This month, we used AI to stress-test three repeatable patterns: we ran a full Java 8 to 21 migration to compare Claude Code with Cursor, scaled agents on a large codebase by right-sizing context with Cline’s Focus Chains, and we put two specialized agents into a review loop. We also did a rapid proof of concept with Cursor in agentic mode.
In this edition of GitHub All-Stars, we look at a key building block of agentic applications - UI- and how Human-in-the-Loop can be implemented in practice. Let’s be honest: despite the marketing, no agent solution is perfect, and there will always be moments when we, the protein-based organisms, are needed. That’s why any application that aims to solve the problem realistically has to face this challenge head-on.
In this third and final installment of the series, Krzysztof Korbacz will take a deep dive into the role of AI agents. Anyone who has seen the underwriting process from the inside knows it is a complex beast and, interestingly, still largely manual and based on fragmented data. How could AI agents fit into it, and how are they already doing so?
To stay up to date in the Artificial Intelligence solutions era, it's critical to become acquainted with the fundamental terms, concepts, and jargon that characterize this fast-developing discipline. This article will help you discover the foundational concepts of neural networks, deep learning, natural language processing, and beyond.
Every Wednesday, we’ll pick one trending repository from the previous week and give it attention, preparing a tutorial, article, or code review—learning from its creators along the way. Today, we’re taking a fresh project from Google engineers to the bench: Google/langextract.
In the previous entry of the series, Bartek Antoniak shared his market-wide observations from the perspective of a professional software engineering and consulting firm. In the second article in the series, Peter Ratcliffe takes a deep dive into how AI has already reshaped the insurance industry, particularly the underwriting process.
Every morning, I scan Hacker News, newsletters, research, and weird corners of the web before preschool drop-off. I collect the gems that don’t make the headlines but teach useful lessons or spark technical ideas. Now I’m sharing them here. My rules are simple: no chasing every new model, no “top 50” lists, and no breathless marketing. Read on for this edition’s hand-picked stories and lessons. If that sounds like your kind of newsletter, welcome to issue two.
As part of our GitHub All-Stars series, where we examine open-source gems, I stumbled upon a project that strikes at the heart of one of the most fundamental problems in modern AI. After analyzing deepagents and its system-level approach to reasoning, the natural next step was to examine how we solve the problem… of memory.
The Insurance industry needs modernization. Current accelerated digitisation makes it hard for laggards to keep up. There is a clear efficiency gap that needs bridging. The market's connectivity is low-tech, hugely inefficient, and the market lacks the will to adapt to modern technology standards - particularly among traditional insurers that have expanded organically or through acquisitions.
I’ve always claimed there’s no better way to learn anything than by building something yourself… and the second-best way is reviewing someone else’s code. From now on, every Wednesday we’ll pick one trending repo from the previous week and give it some attention: a tutorial, an article, or a code review - learning directly from its authors.
As we go full-AI mode, we want to inspire fellow developers with all the cool projects and experiments our devs are running with AI tools and agents. This is why we are launching the new series: “This Month We AIed” initiated by the Scala expert and SoftwareMill co-founder, Adam Warski.
This first edition curates in-depth, hype-free AI insights for engineers, from the pitfalls of multi-agent systems to the rise of AI-powered browsers. Expect contrarian takes, practical engineering tips, and real-world case studies over fleeting trends.
