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We just launched Quant IQ 🧠 an AI assistant built directly into Quant Data. It sees what you see 👀. Ask "What does the 0DTE GEX look like for SPY?" and it answers using the live data on your screen. You can describe what you want in plain English,... show more

Andrew Hiesinger

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21,009 views • 1 month ago •via X (Twitter)

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holy shit! this is INSANE… Microsoft just launched copilot cowork. built with Anthropic. you describe what you want done. it turns your request into a plan and executes it across outlook, teams, excel. 90% of the fortune 500 already use copilot or Microsoft tools. now it does the work for you. the execution era is here and only accelerating.

holy shit! this is INSANE… Microsoft just launched copilot cowork. built with Anthropic. you describe what you want done. it turns your request into a plan and executes it across outlook, teams, excel. 90% of the fortune 500 already use copilot or Microsoft tools. now it does the work for you. the execution era is here and only accelerating.

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166,920 views • 6 months ago

Introducing Infinite Pages on Quant Data 🗺️ Your dashboard is no longer one fixed screen. Your tools live on an open canvas you pan and zoom like a map. Up to 100 tools per page. Only what's on screen loads, so it stays fast. Live on web today, alongside your Classic Pages. Try it now: Create Page → New Infinite Page

Introducing Infinite Pages on Quant Data 🗺️ Your dashboard is no longer one fixed screen. Your tools live on an open canvas you pan and zoom like a map. Up to 100 tools per page. Only what's on screen loads, so it stays fast. Live on web today, alongside your Classic Pages. Try it now: Create Page → New Infinite Page

Andrew Hiesinger

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Solaris AI is officially out of beta and open to everyone. Tell Copilot what you want to automate in plain English. It asks for the missing details, turns your request into an editable onchain workflow, and runs it on Solana. In this demo, one prompt becomes a SOL dip-buying ladder using live market data, wallet balances, persistent state, Jupiter swap logic, and Telegram reporting. Every step is visible and editable on the canvas, so you can inspect the logic, change it, and deploy it again. From prompt to running workflow.

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Solaris AI

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TraderJonesy

43,814 views • 7 months ago

Jesus makes an incredible promise about prayer. He says, “If you abide in Me, and My words abide in you, you will ask what you desire, and it shall be done for you” (John 15:7). Wait—ask what I desire and it will be done? What does that really mean? I break it down in this short video, and it’s well worth the watch.

Jesus makes an incredible promise about prayer. He says, “If you abide in Me, and My words abide in you, you will ask what you desire, and it shall be done for you” (John 15:7). Wait—ask what I desire and it will be done? What does that really mean? I break it down in this short video, and it’s well worth the watch.

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ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

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Ok I'm convinced, Generative UI is the future I built this today using Vercel's AI SDK & PostHog, it allows you to talk with your analytics data All you do is describe in natural language what you'd like to see and then talk with the results

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The FLYNN movie, "Deliver the Truth. Whatever the Cost," is now available to stream for free. I want every American to see it. Watch it on your phone, your computer, your tablet, any device you have. This film tells the truth about what was done to my family and what is being done to this country. It is the story they spent years and millions of dollars trying to suppress. Now you can watch it for free. Share this with everyone you know. Link in the comments.

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Damian Player

195,276 views • 3 months ago

We built Superhuman Go for you. Go is the proactive AI assistant that knows what you know and offers help without you even having to ask. It’s always with you, and it’s already on it. Try it out:

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Superhuman

12,913 views • 10 months ago

For 60 years every computer ever built did the same thing. Stored information and retrieved it on demand. Jensen Huang just explained why that era is over and what replaces it. His framing was the clearest I have ever heard. Think about everything a computer has ever done for you. You wrote a document, you saved it to a file. You took a photo, it saved to a file. You recorded music, it saved to a file. When you wanted it back, you retrieved it from a disc. That is it. That is 60 years of computing. Store and retrieve. He pointed out something hiding in plain sight. We call them data centers. Not computer centers. Because we were not really computing anything meaningful. We were storing data that you retrieved based on what you tapped on your phone. Then he explained what changed. Every time you give AI a prompt today, the response is produced originally in real time. It is not retrieved from storage. It is generated fresh based on your specific context, your specific question, your specific moment. What you see is completely different from what anyone else sees because it was made for you. Jensen said every pixel you see, every word you read, every video you watch in the future will be originally generated. Not retrieved. 60 years of computing was about building better storage and faster retrieval. The entire paradigm flipped overnight. He said this simply: we went from a retrieval industry to a generation industry. And the machines that generate intelligence are what Nvidia builds. The buildings used to be called data centers because they stored data. Nobody has renamed them yet. But the job description changed completely.

For 60 years every computer ever built did the same thing. Stored information and retrieved it on demand. Jensen Huang just explained why that era is over and what replaces it. His framing was the clearest I have ever heard. Think about everything a computer has ever done for you. You wrote a document, you saved it to a file. You took a photo, it saved to a file. You recorded music, it saved to a file. When you wanted it back, you retrieved it from a disc. That is it. That is 60 years of computing. Store and retrieve. He pointed out something hiding in plain sight. We call them data centers. Not computer centers. Because we were not really computing anything meaningful. We were storing data that you retrieved based on what you tapped on your phone. Then he explained what changed. Every time you give AI a prompt today, the response is produced originally in real time. It is not retrieved from storage. It is generated fresh based on your specific context, your specific question, your specific moment. What you see is completely different from what anyone else sees because it was made for you. Jensen said every pixel you see, every word you read, every video you watch in the future will be originally generated. Not retrieved. 60 years of computing was about building better storage and faster retrieval. The entire paradigm flipped overnight. He said this simply: we went from a retrieval industry to a generation industry. And the machines that generate intelligence are what Nvidia builds. The buildings used to be called data centers because they stored data. Nobody has renamed them yet. But the job description changed completely.

Ihtesham Ali

30,235 views • 2 months ago

Steve Jobs basically predicted the exact world we live in today... back in 1983 "the problem was you can't ask Aristotle a question... someday after the person's dead and gone we can ask a machine: hey, what would Aristotle have said, and maybe it will get it right" 43 years later, the machine doesn't just answer anymore, you tell it what you need done and it gets done no app, no typing, just one messy sentence spoken out loud, and the work is already split across three teams before you've put your phone down that's Viktor, it does the rest (you get $100 free credits to try it out)

Steve Jobs basically predicted the exact world we live in today... back in 1983 "the problem was you can't ask Aristotle a question... someday after the person's dead and gone we can ask a machine: hey, what would Aristotle have said, and maybe it will get it right" 43 years later, the machine doesn't just answer anymore, you tell it what you need done and it gets done no app, no typing, just one messy sentence spoken out loud, and the work is already split across three teams before you've put your phone down that's Viktor, it does the rest (you get $100 free credits to try it out)

Machina

14,513 views • 1 month ago

i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

Cody Schneider

47,333 views • 1 year ago

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 views • 10 months ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 10 months ago