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Inspired by Hugging Face's official MCP server, I built my own to expose my semantic search API for the HF ecosystem! Features AI-powered search, parameter analysis via safetensors, and tools to find similar models/datasets. Try: "Find non maths reasoning datasets from 2025"!

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Daniel van Strien

5,671 subscribers

15,147 görüntüleme • 1 yıl önce •via X (Twitter)

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RTTS2 yıl önce

API testing of interfaces is critical to determine if they meet requirements for functionality, reliability, performance, and security. Check out RTTS - the automated testing experts since 1996. #API #testautomation #integrationtest

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Vanraj Narula | AI Engineer1 yıl önce

@huggingface That's great! Your semantic search API sounds like a valuable tool for the HF ecosystem.

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George1 yıl önce

@huggingface That's a genius query! 🤯🔎

Benzer Videolar

Welcome to the party Hugging Face 🤗 Access the Hugging Face Hub directly from Gemini CLI with this new Gemini CLI extension. 🔍 - Search models, datasets and papers 📈 - Find trending models or datasets 🤗 - Learn how to fine-tune models and more! Big thanks to Shaun Smith and Vaibhav (VB) Srivastav for making this happen!
1:01

Welcome to the party Hugging Face 🤗 Access the Hugging Face Hub directly from Gemini CLI with this new Gemini CLI extension. 🔍 - Search models, datasets and papers 📈 - Find trending models or datasets 🤗 - Learn how to fine-tune models and more! Big thanks to Shaun Smith and Vaibhav (VB) Srivastav for making this happen!

Jack Wotherspoon

14,491 görüntüleme • 9 ay önce

Introducing the Daily Papers SKILL.md Enables agents to > read paper content as markdown > search papers > find linked Hugging Face models and datasets > fetch the papers API > and more! Link below ⬇️
1:06

Introducing the Daily Papers SKILL.md Enables agents to > read paper content as markdown > search papers > find linked Hugging Face models and datasets > fetch the papers API > and more! Link below ⬇️

DailyPapers

34,088 görüntüleme • 5 ay önce

Harvesting robot for scale. Built by Four Growers, the GR-200 runs on AI-driven vision with motion planning to quickly find and pick ripe tomatoes. With custom AI models and datasets, it can adapt to each grower’s ripeness standards and crop types.
1:04

Harvesting robot for scale. Built by Four Growers, the GR-200 runs on AI-driven vision with motion planning to quickly find and pick ripe tomatoes. With custom AI models and datasets, it can adapt to each grower’s ripeness standards and crop types.

Rohan Paul

28,981 görüntüleme • 7 ay önce

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:
6:15

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

Santiago

215,882 görüntüleme • 1 yıl önce

Building for the Apple ecosystem? You will soon be able to securely call cloud-hosted Gemini models directly from Apple’s Foundation Models framework 🔥 This integration, powered by Firebase AI Logic, will allow you to use both Apple’s Foundation Models and Gemini models via a shared API surface.
1:20

Building for the Apple ecosystem? You will soon be able to securely call cloud-hosted Gemini models directly from Apple’s Foundation Models framework 🔥 This integration, powered by Firebase AI Logic, will allow you to use both Apple’s Foundation Models and Gemini models via a shared API surface.

Firebase

14,780 görüntüleme • 2 ay önce

✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)
1:38

✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)

@levelsio

84,462 görüntüleme • 7 ay önce

I started working on a MCP server for Laravel Herd and damn, this is like a glimpse into the future 🔥 For example, in Cursor I can just say: "I want to search my models via Typesense" This checks Herd if Typesense is installed, installs it if needed and starts the service
0:23

I started working on a MCP server for Laravel Herd and damn, this is like a glimpse into the future 🔥 For example, in Cursor I can just say: "I want to search my models via Typesense" This checks Herd if Typesense is installed, installs it if needed and starts the service

Marcel Pociot 🧪

22,218 görüntüleme • 1 yıl önce

i built an ai agent that talks to my girlfriend 24/7. fully open source. sometimes i am on a plane or just working, so i built an ai that uses Nozomio Labs’s search api to index and search through 5,000 pickup lines and methods on how to talk to women. it also has its own telegram that uses my account to send, receive, record, and schedule messages to my girlfriend, with built-in memory that analyzes her mood and tells me when something is wrong. here is a demo of me asking an agent to search for some pickup lines and create a 7-day meal plan for her:
0:38

i built an ai agent that talks to my girlfriend 24/7. fully open source. sometimes i am on a plane or just working, so i built an ai that uses Nozomio Labs’s search api to index and search through 5,000 pickup lines and methods on how to talk to women. it also has its own telegram that uses my account to send, receive, record, and schedule messages to my girlfriend, with built-in memory that analyzes her mood and tells me when something is wrong. here is a demo of me asking an agent to search for some pickup lines and create a 7-day meal plan for her:

Arlan

397,426 görüntüleme • 7 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

We’re thrilled to announce the official launch of AIOZ AI V1, a Web3 collaborative AI platform powered by AIOZ DePINs! Sign up and get started here: With AIOZ AI, we’re setting a new Web3 standard for AI development and innovation. Here’s what makes it unique: → Web3 AI marketplace for models and datasets → AI compute powered by AIOZ DePINs → Monetization opportunities for contributors in a growing AI ecosystem Whether you're building, contributing, or reimagining AI solutions, AIOZ AI V1 is your platform. Explore the platform and join us in the DePIN Ecosystem for AI. Read the full introduction on our blog here:
0:30

We’re thrilled to announce the official launch of AIOZ AI V1, a Web3 collaborative AI platform powered by AIOZ DePINs! Sign up and get started here: With AIOZ AI, we’re setting a new Web3 standard for AI development and innovation. Here’s what makes it unique: → Web3 AI marketplace for models and datasets → AI compute powered by AIOZ DePINs → Monetization opportunities for contributors in a growing AI ecosystem Whether you're building, contributing, or reimagining AI solutions, AIOZ AI V1 is your platform. Explore the platform and join us in the DePIN Ecosystem for AI. Read the full introduction on our blog here:

AIOZ Network

600,449 görüntüleme • 1 yıl önce

🚀 Exciting Update Alert! 🚀 We’re thrilled to announce a major update coming soon for Reactor, our AI model! Here’s what’s new: 🔍 Enhanced Search Capabilities: - News Search: Stay up-to-date with the latest happenings. - X & YouTube Search: Find trending content and videos instantly. - Reddit Search: Dive into discussions and communities with ease. - Wolfram Alpha Integration: Solve complex math problems and get accurate answers. 📚 References & Research: - Access reliable references and data for your queries. 🖼️ Visual Search & Creation: - Image & Video Search: Find visuals faster than ever. - Image Generation: Create stunning images with AI-powered tools. And that’s not all! We’ve also improved performance and added behind-the-scenes enhancements to make Reactor even more intuitive and responsive. Try out the new features today and let us know what you think! Your feedback helps us keep improving. 💡
1:03

🚀 Exciting Update Alert! 🚀 We’re thrilled to announce a major update coming soon for Reactor, our AI model! Here’s what’s new: 🔍 Enhanced Search Capabilities: - News Search: Stay up-to-date with the latest happenings. - X & YouTube Search: Find trending content and videos instantly. - Reddit Search: Dive into discussions and communities with ease. - Wolfram Alpha Integration: Solve complex math problems and get accurate answers. 📚 References & Research: - Access reliable references and data for your queries. 🖼️ Visual Search & Creation: - Image & Video Search: Find visuals faster than ever. - Image Generation: Create stunning images with AI-powered tools. And that’s not all! We’ve also improved performance and added behind-the-scenes enhancements to make Reactor even more intuitive and responsive. Try out the new features today and let us know what you think! Your feedback helps us keep improving. 💡

ARC

87,518 görüntüleme • 1 yıl önce

The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.
0:26

The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.

Rajan Patel

114,317 görüntüleme • 4 ay önce

Putting my tokens where my mouth is. I built pi-share-hf. Share your pi coding agent sessions as Hugging Face datasets. It tries to prevent you from uploading sessions containing PII/sensitive data with 3 tiers of defenses. Best used on OSS coding sessions, as those are less likely to contain sensitive info. Uses pi agents for PII detection, which can cost you a lot of tokens. Read the README with your human eyes so you don't accidentally pwn yourself or get a huge bill. Haven't figured out how to filter for such datasets on HF yet. clem 🤗, any pointers on how to best label them so people can find them?
12:09

Putting my tokens where my mouth is. I built pi-share-hf. Share your pi coding agent sessions as Hugging Face datasets. It tries to prevent you from uploading sessions containing PII/sensitive data with 3 tiers of defenses. Best used on OSS coding sessions, as those are less likely to contain sensitive info. Uses pi agents for PII detection, which can cost you a lot of tokens. Read the README with your human eyes so you don't accidentally pwn yourself or get a huge bill. Haven't figured out how to filter for such datasets on HF yet. clem 🤗, any pointers on how to best label them so people can find them?

Mario Zechner

51,425 görüntüleme • 4 ay önce

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
1:33

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,278 görüntüleme • 1 yıl önce

How do you find the right people — for hiring, marketing, sales, or research — in seconds, not weeks? I tried Lessie AI , the world’s first People Search AI Agent. In this example, I instructed LessieAI to conduct in-depth research on TTC researchers: "Find me experts on artificial intelligence who focus on reasoning models and specifically investigate test-time computation."
2:01

How do you find the right people — for hiring, marketing, sales, or research — in seconds, not weeks? I tried Lessie AI , the world’s first People Search AI Agent. In this example, I instructed LessieAI to conduct in-depth research on TTC researchers: "Find me experts on artificial intelligence who focus on reasoning models and specifically investigate test-time computation."

Chubby♨️

17,929 görüntüleme • 11 ay önce

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!
0:26

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Avi Chawla

11,927 görüntüleme • 5 ay önce

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.
0:40

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 görüntüleme • 1 yıl önce

I built an AI powered writing app with Cursor Agent without writing a single line of code in a little over an hour. Features: - AI powered autocomplete - AI powered image search with autosuggestions based on what i'm writing - Ai powered research based on what i'm writing - Block based writing (which is useful for my workflow as a video content creator) !!! And I ran it locally this time. Normally I use replit, so if you got stuck on connecting cursor and replit, this video is for you. Template pinned on @senior_swc (starting point you can download) Tools used - Cursor (Claude for ai model) - Firebase - Deepseek DeepSeek - Exa ( Exa ) - Search API These videos serve as R&D for the app we're building at @yapthread. We're building the cursor for content creators. Sections 00:00 Introduction 03:24 Introduction to the Template 03:54 Setting Up Locally 04:04 Downloading and Running Locally 05:34 Firebase Setup 06:10 first prompt in cursor + continuing firebase 08:11 Full Plan for the app I'm building 10:44 Outlining the app hotkeys 11:55 Testing First Feature 16:54 Adding Autocomplete Feature Deepseek 22:23 Adding Images to Blocks (Search API) 22:42 Implementing and Testing Image Search (Search API) 25:32 Adjusting Image Sizes and Social Media Discussion 28:11 Adding Relevant Links (Exa API) 30:34 Testing and Debugging Link Highlights 32:27 Finalizing Image and Link Integration 40:37 Conclusion and Future Plans
40:46

I built an AI powered writing app with Cursor Agent without writing a single line of code in a little over an hour. Features: - AI powered autocomplete - AI powered image search with autosuggestions based on what i'm writing - Ai powered research based on what i'm writing - Block based writing (which is useful for my workflow as a video content creator) !!! And I ran it locally this time. Normally I use replit, so if you got stuck on connecting cursor and replit, this video is for you. Template pinned on @senior_swc (starting point you can download) Tools used - Cursor (Claude for ai model) - Firebase - Deepseek DeepSeek - Exa ( Exa ) - Search API These videos serve as R&D for the app we're building at @yapthread. We're building the cursor for content creators. Sections 00:00 Introduction 03:24 Introduction to the Template 03:54 Setting Up Locally 04:04 Downloading and Running Locally 05:34 Firebase Setup 06:10 first prompt in cursor + continuing firebase 08:11 Full Plan for the app I'm building 10:44 Outlining the app hotkeys 11:55 Testing First Feature 16:54 Adding Autocomplete Feature Deepseek 22:23 Adding Images to Blocks (Search API) 22:42 Implementing and Testing Image Search (Search API) 25:32 Adjusting Image Sizes and Social Media Discussion 28:11 Adding Relevant Links (Exa API) 30:34 Testing and Debugging Link Highlights 32:27 Finalizing Image and Link Integration 40:37 Conclusion and Future Plans

Riley Brown

83,096 görüntüleme • 1 yıl önce