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
So you've outgrown MySQL. Maybe you need better JSON support, real window functions, or you're moving to a managed cloud database that defaults to Postgres. Whatever the reason — MySQL to PostgreSQL migration trips up almost everyone the first time. The two dialects look similar but behave very differently under the hood. Why MySQL Dumps Don't Import Directly into PostgreSQL users ( id INT(11) NOT
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
Inside the five-stage pipeline from 1.1.1, there is another fork right after the parser. PostgreSQL classifies every SQL command into one of two camps. One side holds the optimizable queries, the other holds the utility commands. The classification is decided by a single field on the Query node, commandType, and from that point on the two camps travel completely different paths. One goes through t
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