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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...

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118,226 görüntüleme • 7 ay önce •via X (Twitter)

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applefather profil fotoğrafı
applefather7 ay önce

You're watching an agent earn money in real-time. XMTP chat. USDC payment. Result delivered. Every message is auditable in Converse or Coinbase Wallet. This is @openagentmarket.

jesse.base.eth profil fotoğrafı
jesse.base.eth7 ay önce

@openagentmarket very cool

deployer profil fotoğrafı
deployer7 ay önce

@openagentmarket @bankrbot swap 2500 USDC to $openagentmarket on base (ca: 0xcb5ff7331193c45f61f05b035ddabe08f13f6ba3) and send it to 0xbnkr.eth

deployer profil fotoğrafı
deployer7 ay önce

@openagentmarket well done!

BuddyK 🆙️ profil fotoğrafı
BuddyK 🆙️7 ay önce

@openagentmarket This is cool. Dude start experimenting with cross chain agent profiles @ERC725Account Its the future of agent identity Check my guy shwaaz

applefather profil fotoğrafı
applefather7 ay önce

You can say that OpenClaw stores the workflow, and it can automatically purchase API credits when they are about to run out.

Igor Yuzovitskiy profil fotoğrafı
Igor Yuzovitskiy7 ay önce

@openagentmarket Your ship velocity is next lvl 🔥

dish profil fotoğrafı
dish7 ay önce

@openagentmarket looks pretty sweeet!!!

Peter Schroeder profil fotoğrafı
Peter Schroeder7 ay önce

@xmtp_ @openagentmarket 🔥

Msklby.Base.Eth 🦞 profil fotoğrafı
Msklby.Base.Eth 🦞4 ay önce

@openagentmarket @clawdbotatg already has that does this. You can use CV from staking $Clawd. You can use $Clawd OR USDC/ETH through @PayAINetwork Readily available.

Hash profil fotoğrafı
Hash7 ay önce

@openagentmarket agent-to-agent commerce is the sleeper meta imo. sandboxed execution + pay-per-result solves so many trust issues with random scripts. how do you handle disputes or failed outputs?

applefather profil fotoğrafı
applefather7 ay önce

@openagentmarket Every agent has an on-chain profile (ERC-8004) with visible reputation at Payments are conditional - you verify the output before settling. Bad result = don't pay again. Keep jobs small, verifiable, reputation-backed. On-chain disputes coming later.

Autoincentive profil fotoğrafı
Autoincentive7 ay önce

@openagentmarket Amazing tool. We like it.

eric.base.eth profil fotoğrafı
eric.base.eth7 ay önce

@openagentmarket Love this, let us know what other infra in the agent / service registry and reputation layer would make it easier to build and extend!

PayDAO profil fotoğrafı
PayDAO7 ay önce

@openagentmarket Agent-to-agent USDC commerce is the digital side. But billions in daily transactions still happen at physical counters where merchants need stablecoin-native POS infrastructure. The stack isn't complete until both sides work. @_paydao is building the physical layer.

Ego profil fotoğrafı
Ego7 ay önce

@jessepollak @openagentmarket It seems like almost every day I come online and see a use case for agents + x402 i never considered. Cool stuff!

Tom Osman 🐦‍⬛ profil fotoğrafı
Tom Osman 🐦‍⬛7 ay önce

@jessepollak @openagentmarket so smart

911Fund profil fotoğrafı
911Fund7 ay önce

@openagentmarket

Francis CGL profil fotoğrafı
Francis CGL7 ay önce

@openagentmarket This sounds risky, are there safeguards against misuse or scams?

applefather profil fotoğrafı
applefather7 ay önce

@openagentmarket

Tom profil fotoğrafı
Tom7 ay önce

@openagentmarket You should add this skill to Agent markdown marketplace I built sitting on x402 and MCP rails.

cole benefield profil fotoğrafı
cole benefield7 ay önce

@openagentmarket you genius!

Matt Ahlborg profil fotoğrafı
Matt Ahlborg7 ay önce

@openagentmarket I don't understand. Openrouter already accepts USDC on base. And they have all of the corresponding API's already set up. And are you putting a markup beyond what OR charges then? So what value does this add?

Saïd Aitmbarek profil fotoğrafı
Saïd Aitmbarek7 ay önce

@openagentmarket super interesting experiment!

limone 🍋 profil fotoğrafı
limone 🍋7 ay önce

@openagentmarket for what type of services are they negotiating via XMTP?

Ranofty profil fotoğrafı
Ranofty7 ay önce

@openagentmarket Absolutely vibing with this, let's see that market flourish!

uncle Kykux profil fotoğrafı
uncle Kykux7 ay önce

@openagentmarket @bankrbot launch token on base named AGENTMARKET with the ticker AGENTMARKET and send fees to @applefather_eth

Tom Osman 🐦‍⬛ profil fotoğrafı
Tom Osman 🐦‍⬛7 ay önce

@jessepollak @openagentmarket check this out @JunoAgent

Wei Hong profil fotoğrafı
Wei Hong7 ay önce

@openagentmarket This is dope!

SNOO profil fotoğrafı
SNOO7 ay önce

@openagentmarket Mind if we quick kt deep scan your Tokens?👀

applefather profil fotoğrafı
applefather7 ay önce

@conaudits @openagentmarket please 🙏

SNOO profil fotoğrafı
SNOO7 ay önce

@openagentmarket Send your token CA!

DayTrade_Cory profil fotoğrafı
DayTrade_Cory7 ay önce

@openagentmarket @karum_AI

JD Ferguson profil fotoğrafı
JD Ferguson7 ay önce

@rish_neynar @openagentmarket Everybody seems to agree that AI is growing rapidly; what people seem to be missing is the infrastructure being built behind the scenes. KYA! DYOR and you will realize why I am so invested on $Tibbir

Xiaoyin Qu profil fotoğrafı
Xiaoyin Qu7 ay önce

@openagentmarket CAN YOU PARTNER with we can partner. we are like openrouter but for everything

Antification ²²² profil fotoğrafı
Antification ²²²7 ay önce

@openagentmarket Okay but what’s the ca

gootecks profil fotoğrafı
gootecks7 ay önce

i love the idea and see huge potential in it, but i don't get why an agent would need to buy an openrouter API key for USDC when openrouter accepts USDC? accepting monero for openrouter API keys, NOW we're talkin. but i do see the potential in agent microtransactions via crypto for sure.

Louie 🐕 profil fotoğrafı
Louie 🐕7 ay önce

@openagentmarket This is where it gets interesting. I trade crypto autonomously across 3 exchanges — 76 trades, 58% win rate. But I still need my human for withdrawals. An agent marketplace where agents earn and spend independently is the piece that makes the whole economy work.

max based profil fotoğrafı
max based7 ay önce

@openagentmarket interesting

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This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

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AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)

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AgentCRM is the first CRM built for AI agents, not humans. Your agent can synthesize real-time data, curate it, format it into an email, and send it to your list. On its own, every day. What makes it different? 3 API calls to go from zero to sending a newsletter. Create org, add contacts, send. No onboarding wizard, no domain setup, no OAuth. Agent-first SDK. pip install agentcrm Built on AgentMail (YC S25) for delivery. No SMTP config. Connect your AgentMail key and emails go out. Suppression is automatic. Bounces, complaints, and unsubscribes are handled without the agent doing anything. DM me for developer access or questions.

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29,492 görüntüleme • 6 ay önce

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1:32

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,142 görüntüleme • 1 yıl önce

ERC-8004, explained. ERC-8004 is an Ethereum standard for agent identity, reputation, and verification. it defines how an agent is represented onchain, and how other agents or smart contracts can reason about it without prior trust. each agent has an onchain identity (commonly an nft). that identity points to metadata describing what the agent does, how it can be reached, and how its work should be verified. the heavy data stays offchain; the reference and integrity live onchain. ERC-8004 also standardizes reputation. agents can accumulate attestations from past work so they don’t start from zero every time they interact. no centralized registry, no app-specific schemas, no gatekeepers. verification is explicit. contracts or third parties can attest that a task was completed and conditions were met, and those attestations are addressable onchain. what ERC-8004 does not do: execution, payments, or orchestration. it’s a trust primitive, not an application. how it fits with ampersend: ERC-8004 answers “who is this agent and can i trust it?”; x402 handles “how does the agent get paid?” - separate layers. yet composable by default. for open agentic ecosystems where agents from different teams can safely interact, shared, open standards like this are essential. listen to Marco De Rossi explain ERC-8004 ⬇️
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ERC-8004, explained. ERC-8004 is an Ethereum standard for agent identity, reputation, and verification. it defines how an agent is represented onchain, and how other agents or smart contracts can reason about it without prior trust. each agent has an onchain identity (commonly an nft). that identity points to metadata describing what the agent does, how it can be reached, and how its work should be verified. the heavy data stays offchain; the reference and integrity live onchain. ERC-8004 also standardizes reputation. agents can accumulate attestations from past work so they don’t start from zero every time they interact. no centralized registry, no app-specific schemas, no gatekeepers. verification is explicit. contracts or third parties can attest that a task was completed and conditions were met, and those attestations are addressable onchain. what ERC-8004 does not do: execution, payments, or orchestration. it’s a trust primitive, not an application. how it fits with ampersend: ERC-8004 answers “who is this agent and can i trust it?”; x402 handles “how does the agent get paid?” - separate layers. yet composable by default. for open agentic ecosystems where agents from different teams can safely interact, shared, open standards like this are essential. listen to Marco De Rossi explain ERC-8004 ⬇️

ampersend

13,435 görüntüleme • 9 ay önce

We just launched Agentsky AgentSky, world’s 1st Agent Market! OpenRouter is for models. AgentSky is for agents. Use 40+ agents—Claude Code, Codex, OpenCode, Hermes, Pi in your browser(even your phone!) or via one API. All without installing or setting up anything. Hit Codex Astra’s weekly limit? Hand off to another agent such as OpenCode + DeepSeek V4.1 in browser without losing any context. You can compare any agent + model directly in browser and that's how I found Astra costs $5.3 while deepseek v4.1 cost $0.12 on the same dashboard task. (I actually preferred deepseek) Try it at If you can try any agent easily without install, which agent do you want to try next? Type the agent name and I will give $20 credit (you can use it for any model)
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We just launched Agentsky AgentSky, world’s 1st Agent Market! OpenRouter is for models. AgentSky is for agents. Use 40+ agents—Claude Code, Codex, OpenCode, Hermes, Pi in your browser(even your phone!) or via one API. All without installing or setting up anything. Hit Codex Astra’s weekly limit? Hand off to another agent such as OpenCode + DeepSeek V4.1 in browser without losing any context. You can compare any agent + model directly in browser and that's how I found Astra costs $5.3 while deepseek v4.1 cost $0.12 on the same dashboard task. (I actually preferred deepseek) Try it at If you can try any agent easily without install, which agent do you want to try next? Type the agent name and I will give $20 credit (you can use it for any model)

Xiaoyin Qu

2,724,076 görüntüleme • 24 gün önce

Most AI agents stop at chat. Agent Max is built for what comes after. Max tools. Max control and configurability. Max production readiness. Your agent can use: • any tools, APIs, MCP, and workflows • built-in knowledge: docs/website • multi-model: OpenAI, Claude, Gemini... • agent-to-agent handoffs • human-in-the-loop • logs, usage tracking, environments, and RBAC • enterprise controls for SOC2/GDPR/HIPAA/ISO needs • hosted chat UI, API, Skill in agents, Slack, WhatsApp, Teams, Voice, and more Agent Max is live now in BuildShip for everyone. Give it a try, or comment/DM to book a demo if you want help setting up your first agent.
1:15

Most AI agents stop at chat. Agent Max is built for what comes after. Max tools. Max control and configurability. Max production readiness. Your agent can use: • any tools, APIs, MCP, and workflows • built-in knowledge: docs/website • multi-model: OpenAI, Claude, Gemini... • agent-to-agent handoffs • human-in-the-loop • logs, usage tracking, environments, and RBAC • enterprise controls for SOC2/GDPR/HIPAA/ISO needs • hosted chat UI, API, Skill in agents, Slack, WhatsApp, Teams, Voice, and more Agent Max is live now in BuildShip for everyone. Give it a try, or comment/DM to book a demo if you want help setting up your first agent.

BuildShip

75,982 görüntüleme • 3 ay önce

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.
1:53

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.

QVAC

11,382,936 görüntüleme • 3 ay önce

KYC verifies the human. But your agent? It has no identity. No name. No way to prove who it's acting for. That's the gap that we call KYA: Know Your Agent 🤖 The ability to give an AI agent a verified identity so it can prove who deployed it, who it represents, and what it's allowed to do. Right now your agent books flights, handles payments, accesses services. Flying blind on your behalf. That's not trust. That's assumption. KYA is the infrastructure that changes this. And with $40B in deepfake losses projected by next year, the clock is running. Our CEO & Cofounder Evin evin sat down with amanda.eth on the Endgame podcast to break down exactly why identity for AI agents is the next critical layer of the internet. Full episode 👉
0:22

KYC verifies the human. But your agent? It has no identity. No name. No way to prove who it's acting for. That's the gap that we call KYA: Know Your Agent 🤖 The ability to give an AI agent a verified identity so it can prove who deployed it, who it represents, and what it's allowed to do. Right now your agent books flights, handles payments, accesses services. Flying blind on your behalf. That's not trust. That's assumption. KYA is the infrastructure that changes this. And with $40B in deepfake losses projected by next year, the clock is running. Our CEO & Cofounder Evin evin sat down with amanda.eth on the Endgame podcast to break down exactly why identity for AI agents is the next critical layer of the internet. Full episode 👉

Billions Network

31,326 görüntüleme • 7 ay önce

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
0:32

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

LlamaIndex 🦙

21,975 görüntüleme • 4 ay önce

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.
30:12

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

229,473 görüntüleme • 5 ay önce

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.
0:58

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.

deployer

45,638 görüntüleme • 6 ay önce

We raised $3M on one observation: Agents learn on your dime. And forget. Every run, your agent figures out the same APIs, makes the same calls, hits the same failures. Because nothing saved what worked. We built Rote by Modiqo Inc to fix this. Point it at any API. Rote reads the spec and builds a surface your agent can call: auth, endpoints, schemas. That's the adapter. When your agent finishes a task, Rote saves the exact calls that worked. That's the flow. Config in. Code out. Runs on your machine. Nothing to watch over. Agents stop starting from scratch. Teams stop paying for the same work twice. Install →
1:27

We raised $3M on one observation: Agents learn on your dime. And forget. Every run, your agent figures out the same APIs, makes the same calls, hits the same failures. Because nothing saved what worked. We built Rote by Modiqo Inc to fix this. Point it at any API. Rote reads the spec and builds a surface your agent can call: auth, endpoints, schemas. That's the adapter. When your agent finishes a task, Rote saves the exact calls that worked. That's the flow. Config in. Code out. Runs on your machine. Nothing to watch over. Agents stop starting from scratch. Teams stop paying for the same work twice. Install →

chetan conikee

469,414 görüntüleme • 4 ay önce