AI-generated code is often close to correct. That is exactly what makes it dangerous. Obviously broken code is easy to reject. Code that compiles, looks reasonable and passes the happy path is much harder to distrust. In software, small gaps matter: one missing null check one unhandled timeout one weak authorization condition one unsafe default one test that only covers the obvious path AI tools c
The Model Context Protocol has transformed how we connect AI to tools. But connecting agents to tools is only half the battle — connecting agents to each other is where the real challenge begins. I recently read @raviteja_nekkalapu_'s excellent article "I built an AI security Firewall and made it open source because production apps were leaking SSNs to OpenAI" and it resonated deeply with challeng
A deeply-synthesized, opinionated reference distilled from five canonical sources: donnemartin/system-design-primer · ByteByteGoHq/system-design-101 · karanpratapsingh/system-design · ashishps1/awesome-system-design-resources · binhnguyennus/awesome-scalability Use it as: a study guide for interviews, a checklist for design reviews, and a vocabulary for cross-team discussions. 📖 How to Use This
Building a Translation Pipeline for International Contract Bidding If your company bids on international contracts, you've probably dealt with the translation bottleneck. Technical proposals need precise translation, certified documents have strict formatting requirements, and procurement deadlines don't wait for anyone. After seeing how UK public procurement translation requirements can make or
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
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
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
If you've tried building an AI agent in the last six months, you've hit the same wall: there are half a dozen frameworks, each with a different philosophy, a different API surface, and a different definition of what an "agent" even is. I spent a weekend writing the same simple agent — "read a GitHub issue, classify it as bug/feature/question, and post a comment" — in six different frameworks. This