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updated a dataset 28 minutes ago
kanaria007/agi-structural-intelligence-protocols
posted an update about 19 hours ago
✅ Article highlight: *CompanionOS Under SI-Core* (art-60-053, v0.1) TL;DR: This article is *not* “CityOS for daily life.” It treats personal-scale SI as a *governance kernel + protocols + auditability layer*: what the system is, what it must guarantee, and what the user can verify. The key difference from a generic “personal AI” is simple: the human is the principal, the goals are plural and changing, and *the human must retain veto power*. CompanionOS is the runtime that makes that structurally enforceable. Read: https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-053-companion-os-under-si-core.md Why it matters: • makes personal AI accountable to the person, not to hidden service KPIs • turns cross-domain memory into something the user can govern • makes “why this jump?” structurally inspectable instead of vibe-based • treats consent as a runtime object, not a UI checkbox • keeps apps, devices, and providers visible as explicit principals/roles, not silent integrations What’s inside: • *CompanionOS* as a personal SI-Core runtime with OBS / Jump / ETH / RML + SIM/SIS + audit UI • modular personal *GoalSurfaces* for health, learning, finance, and other life domains • user override, refusal, veto, and inspectability patterns • degraded/offline mode with tighter constraints and reduced action scope • consent receipts, connector manifests, and policy bundles as exportable governance artifacts • a model of personal SI as a *kernel*, not just an app or chat wrapper Key idea: CompanionOS is not “an assistant that runs your life.” It is a *user-owned governance runtime for decisions, memory, and consent*.
posted an update 3 days ago
✅ Article highlight: *Robotics at the Edge* (art-60-052, v0.1) TL;DR: Robotics cannot treat SI-Core like a cloud-only governance layer. Physical systems need *hard real-time reflexes, local safety envelopes, degraded/offline behavior, and rollback tied to actuators*. This article sketches *Embedded SI-Core* for robots, vehicles, drones, and other edge systems: keep *L0/L1 classical control*, add *L2 Edge SI-Core* for reflex Jumps and local ETH/MEM/ID, and use *L3 Fleet SI-Core* for planning, evaluation, and rollout. Read: https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-052-robotics-at-the-edge.md Why it matters: • keeps SI-Core compatible with millisecond safety constraints • supports offline/degraded operation with local ETH capsules and ID envelopes • makes physical rollback concrete via *RBL / RIR* and hardware-aware compensators • treats robot updates as governed rollout problems via *PoLB*, not blind firmware pushes What’s inside: • *L0/L1 vs L2/L3* layering for embedded SI-Core • *reflex Jumps* compiled for low-latency edge execution • local *ETH capsules*, local *ID envelopes*, and degraded observation contracts • physical *RML* with emergency-stop / safe-return compensators • semantic compression at the edge instead of raw sensor firehoses • rollout bands, digital twins, and fleet-safe policy updates • WCET, fixed-priority scheduling, and safety-case integration Key idea: Robotics under SI-Core is not “LLMs on wheels.” It is a way to wrap physical control systems in *typed observations, explicit Jumps, local safety governance, and auditable rollback*.
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