Release notes¶
Releases are tagged system1--v<version>, the Claude Code plugin convention (claude plugin tag . --push),
and listed on GitHub. Each one also gets a
one-word codename from Kahneman's Thinking, Fast and Slow, in alphabetical order: minor and patch releases
take the next letter, and major releases break the alphabet and take the authors' names.
Unreleased¶
- Providers:
systemonefor any server speaking the/v1/systemonecontract (llama.cpp'sllama-serverwith Liquid AI's d1, orengines/systemone/serve.pyfor the gte reranker, Qwen3-Reranker, AnyJev and OpenThai-SystemOne) andanthropicfor a hosted Claude model over HTTPS (key fromANTHROPIC_API_KEY). - Naga study, round 3: Liquid AI d1-omni-600M and d1-3B, OpenThai-SystemOne and AnyJev on the 100-request TEST2 set and the model-building sets; d1-3B matches Gemma 4 E4B (licence: commercial use below USD 10 M revenue).
- New study: which model tier a Claude Code subagent task needs, with a rerun on the public seed sets.
0.2.0 Econs (2026-10-20)¶
The first public release of the System-1 Gateway: a local MCP server that gives Claude Code and other MCP clients fast multiple-choice decisions from a pluggable engine, and the studies that show which engine to pick.
- Gateway: three stable tools (
system1_decide,system1_capabilities,system1_health) in front of Laya, Von, any Ollama chat model or an adapter you add. Python 3.11+ and the standard library only; installs as a Claude Code plugin with guided setup or with one script. - Study: which System-1 model for naga decisions: eleven engines on tool choice and model building for the naga simulation engine, measured on frozen blind test sets on one 4 GB laptop GPU. The short answer: Claude Haiku 5.5 is the most accurate (22 of 25 tool choices over all 90 tools, 37/40 blocks, 30/30 distributions, about 0.9 s, hosted); Gemma 4 E4B is the most accurate local engine; gte-reranker-modernbert-base is the fast one (about 0.1 s). The report covers retries, fewer-choice designs that did not pay, and fine-tuning that did not hold on the blind set.
- Bench: every number comes from scripts in
bench/nagathat rerun against a clone of the public naga repository; new engines plug in with oneeng_*.pyfile. - Open source: Apache-2.0, with contribution and security guides and CI on Ubuntu and Windows.
- Known limit: the study's sets are small (25 tool questions, 40 blocks, 30 distributions), so a difference of one or two rows is noise.
Codenames¶
| Release | Codename | Concept |
|---|---|---|
| 0.1.9 | Anchoring | An initial number drags every later estimate toward it. The routing hint anchors Claude's model choice |
| 0.1.10 | Baserate | Base-rate neglect: a vivid story beats the statistics. Routing now gathers its own base rate, silently, before trusting the hint |
| 0.1.11 | Coherence | System 1 builds the most coherent story, not the most likely one. Routing stays out of follow-ups it can't see the story for, and the skill picker waits for an engine that can hold the whole list |
| 0.1.12 | Denominator | Denominator neglect: "1 in 10" feels riskier than "10%". Routing is now judged on outcome counts, not on how sure the engine sounds |
| 0.2.0 | Econs | The perfectly rational agents of economics, as opposed to Humans. The first public release: engines are chosen by measurement on frozen blind tests (the naga decisions study), not by how sure they sound |
| next | Framing | The same facts described differently lead to different choices |
| Halo | Liking one trait colors judgment of all the others | |
| Illusion | The illusion of validity: confidence without accuracy, which is exactly what calibration corrects | |
| 1.0 | Kahneman | |
| 2.0 | Tversky | Amos Tversky, Kahneman's research partner behind heuristics and prospect theory |