Practical post for engineers who've hit the wall where an AI proof-of-concept works on clean data but can't connect to the legacy systems that hold actual production data. Disclosure: I work at Ailoitte, which builds AI integration layers connecting legacy infrastructure to production AI. Sharing what the engineering actually looks like. AI models expect structured, consistently formatted data. Le
You write a detailed design doc. You paste it into your AI assistant. You wait. The output compiles. Tests pass. And yet — it's not quite what you designed. The auth middleware is in the wrong layer. The error handling pattern differs from the rest of the codebase. The field names don't match the schema. You fix it. Next task, same thing. This happens constantly, and it's not a model capability pr
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
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
Originally published on TechSaaS Cloud Originally published on TechSaaS Cloud An API gateway sits between clients and your backend services. It handles cross-cutting concerns so your services do not have to: authentication, rate limiting, request routing, load balancing, caching, and observability. WebMobileIoTGatewayRate LimitAuthLoad BalanceTransformCacheService AService BService CDB / Cache API
A deep, opinionated, practical guide for the engineer who has crossed the mid-level threshold — or is about to. The mental models, technical habits, ownership patterns, communication skills, and career mechanics that separate "solid senior" from "engineer the whole team builds around." Grounded in 2026 reality — AI-augmented coding, distributed async teams, post-ZIRP efficiency pressure, and a mar
One thread. Multiple AIs. Deliberation, not polling. Most people use AI like this: 🤦 Ask one model → get one answer Ask multiple models → compare results That’s not thinking. That’s polling. Not side by side. Not isolated. But in sequence — where each one reads what the previous one said before responding. Manual Council is the simplest form of that idea. No backend. No orchestration. No