Deep Dives into AI Engineering
In-depth explorations of AI techniques, LLM internals, agent architectures, and the engineering patterns shaping modern AI-assisted development.
Claude Code Best Practices: A Practical Playbook for Everyday Developers
Most people use Claude Code like a faster autocomplete. This beginner-friendly playbook shows the habits that separate occasional users from power users — managing the context window, the explore-plan-code-commit workflow, giving Claude a way to verify its work, plus intermediate moves like hooks, subagents, and MCP. Start with two habits and compound from there.
Same Job, Opposite Bets: Cline vs OpenCode vs Pi
Three open-source coding agents solve the identical task but make opposite design bets: Cline is a batteries-included platform, OpenCode is a client/server engine, and Pi is a minimal core you reshape. A beginner-friendly head-to-head across multi-agent, safety, providers, context, and architecture — with a decision framework for choosing.
Pi: The Anti-Claude-Code — a Coding Agent That Ships With Almost Nothing
Pi is a terminal coding agent that deliberately ships without sub-agents, plan mode, to-dos, or permission pop-ups — because every one of those is something you add with a short TypeScript extension instead. A beginner-friendly tour of its layered design, how it compares to Claude Code, and the one security fact you must understand before running it.
MCP 2026-07-28: The Protocol Grew Up
The Model Context Protocol just shipped its biggest update — a stateless core, multi-round-trip requests, header-based routing, and cacheable results. This post explains what changed, why, and shows old vs new server implementations side by side.
The New Rules of Context Engineering for Claude 5 Generation Models
Anthropic removed 80% of Claude Code's system prompt for newer models with zero regression. This post breaks down the six new rules — from trusting model judgement to progressive disclosure — with practical before/after examples for each.
The Code Knowledge Graph Tool I Missed: codebase-memory-mcp
A supplementary comparison adding codebase-memory-mcp to the Graphify vs GitNexus vs CodeGraph showdown. 158 languages, a Cypher query engine, embedded semantic search, and 120x fewer tokens — all from a single zero-dependency binary.
Grok Build Architecture Deep Dive: How It Works and How It Compares to Claude Code
SpaceXAI open-sourced 250K+ lines of Rust after a privacy scandal. This post breaks down Grok Build's architecture — actor model, leader daemon, kernel sandbox, parallel sub-agents — and compares each system head-to-head with Claude Code.
Same Skeleton, Opposite Souls: Hermes vs Claude Code
Hermes Agent and Claude Code share the same architecture — agentic loop, tool registry, markdown memory, MCP, hooks. But they split on one fundamental question: what is memory for? A deep comparison of memory-as-influence vs memory-as-product in AI agent design.
The Dark Side of Agent Skills: Security Risks in AI Coding Agents
Agent skills supercharge your coding workflow — but they also open doors to prompt injection, data theft, and remote code execution. Two concrete attack walkthroughs and a practical defense checklist.
Stop Wasting Tokens: Graphify vs GitNexus vs CodeGraph
A head-to-head comparison of three code knowledge graph tools that give AI agents structural awareness of your codebase — with clear winners per category and a decision framework for choosing the right one.
Production-Grade Engineering Skills for AI Coding Agents
AI agents don't lack knowledge — they lack discipline. This post explores agent-skills: 24 structured workflows that constrain AI agents to follow production-grade engineering practices, with a Tetris experiment showing the dramatic difference.
Loop Engineering: Designing Systems That Prompt Your Agents For You
From prompt engineering to loop engineering — how developers are shifting from crafting individual prompts to designing autonomous systems that find work, run agents, verify results, and persist state without human input at each step.
Building an AlphaEvolve Skill for Claude Code
Turn Claude Code from a one-shot code generator into an iterative optimizer. This post introduces alphaevolve-skill — an open-source skill that brings evolutionary optimization to your coding workflow.
AlphaEvolve: Why One LLM Call Isn't Enough
A single LLM call generates plausible code — AlphaEvolve wraps LLMs in an evolutionary loop with automated evaluators to discover optimal code. Understanding this distinction unlocks a new mental model for AI-driven optimization.
Graphify: Turn Your Codebase into a Knowledge Graph
An introduction to Graphify — a tool that maps your entire project (code, docs, images, videos) into a persistent knowledge graph you can query instead of grepping through files.
Why Public LLM Benchmarks Are Misleading
Public benchmark scores are a poor proxy for real-world model quality. This post covers data contamination, benchmark overfitting, and how to actually evaluate models for production.
Claude Code: Architecture Deep Dive
A deep dive into the architecture of Claude Code — Anthropic's agentic coding tool that can read your files, run commands, and make edits across your entire project.
More notes on the way
New deep dives are in progress. Stay tuned.