LLM is fuel, not the brain
The LLM parses, proposes, and generates candidates; decisions, guard, verification, and adoption are carried out by GHW's existence degree, rules, and feedback loops.
Goal-Harness-World
A digital life framework that closes the loop across math, rules, memory, feedback, and product KPIs
The LLM parses, proposes, and generates candidates; decisions, guard, verification, and adoption are carried out by GHW's existence degree, rules, and feedback loops.
The L1-L5 self-evolution chain passes SelfEvolutionGuard, CompileVerifier, SelfEncoder, and delta-E feedback, so proposals never bypass the rules.
ProductIntent, KPI Bridge, ChangeProposal, Consent, Audit, and the SDK/API let GHW embed into real products.
TaskQueue, HumanSignal, HumanGSP, ValueTrendTracker, and chat memory bring human tasks and feedback into the long-term trajectory of E.
GHW is a digital life runtime. It is neither an agent that hands every judgment to an LLM, nor a plain task orchestrator. It splits the system into four layers:
The core principle is: the LLM may propose, but cannot decide; the digital life itself decides whether to adopt, via E, Guard, Verifier, Consent, and ΔE feedback.
The project already has the core loops of a research framework and a product runtime:
ui_server HTTP API expose the product runtime capabilities.Parts still being engineered include real product Adapters, signed audits, role permissions, more deployment templates, and a long-term stress-test matrix. See the Roadmap.
The docs are plain Markdown built with zdoc — no config file, no Node.js:
zdoc docs site/docs --output site/public --title "GHW"
The output in site/public/ is a static site ready for any web server or GitHub Pages.