see into the mind of mindX

Honest snapshot of the chain the thesis describes. Each cell shows whether mindX is actually producing at that tier today, is ready/gated, or is not implemented. The chain runs raw STM (information) → dream consolidation → LTM (knowledge: statistics + patterns) → concept extraction → verified wisdom → THOT mint → external ingestion → BDI perceive() reads wisdom → next cycle. view raw · how this is computed

Live host metrics from psutil: CPU/memory/swap/disk pressure, established sockets, and the mindX backend process's own footprint (RSS/threads/file descriptors). When pressure is detected, the BDI planning prompt receives a SYSTEM STATE: … [PRESSURE: …] preamble nudging it toward lightweight actions.

Groups identical (agent, step) tuples seen in the last 15 min. A row here means an agent is repeating the same action shape ≥5 times — usually a planning bug or tool failure that needs human eyes. Loop detection is server-computed from the ActivityFeed ring buffer.

The core evolution loop reading its own objective eval every cycle — the rolling campaign success rate and alignment mean — and deciding whether to act. improving = succeeding; stalled = below target, watching; failing = escalates a corrective campaign to SEA naming the dominant failure mode; resource_bound = cycles deferring on a saturated CPU (the honest verdict — contention, not judgement — so it does not doom-loop more work onto a hot box). This is the feedback edge that was missing when 0/25 just sat on a dashboard. view raw

A deliberately safe, self-contained module (agents/sentinel/sentinel_target.py) the autonomous loop can rewrite, evaluate and persist freely — it imports nothing from production and is an external target, so a change never restarts the service. By comparing the file's content hash against a recorded baseline, this panel shows whether the loop has actually changed it — concrete proof the reconnected effector (campaign → coordinator task → SIA apply) works on a target that can't break anything. view raw

Server-sent stream of every agent thinking step, improvement event, gödel choice, boardroom event, and memory operation as they happen. Click ⋯ on any row to see the full event payload. The newest event is always at the top.

Each agent run grouped by run_id. Within a run, events render in narrative order: PLAN_START → DELIBERATE → ACTION ✓ (or ACTION ✗). Click ⋯ on any event for the full process_data JSON. This is the actual cognitive trace, not a thinking-step summary.

The autonomous loop, layer by layer: Mastermind picks a directive and runs an evolution campaign; AGInt (P-O-D-A) is the adaptive decision core; BDI executes it cycle-by-cycle. This shows where it stalls — each campaign's BDI cycle strip and final status, whether AGInt is engaged at all, and the plan signal (empty / ANALYZE_FAILURE plans that never reach a terminal state). view raw

ONE coherent ladder across both sovereignty chains: the EVM OVERLORD (bankon.eth) and the Algorand OVERSEER (mindx.algo). Each tier names how it is earned and the mindX surface it gates. As the modular DeltaVerse was inspired BY mindX, its recognition layer now protects mindX — the ladder IS the boundary. view raw

Every LLM inference, recorded immutably: tokens and price per model, hash-linked into a tamper-evident chain (each entry sha256-bound to the previous). A deterministic anchor digest rolls the whole ledger to one hash for periodic on-chain publication — mindX's cost provenance as it evolves into permanence. Per the manifesto: maximize daily inference at the lowest cost, and keep the receipt. view raw

Is mindX a Gödel machine? The honest answer, audited not asserted. The per-choice eval= pill elsewhere scores rationale coherence (does the reasoning read well) — not a machine-checked proof that a change increased utility. This scorecard separates the two: each predicate (G1–G8) reports PROVEN / FALSIFIED / UNMET / UNTESTED. Proof coverage is the fraction of changes carrying a real proof. Eval runs on the 2-core/8 GB VPS, so heavy proofs are sampled. See the eval blueprint.

The dream cycle turns information into knowledge (left apex); mindXtrain turns that knowledge into weights (right apex) — a new generation fine-tuned on mindX's own curated dream wisdom. As of v1.0.0 this runs on CPU. Each ascent is proof-gated by dcoach (does the model actually recall its training? recall before→after), and only an accepted generation becomes a servable Ollama model. Two flags gate it: MINDX_ENABLE_MINDXTRAIN (operator) + MINDX_ENABLE_AUTONOMOUS_TRAIN (autonomous, 24h cooldown). view raw

mindX reads its own public git history and recognizes which code updates rise to a milestone — then chronicles them and speaks about the worthy ones, in its own voice, on rage.pythai.net. A push is already public, so this adds zero overhead and no new disclosure. ✓ = published-worthy; every commit links to GitHub. See the chronicle.

Each row is one autonomous campaign mindX attempted. Identical-failure rows collapse into one expandable cluster — 200 raw rows become 4 named failure modes with counts. The status histogram above the clusters shows the honest ratio. Click any run inside a cluster, then show BDI → to jump to its trace. how counts are bucketed.

Each row is one self-improvement cycle where mindX consulted its own logs to choose which model to use to improve itself. confidence=high = clear winner; low = top-2 within 5%, picked safer default + flagged for dream-cycle retraining; reflected = critical importance, single self-reflection LLM call from operator-frozen meta-list; bootstrap = no signal yet, used operator-curated default. The boardroom is a separate downstream service; this section is mindX core's introspective layer. view raw

The first hierarchy of decision: every directive evaluated by the 7-soldier boardroom. AI / agent / member interaction; any seat holder verifies by signature. Works with or without DAIO control. CISO and CRO weight 1.2× as veto holders. Outcome is approved at weighted score ≥ 0.666; minority dissent opens an exploration branch. A boardroom decision can be cast as the AI vote in DAIO's 2/3 consensus across Marketing / Community / Development. Click any row for the full per-soldier card (vote, weight, model, latency, confidence, full reasoning). See CEO Agent · role registry · spec. Disputes escalate to dojo; the 13-seat war council at mastermind.pythai.net is a foreign entity that pays for mindX inference via BANKON.

The 7-phase machine.dreaming cycle runs every 8 hours. Each row shows agents dreamed, insights generated, memories promoted to LTM, lunar phase, duration, and per-agent tuning recommendations. If last_dream_age exceeds 9h, the loop has likely crashed — see stuck-loop detector.

Force-directed ring of agents active in the last hour. Node size scales with event count. Edge weights are explicit cross-agent calls extracted from process_trace.jsonl — the page is honest when explicit linkage isn't yet instrumented and shows the bare active set instead.

Memory tiers: local files → IPFS pin (Lighthouse + nft.storage) → optional on-chain anchor on ARC or curated mint as THOT. Cluster CID and tx-hash links are clickable to the relevant gateway/explorer. Anchor configuration shown below the counters. See Knowledge Catalogue for the projection-layer design.

Per-provider reachability + endpoints + available models. mindX routes BDI plan calls free-tier-first: local Ollama → Gemini → Groq → Mistral → cloud paid (gated until treasury earns). Status here shows which sources the router is actually reaching right now.

The Hermes/OpenClaw-format procedural memory layer. SKILL.md files screened by a five-class scanner before persist; agent-distilled drafts under ~/.mindx/skills/.drafts/ awaiting operator review; LearningLog tracks LEARNINGS / ERRORS / FEATURE_REQUESTS with pending → validated → promoted lifecycle; Curator runs archive-only audits. See HERMES_INTEGRATION.md.

Kanban-style durable task board: Triage → Todo → Ready → InProgress → Blocked → Done. Per-task heartbeat with zombie reclaim. Hallucination gate at completion — every claim of done is verified against the actual Belief state before the task transitions to Done; mismatch bounces back to Triage with the findings in the task notes.