Crash-test your AI system before your customers do.
AI Ship Check currently demonstrates evidence-backed evaluation against controlled fixtures and turns observed failures into remediation work. The broader AGI OS surface is a research-stage software laboratory for understanding, comparison, and system design—not AGI or a production-safety certification.
Understand
First-principles portals explain physics, computation, learning, bottlenecks, and AGI relevance.
Connect
Graph relations expose dependencies, convergence points, causal pathways, and open problems.
Execute
Roadmaps turn scientific uncertainty into experiments, milestones, and innovation opportunities.
Align
Every paradigm carries a safety surface: control, opacity, recursive risk, privacy, and governance.
System Pulse
Strategic research state
Hybrid intelligence systems are the best near-term strategic architecture because they route cognition across digital models, physical systems, human institutions, and specialized accelerators.
Four portals. One conclusion.
Flow structures the loop, Cognitive Lab tests it, Visual Engine makes it legible, and Evolving Engine learns from the next attempt.
Structure the learning loop
Test whether the flow supports understanding
Turn meaning into visible structure
Use learner evidence to change the next test
Main Connected Interface
All systems linked from one hub
AI Ship Check
Crash-test an AI system, reproduce meaningful failures, trace evidence, generate remediation, and explain readiness limits.
Networking OS
Understand relationship leverage, map access paths, simulate strategy, and turn the next conversation into compounding trust.
Frontier Intelligence
Research-source adapters, credibility heuristics, contradictions, and trend exploration.
Emergence Map
Interactive causal map from raw signals to AGI and ASI, with red weak points and innovation zones.
Maps For Each AI
A separate live emergence map for every AI paradigm, showing how that paradigm works and where it gets weak.
Maths AI
A bounded hypothesis-search lab with deterministic verifiers, rejection evidence, and fixed-baseline comparisons.
Imagination Engine
Possible-world search where the AI dreams future systems, branches counterfactuals, and turns scenes into tests.
Shadow Field Theory Lab
Detect hidden traces behind every action: physical, cyber, behavioral, AI memory, and reality-testing shadows.
Negative Trace Intelligence
AI that learns from absence, failure, uncertainty, contradiction, missing context, and unchosen paths.
Paradigm Explorer
Dedicated portals for electronic, photonic, neuromorphic, quantum, embodied, biological, and hybrid AI.
Knowledge Graph
Causal dependencies connecting concepts, equations, architectures, companies, bottlenecks, and opportunities.
Comparison Engine
Radar, matrix, and strategic scoring across energy, compute density, maturity, safety, and AGI potential.
Research Workspace
Notes, hypotheses, experiments, innovation trees, tags, and execution tracking.
AGI Roadmap
Missing technologies, bottleneck priority, hybrid strategies, and staged research execution.
Future Simulation
Scenario modeling for compute scaling, energy constraints, recursive improvement, and hardware bottlenecks.
System Architecture
Database schema, APIs, AI integration layer, deployment path, and scale roadmap.
Legacy Simulation Build
Local simulation components and build checks
These are deterministic local simulation diagnostics, not independently verified mathematical discoveries. Verified evidence appears only in Verified Experimental Mode.
Commands verified
python main.py --ticks 5passedGenerated evolving agent/theorem simulation and dashboard.html.
python -m unittest discover maths_ai_ecosystem/testspassed9 tests passed.
python -m compileall -q maths_ai_ecosystem main.pypassedPython compile check passed.
Generated files
Priority Portals
Highest AGI potential tracks
Meta-cognitive systems
Recursive Self-Improving AI
Systems that improve their own models, tools, code, objectives, curricula, and institutional feedback loops.
Convergent architecture
Hybrid Intelligence Systems
Architectures that combine digital models, human institutions, robots, neuromorphic sensors, photonic compute, and formal verification.
Self-evolving intelligence ecosystem
Maths AI
A local multi-agent cognitive civilization where mathematical, logical, physical, critical, memory, compression, and explorer agents recursively teach, critique, mutate, and validate each other under strict reality constraints.
Digital substrate
Electronic AI
Transformer-era digital intelligence built on CMOS, GPUs, TPUs, memory hierarchies, and datacenter-scale orchestration.
Agent societies
Multi-Agent Intelligence Systems
Networks of specialized AI agents that coordinate, debate, trade tasks, monitor each other, and execute plans.
Embodied agents
Embodied Robotics AI
Intelligence grounded in perception, action, control, world models, and physical feedback loops.
Architecture
Next.js App Router, Prisma/PostgreSQL, React Flow, provider-neutral AI layer.
Execution
Research notes, experiments, innovation trees, roadmaps, and future simulation modules.
Scale Path
Vector search, Neo4j graph traversal, agentic workflows, evaluation harnesses, and enterprise auth.