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
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
I opened IBM Course 4 — Python for Data Science, AI and Development — fully expecting to breeze through it. I'd used Python before. In college. In personal projects. It was supposed to be the comfortable one. Then **kwargs showed up. My previous post went up on May 2. After that, I finished IBM Course 3 on Prompt Engineering. May 3 — started Course 4. Finished a major chunk of it the same day. May
At some point, coding stopped being engaging. Most dev tools optimize for speed but I wanted to optimize for feeling. It adds subtle feedback while you work: small cues as you type a sense of momentum a smoother flow state Nothing loud. Just enough to make coding feel less “dead”. When coding feels better, you: stay focused longer switch context less enjoy the process more Small improvements in ho
Two and a half months ago we published Why We Built UCP Playground, which closed on 114 agent sessions and an honest acknowledgement that the dataset was thin — most models had single-digit sample sizes, store coverage was uneven, and the headline rates moved meaningfully with every new run. A month later we crossed a different threshold: the first fully autonomous AI agent purchase through UCP —