Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Built AI Launch Radar this week. One thing I underestimated was how hard it is to get reliable, structured web data for monitoring product launches and market sentiment. After trying different approaches, I ended up using Nimble's APIs to power the data layer, which saved me a lot of... show more

Nayeem Sheikh

62,377 subscribers

12,349 views • 2 months ago •via X (Twitter)

Education Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:

I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:

Hasan Toor

100,511 views • 7 months ago

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

CherryAI Robotics

90,786 views • 1 year ago

Here is why Nimble has 1.1M Claude plugin installs… I tested the Nimble Studio for a few minutes and immediately understood the appeal. Instead of manually scraping sites or fighting APIs, Nimble lets AI agents pull structured live data from platforms like TikTok, YouTube, Instagram, Reddit, and more. I tried pulling trending AI tool content and it turned messy social pages into a clean dataset almost instantly, complete with engagement metrics, creators, hashtags, and trends. The interesting part is how natural it feels. You’re basically giving AI agents the ability to interact with live web data in a usable format instead of static search results. That’s why the 1.1M installs matter. It solves a real problem for developers, researchers, marketers, and teams building AI workflows around real-time internet data.

Here is why Nimble has 1.1M Claude plugin installs… I tested the Nimble Studio for a few minutes and immediately understood the appeal. Instead of manually scraping sites or fighting APIs, Nimble lets AI agents pull structured live data from platforms like TikTok, YouTube, Instagram, Reddit, and more. I tried pulling trending AI tool content and it turned messy social pages into a clean dataset almost instantly, complete with engagement metrics, creators, hashtags, and trends. The interesting part is how natural it feels. You’re basically giving AI agents the ability to interact with live web data in a usable format instead of static search results. That’s why the 1.1M installs matter. It solves a real problem for developers, researchers, marketers, and teams building AI workflows around real-time internet data.

Kaitee

28,590 views • 3 months ago

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 views • 1 year ago

Introducing Orbit Scout a Bloomberg for crypto. One clean command center for real-time data, charts, and insights, no more juggling 20+ tabs. Orbit Scout uses AI to pull from news, on-chain data, and social sentiment so you can ask why the market is moving and get instant answers. Private beta is live, DM me for access.

Introducing Orbit Scout a Bloomberg for crypto. One clean command center for real-time data, charts, and insights, no more juggling 20+ tabs. Orbit Scout uses AI to pull from news, on-chain data, and social sentiment so you can ask why the market is moving and get instant answers. Private beta is live, DM me for access.

Orbit

13,339 views • 11 months ago

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,412 views • 1 year ago

The cool thing about local-first web apps is you don't have to choose anymore, which was one of the goals for Thymer: real-time collaboration, E2EE sync, and web access, while your data also lives locally and the app works offline. That means tools like Claude (or local LLMs) can get fast access to your workspace data. There's already an MCP plugin and here's a quick prototype where Claude Code talks directly to offline workspace data stored in the browser DB.

The cool thing about local-first web apps is you don't have to choose anymore, which was one of the goals for Thymer: real-time collaboration, E2EE sync, and web access, while your data also lives locally and the app works offline. That means tools like Claude (or local LLMs) can get fast access to your workspace data. There's already an MCP plugin and here's a quick prototype where Claude Code talks directly to offline workspace data stored in the browser DB.

Wim Cools

31,293 views • 8 months ago

I finally built my own self-driving, personal CRM. After many failed attempts and trying a lot different tools/products, I finally ended up with a system/approach I like built on top of Notion. The best part: It auto-updates, so data never goes stale.

I finally built my own self-driving, personal CRM. After many failed attempts and trying a lot different tools/products, I finally ended up with a system/approach I like built on top of Notion. The best part: It auto-updates, so data never goes stale.

Johannes Schickling

55,908 views • 1 month ago

Ok I went a little too hard on this Biologicals trial building. I’ve always been a big fan of biologicals, completely underutilized imo, likely because it has a bad reputation often marketed by companies as the cure for plant AIDS. I have a Biostimulant, lignin-derived, that sounds like a real dinger if it can do what it is supposed to do, so I have negotiated a good price per acre that ensures the manufacturer isn’t chasing the “hit it and quit it” mentality retailers in agriculture seem to have towards farmers. I am tracking everyone in the trial and open sourcing all the data in real time. No compilation afterwards to stroke the data, all raw and interpreted in real time. If the product doesn’t do anything, the retailer never sells that product ever again. Even easier for the farmers, I set up a chat bot in Telegram so to get the data in your dashboard and online is take a picture or type in your data. This is borne out of curiosity and dismay towards all the scammers. I’m really hoping this does some good. There is a link at the end of the video that took me an uncomfortable amount of time to make so I’m forcing you to watch it to get the link. More product data there.

Ok I went a little too hard on this Biologicals trial building. I’ve always been a big fan of biologicals, completely underutilized imo, likely because it has a bad reputation often marketed by companies as the cure for plant AIDS. I have a Biostimulant, lignin-derived, that sounds like a real dinger if it can do what it is supposed to do, so I have negotiated a good price per acre that ensures the manufacturer isn’t chasing the “hit it and quit it” mentality retailers in agriculture seem to have towards farmers. I am tracking everyone in the trial and open sourcing all the data in real time. No compilation afterwards to stroke the data, all raw and interpreted in real time. If the product doesn’t do anything, the retailer never sells that product ever again. Even easier for the farmers, I set up a chat bot in Telegram so to get the data in your dashboard and online is take a picture or type in your data. This is borne out of curiosity and dismay towards all the scammers. I’m really hoping this does some good. There is a link at the end of the video that took me an uncomfortable amount of time to make so I’m forcing you to watch it to get the link. More product data there.

bu/ac

29,897 views • 4 months ago

We’re beginning to implement Polymarket Dashboards directly into the $SPECT App. This is an interface showcase - a first look at how on-chain prediction markets will live inside the Spectre AI ecosystem. Data will be synced in tomorrow. ✅ A Polymarket Landing Dashboard MVP is now being added to the homepage ✅ Full category analytics, trend mapping & sentiment overlays are being built ✅ AI Market Analysis and Bubble Engine integrations are in progress ✅ The end goal: real-time narrative + odds + influence visualization in one view This is just the start. The full suite will dig into market flows, sentiment velocity, X narratives and behavioral patterns behind each side of a prediction market. #Predictions becomes a clear window into why the market is shifting, where the general sentiment is heading. PolymarketBuild #PredictionMarkets #polymarket

We’re beginning to implement Polymarket Dashboards directly into the $SPECT App. This is an interface showcase - a first look at how on-chain prediction markets will live inside the Spectre AI ecosystem. Data will be synced in tomorrow. ✅ A Polymarket Landing Dashboard MVP is now being added to the homepage ✅ Full category analytics, trend mapping & sentiment overlays are being built ✅ AI Market Analysis and Bubble Engine integrations are in progress ✅ The end goal: real-time narrative + odds + influence visualization in one view This is just the start. The full suite will dig into market flows, sentiment velocity, X narratives and behavioral patterns behind each side of a prediction market. #Predictions becomes a clear window into why the market is shifting, where the general sentiment is heading. PolymarketBuild #PredictionMarkets #polymarket

SPECTRE AI

16,024 views • 10 months ago

Shopify’s latest Edition has SO MANY new features for ecommerce operators, here's the highlights: - native A/B testing on theme and checkout - Shopify is building the data layer for your products to be sold with AI - Shop Campaigns can now be run in ChatGPT - in-dashboard monitoring of how and when products show up in agentic search - Markets got a visualization redesign... and this is just scratching the surface. See all the updates, in my profile.

Oren John

28,986 views • 2 months ago

For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.

For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.

Satya Nadella

351,122 views • 11 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

$NVG8 Is officially LIVE! A new era begins as the first decentralized AI data marketplace is now powered by its own token, built on Base. This isn’t just another launch, it’s a fundamental shift in how AI is built, trained, and monetized. The backbone of the AI data economy is here, creating a decentralized ecosystem where value flows to the creators, contributors, and builders. AI needs data, now you OWN it. AI needs fuel, now you EARN it. We’re stepping into a new frontier and it’s time to change the game. Welcome to the AI economy. Trade now on KuCoin & Gate.io! (Contract Address: 0x5d6cae0422a950dbd7918d1e74434a35156b3ba4)

$NVG8 Is officially LIVE! A new era begins as the first decentralized AI data marketplace is now powered by its own token, built on Base. This isn’t just another launch, it’s a fundamental shift in how AI is built, trained, and monetized. The backbone of the AI data economy is here, creating a decentralized ecosystem where value flows to the creators, contributors, and builders. AI needs data, now you OWN it. AI needs fuel, now you EARN it. We’re stepping into a new frontier and it’s time to change the game. Welcome to the AI economy. Trade now on KuCoin & Gate.io! (Contract Address: 0x5d6cae0422a950dbd7918d1e74434a35156b3ba4)

Navigate

276,292 views • 1 year ago

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,254 views • 5 months ago

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,107 views • 1 year ago

AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉

AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉

OptimAI Network

14,686 views • 4 months ago

Thiel Fellow and cofounder of The Antifraud Company Alex Shieh explains how his company is using AI to identify and collect bounties on fraud: "There are essentially two steps here. The first is that a lot of this data is unstructured. A lot of it is documents. So LLMs really speed that up — getting structured entities out of the unstructured data." "And then we ontologize it. 'This company has this contract, has this relationship with this employee,' etc. We build this ontology knowledge-graph-like thing." "And then we have the second layer. The rules model, essentially. My cofounders are both lawyers. They have a legal background. They've been working on the fraud issue in the legal sphere, and so they know what that looks like." "With their input and expertise, we're able to develop this rules model, compare that to what we're seeing in the real world, and flag things that look like violations in real time."

Thiel Fellow and cofounder of The Antifraud Company Alex Shieh explains how his company is using AI to identify and collect bounties on fraud: "There are essentially two steps here. The first is that a lot of this data is unstructured. A lot of it is documents. So LLMs really speed that up — getting structured entities out of the unstructured data." "And then we ontologize it. 'This company has this contract, has this relationship with this employee,' etc. We build this ontology knowledge-graph-like thing." "And then we have the second layer. The rules model, essentially. My cofounders are both lawyers. They have a legal background. They've been working on the fraud issue in the legal sphere, and so they know what that looks like." "With their input and expertise, we're able to develop this rules model, compare that to what we're seeing in the real world, and flag things that look like violations in real time."

TBPN

10,209 views • 4 months ago