· Structured Context
TOON vs JSON: why smaller doesn't mean cheaper for LLMs
TOON looks more compact than JSON, but a 9,649-test study found it cost LLMs 38% more tokens. The reason: model training distribution beats format size.
· AI Hallucination
GPT-5.5 didn't cut hallucinations 60%. Here's what it did.
OpenAI's GPT-5.5 system card reports 23% better claim-level accuracy, not the 60% hallucination reduction making press rounds. Here's what actually changed.
· Agent Drift
Agent drift: why long-running AI agents lose the plot
Agent drift is how AI agents silently deviate from their goal over long tasks. Goal drift, stale context, and four more mechanisms cause it, not the model.
· Context Engineering
Provenance is a context engineering primitive, not a trust score
Retrieval provenance for AI agents isn't an audit log or a trust verdict. It's structural metadata (source, position, time, edges) agents use to plan.
· Context Engineering
Why token cost doesn't 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.