos into clarity, reveal how to use ChatGPT to transform a lightning talk into presentation-as-code, and take you on a journey from chaotic vibe coding to disciplined specification-driven development.
If you're a developer exploring AI coding assistants, you might have encountered Claude Code and wondered how it actually works under the hood. What's the relationship between the command-line tool you install and the large language model that powers it? How does the AI decide when to read your files or run commands? And how do those CLAUDE.md instruction files actually get interpreted? This article will walk you through these questions by clarifying a fundamental distinction that often gets overlooked when people first encounter Claude Code.
In our “GitHub All-Stars” series, we take “new or little-known” open-source gems that solve real engineering problems and put them under the microscope. Today, we’re looking at toon —a tool that directly tackles the financial and performance overhead of data serialization in the age of AI.
To see how code assistants work in reality, our engineers ran a focused, half-day AI hackathon inside an active commercial project - a large-scale logistic platform built in Scala and deployed on Kubernetes. Their goal was to see how AI can be used responsibly in a real, mature system and explore its applications and possibilities directly in our project domain, making sure the outcomes were practical, safe, and genuinely valuable. Read on to learn about their results.
This week, we’re diving into a project that tackles one of the most fundamental problems in the world of science and engineering: the frustrating gap between the theory described in a research paper and its practical implementation. Anyone who’s ever tried to reproduce results from a paper knows the pain. The code, if it’s even available, is often a tangled mess of one-off scripts and Jupyter notebooks - making reproducibility, the cornerstone of science, more of an art than a craft.
