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Airtable's data team was spending 70–80% of every week on ad hoc analytics. So they built a data agent on Hyperagent. $1000 in compute. 200+ requests answered over 4 weeks. Work that would've taken 300+ analyst hours.

Hyperagent

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5,810,690 views • 3 months ago •via X (Twitter)

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🚨 RFK Jr. claims a CDC scientist was ordered in 2002 to destroy data showing that Black children who got the measles vaccine on time had over a 200% higher risk of autism than those who waited.

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Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

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36,504 views • 10 months ago

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

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The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

Drey

13,996 views • 2 months ago

Galaxy CFO: Half of planned US data centers won't be built on time - or at all. Galaxy thinks it has the edge everyone else is missing. When they converted Helios from a Bitcoin miner to an AI data center, they weren't starting from zero. Long lead-time equipment was already on site, substations already built. Now they have 1,500 acres, approved for 1.6 GW, with another 1.8 GW in various stages of study. "On time, on budget. That is our MO." FT Mike Novogratz Galaxy.

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Milk Road

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Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."

Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."

Goshawk Trades

183,992 views • 29 days ago

"The most important thing in the next 3-4 years is data centers in space. In every way, data centers in space, from a first principles perspective, are superior to data centers on earth. In space, you can keep a satellite in the sun 24 hours a day. The sun is 30% more intense, which results in six times more irradiance than on Earth. So you don't need a battery. The cooling in these data centers is incredibly complicated. Space cooling is free. You just put a radiator on the dark side of the satellite. The only thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. Link satellites with lasers, and you have a faster and more coherent network than any data center on Earth."

"The most important thing in the next 3-4 years is data centers in space. In every way, data centers in space, from a first principles perspective, are superior to data centers on earth. In space, you can keep a satellite in the sun 24 hours a day. The sun is 30% more intense, which results in six times more irradiance than on Earth. So you don't need a battery. The cooling in these data centers is incredibly complicated. Space cooling is free. You just put a radiator on the dark side of the satellite. The only thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. Link satellites with lasers, and you have a faster and more coherent network than any data center on Earth."

Patrick OShaughnessy

8,214,549 views • 8 months ago

“We’ve seen students generate multi-million-dollar contracts within weeks of getting hired.” Austen Allred on how AI is collapsing sales cycles: “A team met with the FBI, took feature requests, and an engineer built everything in three or four hours.” “By dinner that night, all the requests were done.” “That turned into a major contract.” “It’s a fundamentally different world when execution happens at that speed.”

“We’ve seen students generate multi-million-dollar contracts within weeks of getting hired.” Austen Allred on how AI is collapsing sales cycles: “A team met with the FBI, took feature requests, and an engineer built everything in three or four hours.” “By dinner that night, all the requests were done.” “That turned into a major contract.” “It’s a fundamentally different world when execution happens at that speed.”

Forward Future

20,835 views • 6 months ago