Ai

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. ...

This begins a two-part exploration of evolving from intuition-based to evidence-based prompt engineering, revealing fundamental insights about AI system limitations and systematic evaluation. The Problem That Nearly Broke Me..Not Really I was building a multi-phase HTTP reconnaissance workflow using Nerve ADK - a sophisticated chain of AI agents working together to systematically probe web services. The architecture was elegant (if I do say so myself!): a Discovery Planner would identify what information we needed, a Command Generator would translate those requirements into concrete tool commands, and executors would run the actual reconnaissance. ...

This concludes our two-part exploration of evolving from intuition-based to evidence-based prompt engineering. Read Part 1 for the context and methodology behind these findings. The Real Results: Validating and Expanding “Curious Case” Findings After fixing the flag format bias and running the complete four-strategy comparison across all models, the results were definitive - and they validated the core insight from “The Curious Case of the Thinking Machine” while revealing much broader patterns: ...

This continues my exploration of AI-assisted development workflows, building on insights from Making Security Ambient and the practical lessons learned from daily Claude Code usage. The Evolution of AI-Assisted Development The progression has been fascinating to observe—and participate in. We started with simple one-shot prompts: “Generate this function.” Then we got smarter about light planning, adding some structure to our requests. From there, we developed systematic AI-assisted workflows like the /plan-task approach I’ve written about, where architectural guardrails transform unpredictable AI assistance into something more craftsperson-like. ...

In the previous article on Beyond Comprehensive Context, I explored how AI-assisted development revealed challenges with comprehensive documentation and token efficiency. What follows are my approaches for memory optimization that emerged from daily use—not as a definitive solution, but as a possible option. The Layered Memory Paradigm What I’ve discovered through daily practice is that Claude Code’s memory system reveals something pretty cool about how expertise actually functions. The system’s three-layered structure mirrors the way I think about knowledge—from universal principles to contextual applications. ...

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. ...