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We built the geometric intelligence layer for manufacturing and AI. Blazing fast, critical manufacturing data from just a CAD file. Features: - Quick DFM & feature checks - Rapid thickness, clearance, and draft analysis - Deterministic, configurable rulesets for mfg method - CNC (3ax & 5ax), Casting, Additive, Molding...

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Connor Kapoor

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19,031 görüntüleme • 2 ay önce •via X (Twitter)

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We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.
1:23

We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.

Ayush S

730,614 görüntüleme • 2 ay önce

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,137 görüntüleme • 1 yıl önce

AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:36

AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

25,458 görüntüleme • 4 ay önce

Molecular design for AI agents: announcing the Tamarind MCP Server. Today, scientists can use the Tamarind Bio library of 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in any AI chat interface. We serve not just open-source models, but the internal protocols your team has onboarded to Tamarind. Any tool added to Tamarind is then available across the MCP server, Tamarind web app, and API, so it can be used in chat-based agents, multi-step pipelines, ELNs, and LIMS-connected workflows. Our goal is simple: make Tamarind the place where scientists can access the BioAI tools they need, wherever they want to work, while we handle the infrastructure. Many users have already incorporated our MCP into their internal AI agents, along with community efforts like Blatant-Why building apps on top of Tamarind. Try out our tooling for antibody design, small molecule virtual screening, developability/ADMET scoring and more!
0:41

Molecular design for AI agents: announcing the Tamarind MCP Server. Today, scientists can use the Tamarind Bio library of 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in any AI chat interface. We serve not just open-source models, but the internal protocols your team has onboarded to Tamarind. Any tool added to Tamarind is then available across the MCP server, Tamarind web app, and API, so it can be used in chat-based agents, multi-step pipelines, ELNs, and LIMS-connected workflows. Our goal is simple: make Tamarind the place where scientists can access the BioAI tools they need, wherever they want to work, while we handle the infrastructure. Many users have already incorporated our MCP into their internal AI agents, along with community efforts like Blatant-Why building apps on top of Tamarind. Try out our tooling for antibody design, small molecule virtual screening, developability/ADMET scoring and more!

Deniz Kavi

14,588 görüntüleme • 4 ay önce

It takes a lot of time for technology to mature and become magical. 🤩 AI is there now. When we envisaged AI Soundbox, soon it was clear that it won’t be just a payment device anymore. 🤖 It would rather be like a smartphone in the age of feature phones. QR was like a rotary phone, Soundbox a cellular, and this: a smartphone. 📱 A superhuman capability for a small and micro business that has never been available to these businesses. A very smart and capable business partner on the counter for 100s of millions of small businesses. At Paytm, we are very proud to lead innovations for small and micro businesses in India. Today we bring the perfect AI device for millions of businesses. Making technology and AI more inclusive. An AI system, with power and training of their payment and business data. It doesn’t anymore remain a payment device, rather becomes a business partner on the counter. Proud to launch and reinforcing our commitment to small and medium businesses to make Made-in-India AI 🇮🇳 🤖. Here is the launch demo of Paytm AI. Paytm AI Soundbox. You will love the use case which even we discovered in the launch. 🚀
2:22

It takes a lot of time for technology to mature and become magical. 🤩 AI is there now. When we envisaged AI Soundbox, soon it was clear that it won’t be just a payment device anymore. 🤖 It would rather be like a smartphone in the age of feature phones. QR was like a rotary phone, Soundbox a cellular, and this: a smartphone. 📱 A superhuman capability for a small and micro business that has never been available to these businesses. A very smart and capable business partner on the counter for 100s of millions of small businesses. At Paytm, we are very proud to lead innovations for small and micro businesses in India. Today we bring the perfect AI device for millions of businesses. Making technology and AI more inclusive. An AI system, with power and training of their payment and business data. It doesn’t anymore remain a payment device, rather becomes a business partner on the counter. Proud to launch and reinforcing our commitment to small and medium businesses to make Made-in-India AI 🇮🇳 🤖. Here is the launch demo of Paytm AI. Paytm AI Soundbox. You will love the use case which even we discovered in the launch. 🚀

Vijay Shekhar Sharma

74,029 görüntüleme • 10 ay önce

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.
0:54

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.

Brian Roemmele

121,948 görüntüleme • 5 ay önce

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.
1:12

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,934 görüntüleme • 9 ay önce

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,126 görüntüleme • 1 yıl önce

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 görüntüleme • 10 ay önce

Today we are launching the Hercules MCP. It is probably the craziest thing I have ever built - Hercules makes it easy to build apps and websites by chatting with AI - Hercules MCP makes it easy to do this at 100x the scale The Hercules MCP quite literally enables you to build 100 websites, purchase 100 domains, and publish all of them with just one prompt. It takes ~5 minutes. It's the best SEO-maxxing strategy for local businesses. Here is how some of our top customers have used it: - A plumber in the Netherlands created 4,000 unique websites for his business, one for every city in the Netherlands - An equipment wholesaler in Tennessee made ~100 unique websites, one for every product they sell A lot of these websites are already ranking #1 on Google To do this yourself: 1. Go to your favourite AI chat app (ChatGPT, Claude, Grok, etc) 2. Add the Hercules MCP (link in comment) 3. Ask Hercules to build 100 websites for whatever you'd like We are the only AI App Builder or coding agent that can build and publish websites at this scale. Enjoy!
0:28

Today we are launching the Hercules MCP. It is probably the craziest thing I have ever built - Hercules makes it easy to build apps and websites by chatting with AI - Hercules MCP makes it easy to do this at 100x the scale The Hercules MCP quite literally enables you to build 100 websites, purchase 100 domains, and publish all of them with just one prompt. It takes ~5 minutes. It's the best SEO-maxxing strategy for local businesses. Here is how some of our top customers have used it: - A plumber in the Netherlands created 4,000 unique websites for his business, one for every city in the Netherlands - An equipment wholesaler in Tennessee made ~100 unique websites, one for every product they sell A lot of these websites are already ranking #1 on Google To do this yourself: 1. Go to your favourite AI chat app (ChatGPT, Claude, Grok, etc) 2. Add the Hercules MCP (link in comment) 3. Ask Hercules to build 100 websites for whatever you'd like We are the only AI App Builder or coding agent that can build and publish websites at this scale. Enjoy!

Brendan Falk

21,688 görüntüleme • 2 ay önce

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.
1:10:04

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

Michael Grinich

37,894 görüntüleme • 9 ay önce

Agents Are Replacing SaaS 🟢 And We’re Living It At Assisterr, we don’t just build AI agents - we use them Our own infrastructure has already replaced most traditional SaaS tools with custom-built AI agents that handle everything from analytics and reporting to workflow automation Need deep product insights? Our Analytics Assistant tracks real-time data, generates reports, and delivers actionable insights—automatically 🚀 Need a new tool? We don’t waste time looking for SaaS alternatives - we just build a custom agent. No code, no hassle, just instant automation We’re committed to making this accessible to everyone—so anyone can create, customize, and deploy AI agents tailored to their needs The future isn’t SaaS. The future is agents. And with Assisterr, it’s already here
3:43

Agents Are Replacing SaaS 🟢 And We’re Living It At Assisterr, we don’t just build AI agents - we use them Our own infrastructure has already replaced most traditional SaaS tools with custom-built AI agents that handle everything from analytics and reporting to workflow automation Need deep product insights? Our Analytics Assistant tracks real-time data, generates reports, and delivers actionable insights—automatically 🚀 Need a new tool? We don’t waste time looking for SaaS alternatives - we just build a custom agent. No code, no hassle, just instant automation We’re committed to making this accessible to everyone—so anyone can create, customize, and deploy AI agents tailored to their needs The future isn’t SaaS. The future is agents. And with Assisterr, it’s already here

assisterr

28,822 görüntüleme • 1 yıl önce

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.
1:44

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,736 görüntüleme • 4 ay önce

Spectre AI’s founder steps in front of the camera for Y Combinator . As part of our Y Combinator Summer Batch 2026 application, our founder Sunny Enzo recorded a founder video walking through the Spectre AI app and the direction of the company. Sunny is doxxed, but this is the first time he is appearing on video to speak directly about Spectre AI, the product, and the market intelligence layer we are building. Due to YC’s time format, the video only shows around 5 to 6 features from an ecosystem with 100+ features already built or in progress. A full Spectre AI walkthrough would need far more time. Still, it gives a clear look at where we are heading: AI market intelligence, on-chain data, social intelligence, real-time context, and a cleaner way to understand financial markets through one platform. Another step in taking Spectre AI from product delivery into serious company building.
3:03

Spectre AI’s founder steps in front of the camera for Y Combinator . As part of our Y Combinator Summer Batch 2026 application, our founder Sunny Enzo recorded a founder video walking through the Spectre AI app and the direction of the company. Sunny is doxxed, but this is the first time he is appearing on video to speak directly about Spectre AI, the product, and the market intelligence layer we are building. Due to YC’s time format, the video only shows around 5 to 6 features from an ecosystem with 100+ features already built or in progress. A full Spectre AI walkthrough would need far more time. Still, it gives a clear look at where we are heading: AI market intelligence, on-chain data, social intelligence, real-time context, and a cleaner way to understand financial markets through one platform. Another step in taking Spectre AI from product delivery into serious company building.

SPECTRE AI

17,501 görüntüleme • 3 ay önce

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.
1:16

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

FOMO

26,338 görüntüleme • 1 yıl önce

We’re excited to introduce Taste Labs. Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste. And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by CRV and Amplify Partners AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design. There are two sides to cracking this, the foundation model layer and the agent layer: - We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments. - We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs. We want a future where AI feels right. If you’re passionate about this mission, join us!
1:23

We’re excited to introduce Taste Labs. Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste. And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by CRV and Amplify Partners AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design. There are two sides to cracking this, the foundation model layer and the agent layer: - We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments. - We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs. We want a future where AI feels right. If you’re passionate about this mission, join us!

Thais Castello Branco

2,695,782 görüntüleme • 2 ay önce

I've never recorded a video announcement before. But after 15 years of working in AI and 1.5 years heads down building Cambrian Network 🪴, I wanted to share this news myself. I'm proud to announce the public launch of the Cambrian API, which you can try free on In 2024, while building an AI copilot for trading, I became convinced that the future of finance would be increasingly operated by agents. I also realized that existing data solutions were far from meeting the needs of a burgeoning industry called agentic finance. So I assembled a team of data, infrastructure, and finance veterans, earned support from a16z crypto Polychain Franklin Templeton Digital Assets and many other amazing investors, and embarked on a mission to build the world's financial intelligence layer. Today, the Cambrian API is out of beta. It's the best source of onchain and offchain signal for AI agents and institutions to make winning financial decisions, such as: - Smarter trading strategies - Automated yield-maximization - Social sentiment-based trades - Risk analyses & much more Start using it for free and scale as your needs grow.
1:46

I've never recorded a video announcement before. But after 15 years of working in AI and 1.5 years heads down building Cambrian Network 🪴, I wanted to share this news myself. I'm proud to announce the public launch of the Cambrian API, which you can try free on In 2024, while building an AI copilot for trading, I became convinced that the future of finance would be increasingly operated by agents. I also realized that existing data solutions were far from meeting the needs of a burgeoning industry called agentic finance. So I assembled a team of data, infrastructure, and finance veterans, earned support from a16z crypto Polychain Franklin Templeton Digital Assets and many other amazing investors, and embarked on a mission to build the world's financial intelligence layer. Today, the Cambrian API is out of beta. It's the best source of onchain and offchain signal for AI agents and institutions to make winning financial decisions, such as: - Smarter trading strategies - Automated yield-maximization - Social sentiment-based trades - Risk analyses & much more Start using it for free and scale as your needs grow.

Sam Green

35,450 görüntüleme • 17 gün önce

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!
0:34

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!

Abhinav Asthana

18,140 görüntüleme • 3 ay önce

OpenClaw Memelord skill is officially live. You can now use Memelord's trending memes and video templates to: - Generate AI memes and videos (that are both funny and hyper-trending) - Edit captions for *both* memes and videos just by replying What can you do with this? The use cases for the OpenClaw Memelord skill are infinite: - Set a cron job cook up on-brand memes for your marketing team - Toss it in a n8n workflow for TikTok slideshows - Put your claw in a group chat with your community - Sync it with Polymarket's API to make new memes for new markets - Plug into NCAA data to make memes for March Madness - Link up with Quartr API to make new memes for quarterly reports - Connect to GitHub or Linear to market your new features A lot of people asked me "Why'd you raise $3M for a meme editor?" This is why. The future of memeing is agentic. We achieved Memetic General Intelligence internally earlier this year making hilarious AI memes. Now we can share it with the world. It's time to take your memes more seriously.
1:26

OpenClaw Memelord skill is officially live. You can now use Memelord's trending memes and video templates to: - Generate AI memes and videos (that are both funny and hyper-trending) - Edit captions for *both* memes and videos just by replying What can you do with this? The use cases for the OpenClaw Memelord skill are infinite: - Set a cron job cook up on-brand memes for your marketing team - Toss it in a n8n workflow for TikTok slideshows - Put your claw in a group chat with your community - Sync it with Polymarket's API to make new memes for new markets - Plug into NCAA data to make memes for March Madness - Link up with Quartr API to make new memes for quarterly reports - Connect to GitHub or Linear to market your new features A lot of people asked me "Why'd you raise $3M for a meme editor?" This is why. The future of memeing is agentic. We achieved Memetic General Intelligence internally earlier this year making hilarious AI memes. Now we can share it with the world. It's time to take your memes more seriously.

Jason Levin

30,693 görüntüleme • 5 ay önce

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.
0:22

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,556 görüntüleme • 6 ay önce