· Context Engineering
Does AI token usage scale with knowledge base size?
AI token usage scales with knowledge base size only when the full corpus loads per query. The real variable is selective context delivery, not KB size.
· MCP (Model Context Protocol)
One job per tool: why adding wire_navigate cut agent calls 24%
We restructured Wire's MCP surface from 2 overloaded tools to 3 single-purpose ones. The counterintuitive result: adding a tool cut total calls 24%.
· AI Hallucination
GPT-5.4-pro hallucinates more than GPT-5.4-nano
Vectara's 2026 benchmark shows OpenAI's flagship GPT-5.4-pro hallucinates at 8.3% while its nano variant stays at 3.1%. The reasoning-model tradeoff, explained.
· AI Second Brain
How to build a private AI second brain
Native Notion and Obsidian MCP give every connected agent the same coarse scope. Build a private AI second brain with per-agent, revocable access across tools.
· RAG (Retrieval-Augmented Generation)
RAG vs fine-tuning: when to use each
RAG vs fine-tuning: RAG wins for knowledge injection and freshness, fine-tuning wins for style and format. The right choice is a context engineering call.
· Context Engineering
Context budgets: how to allocate tokens for AI agents
A practical guide to context budgets for AI agents. How to allocate tokens across system prompts, tools, retrieval, history, and a buffer in production.
· Context Engineering
Context Poisoning: When Bad Data Becomes AI Ground Truth
Context poisoning plants false data into an AI agent's memory or RAG index. The model treats it as truth. It's a context engineering problem, not a model bug.
· Context Engineering
Why your AI costs are a context problem
Token prices fell 280x but enterprise AI spend rose 320%. Poor context architecture drives 60-70% of total AI costs. Here is where the money actually goes.
· RAG (Retrieval-Augmented Generation)
Long Context Didn't Kill RAG. Here's What the Data Shows.
Long context windows haven't replaced RAG. New 2026 benchmarks reveal the cost, speed, and accuracy tradeoffs, and when each approach wins in production.
· Context Engineering
How context engineering reduces AI hallucinations
Most AI inaccuracies in production are context quality failures, not model fabrications. Here's the research on what context engineering actually changes.