Development

TL;DR Context creation was still ad-hoc even with the three-layer memory system in place. Every new language meant manually crafting another CLAUDE.local.md file and repeating the same research process. The research process was repeatable. Looking at what made my Python context effective, I noticed the same pattern: current tools, common mistakes, workflow problems. The structure was consistent across languages. Prompt template generates research-backed contexts. Feed it “python” or “golang” and get comprehensive development contexts through 12-15 targeted searches of authoritative sources. ...

You ask AI to implement something seemingly straightforward. It builds exactly what you asked for. Then you realize it’s not what you actually needed - wrong UX behavior, missing security considerations, breaks existing functionality. There’s a pattern emerging to address this: let the AI interview you first. TL;DR Assumptions hide in seemingly straightforward requests. Even simple tasks like “add email validation” contain multiple implementation decisions that aren’t obvious until you start building. ...

How I discovered that LLM limitations reveal fundamental truths about software architecture in the AI era The Breaking Point Isn’t What You Think After months of AI-assisted development, I hit a wall that had nothing to do with prompting skills or context limits. The problem was deeper and more fundamental: LLMs lose their minds when applications exceed their cognitive capacity. I wasn’t building monoliths. My applications had proper separation of concerns, clean data models, comprehensive tests, and type safety. The repository pattern, service layers, configuration management - all the architectural best practices were there. But as these well-structured applications grew in complexity, something strange happened. ...

AI-assisted development tools are everywhere - systems that understand code, generate tests, fix bugs, and accelerate delivery. But practitioners are hitting a wall. Despite sophisticated prompts, quality controls, and multi-agent workflows, LLMs consistently cut corners on complex work. The problem isn’t training or prompt engineering - it’s architectural. TL;DR LLMs optimize for different objectives than human developers. The “laziness” comes from training to satisfy human approval patterns rather than correctness. My sophisticated workflows work against this optimization. ...

Over the past ten years, I’ve spent considerable time and energy trying to shorten critical time gaps. The time it takes attackers (the good kind) to find bugs, defenders to mitigate, and responders to act when things go sideways. This has always been about data—getting the right people the right information at the right time. I’ve built systems designed to reduce friction, perform the drudgery that bug hunters do at scale (before AI made this interesting), and tackle attack surface problems before ASM was a thing. In every case, success hinged on that fundamental principle: right people, right data, right time. ...

While building my own JARVIS-like assistant (yes, another one of those), I noticed something: AI output is perfectly readable as text, but throw it at a text-to-speech system and… well, let’s just say it’s not winning any audiobook awards. Here’s what I mean. Take this perfectly clear output from Claude: T h e s e r v e r a t I P 1 3 0 . 3 5 . 2 2 9 . 1 2 7 i s r u n n i n g H T T P / 4 4 3 Read that with your eyes? No problem! Crystal clear. But have your TTS system read it out loud and suddenly your technical briefing sounds like a robot having a stroke. ...