Reported analysis on AI, infrastructure and policy. One long read a week, plus briefs.
Evaluating current engineering discussions reveals a recurring pattern: teams slowing down AI adoption, waiting for the market to stabilize, or restricting tool usage to protect traditional code quality. This hesitation is often driven by four widespread assumptions.
Over the last 6 months, I built five personal projects from scratch — all running fully-functional, spanning a wide variety of domains: kissidougou.com: a fully automated AI news platform Locus: Yet another trading app Syntropy: the quest for a "free-lunch" strategy for retail trading.
📌 This is Article 2 of my newsletter series: Building a sub‑ms pricer with full observability – a real‑world dev log. If you missed it, check out Article 1 on the core challenge.
📌 This is Article 3 of my newsletter series: "Building a sub-ms pricer with full observability – a real-world dev log" In this article: Why traditional logging is a "suicide pact" for p99s. Why "We already have ELK" is a dangerous argument. The Process Boundary: Why shared heaps are a liability.
📌 This is Article 1 of my newsletter series: "Building a sub‑ms pricer with full observability – a real‑world dev log" Next articles: achieving ultra-low latency on the hot path, latency‑free metrics, auto-healing cluster, the AI in the loop, and the code release - try it yourself.
Recently, I’ve gained a lot of interest in Java Spring Boot. My background is mainly C# and Microsoft technologies, but the last time I wrote Java was a decade ago. I decided it was time to dust off those skills and see what the ecosystem looks like today.
In software development, reading is easy; writing is hard. We spend hours navigating existing codebases. We can trace execution paths, understand dependencies, and debug complex issues. But when faced with a blank file and a complex problem, many of us freeze.
You’ve been dying to get the funding to finally dismantle that monstrous, creaking legacy architecture. You know the one. The monolithic core that makes every new feature a Herculean effort, that ties your best engineers in knots with its strange, in-house protocols, and silently bleeds millions in
Back in 2015, I was a big believer in Test-Driven Development (TDD). I followed the "Red-Green-Refactor" cycle religiously: write a failing test, write the minimum code to pass it, then refactor the code; and repeat. I inspired (I hope so) and annoyed countless people with TDD concepts and rituals.