We’ve been putting AI to the test. Not in theory, but in controlled experiments. In this edition, you will learn when to use AI to get things done (not to plan them); how to guide it with the right structure, and where the “AI as a teammate” metaphor breaks down today.
Data orchestration can be defined either as an automation or as a data management process. In this article, we will look closely at both of these definitions, how they are different from each other. We will also talk about some of the most popular tools used by engineers worldwide.
There is a fundamental gap in understanding our own productivity, for those who feel an inner need to close it, creates an opportunity for Dayflow - a project that aims to redefine how we perceive and analyze our screen time - for the good or the bad. Dayflow is not yet another time tracker - it’s an ambitious attempt to build a “semantic timeline,” or, to use the project’s own metaphor, “a git log for your day”.
In this article, we’ll break spec-kit project down to its core components. We’ll explore its philosophy, architecture, and the powerful engineering patterns behind it to understand how GitHub is transforming chaotic “vibe coding” into a structured software development process.
Discover how Gradle and Bazel compare for Android builds, from IDE support to scalability. Learn which tool suits your team’s size and project goals.
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.
