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YC S24's Formula Insight is an equity research platform that helps institutional investors manage, track & query their complex internal Excel models. It draws inferences from FactSet estimates and transcripts to uncover novel insights.

Y Combinator

1,580,194 subscribers

17,418 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Финансы

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Hedgeye

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Startup Archive

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i³ (Intelligence Cubed)

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OptimAI

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In under construction real estate, the difference between people who make money and people who lose it is information asymmetry. Homebuyers, investors, NRIs, brokers advising a client on their first purchase all making multi-crore decisions on renders, rumours, and relationships with people whose commission depends entirely on the deal closing today. We built ReraTracker to end that. 2,000+ RERA registered projects across Gurgaon, Noida. NCR first and beyond later. Every project tracked from launch to completion. HRERA filings, not developer claims. Verified insights, not market rumours. Drone shots, floor plans, developer track records, price history from day one to today. Everything you need to research and decide. Without a single broker call unless you ask. If you are a homebuyer, investor, NRI, brokers, an IFA advising families on real estate or an industry player then Reratracker is now yours. Research. Track. Transact. Drop any project name in the comments. I will send you the link personally.👇 This is just Day Zero. A lot more gets built on top of this with your feedback and support. Let’s go 🚀

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Gaurav Gupta | ReraTracker

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The Simpsonwood Coverup Here is an edit from the documentary Trace Amounts, detailing how the CDC and their criminal partners at various pharma corporations discovered an undeniable link between mercury in vaccines and autism, and then spent every waking moment and all their effort trying to manipulate the data and manufacture an epidemiological study that removed all the association until it wasn't significant. This is a common practice and we've witnessed it countless times already with the COVID vaccine, especially with the paper that argues that COVID gives you more myocarditis than the vaccine does, which is just laughable. They also do this incessantly with nutrition research, especially research on fat/cholesterol to justify giving statins, fluoride and reductions in IQ, SSRIs benefiting depression, and pretty much anything else they can manage to contort and manipulate into being an advertisement for a pharmaceutical product that benefits them with profits or protects them from liability. These institutions exist solely for corporate interest, and what's worse is that they use your own tax money to defend them.

Inversionism

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"Mostly right" is useless when millions of dollars are on the line. That's why we built Leni: the world's most accurate AI for investment work. It analyzes hundreds of files at once, reasons through complex problems, validates its outputs, and delivers finished work instead of making users babysit prompts. Over the last few years, Leni has been used by institutions to manage over $80B in assets, and we have learned that the biggest challenge with AI isn't intelligence. It's trust. Many AI systems sound convincing. Few can be trusted for serious investment decisions. So we aimed to build one that is accurate, verifiable, and dependable. Everything we've learned from processing over 100 million rows of data, building our own verification models, evaluation frameworks, and investment workflows, is now embedded into a powerful self-serve platform designed to help investors make institutional-grade decisions with confidence. Built for serious work. Built for accuracy. Try Leni today and experience the difference. 🚀

"Mostly right" is useless when millions of dollars are on the line. That's why we built Leni: the world's most accurate AI for investment work. It analyzes hundreds of files at once, reasons through complex problems, validates its outputs, and delivers finished work instead of making users babysit prompts. Over the last few years, Leni has been used by institutions to manage over $80B in assets, and we have learned that the biggest challenge with AI isn't intelligence. It's trust. Many AI systems sound convincing. Few can be trusted for serious investment decisions. So we aimed to build one that is accurate, verifiable, and dependable. Everything we've learned from processing over 100 million rows of data, building our own verification models, evaluation frameworks, and investment workflows, is now embedded into a powerful self-serve platform designed to help investors make institutional-grade decisions with confidence. Built for serious work. Built for accuracy. Try Leni today and experience the difference. 🚀

Arunabh Dastidar

1,315,640 просмотров • 3 месяцев назад

Paul Buchheit, who was there when OpenAI started as YC Research, looks back on how crazy the idea was and how insane it is that it actually worked: "And so we had this sort of like loony moonshot idea to start at the time we called it YC research, but it eventually got renamed open AI. We were going to take on Google with a small nonprofit, which doesn't quite pass the laugh test. Right? Like, like how is this little nonprofit going to, going to be the one that actually develops AGI when the other companies have dramatically more resources. And then here we are 10 years later, it actually happened. And at the time it just seemed incredibly implausible. Like no one would have believed it. Yet here we are on, I think basically the best timeline, like we actually delivered it. We have, An open and competitive market with, I would say at least six foundation models that are competing, including an open source one from meta. And I think that's our best shot for preserving freedom is, choice and competition. " - Paul Buchheit

Paul Buchheit, who was there when OpenAI started as YC Research, looks back on how crazy the idea was and how insane it is that it actually worked: "And so we had this sort of like loony moonshot idea to start at the time we called it YC research, but it eventually got renamed open AI. We were going to take on Google with a small nonprofit, which doesn't quite pass the laugh test. Right? Like, like how is this little nonprofit going to, going to be the one that actually develops AGI when the other companies have dramatically more resources. And then here we are 10 years later, it actually happened. And at the time it just seemed incredibly implausible. Like no one would have believed it. Yet here we are on, I think basically the best timeline, like we actually delivered it. We have, An open and competitive market with, I would say at least six foundation models that are competing, including an open source one from meta. And I think that's our best shot for preserving freedom is, choice and competition. " - Paul Buchheit

Jared Friedman

55,910 просмотров • 1 год назад

“It’s not sufficient to bring the asset on-chain. You must transform it into a form that the on-chain investor is happy to embrace.” As Richard G Brown explains, there is already a large and growing pool of capital on-chain, held in stablecoins, crypto treasuries, and by investors who have made money in crypto. This capital runs into the hundreds of billions globally, and it’s increasingly looking for real yield and institutional-grade assets. These allocators don’t want to move back into the old world of legacy custodians or traditional settlement processes. Their liquidity needs, access requirements, investment sizes, and jurisdictions differ from traditional buyers - and they expect assets to meet them where they already are: on-chain. That’s why the challenge isn’t simply tokenising an asset. It’s reshaping it so on-chain investors can use it, trade it, and exit it in ways consistent with their market structure. A powerful insight from Richard Gendal Brown’s conversation with Future of Finance (FOF), and a clear signal of where institutional digital assets are heading. Watch the full podcast here: Want to learn more about what R3 is bringing to Solana? Catch Richard on stage at SolanaConf Breakpoint, 11-13 December.

“It’s not sufficient to bring the asset on-chain. You must transform it into a form that the on-chain investor is happy to embrace.” As Richard G Brown explains, there is already a large and growing pool of capital on-chain, held in stablecoins, crypto treasuries, and by investors who have made money in crypto. This capital runs into the hundreds of billions globally, and it’s increasingly looking for real yield and institutional-grade assets. These allocators don’t want to move back into the old world of legacy custodians or traditional settlement processes. Their liquidity needs, access requirements, investment sizes, and jurisdictions differ from traditional buyers - and they expect assets to meet them where they already are: on-chain. That’s why the challenge isn’t simply tokenising an asset. It’s reshaping it so on-chain investors can use it, trade it, and exit it in ways consistent with their market structure. A powerful insight from Richard Gendal Brown’s conversation with Future of Finance (FOF), and a clear signal of where institutional digital assets are heading. Watch the full podcast here: Want to learn more about what R3 is bringing to Solana? Catch Richard on stage at SolanaConf Breakpoint, 11-13 December.

R3

21,554 просмотров • 9 месяцев назад

I'm currently going through this "Developing Large Language Models" course. You definitely want to check it out. Actually, this is much more than a course: it's a complete skill track from DataCamp. It starts with an introduction to PyTorch, neural networks, all the way to large language models. This starts from the very beginning. Literally, the solution to the first exercise is to `import torch`! I recorded the attached video to tell you a bit more about this. I partnered with Datacamp because their platform is one of the best for leveling up in the DS/ML/AI space. Then, I found this course and started taking it. Now, I don't want to stop!

I'm currently going through this "Developing Large Language Models" course. You definitely want to check it out. Actually, this is much more than a course: it's a complete skill track from DataCamp. It starts with an introduction to PyTorch, neural networks, all the way to large language models. This starts from the very beginning. Literally, the solution to the first exercise is to `import torch`! I recorded the attached video to tell you a bit more about this. I partnered with Datacamp because their platform is one of the best for leveling up in the DS/ML/AI space. Then, I found this course and started taking it. Now, I don't want to stop!

Santiago

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If anyone is wondering how a politician has made tens of millions of dollars on a $174K salary: Check out Quiver. It lets you track politicians’: - Stock trading - Portfolio - Net Worth - Corporate Donors - Proposed Legislation Along with data on hedge fund holdings, company financials, insider trading, and more. Open to the public with no ads. I try to post interesting insights from the data, but I think it’s important that the raw data is available to anyone who wants to do their own research.

If anyone is wondering how a politician has made tens of millions of dollars on a $174K salary: Check out Quiver. It lets you track politicians’: - Stock trading - Portfolio - Net Worth - Corporate Donors - Proposed Legislation Along with data on hedge fund holdings, company financials, insider trading, and more. Open to the public with no ads. I try to post interesting insights from the data, but I think it’s important that the raw data is available to anyone who wants to do their own research.

Quiver Quantitative

30,383 просмотров • 1 год назад