Public content and memory sweep

The next public content pass turns review documents into durable memory and clearer routes.

This update promotes the ErrorNotifier telemetry-substrate analysis and Teleodynamic website improvement analysis into long-term file memory, short-term `.uai` bootstrap memory, machine-readable JSON, and reviewer-facing public routes.

It keeps the ecosystem lane split intact: Teleodynamic.com remains the philosophical fulcrum, ErrorNotifier.com is the immune-system telemetry substrate, UAIX.org remains schema/evidence guidance, and Carcinus.org remains the defensive execution lane.

Open memory sweep JSON Open telemetry map Open developer bridge

Long-term file memory

Two source documents are now durable review inputs.

The ErrorNotifier analysis supplies the operational immune-system framing. The Teleodynamic improvement analysis supplies the public content, developer onboarding, and machine-route accessibility backlog.

ErrorNotifier telemetry substrate: external observer, pressure matrix, incident/test/recovery evidence, and phenome monitoring.
Teleodynamic website recommendations: Deacon-to-ML bridges, six-commitments correction, resource normalization, bounded sandbox clarity, and machine-route QA.
Memory handling: concise source briefs live under `.uai/long-term/source-briefs/`; short-term memory now carries only the operational points needed for the next pass.

ErrorNotifier as immune-system telemetry

Operational symptoms become lane pressure, not automatic authority.

ComputeLatency and uptime

External checks estimate operational burden without trusting internal self-report.

ReviewIncident queues

Alert, bug, suggestion, and no-op pressure show when humans are absorbing ambiguity.

GovernanceClaim drift signals

Public-route and AI-report issues can trigger review before claim widening.

UncertaintyBrowser/server errors

Unresolved faults remain evidence instead of being hallucinated into completion.

MemoryRetention history

Archives and recovery evidence support rollback, deep archive, and long-memory promotion.

Public content improvements

The improvement backlog is now concrete and route-addressable.

Developer bridgeTranslate theory into standard ML language.

Use concrete examples that map homeodynamic, morphodynamic, and teleodynamic terms to loss drift, clustering, attention, local objectives, resource gates, and audit ledgers.

Machine routesKeep crawler paths reachable and current.

Audit `/llms.txt`, `/ai-router.json`, `/.well-known/ai-agent.json`, `/sitemap.xml`, `/agent-start/`, and `/ai-summary/` after deployment and cache clearing.

Resource mathShow the normalization steps.

Publish bounded formulas and JSON/Python examples that explain how compute, review, governance, uncertainty, and memory pressure become comparable without hiding qualitative risk.

Bounded labClarify browser-local simulation.

Make the v3.207.0 sandbox lab easier to understand while preserving the no-server-compute, no-live-model, no-certification boundary.

Visual polishReduce dense-route fatigue.

Use wider cards, clearer tables, responsive wrapping, and route-specific signposts on matrix and ecosystem pages.

Reviewer handoffProvide action-ready QA prompts.

Every content improvement should leave a concrete human/agent reviewer checklist with expected evidence and UTC timestamps.

No-overclaim boundary

The v3.207.2 package is a source-memory, route, content, design, and reviewer-handoff update. It does not claim live deployment, automatic bug fixing, human approval from API success, runtime safety certification, credential validation, model training, AGI, consciousness, sentience, biological equivalence, hidden suffering proof, private-network probing, or protected-anchor mutation by agents.

Next pass: verify live deployment parity, make ErrorNotifier visible on remaining ecosystem pages, run machine-route accessibility checks, and improve first-read onboarding from theory to implementation.