Originally published at Perl Weekly 771 Hi there, I put the 'Testing in Perl' course on hold for now. Instead of that we are going to explore the use of some of the mocking libraries we saw during the course. In the next session we'll pick one of the Perl modules used for mocking and we'll look for modules that use it. We'll try to understand how it is being used and we'll try to contribute someth
Building CLMA: A Self-Verifying Multi-Agent Framework from Scratch Posted on May 4, 2026 · #LLM #MultiAgent #CodeGeneration #OpenSource #SystemDesign #WebUI #SSE All code is open source on GitHub: github.com/kriely/CLMA If you've spent any time using AI for coding, you've experienced this cycle: ask → get code → try to run → it fails → paste error → get fix → something else breaks → lather, rins
Si usas Claude Code para programar ya sabes lo que pasa: abres una nueva sesión y el agente vuelve a improvisar. El contexto de la sesión anterior desapareció. Le describes la feature de nuevo, asume cosas distintas, y acabas corrigiendo código que nadie pidió. OpenSpec resuelve exactamente eso. Es un CLI open-source que inserta una capa de especificación versionada dentro de tu proyecto. Claude C
Manual content discovery is a core skill in application security testing. Instead of relying only on automated scanners, you can use simple HTTP requests and browser tools to find exposed files, hidden paths, and technology fingerprints. This covers techniques like checking robots.txt, fingerprinting favicons, reading sitemap.xml, inspecting HTTP headers, and spotting framework markers in HTML sou
Series: AI Isn’t an Engineering Problem Anymore (Part 2) In the last post, I talked about hitting a usage limit while debugging my robot and realizing how repetitive my own AI usage had become. When we use LLMs, whether through APIs or tools, it feels like every request is new. The inefficiency isn’t from using AI too much. You don’t ask once, you iterate. These are the most interesting ones. Some
Series: How Machines Learn: A Complete Guide from Zero to AI Engineer Phase 6: Machine Learning (The Core) You've been hearing "machine learning" for years now. Your phone uses it. Netflix uses it. Your spam filter uses it. Every tech company puts it in their job posts. And yet, if someone asked you right now to explain what machine learning actually is in plain words, you might freeze up a little
The Problem If you're like me, you live in your terminal. You've got Docker containers running for databases, Redis instances for caching, microservices doing their thing — and you're constantly context-switching to check on them. # The old way: docker ps docker logs my-app -n 50 docker stats docker inspect some_container # ... back and forth, breaking your flow Now imagine you're working with
I have a confession: I used react-i18next for years and genuinely never questioned it. It worked. It was everywhere. Every project I joined during my internships at DNB had it set up. You install it, you configure it, you wrap your app in a provider, and you ship. Done. But then I started building more things on my own, projects where I got to choose the stack from scratch, and I started noticing