Notes from the context layer.

Your AI Coding Agent Reads 166 Tokens for Every 1 It Writes. Here's Why That's a Problem.

For every 1 token your AI writes, it reads 166. That 165:1 ratio explains why AI coding is expensive, slow, and hitting limits constantly. Here's the data.

Claude Code Keeps Compacting and Losing My Work. Here's What's Actually Happening.

Claude Code compacts your session and suddenly forgets which files it modified, what errors it found, and what it was working on. Here's the technical explanation of why — and how to prevent it.

What Happens When Your AI's Context Window Fills Up (The Technical Explanation)

Every AI coding assistant has a fixed context window — the maximum information it can hold at once. Here's what happens step by step when that window fills up, and why bigger windows don't fix the problem.

I Tracked 100 Million Tokens of Claude Code Usage. 99.4% Were Wasted on Reading.

A developer tracked every token Claude Code consumed for a month. The result: 99.4% were input tokens. For every 1 token written, 166 were consumed reading. Here's what that means for your bill.

Why You Get Banned From Claude for 5 Hours (And Why It's Not Really About Your Message Count)

Claude locks you out for 5 hours and you barely sent any messages. The real reason: your AI agent silently consumed 100,000+ tokens reading files you didn't ask it to read.

"Context Left Until Auto-Compact: 0%" — What This Warning Means and Why Your AI Just Forgot Everything

When Claude Code shows 'context left until auto-compact: 0%', it's about to summarize everything and throw away the details. Here's exactly what gets lost and why it matters.

Why Your $20/Month Claude Pro Plan Runs Out in 3 Prompts (And What's Actually Eating Your Tokens)

Your Claude Pro subscription burns through its 5-hour session limit in minutes. Here's the technical reason: your AI agent wastes 70% of your tokens reading files instead of answering your question.

Why "Context Graph" Has Become the Most Misunderstood Term in AI Engineering

A technical deep-dive into LLM context management, the computational limits of context windows, and why every AI tool's 'context graph' solution might just be clever marketing around semantic search and RAG.

End-to-End System Evaluation: The Stress Test of GraphRAG

Individual layers may pass, but systems often fail at the seams. This blog details how to conduct holistic 'System-in-the-Loop' tests, measuring how retrieval noise compounds into generation errors across 25+ repositories. We provide a blueprint for evaluating the full journey from a vague natural language query to a multi-repo pull request.

Evaluating Generation and Grounding in Multi-Repo Systems

Retrieving nodes is only half the battle; the LLM must synthesize code that adheres to cross-repo constraints. This post explores measuring faithfulness, checking execution-level correctness against internal SDKs, and using LLM-as-a-Judge to verify that generated code respects the security and type contracts of separate repositories.