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Topview MCP is here 🚀 Your full-stack marketing MCP now comes with built-in access to data signals from Amazon, YouTube, TikTok Shop, and Shopee. No separate data integrations. No scattered research tools. Analyze market insights, find content opportunities, plan creative angles, generate images/videos, then organize everything on an infinite... show more

TopviewAI

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1,028,015 次观看 • 1 个月前 •via X (Twitter)

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Topview Plugin for Codex is here: the creative execution layer for your AI workflow. To celebrate the launch, Topview subscribers can get 10 free MiniMax H3 video generations when using the plugin. Generate video concepts, storyboards, images, and videos directly from Codex . No switching between AI tools. No manual setup across different creative platforms. Just install the plugin, describe what you want, and let Codex call Topview to create the asset. From product videos and social clips to avatar explainers and voiceovers, your AI assistant can now produce real creative outputs. #TopviewAI #Codex #AIVideo #AIMarketing #AIContentCreation

TopviewAI

602,353 次观看 • 20 天前

Can AI agents analyze market data and internal research without a compliance nightmare? We built a secure equity research workflow using the Box MCP server + Anthropic's Claude to analyze NVIDIA. Claude pulls internal research from Box, layers in live market data, and generates a polished report via Box Doc Gen. For asset managers and hedge funds, this solves the trade-off between speed and security. No custom connectors, no data leaving Box, and your existing governance is inherited automatically. Watch the full demo 👇

Can AI agents analyze market data and internal research without a compliance nightmare? We built a secure equity research workflow using the Box MCP server + Anthropic's Claude to analyze NVIDIA. Claude pulls internal research from Box, layers in live market data, and generates a polished report via Box Doc Gen. For asset managers and hedge funds, this solves the trade-off between speed and security. No custom connectors, no data leaving Box, and your existing governance is inherited automatically. Watch the full demo 👇

Box

289,025 次观看 • 2 个月前

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 次观看 • 1 年前

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!

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

142,234 次观看 • 1 年前

Grok 4.3 Beta is insanely good and now comes with powerful new tools that are super helpful for everyday use: • Create clean slides, graphs, images and dashboards • Pull real-time data and images directly from the web • Run smooth multi-step workflows • Analyze videos Grok is being built for real-world use - better tools, richer context, and real day-to-day signals make this a big step toward Grok becoming part of everyday life You can now research, visualize, and package your ideas end-to-end..... from concept to finished presentation or dashboard all in one place And the Grok Computer is coming very soon 🔥

Grok 4.3 Beta is insanely good and now comes with powerful new tools that are super helpful for everyday use: • Create clean slides, graphs, images and dashboards • Pull real-time data and images directly from the web • Run smooth multi-step workflows • Analyze videos Grok is being built for real-world use - better tools, richer context, and real day-to-day signals make this a big step toward Grok becoming part of everyday life You can now research, visualize, and package your ideas end-to-end..... from concept to finished presentation or dashboard all in one place And the Grok Computer is coming very soon 🔥

X Freeze

572,489 次观看 • 4 个月前

Today, is launching Aim, the first background agent purpose-built for marketers. For months, we've been obsessed with one problem: dashboards tell you what's happening, but not how to act on the data. Aim is the agent harness designed specifically for marketing. Aim is trained from scratch on Profound's proprietary data, grounded in our research, and understands how marketers get work done. Aim analyzes your AI Search data, Prompt Volumes, competitive insights, and Knowledge Base to surface the opportunities worth acting on. It finds the anomalies that matter so you can spend your time on what moves the needle. Every Project comes with a data-backed brief and recommended tasks. From there, you can: • Chat with Aim to refine the plan • Deploy the custom Agent in one click • Track progress automatically Aim is your newest teammate keeping you moving in the right direction, working 24/7.

Today, is launching Aim, the first background agent purpose-built for marketers. For months, we've been obsessed with one problem: dashboards tell you what's happening, but not how to act on the data. Aim is the agent harness designed specifically for marketing. Aim is trained from scratch on Profound's proprietary data, grounded in our research, and understands how marketers get work done. Aim analyzes your AI Search data, Prompt Volumes, competitive insights, and Knowledge Base to surface the opportunities worth acting on. It finds the anomalies that matter so you can spend your time on what moves the needle. Every Project comes with a data-backed brief and recommended tasks. From there, you can: • Chat with Aim to refine the plan • Deploy the custom Agent in one click • Track progress automatically Aim is your newest teammate keeping you moving in the right direction, working 24/7.

Dylan Babbs

79,539 次观看 • 2 个月前

Big launch today: Custom MCP integrations for Replit Agent! 🚀 You can now connect Replit Agent to remote MCP servers, letting it pull context and take actions across your tools- so it can build, update, and act with deeper context than ever. Use cases this unlocks for you: - Build a webpage from content stored in Notion - Update tasks in Linear/Jira automatically - Build an app based on content in your Miro board We can’t wait to see what you build with it. ✨

Big launch today: Custom MCP integrations for Replit Agent! 🚀 You can now connect Replit Agent to remote MCP servers, letting it pull context and take actions across your tools- so it can build, update, and act with deeper context than ever. Use cases this unlocks for you: - Build a webpage from content stored in Notion - Update tasks in Linear/Jira automatically - Build an app based on content in your Miro board We can’t wait to see what you build with it. ✨

Replit ⠕

53,075 次观看 • 9 个月前

$Data encryption is now live on Veilnet. Available in @veilnet/mcp-server@0.3.0. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET$

Data encryption is now live on Veilnet. Available in @veilnet/[email protected]. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET

Veilnet

11,761 次观看 • 3 个月前

Claude + Apify MCP is insane 🤯 This setup allows you to chat with TikTok and Instagram Reels data in natural language. All inside Claude Desktop. Perfect for DTC brands & agencies who need fresh creative angles fast. Here's the problem: You want to know what hooks are working in your niche. But that means hours of scrolling, screenshot folders, and spreadsheets you'll never look at again. This Claude MCP setup solves it: → Ask Claude to scrape 10 viral IG Reels in your niche → It pulls transcripts, engagement, and comments automatically → Then you just... chat with the data → "What pain points keep coming up?" → "Give me the top 5 hooks from these videos" → "What emotional triggers are driving engagement?" No automations. No exports. No learning new tools. What you get: - Instant access to viral content data - Natural language questions, instant answers - Hook formulas backed by millions of views - A full report in under 3 minutes Just ask Claude like you're talking to a research assistant who already watched 100 videos. I recorded a full 11-minute breakdown showing you how to set this up step-by-step. Want access? > Like this post > Comment "CHAT" And I'll sent it right over (must be following so I can DM)

Claude + Apify MCP is insane 🤯 This setup allows you to chat with TikTok and Instagram Reels data in natural language. All inside Claude Desktop. Perfect for DTC brands & agencies who need fresh creative angles fast. Here's the problem: You want to know what hooks are working in your niche. But that means hours of scrolling, screenshot folders, and spreadsheets you'll never look at again. This Claude MCP setup solves it: → Ask Claude to scrape 10 viral IG Reels in your niche → It pulls transcripts, engagement, and comments automatically → Then you just... chat with the data → "What pain points keep coming up?" → "Give me the top 5 hooks from these videos" → "What emotional triggers are driving engagement?" No automations. No exports. No learning new tools. What you get: - Instant access to viral content data - Natural language questions, instant answers - Hook formulas backed by millions of views - A full report in under 3 minutes Just ask Claude like you're talking to a research assistant who already watched 100 videos. I recorded a full 11-minute breakdown showing you how to set this up step-by-step. Want access? > Like this post > Comment "CHAT" And I'll sent it right over (must be following so I can DM)

Mike Futia

17,788 次观看 • 9 个月前

Your Favorite Model, Now In Your Favorite Agent Kling MCP & CLI is officially live! Now, you can call Kling directly within your favorite AI Agents. Your go-to Skills are all here—no switching tools frequently, no move assets around manually. With just one sentence, you can hand your creative tasks over to your “AI assistant.” Strong realism, high-fidelity details, and cinematic aesthetics—e-commerce sellers, small business, and content creators can all produce at scale with low cost, double their efficiency, and keep inspiration flowing. Get started with Kling MCP and build your unique AI creative workflow.

Your Favorite Model, Now In Your Favorite Agent Kling MCP & CLI is officially live! Now, you can call Kling directly within your favorite AI Agents. Your go-to Skills are all here—no switching tools frequently, no move assets around manually. With just one sentence, you can hand your creative tasks over to your “AI assistant.” Strong realism, high-fidelity details, and cinematic aesthetics—e-commerce sellers, small business, and content creators can all produce at scale with low cost, double their efficiency, and keep inspiration flowing. Get started with Kling MCP and build your unique AI creative workflow.

Kling AI

1,045,296 次观看 • 2 个月前

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:

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 次观看 • 1 年前

Every other chat I do now in cursor is to use an MCP to analyze my usage data. And it all started after I recorded this episode. This week on How I AI, Tim & Alexa at Faire show us how they analyze everything from experiment results to weird data anomalies with Cursor + various MCPs. We'll walk through - investigating a mysterious conversion rate drop across various data sources, including PRDs in Notion and code - programmatically creating Mode Analytics data visualizations via chat - defining agents to do repetitive tasks like writing eppo experiment reports After this ep, I realized I was massively underutilizing data MCPs and I hope you do, too. As always, a big thanks to our amazing sponsors! ⚡️ Zapier - the most connected AI orchestration platform (with a killer MCP I must add) 🤑 Brex - the intelligent finance platform built for founders

Every other chat I do now in cursor is to use an MCP to analyze my usage data. And it all started after I recorded this episode. This week on How I AI, Tim & Alexa at Faire show us how they analyze everything from experiment results to weird data anomalies with Cursor + various MCPs. We'll walk through - investigating a mysterious conversion rate drop across various data sources, including PRDs in Notion and code - programmatically creating Mode Analytics data visualizations via chat - defining agents to do repetitive tasks like writing eppo experiment reports After this ep, I realized I was massively underutilizing data MCPs and I hope you do, too. As always, a big thanks to our amazing sponsors! ⚡️ Zapier - the most connected AI orchestration platform (with a killer MCP I must add) 🤑 Brex - the intelligent finance platform built for founders

claire vo 🖤

22,942 次观看 • 10 个月前

Stop Creating Images One by One (Automate Everything with Google Flow) Creating AI images manually is one of the biggest time wasters for content creators. This tutorial shows you how to automate the entire process with Google Flow, so you can generate hundreds of high quality images in bulk instead of repeating the same prompts over and over. You'll learn how to build an automated workflow, batch generate AI images, download them automatically, and scale your content creation with far less effort. If you're creating YouTube videos, social media content, marketing assets, or AI projects, this workflow can save you hours every week. Like, Retweet and Bookmark. Follow BIG CHRIS 🤝 Video Credit: AI Content Insights (

Stop Creating Images One by One (Automate Everything with Google Flow) Creating AI images manually is one of the biggest time wasters for content creators. This tutorial shows you how to automate the entire process with Google Flow, so you can generate hundreds of high quality images in bulk instead of repeating the same prompts over and over. You'll learn how to build an automated workflow, batch generate AI images, download them automatically, and scale your content creation with far less effort. If you're creating YouTube videos, social media content, marketing assets, or AI projects, this workflow can save you hours every week. Like, Retweet and Bookmark. Follow BIG CHRIS 🤝 Video Credit: AI Content Insights (

BIG CHRIS

54,520 次观看 • 2 个月前

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

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

Mario Nawfal

59,976 次观看 • 2 个月前

Zapier MCP is now connected to Claude. That means you can plug Claude into 8,000+ apps and 30,000+ actions using Zapier. Here are a few things you can now ask Claude to do: * Pull CRM data before a sales call * Assign tasks in Asana or Jira * Update spreadsheets across tools * Draft and send emails (in your voice, without leaving Claude!) All from within the Claude chat (tab switching, no custom integrations, no dev work). If you’ve been waiting for AI that takes action, not just notes, this is it. Curious how it could help you and your org? Learn more about Claude + Zapier MCP here:

Zapier MCP is now connected to Claude. That means you can plug Claude into 8,000+ apps and 30,000+ actions using Zapier. Here are a few things you can now ask Claude to do: * Pull CRM data before a sales call * Assign tasks in Asana or Jira * Update spreadsheets across tools * Draft and send emails (in your voice, without leaving Claude!) All from within the Claude chat (tab switching, no custom integrations, no dev work). If you’ve been waiting for AI that takes action, not just notes, this is it. Curious how it could help you and your org? Learn more about Claude + Zapier MCP here:

Bryan Helmig 🍻

93,625 次观看 • 1 年前

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

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 🚀

40,562 次观看 • 6 个月前

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 👇

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 次观看 • 1 年前

Introducing the end of influencer marketing. Startups spend thousands on creators and agencies before they even get their first 1000 customers. We built Fastlane to fix that. Fastlane can: ➤ Create hyper-realistic AI influencers tailored for your brand ➤ Generate thousands of viral TikToks, Reels and Shorts cloned from trending content ➤ Schedule all content directly to TikTok, Instagram and YouTube in one click ...and more Fastlane gives every company its own infinitely scalable influencer army. No dealing with creators. No managing campaigns. No more burning cash. Just enter your website and start generating content in seconds. The influencer hire is now optional.

Fastlane

272,437 次观看 • 3 个月前

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.

sachin

38,485 次观看 • 5 个月前

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 次观看 • 1 年前