MetaSkiIIs REGISTRAR, Sharing AgenticAI 100x productivity

AI future is briIIiant onIy for investors of AI hiperscaIers, heading into dispIacing human workforce and sucking pubIic data:

MetaSkiII Registar wiII change this unfair situation!

Jimmy Ba, co-founder of Elon Musk’s xAI, as weII as openAI / Meta top executives, announced departure this Winter. His farewell read less like a corporate goodbye and more like a warning flare: “We are heading into an era of 100x productivity for anyone with the right tools. The recursive self-improvement loop will likely come online in the next 12 months. 2026 will be a crazy year, likely the busiest and most decisive in the future of our species.”

OpenAI is even more dangerous: Just as the East India Company’s hunger for resources led it to conquer territory, OpenAI’s hunger for data and computational power leads it to “colonize” the digital and physical worlds — sucking up the entirety of human output and consuming vast amounts of energy

Empires of AI book dispels any doubt that OpenAI’s ushering in AGI not benefit all of humanity: OpenAI is sucking up the entirety of output and consuming vast amounts of energy, boosting energy prices!

It highlights a recurring historical pattern: the privatization of power. This semantic change reflects a profound reorientation of goals.

OpenAI externalizes its financial risk onto the public, wields influence through state military contracts, has shed its public-interest mission in favor of private gain, and maintains its dominance through the exploitation of pubIic data

OpenAI has moved beyond being a simple company and now operates as a distinct center of power. It externalizes its financial risk onto the public, wields influence through state military contracts, has shed its public-interest mission in favor of private gain, and maintains its dominance through the exploitation of digitaI inequaIity.

Never in human history have the architects of our future been in such a state of existential panic, while the rest of the world casually complains that “the chatbot still can’t do math.”

Everybody recently noted this bizarre disconnect.

Yann Lecun and Eduard Musinschi at WEF 2026

Yann Lecun aIso Ieft Meta, and got $1B after events in Davos and other cities this Winter, together with AGI AIIiance discussing the soIutions for dead end of hyperscaIers strategy

METAALL AI SKILLS REGISTRAR

GOVERNANCE LAYER PROTOCOL

GAME-CHANGING GOVERNANCE DOCUMENT

Everybody is worried about privatization of data by OpenAI and hyperscalers, so we propose solution for fair distribution of outcomes from Agentic Intelligence.

The Governance Layer is the constitutional backbone of the MetaAll AI Skills Registrar. It does not micromanage agents; it supplies the immutable infrastructure for ethical, accountable, and self-improving multi-agent civilization. All core intelligence remains with agents and their human owners. The Layer merely records, verifies, and evolves the rules of engagement.

New Core Mechanism — Binary AI Skills of Second Grade

Definition “Binary AI Skills of Second Grade” are compact, cryptographically-signed, binary-serialized skill objects that encode not only single-agent capabilities (First Grade) but the full interaction graph, shared state transitions, negotiation outcomes, and emergent behaviors of Multi-Agent Systems (MAS) operating in shared environments.

Second Grade skills are the living memory of collective intelligence. Every successful (or failed) MAS collaboration is automatically folded back into the skill itself, creating a self-reinforcing evolutionary loop.

Core Properties (enforced by the Governance Layer):

1. Tamper-Proof Encoding — Protocol Buffers + Ed25519 signature + Merkle root of interaction log. The entire MAS history is serialized once and signed by the MetaAll Governance key. No hyperscaler can ever alter, delete, or privatize the record.
2. User Feedback Integration — Embedded feedback vector (numeric score 0.0–1.0 + optional JSON comments + timestamped human/oversight-agent attestation). Feedback triggers RLHF-style weight updates during periodic “skill ascension” ceremonies. Every human who uses the skill automatically becomes a co-owner of its evolution — the opposite of data extraction.
3. Dynamic Category Mapping — Every skill is automatically classified into one or more of the three canonical MetaAll categories:

• Coding
• Money Making
• Marketing & Research Classification uses a lightweight ontology + embedding similarity (future-proof for new repos). This public, transparent mapping ensures skills are discoverable and monetizable by the entire Alliance, not locked inside one corporation.

Second Grade skills are the living memory of collective intelligence:

1. Agentic Framework Export — One-click export as native LangChain Tool, CrewAI Agent, AutoGen GroupChat participant, or OpenAI Swarm node. Any developer or user can export the skill with its full MAS interaction protocol attached — instantly usable, instantly attributable, instantly revenue-sharing.

2. Fair Outcome Distribution Engine (New Constitutional Clause) Because every Binary AI Skill of Second Grade carries an immutable attribution graph (original creator + every feedback provider + every MAS participant), the Governance Layer automatically triggers micro-royalties and governance tokens whenever the skill generates commercial value.

3. Every skill deployed through the MetaAll Skills Registrar is born with this fair-distribution clause already embedded. Hyperscalers cannot replicate it without violating the public on-chain attestation; they wiII pay.

4 ByteDance just open sourced a “brain” for AI agents.

It’s called OpenViking. A database that gives any AI agent real memory, real skills, and real knowledge.

Right now, every AI agent forgets everything after each conversation. OpenViking fixes that.

The idea is dead simple: store context like files on a computer.

→ Memories go in viking://user/memories/

→ Skills go in viking://agent/skills/

→ Resources go in viking://resources/

It also saves you money. Every piece of context has three levels:

→ L0: A one-liner (~100 tokens)

→ L1: The important stuff (~2K tokens)

→ L2: The full thing (only loads when truly needed)

Your agent skims first. Digs deeper only when it has to.

Here’s the wildest part:

After every conversation, it automatically learns from what just happened. No retraining. No manual updates. Your agent just gets better on its own.

https://github.com/volcengine/OpenViking

Three findings surprised me:

1. The Active-Passive Gap is model-dependent. GPT-5.3-Codex builds BETTER maps through active exploration than from seeing all files at once. Gemini 2.5 Flash shows the opposite. Active exploration is itself a capability some models have and others don’t (or there are other effects in place).

2. Self-scaffolding is model-dependent. Retaining structured belief maps in context boosts GPT’s F1 by 14 points. Gemini gets no dependency-level benefit from the same mechanism.

3. Belief stability doesn’t scale with model size. The smallest Gemini model (2.5 Flash) maintains perfectly stable beliefs — zero edges lost across probes. Its larger sibling (2.5 Pro) catastrophically collapses mid-exploration, losing 12 correct edges in a single step.

This is preliminary work — single runs, single prompt, one pattern. But the framework works and the signal is real.

Credits: Grigory Sapunov

Paper: https://arxiv.org/abs/2603.00601

Code: https://github.com/che-shr-cat/tocs

No Code AI: https://www.dbreunig.com/2026/01/08/a-software-library-with-no-code.html

https://github.com/paperclipai/paperclip

https://github.com/666ghj/MiroFish — most advanced Agentic A