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
Most "self-hosting" articles are basically a list of Docker Compose files. They tell you what to run. They don't tell you why the smart money is moving away from managed cloud services — or what a real production stack looks like when you do it right. The shift isn't about being cheap. It's about control. Your data. Your pipeline. Your infra. No vendor lock-in, no surprise pricing changes, no term
One of the things I didn't expect when I started building Neuron AI was how much the design of the framework would be shaped by the people using it. I started this project to solve my own problems: I wanted PHP developers to have a clean, idiomatic way to integrate AI into their applications without having to learn Python or rewire their entire mental model. But at some point, the users started dr
Today, the frontend starts. Day 91 was the most infrastructure-heavy day on the Next.js side, not because of what's visible, but because of what everything else depends on. The Axios instance with JWT interceptors, the auth context, protected routes, and the login and register pages. By the end of today, a user can register in the browser, be redirected to a dashboard, log out, log back in, and ha
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
AutoGen-style workflows usually look harmless at the message level. One agent reads something. The problem starts when the first thing was not trusted. Maybe it was a support ticket. Maybe a PDF. Maybe a web page. Maybe an email thread. The first agent reads it, produces a clean summary, and that summary moves into the next agent step. After one or two turns, the original source is no longer visib
Most developers don’t trust AI. Until it writes code that works. Then suddenly… they do. You paste a prompt. You move on. No deep review. No second guessing. Because it looks right. That’s the moment trust creeps in. AI-generated code isn’t the real issue. We assume: the logic is correct the inputs are handled safely the dependencies are fine the security is “good enough” But AI doesn’t know your
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