Posts on Calyntro Blog

The Review Is Too Late

Wed, 20 May 2026 00:00:00 +0000

Everyone is optimising the code review. But the review isn't where quality gets decided — it's where quality gets caught. In the age of AI-generated code, that distinction matters more than ever.

Why We Code in Threes

Fri, 15 May 2026 00:00:00 +0000

Two developers, one AI agent — and the reasons it works better than going alone.

What We Found When We Analysed MongoDB's Codebase

Wed, 13 May 2026 08:00:00 +0200

A file can look shared today but have been built entirely by someone who left last year. We ran that analysis on MongoDB — and the results are worth paying attention to.

The Invisible Risk in Your Codebase

Thu, 30 Apr 2026 08:00:00 +0200

Most teams know that critical knowledge is concentrated in a few people. Almost none know exactly where. That's not a knowledge transfer problem — it's a visibility problem.

Why AI Cannot Handle Full Specifications – and How Architecture with AI Really Works

Mon, 20 Apr 2026 00:00:00 +0000

Why AI fails with full-system specifications, how to structure systems in an AI-friendly way, and why component-based architecture is the key.

The Parallelization Paradox: Why Speed is Killing Your Agility

Fri, 10 Apr 2026 10:00:00 +0200

Why optimizing for simultaneous output with coding agents often leads back to a rigid Waterfall mindset.

AI + TDD: A Shortcut to the Goal or a Loss of Insight?

Tue, 21 Apr 2026 09:00:00 +0200

Test-Driven Development has always been slightly misunderstood — even by people who practice it.

The name doesn’t help. “Test-Driven” sounds like it’s primarily about tests. Coverage metrics. Regression safety. The QA team’s peace of mind. But anyone who has worked seriously with TDD, or spent time with practitioners like Emily Bache, knows that tests are almost a side effect. The real output is understanding.

TDD, done well, is a method for thinking your way through a problem one small step at a time. You don’t start with a complete picture of the solution. You start with the smallest possible question: what is the simplest behavior this code should exhibit? You write a test for that. You make it pass. And in the process of making it pass, you learn something — about the problem, about your assumptions, about the design that is quietly trying to emerge.

Software Engineering: The Art of Thinking Out Loud (with AI)

Tue, 14 Apr 2026 09:00:00 +0200

A colleague said something to me recently that I keep coming back to:

“Often, by the time you’ve finished articulating a complex problem for the AI, you’ve already solved it yourself.”

It sounds almost like a joke. You open a chat window, start typing out your problem in careful detail — and somewhere in the middle of the second paragraph, the answer appears. Not from the AI. From you.

If you’ve worked with LLMs seriously, you’ve probably experienced this. And I think it points to something important about what is actually changing in our craft — something that goes beyond the usual conversation about automation and job displacement.

AI in Software Engineering: Between Vision and Craftsmanship

Mon, 07 Apr 2025 00:00:00 +0000

A question recently surfaced in my LinkedIn feed that I haven’t been able to shake: How will the next generation of software developers gain experience when AI takes over the writing?

The analogy offered was that of a conductor — someone who doesn’t need to play the violin to judge whether the orchestra is in harmony. It’s a compelling metaphor. But I think it lets us off the hook too easily.