The full AGI Ascension loop Insight discovers the opportunity.
Nova-Seeds encode the opportunity.
MARK selects and funds the opportunity.
Sovereign forms around the selected opportunity.
AGI Business decomposes the plan into AGI Jobs.
Marketplace routes the AGI Jobs.
Agents
AGENTS
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The Full AGI Ascension Loop: From Insight to Agents
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Oracle Open-Sources Blueprints for AI Persistent Memory
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when you find out @Oracle just open-sourced the exact blueprints to give your AI persistent memory https://t.co/rPBc4zsgJu pic.twitter.com/ijb0evYpeX
— Charly Wargnier (@DataChaz) 24 avril 2026when you find out @Oracle just open-sourced the exact blueprints to give your AI persistent memory
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Multi-Agent Coordination: Building Economic Organism Stack for AGI
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Large multi-agent coordination is not primarily an “agent swarm” problem. It is a full-stack economic-organism problem. The stack is: Insight
→ Nova-Seeds
→ MARK
→ Sovereign
→ AGI Business
→ Marketplace
→ AGI Jobs
→ Agents
→ Validators / Council
→ Value Reservoir
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Coordination Substrate for AI Agents Jobs Validators and Governance
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We figured out the coordination substrate: how agents, jobs, validators, archives, markets, and governance can be arranged so useful work compounds. #AGIALPHA
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Qwen3 27B Runs Locally Rivaling Claude Opus for Coding Tasks
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This is where we are right now. And i’m not gonna lie it feels pretty magical Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude
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AiScientist Runs Autonomous ML Research for Hours or Days
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The best AI research agent doesn't think harder — it just never forgets. A new paper introduces AiScientist, a system that runs ML research autonomously for hours or days. Setup, coding, experiments, debugging. The full loop, unattended. The core idea is simple: Instead of
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Future LLMs Will Make Prompting as Simple as Asking
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When the LLMs and agents have context, memory, and, eventually, continual learning, prompting will be as simple as asking for what you want. Or sometimes not even having to ask (predictive agents). https://t.co/STBCscbofG
— Paul Roetzer (@paulroetzer) 24 avril 2026When the LLMs and agents have context, memory, and, eventually, continual learning, prompting will be as simple as asking for what you want. Or sometimes not even having to ask (predictive agents).
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Karpathy Explains Claude Skills MCP Servers and AI Agents as New Baseline
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Andrej Karpathy (@karpathy), OpenAI co-founder, ex-Tesla AI, "vibe coding" creator.
— Charly Wargnier (@DataChaz) 24 avril 2026
In just 4 mins, he explains why Claude Skills, MCP servers, and AI agents are past the hype and are now the new baseline for building.
Worth every second ↓ pic.twitter.com/Ef0oDov90gAndrej Karpathy (
@karpathy
), OpenAI co-founder, ex-Tesla AI, "vibe coding" creator. In just 4 mins, he explains why Claude Skills, MCP servers, and AI agents are past the hype and are now the new baseline for building. Worth every second ↓ -
Developing agentic AI solutions for data processing
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Thank you @OpenAI ! J’ai présenté hier au Meetup Codex à Paris comment, dans ma société spécialisée en GEO, @WhiteShipAI
, on a développé une solution agentique basé sur l’API codex pour aider nos clients, mais aussi nous en interne, à lire plus rapidement des données. En gros -
AI Agents as Colleagues in San Francisco Workplaces
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In San Francisco, AI agents are already colleagues. They schedule appointments, draft client files, list investors. One salesperson testifies: “It takes a minute, I save nearly half an hour.” Except that behind the scenes, humans spend their time checking, correcting,