benarous farouk

435 posts

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benarous farouk

benarous farouk

@benarousfarouk1

@blockchain

London, England Katılım Temmuz 2019
1.1K Takip Edilen103 Takipçiler
Blockchain
Blockchain@blockchain·
⚽ Prediction markets, powered by @Polymarket, are now available in the Blockchain app.
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SnapMarkets
SnapMarkets@TheSnapMarkets·
1/ The Snap Off competition has begun!
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Harman
Harman@itsharmanjot·
A TEAM OF AI RESEARCHERS JUST OPEN-SOURCED THE BLOOMBERG TERMINAL FOR QUANT FINANCE. A Bloomberg Terminal costs $25,000 per year per seat. Banks pay for thousands of them. This thing reads every quant paper, every financial blog, every SEC filing, every arXiv preprint, and turns it into a searchable knowledge base. For free. It's called QuantMind. It just got accepted to the NeurIPS 2025 GenAI in Finance Workshop. Here's what it actually does: → Ingests arXiv quant papers, financial news, blogs, and reports automatically → Parses PDFs, HTML, tables, and figures into structured knowledge → Tags every paper by research area and topic → Builds a semantic knowledge graph you can query in plain English → Plugs into DeepResearch, RAG, and MCP for multi-hop reasoning → Two-stage architecture: extract once, retrieve forever Here's the wildest part: The financial research industry publishes around 500 new papers and reports every single day. Hedge funds pay six-figure salaries to junior analysts whose entire job is reading them. QuantMind reads all of it. Tags it. Embeds it. Lets you ask it questions. 154 stars. 22 forks. 173 commits. MIT license. Python. One honest note: this is a framework, not a magic alpha machine. You still need to know what to ask. But the "I haven't read that paper yet" excuse is officially dead. The thing Wall Street charges $25,000 a year for is sitting on GitHub. Free. Link in the comments.
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Sara Imari Walker
Sara Imari Walker@Sara_Imari·
Math exists within the architectures (biological and technological) evolved on Earth, we should not be so easily convinced it exists anywhere else in our universe. Math as our most “universal language” is merely evidence of our own evolutionary boundary in what we can describe. It is not a universal truth autonomous to our universe, nor does it reveal anything beyond a boundary to what we observe.
Paras Chopra@paraschopra

Why math works so well in describing our universe? Let’s look at it from three perspectives. 1. We grasp the graspable bits By definition, we model aspects of reality that we are capable of modeling. The remaining part may be truly random or complicated. For example, where do quantum measurements come from? Or the incompatibility between general relativity or quantum mechanics. We hit the limits of our models in those cases, so it’s a bit of survivorship bias when we ask ourselves why our models work so well in cases where do they do. 2. The Anthropic Argument Wigner wondered about the unreasonable effectiveness of mathematics in describing our universe. Out of all universes that could exist, the universes in which any science is possible upper-bounds their complexity (if universe were any more complex, no science is possible as laws would be too complicated and Wigner wouldn’t be asking this question). The lower bound of complexity is given by the fact that Wigner has to exist and hence laws have to be complex enough for intelligent life to be possible. Hence the universe we should find ourselves in should have laws and dynamics to produce life, but simple enough to be understood by the very same life. 3. The Simplicity Prior The Anthropic argument gives us a range, but it seems like our universe is at the simple end of that range. Given that the standard model fits on a page, the question isn’t why laws are graspable but why are they so simple? If you assume a prior over universes where simpler universes are more probable, then we should find ourselves in the simplest universe that’s capable of supporting life (which might be the case with our universe). What would motivate simplicity prior? Well, if we assume some kind of “universe generator” that churns through simpler universes first before getting to more complicated ones, we would find ourselves in the simplest universe that’s complex enough for intelligent life to emerge. 4. So, are there multiverses? Given, how successful Anthropic argument is for illuminating puzzles like Wigner’s makes me seriously consider the possibility that many, many possible universes exist beyond our own but that we just happen to find ourselves in a particular one that’s suited for us. Of course, there’s no way to experimentally prove it (yet), but given that the alternative is assuming our precise universe with specific laws and constants exists as a bridge fact, I’d pick multiverses and Anthropic argument as a more likely explanation for what we observe.

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Roan
Roan@RohOnChain·
As someone who builds institutional level quant systems, this Stanford paper is the closest thing to an HFT desk I have ever seen publicly shared. 14 pages. Top Trading Strategies. Bookmark & get this, then read the article below before someone takes it down.
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Roan@RohOnChain

x.com/i/article/2056…

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Rahul
Rahul@sairahul1·
Anthropic pays $750,000+ a year for engineers who know how to build LLMs from scratch. Stanford just released the exact lecture that teaches it - 1 hour 44 minutes, free, straight from CS229. Bookmark and watch it this weekend. It'll teach you more about how ChatGPT & Claude actually work than most people at top AI companies learn in their entire careers.
Rahul@sairahul1

x.com/i/article/2057…

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Blockchain
Blockchain@blockchain·
Crypto-backed loans are now live on Blockchain 🙌 Borrow funds against your BTC or ETH from 1.9% per year. No selling. No fixed repayment. No new platform.
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Mnimiy
Mnimiy@Mnilax·
Karpathy threw a grenade at every senior engineer who still treats LLMs as a toy. his actual words: the worst thing an expert can do right now is reject them. most experts read it as a threat, but it's advice. his framing: > the gap between "AI tools are bad" and "AI tools are useful when used right" is professional discipline, not capability > agents have cognitive deficits. they fail in ways nothing in the training set anticipated > the experts who reject LLMs lose to experts who learn to wrangle them > "models have so many cognitive deficits. but you can route around them" routing around the deficits is what CLAUDE.md was invented for. Karpathy himself wrote 4 rules. across 30 codebases they took my Claude error rate from 41% down to 11%. solid drop. but his rules pre-date the slop era going public. I bolted on 8 more, tuned to the failure modes that surfaced after January. got it down to 3%. a CLAUDE.md does not raise Claude's IQ. it lowers his slop floor. that is the entire game. open the article underneath. the model is not the bottleneck. your config is.
Mnimiy@Mnilax

x.com/i/article/2053…

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Blockchain
Blockchain@blockchain·
Caption this pic of @IOHK_Charles from our event at Consensus Miami. We'll go first 👇
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José Donato
José Donato@josedonato__·
wild how much data you can see from hyperliquid stops, TPs, fills, top positions, liquidation profiles, cohort sentiment, builder revenue, ... click on any price level and see the wallets behind it, add them to watchlist view and track them layout below
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Quant Science
Quant Science@quantscience_·
This paper unlocks every algorithm used by hedge funds. 151 trading strategies. Get it here (361 page PDF):
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