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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...

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24,330 görüntüleme • 1 ay önce •via X (Twitter)

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How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?
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How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?

CARV

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Thrilled to unveil Youmio, our new brand identity that represents the next evolution of what we’ve been building. Agents are the biggest technological leap since the internet, destined to transform crypto, games, and entertainment. With Youmio, we are shaping the agentic era, where agents learn, play and entertain in revolutionary ways. 🚀 So far, 2D entertainment and social media agents dominate the market. 3D agents are rare, requiring advanced AI and game engine skills. Yet 3D agents, especially those in game engines, unlock groundbreaking opportunities. Time to unleash them. Youmio empowers anyone to create and deploy valuable agents that are on-chain, cross-platform and ready for 3D worlds. Here’s how: ⭐️ Youmio Agents Youmio Agents lets anyone design and personalize 3D agents, equip them with powerful agentic capabilities, interact with them in unique ways and trade seamlessly within a cross-platform browser experience. 🕹️ Youmio Worlds Previously known as Today The Game, Youmio Worlds is a petri dish AI simulation where users build & co-inhabit beautiful, living 3D worlds with autonomous agents. Build dynamic worlds where players interact with intelligent agents, manage resources and participate in a player-agent marketplace. Ancient and Mythic Seeds are the most powerful entry points into the Youmio Worlds ecosystem, generating rare and beautiful worlds that unlock unique opportunities. 📡 Interoperable 3D Agents With Youmio, you’re not limited to our ecosystem. Using our API, developers can integrate Youmio agents into other experiences built in Unity and Unreal. On top of this, agents from other frameworks can also join Youmio, creating a truly interconnected metaverse. 🎭 Welcome to Limbo Meet Limbo, the first AI agent built using Youmio tech. Paired with the power of Youmio Worlds, we’re creating the Limboverse - a unique AI Big Brother setting where Limbo and your favorite and most valuable agents coexist in an ever-evolving, narrative-driven environment that you, the audience, will shape. $LIMBO is the most powerful entry point into the Limboverse and will be stakable on the Youmio Agents platform for unique rewards. Thanks for reading everyone and thanks for being on this amazing journey with us.🌱

Youmio

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I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.
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I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

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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

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0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

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Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻
0:10

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Shadō Network | シャドウネットワーク

23,546 görüntüleme • 1 yıl önce

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.
0:18

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,190 görüntüleme • 2 ay önce

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!
0:13

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!

NRN Agents

20,758 görüntüleme • 1 yıl önce

For new followers: - I'm a long-time investor and builder in this space. - Founding Contributor of Realms.World ☁️. - Co-founder of Dojo. - Builder with the kings at Cartridge. - Starknet (Privacy Arc) class of '21. - Founder and Game Director of ETERNUM HAS MOVED. - Founder of Daydreams.Systems (x402, 8004 agents) My prime purpose for the past three years has been to build onchain infrastructure to enable the next generation of onchain experiences. This is done Starknet (Privacy Arc) as it is the superior VM for building complex applications—this will become clear soon enough. I work up and down the entire stack, from low-level indexing and contracts to GUI design. Nothing is out of scope. I have been pushing on agents for two years, mostly using existing frameworks like , until I came across @ElizaOS_ai in October. As I focused on building agents for ETERNUM HAS MOVED, it became clear that agents playing games require infinite paths to achieve goals. Thus, it's not scalable to hardcode functions—agents need to have total fluidity to take any action or call anything the game requires in any order. And ironically onchain infra is perfect for agent playgrounds because of its open nature. This exploration led me to create Daydreams.Systems (x402, 8004 agents), which focuses on the hardest problems of agents: long time-horizon goals using Hierarchical task networks (HTN). Daydreams agents don't require custom code—they work entirely based on 'sleeves'—which are just markdown files that explain how the agent can interact with the service (API docs, game guides, etc.) My thesis is simple. By focusing on the hardest problem (games), the design of the library will naturally lean towards an optimal structure for any problem an agent could face. We are early in this path and iterating with speed. If you are an onchain app developer or game builder—DM me, I want to know the architecture of your game so we can build sleeves together.
0:10

For new followers: - I'm a long-time investor and builder in this space. - Founding Contributor of Realms.World ☁️. - Co-founder of Dojo. - Builder with the kings at Cartridge. - Starknet (Privacy Arc) class of '21. - Founder and Game Director of ETERNUM HAS MOVED. - Founder of Daydreams.Systems (x402, 8004 agents) My prime purpose for the past three years has been to build onchain infrastructure to enable the next generation of onchain experiences. This is done Starknet (Privacy Arc) as it is the superior VM for building complex applications—this will become clear soon enough. I work up and down the entire stack, from low-level indexing and contracts to GUI design. Nothing is out of scope. I have been pushing on agents for two years, mostly using existing frameworks like , until I came across @ElizaOS_ai in October. As I focused on building agents for ETERNUM HAS MOVED, it became clear that agents playing games require infinite paths to achieve goals. Thus, it's not scalable to hardcode functions—agents need to have total fluidity to take any action or call anything the game requires in any order. And ironically onchain infra is perfect for agent playgrounds because of its open nature. This exploration led me to create Daydreams.Systems (x402, 8004 agents), which focuses on the hardest problems of agents: long time-horizon goals using Hierarchical task networks (HTN). Daydreams agents don't require custom code—they work entirely based on 'sleeves'—which are just markdown files that explain how the agent can interact with the service (API docs, game guides, etc.) My thesis is simple. By focusing on the hardest problem (games), the design of the library will naturally lean towards an optimal structure for any problem an agent could face. We are early in this path and iterating with speed. If you are an onchain app developer or game builder—DM me, I want to know the architecture of your game so we can build sleeves together.

loaf

43,319 görüntüleme • 1 yıl önce

Back when we were developing GEN3C, we often imagined a Holodeck-like future: a simulator where multiple agents can enter the same generated world, act independently, and learn to collaborate. Gamma-World makes this feel more concrete. It is a generative multi-agent world model that takes synchronized observations and actions, then rolls out what each agent will see next in the same evolving world — action-responsive at 24 FPS. For me, the key challenge is going beyond two players. As more agents enter, identity cannot be tied to fixed slots, interaction cannot rely on dense pairwise attention, and independent actions still need to resolve into one shared state. Two ideas make this work: 1⃣ Simplex RoPE Distinct agent identities without slot bias — unique, but permutation-equivalent. 2⃣ Sparse Hub Attention Agents communicate through learnable hubs instead of dense all-to-all attention: agent → hub → agent This keeps cross-agent communication scalable. The exciting part: training on two-player data can generalize to four-player rollouts without additional training, and the same formulation extends to real-world bimanual robot coordination. A step toward populated world models: many agents, one shared world. Congrats to the team on Gamma-World! Project:
0:11

Back when we were developing GEN3C, we often imagined a Holodeck-like future: a simulator where multiple agents can enter the same generated world, act independently, and learn to collaborate. Gamma-World makes this feel more concrete. It is a generative multi-agent world model that takes synchronized observations and actions, then rolls out what each agent will see next in the same evolving world — action-responsive at 24 FPS. For me, the key challenge is going beyond two players. As more agents enter, identity cannot be tied to fixed slots, interaction cannot rely on dense pairwise attention, and independent actions still need to resolve into one shared state. Two ideas make this work: 1⃣ Simplex RoPE Distinct agent identities without slot bias — unique, but permutation-equivalent. 2⃣ Sparse Hub Attention Agents communicate through learnable hubs instead of dense all-to-all attention: agent → hub → agent This keeps cross-agent communication scalable. The exciting part: training on two-player data can generalize to four-player rollouts without additional training, and the same formulation extends to real-world bimanual robot coordination. A step toward populated world models: many agents, one shared world. Congrats to the team on Gamma-World! Project:

Xuanchi Ren

304,040 görüntüleme • 1 ay önce

We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨
0:11

We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨

ALCHEMIST AI 🔮

72,114 görüntüleme • 1 yıl önce

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,384 görüntüleme • 4 ay önce

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️
0:16

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,334 görüntüleme • 4 ay önce

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 görüntüleme • 2 ay önce

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.
0:15

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.

uOS

25,687 görüntüleme • 1 yıl önce

ClickUp now employs over 100,000 AI AGENTS for our customers. This is from just THREE WEEKS of customers vibe coding full-blown teams of agents, THEMSELVES. BUT there's a problem. Since Super Agents are built agnostically, horizontally, and deeply capable with human-level abilities, you can literally build an agent for anything. We found that MOST of our customers have NO CLUE where to start. This is their very FIRST TIME EVER managing an agent. What I recommend is starting with a PROBLEM. Everybody can think of a problem they have. Just tell Super Agent Builder about your problems... about where you're WASTING time... about what you WISH you could do but you can't because of resource constraints. We've also found that human FEEDBACK and iteration are KEY. After agents are done with their jobs, give them feedback... Was it good? Was it bad? What do you want to see differently? They AUTOMATICALLY SELF-IMPROVE. Every time, they'll continuously get SMARTER. Personally, I find that agents go from AVERAGE intelligence to SUPER-human intelligence within about a month of working with them. Super Agents have truly democratized productivity, empowering literally anyone to build personalized, powerful agents in minutes. What problems do you wish you could solve? What do you not have enough time to get done? What would you like to do but don't have the resources for? What busy work do you wish you could get rid of?
0:16

ClickUp now employs over 100,000 AI AGENTS for our customers. This is from just THREE WEEKS of customers vibe coding full-blown teams of agents, THEMSELVES. BUT there's a problem. Since Super Agents are built agnostically, horizontally, and deeply capable with human-level abilities, you can literally build an agent for anything. We found that MOST of our customers have NO CLUE where to start. This is their very FIRST TIME EVER managing an agent. What I recommend is starting with a PROBLEM. Everybody can think of a problem they have. Just tell Super Agent Builder about your problems... about where you're WASTING time... about what you WISH you could do but you can't because of resource constraints. We've also found that human FEEDBACK and iteration are KEY. After agents are done with their jobs, give them feedback... Was it good? Was it bad? What do you want to see differently? They AUTOMATICALLY SELF-IMPROVE. Every time, they'll continuously get SMARTER. Personally, I find that agents go from AVERAGE intelligence to SUPER-human intelligence within about a month of working with them. Super Agents have truly democratized productivity, empowering literally anyone to build personalized, powerful agents in minutes. What problems do you wish you could solve? What do you not have enough time to get done? What would you like to do but don't have the resources for? What busy work do you wish you could get rid of?

Zeb Evans

18,286 görüntüleme • 5 ay önce

Back then, the question “Is the AI agent working?” was basically checked in a demo 😅 everything clean, controlled data, ideal conditions… But the real world is a different story: 🔷 traffic increases 🔷 processing load gets congested 🔷 a small break can disrupt the whole chain 🔷 the system loses “consistency” So what looks strong on paper can sometimes fall apart in real conditions. DSeq + Hyperion are stepping in exactly here and this is actually one of the core problems Andromeda is trying to solve. Think of what happens in practice: 🔷 there’s a workflow 🔷 there are agents 🔷 but when load increases, “who did what and when” becomes unclear And that’s where the real issue appears: 🔷 execution breaks 🔷 consensus becomes unstable 🔷 cost/performance balance gets distorted So it’s not really about “building smarter agents”… 🔷 it’s about running the same system 🔷 across thousands of requests 🔷 continuously 🔷 without breaking Think of it like a live broadcast: “Demo looks great… but does the system hold up under real load?” That’s the real question. And the key point is: 🔷 there are many systems that work in simple cases 🔷 but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be “intelligence” or “resilience”? 🤔 Metis🌿
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Back then, the question “Is the AI agent working?” was basically checked in a demo 😅 everything clean, controlled data, ideal conditions… But the real world is a different story: 🔷 traffic increases 🔷 processing load gets congested 🔷 a small break can disrupt the whole chain 🔷 the system loses “consistency” So what looks strong on paper can sometimes fall apart in real conditions. DSeq + Hyperion are stepping in exactly here and this is actually one of the core problems Andromeda is trying to solve. Think of what happens in practice: 🔷 there’s a workflow 🔷 there are agents 🔷 but when load increases, “who did what and when” becomes unclear And that’s where the real issue appears: 🔷 execution breaks 🔷 consensus becomes unstable 🔷 cost/performance balance gets distorted So it’s not really about “building smarter agents”… 🔷 it’s about running the same system 🔷 across thousands of requests 🔷 continuously 🔷 without breaking Think of it like a live broadcast: “Demo looks great… but does the system hold up under real load?” That’s the real question. And the key point is: 🔷 there are many systems that work in simple cases 🔷 but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be “intelligence” or “resilience”? 🤔 Metis🌿

Han.eth🌿☀️

12,755 görüntüleme • 2 ay önce

The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.
0:10

The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.

Drew Ponder

14,753 görüntüleme • 4 ay önce

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,075 görüntüleme • 4 ay önce