Claude

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. ...

The Setup We’re building a multi-step reconnaissance workflow using Nerve ADK - a chain of AI agents working together. We built a few pieces to get started: Discovery Planner: “What information do we need?” Command Generator: “How do we get that information?” Simple. Elegant. Each agent has one job, and they pass data between them like a relay race. The Tools Both agents had access to a think() function - a way to explicitly show their reasoning process. Think of it as the difference between solving math in your head vs. showing your work on paper. ...

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. ...