Hey everyone! I just started a blog recently, and last week I finally published my first longer technical blog post: It's about entropy, divergence vs. convergence, and why tests aren’t just verification - they’re convergence mechanisms.

tldr;

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AI tools have dramatically reduced the cost of divergence: exploration, variation, and rapid generation of code and tests.

In healthy systems, divergence must be followed by convergence, the deliberate effort of collapsing possibilities into contracts that define what must remain true. Tests, reframed this way, are not just checks but convergence mechanisms: they encode commitments the system will actively defend over time.

When divergence becomes nearly frictionless and convergence doesn’t, systems expand faster than humans can converge them. The result? Tests that mirror incidental implementation detail instead of encoding stable intent. Instead of reversing entropy, they amplify it by committing the system to things that were never meant to be stable.

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If you're interested, give it a read, I'd appreciate it. If not, maybe let me know what I could do better!

Appreciate any feedback, and happy to partake in discussions :)