5 posts
A token-efficiency reading of the 'harness > model' thesis: the same model solved 6.7% of tasks bare vs 68% with the right harness — at 4× fewer tokens. What a harness is, why it's where the token bill actually lives, and what the Life-Harness and Meta-Harness papers prove. Plus how your Claude Code stack (subagents, skills, hooks, graphs) already is a harness.
My ready-to-use graphify setup for Claude Code: a knowledge graph instead of full-repo reads, semantic extraction on cheap OpenRouter (deepseek) instead of Claude tokens, auto-watch via hooks, protection against leaking secrets into the graph. All packaged in the claude-code-token-savers repository.
GonkaGate hands out $10 of free balance with no card and no crypto top-ups. At ~$0.000334 per 1M tokens, that's enough for billions of Kimi K2.6 tokens — fuel for AI startups that burn through tokens. Inside: TPS tests and step-by-step setup for opencode and Hermes Agent.
A small utility and global /cctoggle slash command that switches off all Claude Code plugins and user-scope MCP servers at once — clearing their tool definitions out of context — and restores exactly what it disabled. No app restart. Inside: apply semantics, selective disable, backups, and limitations.
Working approaches to saving tokens in Claude Code and Opus 4.7/4.8: subagents, skills, hooks, Chinese models, knowledge graphs, RAG, xhigh effort, session management, summarization on a cheap model, input compression (headroom, pxpipe, mcp-compressor), and server-side savings (Tool Search, context editing, prompt caching, Batches API). A checklist to cut costs by 10x.