The traditional large, legacy system migration can cost multiple times the yearly company revenue and still yield limited versions of the original system functionality. However, with the help of AI, it can change. The migration process might become faster, cheaper, and smoother.
This post is about the practical challenges of getting GPU-targeting code tested in environments without GPU hardware - specifically GitHub Actions. The project behind it uses Project Babylon / HAT (an experimental OpenJDK fork that compiles Java to GPU kernels), but the lessons apply to anyone doing OpenCL, CUDA, or heterogeneous compute work in CI.
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.
Agents are LLMs that move in the partially observed environment, interact with it, reason, plan, act, and adapt to the changing environment. They need to use tools to gather information in the iterative cycles. The information required to complete the task is hidden and requires online querying or tool use to obtain it. If your environment is fully observed and determined, tasks are repetitive, and you don’t need to adapt to unplanned changes, a simple LLM pipeline is just enough.
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.
Agentic systems require a new testing paradigm focused on evaluating trajectories, not just outcomes. This post details core test types, metrics, and tools.
