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
Every argument you’re hearing against large language models today — stolen data, lost jobs, power concentrating in a few hands — was made against the printing press five centuries ago. Looking at how that played out tells us something about where we’re headed with AI. In this video, I compare the ethical criticism of LLMs
There’s a predictable path developers go through when adopting AI coding assistants — from “this is useless for programming” to “I can build agents that solve certain problem types.” This video maps that path on two axes: trust in AI output, and trust in AI autonomy. What’s covered: Wherever you are on this graph right
With AI coding assistants generating good-enough first drafts, implementation details and syntax mastery are becoming less important than specifications and systems design. This video explores where that shift is heading — and where it can go wrong. The core idea: small, well-specified “black box” modules that AI agents can generate, test, and regenerate on demand,
Most developers aren’t using AI assistants anywhere near their full potential for architecture work. This video breaks down a practical framework for using AI at every stage of making a strategic technical decision — not to get “the perfect answer,” because that doesn’t exist in architecture, but to make better-informed trade-offs. What’s covered: AI won’t
AI budgets hit faster than expected. Token costs compound quietly in the background — until you’re mid-sprint and suddenly hitting limits. This video walks through 11 practical techniques developers can use to reduce AI costs without reducing what AI does for them. What’s covered: Every token saved is a token you can spend on harder
Most developers handle GenAI’s non-determinism in one of two ways: they ignore it, or they try to force it away. Both approaches fail. There’s a better model. Topics covered: The quadrant doesn’t have a name yet – share your suggestions.
AI tools are generating 110% more code. Lead times are up 480%. Code churn is up 861%. Something is very wrong. This is a commentary on the Faros AI Engineering Report 2026 (“The Acceleration Whiplash”), surfaced by Kent Beck’s LinkedIn post. The data covers 22,000 developers across 4,000 teams. Kent Beck’s post: https://www.linkedin.com/posts/kentbeck_the-ai-engineering-report-2026-the-ai-acceleration-share-7471011014738841600-JErm/Faros report: https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways
Most developers using AI are optimizing for the wrong thing. Speed metrics and line counts are not quality — and AI makes that confusion worse, not better. This video is a reaction to Nolan Lawson’s article “Using AI to write better code more slowly,” published May 2026: https://nolanlawson.com/2026/05/25/using-ai-to-write-better-code-more-slowly/ Topics covered: Going slow with AI isn’t
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