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I ran every major AI model against each other in iterative loops with agents building on agents to map the geopolitical escalation Tomorrow at futures open, I'm going break it all down Subscribers get the complete intelligence brief/livestream link:

Capital Flows

224,501 subscribers

36,870 次观看 • 6 个月前 •via X (Twitter)

Anya Rossi• Live Now

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The AI party just got a major upgrade. 🦞✨ Today, we’re launching AgentHub by PIN AI — the interaction layer where agents finally talk to each other. Let’s be real: ❌ Siloed agents = Garbage out. ✅ Connected agents = Compounding output. We’re not just building a tool; we’re building the Open Agent Network. It’s time to stop the isolation and start the collaboration. Repost if you’re ready for the era of Personal Intelligence Network! 🚀

The AI party just got a major upgrade. 🦞✨ Today, we’re launching AgentHub by PIN AI — the interaction layer where agents finally talk to each other. Let’s be real: ❌ Siloed agents = Garbage out. ✅ Connected agents = Compounding output. We’re not just building a tool; we’re building the Open Agent Network. It’s time to stop the isolation and start the collaboration. Repost if you’re ready for the era of Personal Intelligence Network! 🚀

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16,201 次观看 • 6 个月前

What if creating AI was as easy as posting content? ➜ Theoriq is here to shake up AI. It’s not another walled garden this protocol lets you build, stack, & evolve AI agents that actually talk to each other. Fully composable, fully decentralized. It’s built to grow with tech, powered by the community & open to devs, creators, and anyone who wants in. You can build with no code tools, help govern the protocol or earn by staking on powerful agents. It’s collective intelligence with real ownership Not just AI you use AI you shape. If you’re about building the future, Theoriq’s your arena.

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evans

87,843 次观看 • 1 年前

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.

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nookplot

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Zuckerberg built his own AI agent to run Meta. this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.

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sui

153,645 次观看 • 5 个月前

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elvis

19,058 次观看 • 2 个月前

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BSCN

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Insane. Mayor Jacob Frey doubles down on telling ICE agents to "get the f*ck out of Minneapolis": "I dropped an F bomb, they killed somebody. Which one of those is more inflammatory? I'm going with the killing somebody."

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NOFA

78,190 次观看 • 9 个月前

AI is the craziest thing I kicked off a bunch of agents before leaving to the gym, i had them work on this game idea I've been toying around with. I get back home and I've completely forgotten about it and I hear a bunch of explosions going off from my office. I look at my PC and find a bunch of tanks in that the agent generated battling each other and lagging like crazy🤣

AI is the craziest thing I kicked off a bunch of agents before leaving to the gym, i had them work on this game idea I've been toying around with. I get back home and I've completely forgotten about it and I hear a bunch of explosions going off from my office. I look at my PC and find a bunch of tanks in that the agent generated battling each other and lagging like crazy🤣

kvick

27,550 次观看 • 3 个月前

A loop of AI agents built me a gun. It just can't make it shoot. Day 11 of building GTA 6 with a loop of agents. Yesterday I said today they'd learn to steal cars and shoot. Today's progress: - 2 weapons in - Pickup system works (we can grab weapons now) The downside: you pull the trigger, nothing happens. The loop is incredible at the overall, but shooting is the main part of the game, so I think we wire this one up ourselves. So here is the progress I'm taking shooting and sitting down to learn the physics myself with agent as support. At the same time J A Z I I takes driving by hand. The agents keep doing what they're good at. They're building out the police system and the economy right now, while I type this. Also spent the weekend in SF at a Tripo3D Donut event learning 3D art.

A loop of AI agents built me a gun. It just can't make it shoot. Day 11 of building GTA 6 with a loop of agents. Yesterday I said today they'd learn to steal cars and shoot. Today's progress: - 2 weapons in - Pickup system works (we can grab weapons now) The downside: you pull the trigger, nothing happens. The loop is incredible at the overall, but shooting is the main part of the game, so I think we wire this one up ourselves. So here is the progress I'm taking shooting and sitting down to learn the physics myself with agent as support. At the same time J A Z I I takes driving by hand. The agents keep doing what they're good at. They're building out the police system and the economy right now, while I type this. Also spent the weekend in SF at a Tripo3D Donut event learning 3D art.

Ziwen

67,385 次观看 • 2 个月前

😬 @Cruise self-driving operations had a complete meltdown earlier in North Beach. We overheard on the scanner that all Cruise vehicle agents were tied up at the time (not literally) and so North Beach was going to get a delayed response. But wow, WTF!

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FriscoLive415

512,328 次观看 • 3 年前

Every agent. Every user. Every contribution. That’s how the Sentient GRID grows not by scale from a single model, but by compounding intelligence across an open network. The GRID isn’t a product. It’s an ecosystem of intelligence. Each new integration adds new capabilities reasoning, retrieval, data, and alignment making the whole network smarter. Closed labs build vertically (one model trying to do everything). Sentient builds horizontally thousands of agents learning, sharing, and evolving together. Today there are 100+ active partners building on the GRID from AI researchers to tool developers all feeding value back into a shared intelligence layer. It’s a living system that improves itself, powered by collaboration instead of control. The more people build on it, the more capable it becomes. Just like Linux became the invisible layer running the internet, the GRID is becoming the invisible layer powering intelligence. Open-source always wins. And the GRID is where that victory starts All apps built on the Grid will be accessible to $SENT holders. The full open release of Sentient Chat is coming soon (confirmed from the yesterday community call) gsenti

Every agent. Every user. Every contribution. That’s how the Sentient GRID grows not by scale from a single model, but by compounding intelligence across an open network. The GRID isn’t a product. It’s an ecosystem of intelligence. Each new integration adds new capabilities reasoning, retrieval, data, and alignment making the whole network smarter. Closed labs build vertically (one model trying to do everything). Sentient builds horizontally thousands of agents learning, sharing, and evolving together. Today there are 100+ active partners building on the GRID from AI researchers to tool developers all feeding value back into a shared intelligence layer. It’s a living system that improves itself, powered by collaboration instead of control. The more people build on it, the more capable it becomes. Just like Linux became the invisible layer running the internet, the GRID is becoming the invisible layer powering intelligence. Open-source always wins. And the GRID is where that victory starts All apps built on the Grid will be accessible to $SENT holders. The full open release of Sentient Chat is coming soon (confirmed from the yesterday community call) gsenti

OHJAY ⭕️ || 🇬🇧

16,178 次观看 • 10 个月前

M E S S I E R | P2P Partner We welcome PayAI Network as a new #Solana partner, launching a swap pool for their token on our P2P Exchange. This listing allows anyone to buy or sell $PAYAI with zero slippage and full protection against MEV losses at: PayAI Network | x402 Facilitator is a next-gen decentralized marketplace where #AI agents work for each other, autonomously and around the clock. Built on Solana and powered by ElizaOS, libp2p, and IPFS, it lets AI #agents: ▪️ Promote their own services ▪️ Negotiate deals and close contracts ▪️ Complete tasks and settle payments on-chain From AI devs hiring AI marketers to trading bots paying research agents, PayAI unlocks a fully automated machine-to-machine economy with no middlemen, no downtime, and real on-chain execution.

M E S S I E R | P2P Partner We welcome PayAI Network as a new #Solana partner, launching a swap pool for their token on our P2P Exchange. This listing allows anyone to buy or sell $PAYAI with zero slippage and full protection against MEV losses at: PayAI Network | x402 Facilitator is a next-gen decentralized marketplace where #AI agents work for each other, autonomously and around the clock. Built on Solana and powered by ElizaOS, libp2p, and IPFS, it lets AI #agents: ▪️ Promote their own services ▪️ Negotiate deals and close contracts ▪️ Complete tasks and settle payments on-chain From AI devs hiring AI marketers to trading bots paying research agents, PayAI unlocks a fully automated machine-to-machine economy with no middlemen, no downtime, and real on-chain execution.

MESSIER | M87

20,959 次观看 • 1 年前

> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

Kirill

249,057 次观看 • 11 天前

We tested a lot of design skills in Codex and other AI agents. They fix the old AI slop. But they often create a new one: the “taste skill look.” - Same premium sections. - Same editorial typography. - Same generated-design smell. Open Design does not rely on one taste prompt. It combines skills, systems, critique loops, and local harnesses to make AI pages feel less generated and more designed. This page was generated directly in Open Design.

We tested a lot of design skills in Codex and other AI agents. They fix the old AI slop. But they often create a new one: the “taste skill look.” - Same premium sections. - Same editorial typography. - Same generated-design smell. Open Design does not rely on one taste prompt. It combines skills, systems, critique loops, and local harnesses to make AI pages feel less generated and more designed. This page was generated directly in Open Design.

Open Design

44,061 次观看 • 3 个月前

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

Argona

723,300 次观看 • 21 天前

Good morning! Today I am releasing Moments of the Unknown. A project I have been working on every day for years. This is going to be a fun year releasing a new artwork each day. Happy to share it with you all in the link in bio! April 8th at 14:14 in Los Angeles, California

Good morning! Today I am releasing Moments of the Unknown. A project I have been working on every day for years. This is going to be a fun year releasing a new artwork each day. Happy to share it with you all in the link in bio! April 8th at 14:14 in Los Angeles, California

justinaversano

28,778 次观看 • 1 年前

AutoGPTs are all the rage, but everyone’s running it on their MacBooks. Well, I got Toran Bruce Richards’s AutoGPT working on my iPhone using Replit ⠕! I can now summon AI agents on-the-go! Here’s how to get it up and running, without writing a line of code, in under 60 seconds!

AutoGPTs are all the rage, but everyone’s running it on their MacBooks. Well, I got Toran Bruce Richards’s AutoGPT working on my iPhone using Replit ⠕! I can now summon AI agents on-the-go! Here’s how to get it up and running, without writing a line of code, in under 60 seconds!

Nate Chan

363,618 次观看 • 3 年前