Most security vulnerabilities you can influence while designing and building software come down to one unanswered question: “How do I know that…?” Ask it at every arrow in your architecture diagram and you either find a verification to add, or admit there is no answer and add a protection that limits the damage. The video
Agentic AI turns distributed systems from orchestrating services into orchestrating decisions made at runtime. That sounds powerful, but it adds non-determinism on top of the emergent behavior microservices already produce. This video goes through an InfoQ article on that shift and says where the argument holds and where it doesn’t. Article reviewed: “From Microservices to
“The SDLC is dead, long live the ADLC.” Really? Agents can collapse the build phase to hours, but the lifecycle around it still needs human feedback loops, a cost budget, and a process engineered for your organization. A reaction to Rakesh Gohel’s LinkedIn post on the agentic development lifecycle:https://www.linkedin.com/posts/rakeshgohel01_aiagents-agenticai-generativeai-share-7505965828287397890-b60e/ What we cover: Engineering your AI
Software design focuses on making systems work, but it has no discipline for the opposite question: what should our systems never do? Bugs and security vulnerabilities keep breaking the assumption that software only does what we program it to do. Topics covered: Every module has a list of things it should never do. Most teams
Where does AI coding assistance actually pay off, and where does it quietly cost you more time than it saves? I let Claude generate four hard questions about using LLMs in software Where does AI coding assistance actually pay off, and where does it quietly cost you more time than it saves? I let Claude
Test-driven development is a good candidate to provide AI coding agents with a tight, reliable feedback loop. A small but carefully designed experiment suggests it might not — and in some cases the TDD-driven code came out worse than code written without it. I’m reacting to “TDD inside the agent loop – theater or actual
Anthropic published in May 2026 how they contain Claude across Claude.ai, Claude Code, and Cowork: gVisor sandboxes, full VMs, egress controls, the works. We go through it end to end: what holds up, what’s broken, and what they’re still missing. This is a reaction to “How We Contain Claude Across Products,” published by Anthropic on
AI coding assistants are excellent at generating and validating text formats — which means you can now build custom visualization tools for your team in less than a day. In this video I show Storymap, a Python tool I vibe-coded with Claude that turns a plain markdown file into an interactive, visual product roadmap. It’s
REST has always had a weak spot: complex queries. GET can’t carry enough data in a URL, so most teams fake it with POST — and lose caching, safe retries, and idempotency in the process. HTTP just got a fix: a new verb called QUERY. This video reacts to a LinkedIn post by Martin Forstner,
Adding features feels productive. Often, it is not. Reacting to Oskar Dudycz’s article “Addition by Subtraction in Software Design,” I go through his own admission of over-engineering his contract-testing library, Strictland – and why the fix wasn’t adding more options, it was removing them. This is a commentary on: https://www.architecture-weekly.com/p/addition-by-subtraction-in-software What we cover: Two rules
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