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Someone built a tool where you paste an OpenAPI spec and get a fully stateful sandbox that actually behaves like production No local setup No real API costs It's called FetchSandbox The workflow does this : → explores Stripe APIs → runs workflows → triggers webhooks → verifies integrations...

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35,823 просмотров • 2 месяцев назад •via X (Twitter)

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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 просмотров • 1 год назад

Today we’re launching CredAgent, the most transformational product ever built for credentialing. An autonomous agent that can reason, verify, and execute complex credentialing workflows end-to-end, with human oversight. Credentialing today still relies on outsourced BPOs and manual workflows. This isn’t another workflow tool or co-pilot. It’s a fundamental shift in how credentialing work gets done. CredAgent delivers significant cost savings, better control, and higher quality and early pilots are already showing up to 10× efficiency gains. In a world of autonomous agents, APIs are foundational. We’ve been API-first since day one, and remain the only truly API-first credentialing company , uniquely positioned to power real healthcare agents. If you’re a health plan, virtual care network, or health system ready to modernize credentialing, I’d love to show you CredAgent.
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Today we’re launching CredAgent, the most transformational product ever built for credentialing. An autonomous agent that can reason, verify, and execute complex credentialing workflows end-to-end, with human oversight. Credentialing today still relies on outsourced BPOs and manual workflows. This isn’t another workflow tool or co-pilot. It’s a fundamental shift in how credentialing work gets done. CredAgent delivers significant cost savings, better control, and higher quality and early pilots are already showing up to 10× efficiency gains. In a world of autonomous agents, APIs are foundational. We’ve been API-first since day one, and remain the only truly API-first credentialing company , uniquely positioned to power real healthcare agents. If you’re a health plan, virtual care network, or health system ready to modernize credentialing, I’d love to show you CredAgent.

Nick Macario

114,621 просмотров • 5 месяцев назад

New course added! We’re launching “Knowledge Graphs for AI Agent API Discovery,” built in collaboration with SAP and taught by Pavithra G K and Lars Heling. When agents call APIs in the wrong sequence, entire workflows collapse. The challenge? Most agents lack the contextual understanding of which endpoints depend on others and why order matters in real business processes. This course teaches you to: ✅Construct a knowledge graph from API specifications ✅ Extend it with business-process data so dependencies and order are explicit ✅ Use semantic retrieval and process edges to find required APIs and their sequence, then build an agent to execute them Enroll now!
2:55

New course added! We’re launching “Knowledge Graphs for AI Agent API Discovery,” built in collaboration with SAP and taught by Pavithra G K and Lars Heling. When agents call APIs in the wrong sequence, entire workflows collapse. The challenge? Most agents lack the contextual understanding of which endpoints depend on others and why order matters in real business processes. This course teaches you to: ✅Construct a knowledge graph from API specifications ✅ Extend it with business-process data so dependencies and order are explicit ✅ Use semantic retrieval and process edges to find required APIs and their sequence, then build an agent to execute them Enroll now!

DeepLearning.AI

41,683 просмотров • 11 месяцев назад

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.
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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

75,886 просмотров • 1 месяц назад

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 просмотров • 1 год назад

I am Open-Sourcing PyMolAI! Meet PyMolAI, an AI agent that can talk to your protein structures. Built on top of PyMOL, PyMolAI lets you interact with your structures in plain language. Whether you're: - Analyzing protein structures - Aligning complexes - Creating publication-ready figures - Or running design workflows PyMolAI interprets your request, executes the necessary PyMOL commands, and manages the workflow for you. It integrates with OpenBio APIs, giving you access to tools like Boltz, ProteinMPNN, and BoltzGen — directly from your PyMOL session. It has local chat history with session syncing, so you can pick up exactly where you left off.
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I am Open-Sourcing PyMolAI! Meet PyMolAI, an AI agent that can talk to your protein structures. Built on top of PyMOL, PyMolAI lets you interact with your structures in plain language. Whether you're: - Analyzing protein structures - Aligning complexes - Creating publication-ready figures - Or running design workflows PyMolAI interprets your request, executes the necessary PyMOL commands, and manages the workflow for you. It integrates with OpenBio APIs, giving you access to tools like Boltz, ProteinMPNN, and BoltzGen — directly from your PyMOL session. It has local chat history with session syncing, so you can pick up exactly where you left off.

Ravi Sharma

89,882 просмотров • 5 месяцев назад

Over the past few months, what started as NoelClaw evolved into something much bigger- a platform where AI agents can remember, research, build, automate, trade, and interact with real-world systems. Along the way, we listened to the community. One piece of feedback came up over and over again: the original token distribution wasn't the best foundation for long-term growth. Instead of ignoring it, we decided to rebuild it properly. That's why we rebranded to Finch Agentic and relaunched the token with a healthier foundation. Our mission hasn't changed. We're still building a runtime layer for agentic AI. Today, Finch lets you: >> Build websites and apps with AI >> Deploy persistent AI agents >> Use an AI-powered terminal >> Install reusable skills >> Connect real-world APIs >> Swap assets >> Run deep research >> Create multi-agent workflows And we're just getting started. Next up: - Robinhood MCP integrations - AI financial agents - Staking (USDG & stock pairing rewards) - More skills, APIs, and autonomous workflows Models will keep changing. AI will keep evolving. But every autonomous agent still needs somewhere to remember, work, and operate! That's what we're building with Finch. Welcome to the next chapter. CA : 0xce1981b0431fb495912cab057d2877a290199824 Web : App : Docs : NPM : Github :
0:26

Over the past few months, what started as NoelClaw evolved into something much bigger- a platform where AI agents can remember, research, build, automate, trade, and interact with real-world systems. Along the way, we listened to the community. One piece of feedback came up over and over again: the original token distribution wasn't the best foundation for long-term growth. Instead of ignoring it, we decided to rebuild it properly. That's why we rebranded to Finch Agentic and relaunched the token with a healthier foundation. Our mission hasn't changed. We're still building a runtime layer for agentic AI. Today, Finch lets you: >> Build websites and apps with AI >> Deploy persistent AI agents >> Use an AI-powered terminal >> Install reusable skills >> Connect real-world APIs >> Swap assets >> Run deep research >> Create multi-agent workflows And we're just getting started. Next up: - Robinhood MCP integrations - AI financial agents - Staking (USDG & stock pairing rewards) - More skills, APIs, and autonomous workflows Models will keep changing. AI will keep evolving. But every autonomous agent still needs somewhere to remember, work, and operate! That's what we're building with Finch. Welcome to the next chapter. CA : 0xce1981b0431fb495912cab057d2877a290199824 Web : App : Docs : NPM : Github :

Finch Agentic

11,550 просмотров • 13 дней назад

Claude Code + Shopify AI is f*cking cracked 🤯 Shopify just dropped an official AI Toolkit that connects Claude Code directly to your store. One prompt → Claude reads your products, rewrites your descriptions for AI shopping, and pushes the updates live. All from the terminal. All inside Claude Code. Perfect for DTC brands on Shopify who are still manually editing product pages, writing descriptions in Google Docs, and copy-pasting into the Shopify admin one product at a time. Claude Code + the Shopify AI Toolkit fixes the entire workflow: → Install the official Shopify plugin in Claude Code → Authenticate to your store → Claude reads your entire product catalog → Rewrites every description to be optimized for AI shopping → Pushes the updates directly to your store automatically → Validates every API call against Shopify's official docs before executing No Shopify admin tab-switching. No copy-pasting from a Google Doc. No hiring a copywriter to rewrite 50 product pages. What you get: → Claude Code connected directly to your live Shopify store → Product descriptions optimized for how ChatGPT, Gemini, and Perplexity recommend products → Bulk updates across your entire catalog from a single prompt → Full access to Shopify's GraphQL API — products, themes, inventory, orders, everything Claude can read and write → An official plugin built by Shopify that auto-updates as new features ship I put together a full playbook with the plugin install, the store authentication walkthrough, 5 DTC workflows to run on day one, and the exact prompts I used. Want it for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)
0:20

Claude Code + Shopify AI is f*cking cracked 🤯 Shopify just dropped an official AI Toolkit that connects Claude Code directly to your store. One prompt → Claude reads your products, rewrites your descriptions for AI shopping, and pushes the updates live. All from the terminal. All inside Claude Code. Perfect for DTC brands on Shopify who are still manually editing product pages, writing descriptions in Google Docs, and copy-pasting into the Shopify admin one product at a time. Claude Code + the Shopify AI Toolkit fixes the entire workflow: → Install the official Shopify plugin in Claude Code → Authenticate to your store → Claude reads your entire product catalog → Rewrites every description to be optimized for AI shopping → Pushes the updates directly to your store automatically → Validates every API call against Shopify's official docs before executing No Shopify admin tab-switching. No copy-pasting from a Google Doc. No hiring a copywriter to rewrite 50 product pages. What you get: → Claude Code connected directly to your live Shopify store → Product descriptions optimized for how ChatGPT, Gemini, and Perplexity recommend products → Bulk updates across your entire catalog from a single prompt → Full access to Shopify's GraphQL API — products, themes, inventory, orders, everything Claude can read and write → An official plugin built by Shopify that auto-updates as new features ship I put together a full playbook with the plugin install, the store authentication walkthrough, 5 DTC workflows to run on day one, and the exact prompts I used. Want it for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

113,609 просмотров • 4 месяцев назад

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:
2:50

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

Andrew Ng

125,385 просмотров • 6 месяцев назад

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X
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X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X

Mario Nawfal

59,976 просмотров • 1 месяц назад

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
1:21

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Guohao Li 🐫

223,208 просмотров • 1 год назад

There's a free 120 billion parameter AI you can run on your own computer. Zero internet. Zero API costs. Zero monthly fees. It's called Nvidia Nemotron 3 Super. And it just changed what solopreneurs can do with AI agents. Here's the setup: → Download Ollama. Copy one command into your terminal. → Connect it to OpenClaw in under 10 minutes. → Your AI agent now runs 24/7 on WhatsApp, Discord, or Telegram. → It answers customers, runs tasks, and manages workflows while you sleep. → 256,000 token context window. It reads entire documents without forgetting. No subscriptions. No cloud. No one else touching your data. A full AI employee stack for $0. Save this. Set it up this weekend.
10:33

There's a free 120 billion parameter AI you can run on your own computer. Zero internet. Zero API costs. Zero monthly fees. It's called Nvidia Nemotron 3 Super. And it just changed what solopreneurs can do with AI agents. Here's the setup: → Download Ollama. Copy one command into your terminal. → Connect it to OpenClaw in under 10 minutes. → Your AI agent now runs 24/7 on WhatsApp, Discord, or Telegram. → It answers customers, runs tasks, and manages workflows while you sleep. → 256,000 token context window. It reads entire documents without forgetting. No subscriptions. No cloud. No one else touching your data. A full AI employee stack for $0. Save this. Set it up this weekend.

Julian Goldie SEO

29,325 просмотров • 5 месяцев назад

So before I show you what HowOne actually does, check out this HR workflow in n8n. It's a real one. Tons of nodes. Tons of logic. Definitely not quick to set up. Now here's where it gets crazy: HowOne rebuilt the whole thing, front end, backend, agentic workflows, from just one description. The app screens resumes, figures out who's a good fit, ranks candidates, and even creates interview questions based on the role. And it keeps going. Every workflow can be tweaked or updated just by talking to it. No prompt engineering. No flowcharts. No manual setup. You're not just building an app. You're creating an AI system that actually evolves with your users. I've tried a bunch of these "AI builder" tools, but this one honestly feels different. Like it's the next step. Try it here: I've got an invite link that works for 500 uses:
1:23

So before I show you what HowOne actually does, check out this HR workflow in n8n. It's a real one. Tons of nodes. Tons of logic. Definitely not quick to set up. Now here's where it gets crazy: HowOne rebuilt the whole thing, front end, backend, agentic workflows, from just one description. The app screens resumes, figures out who's a good fit, ranks candidates, and even creates interview questions based on the role. And it keeps going. Every workflow can be tweaked or updated just by talking to it. No prompt engineering. No flowcharts. No manual setup. You're not just building an app. You're creating an AI system that actually evolves with your users. I've tried a bunch of these "AI builder" tools, but this one honestly feels different. Like it's the next step. Try it here: I've got an invite link that works for 500 uses:

Kelsey

131,240 просмотров • 8 месяцев назад

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.
1:10

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.

Akshay 🚀

62,659 просмотров • 7 месяцев назад

AI is getting incredibly good. The problem is it still gets stuck whenever it needs to interact with the real world. Need to host a website? API keys. Need to send emails? Another service. Need to use external tools? More accounts and more setup. That's exactly why I wanted to try Zero. Zero gives Claude and ChatGPT access to **14,000+ real-world services**, so instead of stopping to ask for integrations, they can actually complete real tasks. I tested it by asking Cursor to build an AI healthcare startup. Within a single workflow it: → built and hosted a complete website → generated the assets it needed → emailed me the live URL → showed the total cost of the entire workflow No API keys. No juggling different services. No complicated setup. Just a prompt → a finished result. If you're building with Claude or ChatGPT, it's definitely worth trying. The first 10,000 users can claim $5 in free credits to get started: Try out now: #Ad
1:14

AI is getting incredibly good. The problem is it still gets stuck whenever it needs to interact with the real world. Need to host a website? API keys. Need to send emails? Another service. Need to use external tools? More accounts and more setup. That's exactly why I wanted to try Zero. Zero gives Claude and ChatGPT access to **14,000+ real-world services**, so instead of stopping to ask for integrations, they can actually complete real tasks. I tested it by asking Cursor to build an AI healthcare startup. Within a single workflow it: → built and hosted a complete website → generated the assets it needed → emailed me the live URL → showed the total cost of the entire workflow No API keys. No juggling different services. No complicated setup. Just a prompt → a finished result. If you're building with Claude or ChatGPT, it's definitely worth trying. The first 10,000 users can claim $5 in free credits to get started: Try out now: #Ad

Abdullah AI

12,775 просмотров • 1 месяц назад

Just built an end-to-end AI agent workflow stack in 3 days that automates everything from recruiting to reminders to reporting for electrician franchises. What used to take 6+ SaaS tools and a full ops team? Now handled by 12 specialized agents: •Hiring •Screening •Follow-ups •Reminders •AI call reps •Reviews •Marketing •Finance tracking …and more. Trained each one to plug directly into ServiceTitan, Twilio, Slack, email, and CRM. Next: rolling it out to 50+ franchises, replacing entire workflows with agents that work 24/7, never forget, and never need a break. The SaaS model is being rewritten — one operator at a time.
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Just built an end-to-end AI agent workflow stack in 3 days that automates everything from recruiting to reminders to reporting for electrician franchises. What used to take 6+ SaaS tools and a full ops team? Now handled by 12 specialized agents: •Hiring •Screening •Follow-ups •Reminders •AI call reps •Reviews •Marketing •Finance tracking …and more. Trained each one to plug directly into ServiceTitan, Twilio, Slack, email, and CRM. Next: rolling it out to 50+ franchises, replacing entire workflows with agents that work 24/7, never forget, and never need a break. The SaaS model is being rewritten — one operator at a time.

Bennett Spooner

10,319 просмотров • 1 год назад

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
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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

221,133 просмотров • 5 месяцев назад

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools
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This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

H

29,953 просмотров • 1 месяц назад

The MCP finally has proper authentication! It now supports OAuth 2.1 out of the box. That means proper authentication for AI agents and MCP tools is finally standardized. No more hacked-together API keys or custom token logic. With OAuth 2.1 + MCP, you can now: - Dynamically register clients - Issue & verify scoped JWT tokens - Track which agent acted for which user - Plug into existing IdPs (Auth0, Clerk, Firebase, etc.) I’ve been testing it with Scalekit, a lightweight OAuth layer built for MCP servers. It adds full discovery, token flow, and audit logs with minimal config. This is the missing piece for secure, multi-agent systems. Auth that actually works.
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The MCP finally has proper authentication! It now supports OAuth 2.1 out of the box. That means proper authentication for AI agents and MCP tools is finally standardized. No more hacked-together API keys or custom token logic. With OAuth 2.1 + MCP, you can now: - Dynamically register clients - Issue & verify scoped JWT tokens - Track which agent acted for which user - Plug into existing IdPs (Auth0, Clerk, Firebase, etc.) I’ve been testing it with Scalekit, a lightweight OAuth layer built for MCP servers. It adds full discovery, token flow, and audit logs with minimal config. This is the missing piece for secure, multi-agent systems. Auth that actually works.

Arindam Majumder 𝕏

11,511 просмотров • 10 месяцев назад