I was doing a code review for a colleague when I found it. The component had five useEffect hooks. No errors. No warnings in the console. The PM had signed off on it. It had been in production for three months. But there was a subtle bug that only showed up when the user navigated quickly between pages. Data would flash. State would reset. Sometimes the old user's name would appear for a split sec
As an SDET or Automation Engineer, failing tests are part of the daily grind. With the rise of Agentic AI, fixing scripts is easier than ever—but there’s a catch that tutorials rarely mention: Scale. In a real-world enterprise suite, you aren’t dealing with 10 tests; you’re dealing with 500. When 200 of them fail right before a major release—often due to a single upstream change by another team—fe
REST has served backend development well for two decades. It is simple, predictable, and every developer on your team understands it. So why are more engineering teams moving toward event-driven architecture? The short answer: synchronous communication does not scale the way modern systems need to scale. And the teams discovering this lesson in production are paying for it in outages, latency spik
Twenty-four hours after I swapped Supabase Auth's default SMTP for Resend, my first real user signed up. I'll call him K. I emailed him three questions. He replied in 49 minutes: "make a better ui of admin so we can use." That sentence is now my Q2 roadmap. Three weeks into launch I have 3 users, 2 Google clicks, and zero mentions when you ask ChatGPT or Gemini for "free status page alternatives."
Jack had finally stepped into the world of Docker. It felt like magic, but Jack was never one to just believe in "magic spells." He was curious. He wanted to look under the hood and see what actually made Docker so powerful. He had one big question: How could 50 different people live in the same "apartment building" (the Host OS) without accidentally reading each other's mail or eating each other'
The first stage of AI work is prompting. The last stage is removing the model from most of the workflow. That sounds backwards. It is not. When a workflow is new, the LLM is useful because the work is still ambiguous. You are discovering what good looks like. You try a prompt, read the output, adjust the examples, change the tone, add constraints, and run it again. That is a good use of AI. But if
Last week I had a problem. I'd built an internal documentation site in Trilium Notes — about 30 notes covering product, marketing, and operations stuff. It was for non-technical teammates who needed to answer questions from developers about the platform. The docs were good. Structured, searchable, complete. But reading 30 notes to find one answer is not great UX, especially for someone who's been
Go is a compiled language — the code is converted into machine‑readable form before execution. From a beginner’s perspective, this means Go catches many errors during compilation, giving you cleaner, faster, and more predictable performance at runtime. Go is widely used for: API development CLI tools Microservices architecture Backend server. DEVOPS activity So it fits perfectly with the kind of