Introducing Anchor

Anchor makes less-powerful models behave like a Mythos-class model — through structure, not capability. Plan on the smartest thing you can afford, cut the plan into task specs that fit one context window each, execute on the cheapest model that passes your benchmarks, verify with tooling, review with a fresh-context critic, and escalate after two failures instead of retrying forever.

The premise is economic: frontier models are becoming metered utilities. The operator skill that matters is knowing which tasks actually deserve frontier pricing — and routing everything else to models that are already good enough, whether that's a cheaper API tier, a Mac Mini on your desk, or an H100 node you own. The Savings page sketches how large that gap can be — please consider donating to help support this project.

The repo ships the whole loop, not just the doctrine: per-platform instruction files (Claude Code, Grok Build, NVIDIA NIM/Nemotron, local models, plain chat UIs), a scaffolder (anchor <project-dir>) that drops the right doctrine into any project, fleet scripts for routing/orchestration/benchmarking, and MCP servers so a frontier agent can delegate keystrokes to your local fleet instead of burning credits on them.

Model quirks — Gemma's missing system role, Qwen3's thinking toggle, DeepSeek-R1's no-system-prompt rule, repetition loops under greedy decoding — live in one place (anchor_client.py), keyed by each endpoint's quirks: block, so every caller stays model-agnostic.

Start with the doctrine, then the playbook for the economics, then run ./config.sh and scaffold your first project. If Anchor is already saving you real money, please consider a donation — see also Savings.