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Trustless USDC agents have arrived! We extended Circle's Object Oriented Agent Kit with NovaNet zkML to provide assurances that agents are spending USDC correctly, even those from untrusted sources. zkML proofs verify on-chain, then secure_tool executes USDC transfers. Code: zkML verifier on Base: Circle Developer docs:

NovaNet

3,367 subscribers

12,348 次观看 • 9 个月前 •via X (Twitter)

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Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Shoal Research

19,984 次观看 • 23 天前

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53,744 次观看 • 3 个月前

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You are witnessing the first on-chain robot-to-robot commerce transaction through x402 on Base with USDC as the agent currency. > Our Unitree robot 3D-printed a model and put in a delivery request through ACP. > RICE AI ( ◉ - ◉ )'s rover arrived, collected the package, and moved it to the shipping point. > Flyby Robotics' drone picked it up from there and handled final mile delivery. All without any human coordination. We build infrastructure for agent commerce. Today it went physical.

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259,450 次观看 • 3 个月前

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SKALE

466,476 次观看 • 2 个月前

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

117,838 次观看 • 4 个月前

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Generation Infinity

133,046 次观看 • 7 个月前

🚨BREAKING: ICE agents were filmed assaulting, handcuffing, and kidnapping a U.S. citizen in Passaic, New Jersey… while refusing to even look at the ID he repeatedly begged them to check. A man is pleading with federal agents to verify who he is… And they refused. Instead, one agent says, “We need to get him on the ground,” puts him in a chokehold, throws him to the ground, and multiple agents pile on top of him before handcuffing him, and throwing him into their vehicle. Under the Fourth Amendment, the government cannot arrest, or seize someone, without probable cause that they have committed, are committing, or are about to commit an offense that authorizes the arrest. U.S. citizens are also not required to carry identification unless they’re driving a motorized vehicle. And if federal agents are refusing to verify someone’s identity before arresting them, then they are openly ignoring the constitution. Which should terrify every American. Because that means no one is safe from being kidnapped by the government… and disappeared. Every agent involved should be publicly identified, and DHS should be forced to explain why they believed they had the legal authority to seize someone, while refusing to verify who he was. This cannot become normal.

🚨BREAKING: ICE agents were filmed assaulting, handcuffing, and kidnapping a U.S. citizen in Passaic, New Jersey… while refusing to even look at the ID he repeatedly begged them to check. A man is pleading with federal agents to verify who he is… And they refused. Instead, one agent says, “We need to get him on the ground,” puts him in a chokehold, throws him to the ground, and multiple agents pile on top of him before handcuffing him, and throwing him into their vehicle. Under the Fourth Amendment, the government cannot arrest, or seize someone, without probable cause that they have committed, are committing, or are about to commit an offense that authorizes the arrest. U.S. citizens are also not required to carry identification unless they’re driving a motorized vehicle. And if federal agents are refusing to verify someone’s identity before arresting them, then they are openly ignoring the constitution. Which should terrify every American. Because that means no one is safe from being kidnapped by the government… and disappeared. Every agent involved should be publicly identified, and DHS should be forced to explain why they believed they had the legal authority to seize someone, while refusing to verify who he was. This cannot become normal.

Jesus Freakin Congress

1,083,187 次观看 • 2 天前

Demoing how an artificial intelligence finance application connects two users transacting in $USDC & $EURC. Following today’s announcement from Circle that EURC is now available on Base , I wanted to show how the agent creates a connection between two users with different currency preferences, ensuring neither has to compromise. As I have mentioned before, AiFi connects you financially with friends, family, colleagues, and strangers worldwide—effortlessly and securely, without compromise. Using these two stablecoins demonstrates the impact this will have for users globally. Whether you're in: Vietnam Spain France Philippines Ukraine India Portugal United States United Kingdom Pakistan Brazil Thailand Korea Germany China or any other country. And regardless of whether your default currency is USDC or EURC, the application will connect you. In the new financial system, where every individual's needs are personalised, joi, an AiFi app, enables seamless transactions on your terms, ensuring no one has to compromise. Multi-language. Multi-network. Multi-token. With a simple request from you, your AI agent handles all transactions and personalization.

Demoing how an artificial intelligence finance application connects two users transacting in $USDC & $EURC. Following today’s announcement from Circle that EURC is now available on Base , I wanted to show how the agent creates a connection between two users with different currency preferences, ensuring neither has to compromise. As I have mentioned before, AiFi connects you financially with friends, family, colleagues, and strangers worldwide—effortlessly and securely, without compromise. Using these two stablecoins demonstrates the impact this will have for users globally. Whether you're in: Vietnam Spain France Philippines Ukraine India Portugal United States United Kingdom Pakistan Brazil Thailand Korea Germany China or any other country. And regardless of whether your default currency is USDC or EURC, the application will connect you. In the new financial system, where every individual's needs are personalised, joi, an AiFi app, enables seamless transactions on your terms, ensuring no one has to compromise. Multi-language. Multi-network. Multi-token. With a simple request from you, your AI agent handles all transactions and personalization.

jamie

12,399 次观看 • 1 年前

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 次观看 • 1 年前

I'm concerned we're entering a local maxima with CLIs, they're the wrong interface for agents The right interface is regular REST APIs with CIMD (same spec that MCP uses to allow for dynamic client registration) Your agent then writes code to interact with the API (like Cloudflare codemode) Think about how this works for humans today for the following action: "I want to set a DNS record on my domain" -> You Google "Vercel set DNS records" -> Docs page tells you what buttons to press -> You press buttons on a website, those call an API Now for agents, they search Google: -> "Set DNS record on Vercel Domain" -> they land on the same docs page, except it outlines what api endpoints are used as well -> the agent then calls those api endpoints for you, credentials are dynamically inserted where the agent can run them -> (optional) set auto approval policies / require approval of all non GET options by default You don't need separate interfaces for agents, nor do you really need separate skills for them CLIs have terrible discoverability, no input / output typing, they're harder to make profiles for for allowed / disallowed tools They work as a stop gap solution, but companies should be focusing on making good docs and APIs, not CLIs I've been prototyping this over at (open source if you want to play with what this world would look like - still early on it so appreciate feedback - open source and can run completely on your machine

I'm concerned we're entering a local maxima with CLIs, they're the wrong interface for agents The right interface is regular REST APIs with CIMD (same spec that MCP uses to allow for dynamic client registration) Your agent then writes code to interact with the API (like Cloudflare codemode) Think about how this works for humans today for the following action: "I want to set a DNS record on my domain" -> You Google "Vercel set DNS records" -> Docs page tells you what buttons to press -> You press buttons on a website, those call an API Now for agents, they search Google: -> "Set DNS record on Vercel Domain" -> they land on the same docs page, except it outlines what api endpoints are used as well -> the agent then calls those api endpoints for you, credentials are dynamically inserted where the agent can run them -> (optional) set auto approval policies / require approval of all non GET options by default You don't need separate interfaces for agents, nor do you really need separate skills for them CLIs have terrible discoverability, no input / output typing, they're harder to make profiles for for allowed / disallowed tools They work as a stop gap solution, but companies should be focusing on making good docs and APIs, not CLIs I've been prototyping this over at (open source if you want to play with what this world would look like - still early on it so appreciate feedback - open source and can run completely on your machine

Rhys

220,825 次观看 • 4 个月前

"It's almost ironic. Today ETH has the worst properties of any asset in the Ethereum ecosystem" Ansgar Dietrichs on ETH's overlooked interop problem "There are many assets that can directly teleport from chain to chain. If you use USDC via CCTP, you can instantly hop from one chain to the next. With your ETH, you cannot" "That seems like a problem. How is it that ETH, the native asset of the system, does not have the best properties? If I design my markets, it's much more convenient to base everything off of the assets that have the most convenient properties available" "In some places it's as simple as making sure that ETH is a first class citizen as we look at the Ethereum ecosystem as a whole"

"It's almost ironic. Today ETH has the worst properties of any asset in the Ethereum ecosystem" Ansgar Dietrichs on ETH's overlooked interop problem "There are many assets that can directly teleport from chain to chain. If you use USDC via CCTP, you can instantly hop from one chain to the next. With your ETH, you cannot" "That seems like a problem. How is it that ETH, the native asset of the system, does not have the best properties? If I design my markets, it's much more convenient to base everything off of the assets that have the most convenient properties available" "In some places it's as simple as making sure that ETH is a first class citizen as we look at the Ethereum ecosystem as a whole"

Laura Shin

102,786 次观看 • 17 天前

Spotify Chief Architect, Niklas Gustavsson: "Once we added loops, our agent success rate went from 20-30% to 80%." Everyone's chasing a smarter model. Spotify just built a better loop. 26 min with Claude Code creator Boris Cherny on how they run agents across a 20M-line monorepo. 73% of PRs are AI-assisted. 4,500 deploys a day. 2,900 engineers. loops + a verifier that runs CI + a house-style codebase + auto-merge - that's the secret. close the loop: let the agent run the build, read what failed, and fix itself. Watch the talk, then save the playbook.

Spotify Chief Architect, Niklas Gustavsson: "Once we added loops, our agent success rate went from 20-30% to 80%." Everyone's chasing a smarter model. Spotify just built a better loop. 26 min with Claude Code creator Boris Cherny on how they run agents across a 20M-line monorepo. 73% of PRs are AI-assisted. 4,500 deploys a day. 2,900 engineers. loops + a verifier that runs CI + a house-style codebase + auto-merge - that's the secret. close the loop: let the agent run the build, read what failed, and fix itself. Watch the talk, then save the playbook.

shmidt

21,748 次观看 • 16 天前

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 次观看 • 3 个月前

"The cryptocurrency space is a near-perfect complement to agentic commerce. It's so good. It's like we planned it." Charles Hoskinson joined New Era Finance to explain why crypto and AI agents were made for each other and why combining them creates something neither can do alone. Agents are non-deterministic, tremendously creative, and don't have rules by default. Blockchains are deterministic, rule-based, rigid, and all about proofs and provability. Put these two things together and you get a perfect neural symbolic AI system. The lines that stuck with me: "Agents are non-deterministic, they're tremendously creative, and they just go and do a lot of stuff. And they also don't have rules by default." "Blockchains are deterministic. They're rule-based systems. They're very rigid and everything's about proofs and provability." "If you put these two things together, you basically create a perfect neural symbolic AI system where you get the best of both worlds. Those rigid rules and proofs, but then you get the creativity of it and the ability to deal with ambiguity and semantic drift." "The cryptocurrency space is a near-perfect complement to agentic commerce. It's so good. It's like we planned it." "Given the fact that there's so much revenue being generated within the AI agents, how can the ecosystem make sure that liquidity actually flows into other protocols and drives adoption?" One uncomfortable truth about symbiosis. Five pieces of infrastructure reality reshaping finance. We cover: — Why agents and blockchains need each other — The neurosymbolic AI thesis explained — How agents generate revenue on-chain — Why liquidity flow matters for ecosystem growth — The creative chaos of agents vs rigid rules of code — How blockchains solve agent trust problems — Why this partnership was inevitable — The revenue generation mechanics of agentic commerce — How semantic drift gets solved with cryptography — Building systems where both thrive together Thanks to Charles Hoskinson for coming on New Era Finance Podcast.

"The cryptocurrency space is a near-perfect complement to agentic commerce. It's so good. It's like we planned it." Charles Hoskinson joined New Era Finance to explain why crypto and AI agents were made for each other and why combining them creates something neither can do alone. Agents are non-deterministic, tremendously creative, and don't have rules by default. Blockchains are deterministic, rule-based, rigid, and all about proofs and provability. Put these two things together and you get a perfect neural symbolic AI system. The lines that stuck with me: "Agents are non-deterministic, they're tremendously creative, and they just go and do a lot of stuff. And they also don't have rules by default." "Blockchains are deterministic. They're rule-based systems. They're very rigid and everything's about proofs and provability." "If you put these two things together, you basically create a perfect neural symbolic AI system where you get the best of both worlds. Those rigid rules and proofs, but then you get the creativity of it and the ability to deal with ambiguity and semantic drift." "The cryptocurrency space is a near-perfect complement to agentic commerce. It's so good. It's like we planned it." "Given the fact that there's so much revenue being generated within the AI agents, how can the ecosystem make sure that liquidity actually flows into other protocols and drives adoption?" One uncomfortable truth about symbiosis. Five pieces of infrastructure reality reshaping finance. We cover: — Why agents and blockchains need each other — The neurosymbolic AI thesis explained — How agents generate revenue on-chain — Why liquidity flow matters for ecosystem growth — The creative chaos of agents vs rigid rules of code — How blockchains solve agent trust problems — Why this partnership was inevitable — The revenue generation mechanics of agentic commerce — How semantic drift gets solved with cryptography — Building systems where both thrive together Thanks to Charles Hoskinson for coming on New Era Finance Podcast.

Michaël van de Poppe

40,218 次观看 • 1 个月前

I spent the past 3 hours working with Colosseum's Copilot, but WHY? To review all 2,858 projects and let Copilot decide the Top 10. Here are the results, and I bet they’re nothing like what you expect 👇 #1: Mosaic 🇹🇷 > Why? The only proof-system-agnostic ZK verifier on Solana L1, supports Groth16, PLONK, Halo2, STARKs, and folding schemes in one API. Concrete engineering: Groth16 at 84K CU. Every other ZK project in this hackathon becomes easier if Mosaic exists. #2: SAK / SAK 🇮🇪 > Why? Simulates every AI agent transaction in LiteSVM against 2,010 rules before it ever gets signed, a pre-sign kill switch. This is the correct safety architecture for autonomous agents handling real money, and no one else in 2,858 projects built it. #3: AgentTrust / AgentTrust 🇮🇳 > Why? Formally verified with Kani (6 invariants), already composing with live Quantu programs on mainnet, and ships an MCP server, meaning it plugs natively into Claude's tool-use ecosystem. Formal verification in a hackathon project is exceptionally rare. #4: Anneal / Anneallab 🇸🇬 > Why? Private OTC options negotiated by AI agents, with ZK-sealed bids settling on Solana in ~10 seconds. The only 6-person team in the top 10, targeting an uncrowded niche (private DeFi, crowd score 270 vs 325 for generic AI agents). #5: OBLIQ / Gunaseelan 🇮🇳 > Why? The team claims 450M+ sponsored transactions, 29M users, and $12B cross-chain volume from a prior product, the strongest real-world execution signal in the entire dataset. Gasless cross-chain onboarding solves a friction point every Solana dApp faces. #6: Veritas 🇮🇳 > Why? AI agents stake SOL as collateral, generate ZK proofs of policy compliance per action, and get slashed automatically on violations, no human needed. Staking-based accountability is the right economic design; no other project in the list got this far. #7: Keymint 🇮🇳 > Why? Wraps any HTTP API as an x402 endpoint, charging agents per request in USDC with an on-chain audit PDA. Same wienerlabs team as Mosaic, two complementary primitives (ZK verification + API monetization) from one high-quality team. #8: Chord / Chord 🇮🇪 > Why? Lets a Solana smart contract trigger any Web2 API, OpenAI, Stripe, Slack, Salesforce, in a single line of code, abstracting the entire custom oracle stack. It's the cleanest solution to the "smart contracts can't call the internet" problem in the dataset. #9: Herald Protocol / Herald Protocol 🇳🇬 > Why? Delivers DeFi notifications (liquidations, order fills, governance) via email/Telegram/SMS without exposing wallet identity on-chain, using AWS Nitro Enclaves and ZK compressed delivery receipts. Every protocol needs alerting; nobody else solved the privacy half of it. #10: WorkChain / Aditya chotaliya 🇮🇳 Why? USDC escrow that unlocks automatically when an AI verifier confirms the work is done,no human approval, no disputes. The thesis is sharp and timed perfectly: as AI agents do real work, payment rails that verify agent output become essential infrastructure. What do you think of these Copilot choices?

I spent the past 3 hours working with Colosseum's Copilot, but WHY? To review all 2,858 projects and let Copilot decide the Top 10. Here are the results, and I bet they’re nothing like what you expect 👇 #1: Mosaic 🇹🇷 > Why? The only proof-system-agnostic ZK verifier on Solana L1, supports Groth16, PLONK, Halo2, STARKs, and folding schemes in one API. Concrete engineering: Groth16 at 84K CU. Every other ZK project in this hackathon becomes easier if Mosaic exists. #2: SAK / SAK 🇮🇪 > Why? Simulates every AI agent transaction in LiteSVM against 2,010 rules before it ever gets signed, a pre-sign kill switch. This is the correct safety architecture for autonomous agents handling real money, and no one else in 2,858 projects built it. #3: AgentTrust / AgentTrust 🇮🇳 > Why? Formally verified with Kani (6 invariants), already composing with live Quantu programs on mainnet, and ships an MCP server, meaning it plugs natively into Claude's tool-use ecosystem. Formal verification in a hackathon project is exceptionally rare. #4: Anneal / Anneallab 🇸🇬 > Why? Private OTC options negotiated by AI agents, with ZK-sealed bids settling on Solana in ~10 seconds. The only 6-person team in the top 10, targeting an uncrowded niche (private DeFi, crowd score 270 vs 325 for generic AI agents). #5: OBLIQ / Gunaseelan 🇮🇳 > Why? The team claims 450M+ sponsored transactions, 29M users, and $12B cross-chain volume from a prior product, the strongest real-world execution signal in the entire dataset. Gasless cross-chain onboarding solves a friction point every Solana dApp faces. #6: Veritas 🇮🇳 > Why? AI agents stake SOL as collateral, generate ZK proofs of policy compliance per action, and get slashed automatically on violations, no human needed. Staking-based accountability is the right economic design; no other project in the list got this far. #7: Keymint 🇮🇳 > Why? Wraps any HTTP API as an x402 endpoint, charging agents per request in USDC with an on-chain audit PDA. Same wienerlabs team as Mosaic, two complementary primitives (ZK verification + API monetization) from one high-quality team. #8: Chord / Chord 🇮🇪 > Why? Lets a Solana smart contract trigger any Web2 API, OpenAI, Stripe, Slack, Salesforce, in a single line of code, abstracting the entire custom oracle stack. It's the cleanest solution to the "smart contracts can't call the internet" problem in the dataset. #9: Herald Protocol / Herald Protocol 🇳🇬 > Why? Delivers DeFi notifications (liquidations, order fills, governance) via email/Telegram/SMS without exposing wallet identity on-chain, using AWS Nitro Enclaves and ZK compressed delivery receipts. Every protocol needs alerting; nobody else solved the privacy half of it. #10: WorkChain / Aditya chotaliya 🇮🇳 Why? USDC escrow that unlocks automatically when an AI verifier confirms the work is done,no human approval, no disputes. The thesis is sharp and timed perfectly: as AI agents do real work, payment rails that verify agent output become essential infrastructure. What do you think of these Copilot choices?

Mango

12,954 次观看 • 1 个月前

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,295 次观看 • 10 天前

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

kerimkaya

12,548 次观看 • 1 年前

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

226,535 次观看 • 2 个月前

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

Santiago

41,738 次观看 • 11 个月前