André

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André

André

@oracles

Online Propaganda Department | "Everything is distribution" Mainly into AI × DeFi × capital markets

🇪🇺 Katılım Eylül 2018
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André
André@oracles·
Had a Jane Street interview in 2019. Round 8. Interviewer texts: 'Equinox Brookfield. 6 AM. Bring a calculator you won't use.' I show up. He's on the StairMaster reading a printout of the CBOE VIX term structure. Doesn't get off. Nods at the machine next to him. 'You see that guy on the rower? Goldman MD. Comes here every morning at 6:04. Leaves at 6:38. What's the implied vol on his arrival time?' 'I don't know his variance.' 'Sample size of one year, 250 sessions. Standard deviation is 90 seconds. Annualize it.' I do the math in my head. '90 seconds times sqrt(250). About 24 minutes annualized.' 'Wrong. You annualized like it's a return. Time-of-arrival doesn't compound. It's a Poisson process with drift. The correct answer is his arrival is more punctual than the 6 train. Now price me an option on whether he shows up tomorrow.' I think for a second. 'If he's been here 250 days in a row, base rate is 99.6%. But you have to adjust for his vacation schedule and probability of injury, call it 96%.' 'Strike?' '$10 if he shows, $0 if he doesn't.' 'I'll sell you that option for $9.40.' I think about it. 'No. Expected value is $9.60. You're underpricing by 20 cents.' 'Correct. Now why am I selling it to you?' I freeze. 'Because I just saw him limp on the way in. You're buying my information for 20 cents. You overpaid.' We get off the machines. Walk to the smoothie bar. He orders a $19 smoothie, doesn't drink it. 'Last question. The girl behind the counter makes 200 smoothies per morning. She has perfect information on who's actually here and who's faking it. Citadel guys leak their attendance to her every day for the price of a tip. If I gave you $50,000 to set up a market on which Citadel PM gets fired this quarter, what's your bid-ask?' 'Insider trading.' 'Wrong answer. There's no public security. Try again.' I think. 'I'd quote 8 to 12 percent on any given PM. Spread of 4 points to cover adverse selection. Tighten the spread for PMs I have data on.' 'Where do you get the data?' 'The smoothie girl.' 'Good. How much do you pay her?' '10% of P&L.' 'Wrong. You pay her a flat $200 a week. If you pay her on P&L she becomes your counterparty. Right now she's your data source. Don't conflate edges.' He hands me the untouched smoothie. 'Throw this out on Vesey Street, not in the building. The staff knows what gets wasted. Outside, it was consumed. Same smoothie, different signal.' I do it. Thursday I get the email. 'Offer rescinded. Your bid-ask on the PM market was too tight. 4 points doesn't cover the tail. The girl is a single point of failure and you didn't price her counterparty risk. Also you held the smoothie in your right hand. Right-handers throw with their right hand. Camera saw the hesitation.'
Deedy@deedydas

Jane Street made ~$40B in 2025 with 3,500 employees, a ~2x from the year before. At ~65-70% profit margin, that's $8M profit / employee, the highest for a 1000+ ppl company. High-frequency trading continues to be the most efficient money making engine. I want to share an old story about my Jane Street interview in 2014. Jane Street was known for hiring a lot of math, physics and CS olympiad winners from top universities and putting them through many rounds - including, for trading roles, a gauntlet of mental math. It was my 6th interview and my final round and I recall being asked "What is the next day after today in DD/MM/YYYY where all the digits are unique?" They'd toy with you and say "You can use a pencil and paper, if you want" but you knew that was an instant no. Painstakingly and as quickly as I could, I came to an answer. "How confident are you that this is correct on a 0-1 probability scale?" the interviewer said. "0.95", I blurted out, not fully knowing how to answer that. "Are you sure?" After thinking harder for a few more seconds, I realized I could've flipped the digits around to get a closer date. I gave the interviewer my answer. It was correct. "0.95 huh?" he chuckled. That's when I knew I failed. Note: fwiw, other companies that come close in efficiency are - Tether ($90M+ profit/emp) - Hyperliquid ($80M+ profit/emp) and on revenue: - Valve ($50M/emp) - OnlyFans ($37M/emp) - Craigslist ($14M/emp) - Anthropic ($12M/emp, run rate) - OpenAI ($8M/emp, run rate) For comparison, Nvidia is very efficient at scale and is $4.4M/emp.

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DoubleZero
DoubleZero@doublezero·
If you trade on Solana you are eligible for this notification
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Paul Frambot 🦋
Paul Frambot 🦋@PaulFrambot·
digital assets are such a weird marriage between tech and finance. tech loves short feedback loops, automations, fast growth, ... finance wants time, trust, lindy, relationships, ... frustrating if you're a tech person who wants to move fast, but the few tech infra who will survive the test of time will be standing in front of a monstrous opportunity down the line
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Angelina | itsangelina.eth 🌸
The best time today co-hosting a women’s lunch with @ekang426 today! ❤️ Turns out all those outfits waiting for “the right occasion” just needed a women’s lunch!!✨😂 Loved seeing everyone’s creativity and spending the afternoon with such an amazing group. 👯‍♀️
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zon 🪢
zon 🪢@ItsAlwaysZonny·
we've been quiet at @initia for the past months cuz we're a late game scaling champ and just got boots and refillables the market we built for didn't materialize but we haven't adjusted our beliefs, we have instead adjusted our strategy to success more to share next week!
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André
André@oracles·
WTH?! I didn't know there are recovery audit firms whose entire job is to find duplicate payments in Fortune 500 companies and keep a cut. Basically they go through payment records, find invoices that got paid twice, keep a percentage of what they recover. The biggest one has been doing this for 35 years. It covers $9.5 trillion in client spend and recovers about $9 billion a year. These are companies with full finance teams and Big 4 auditors, losing around $2M per billion of spend to payment errors. Everyone involved knows. Nobody fixes it, because checking an invoice manually costs about $10 and takes ten days. At a 1 to 2% error rate, checking everything costs more than the errors. So for fifty years the rational move was to lose the money and pay someone to find part of it later. That's the gap Freehand works in. Instead of auditing a sample of invoices after payment, their AI agents check every invoice against the actual contract and shipment data before the money leaves. One of their customers, a Fortune 100 electronics company with $2B in freight spend, was auditing 33% of its invoices when they started. The other 67% cleared AP unchecked, including $263M a year in accessorial charges nobody was validating. Full coverage found over $3M a year in errors. Very interesting stuff! (Partnered with @freehand_ai)
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Freehand.ai@freehand_ai

We saved $260M in cash for cos. like Meta, Unilever, and J&J by finding baseless charges across 19M+ invoices. Now, we've raised $75M to extend our guarantee: if we can't find $500K in overpaid invoices, we'll pay you $10K. Book a demo: hubs.li/Q04qP6lp0 ------------------------ How it works ⬇️ PROBLEM: A Fortune 500 gets a million invoices a year. Most of them are for a couple hundred dollars. Checking one properly means opening the contract, pulling the rate card, matching the PO, and hunting down the bill of lading that proves the shipment moved the way the carrier says it did. All of that work to defend $200. So nobody does it. The pile goes to a human. The human audits only a sample (12%) and pays the rest (88%) blindly. That's where the money goes. At a pharma co, we found an uncontracted surcharge billed across global carriers for two years. $26M nobody questioned, because every individual line looked ordinary. Each surcharge is small enough that escalating it costs more than paying it. And there are a million of them. ------------------------ SOLUTION: Freehand’s AI reads everything: every line of every invoice, against every contract, rate card, transaction data, bill of lading, warehouse record, time sheet, and email exchange between you and the supplier. It also analyses every past invoice, and transaction data with that supplier, ever. It holds all of this knowledge in a Context Graph. When an invoice arrives, it knows what you should pay, what actually shipped, if the service was delivered or not, what the SLA was, and what this supplier billed you for the same service last quarter. Then it proactively: > writes up the dispute with the evidence attached > emails the supplier > calls her when the email goes quiet > Slacks your purchases team for more information > Follows up until the invoice comes back corrected. > Approves the correct invoice > Pays it across different currencies and tax structures > And accrues the right amount to your general ledger Freehand’s will do all of that to recover $20, because it is doing it across all millions of invoices at the same time, saving 5-10% of company spend with 100% SOX compliance and auditability. ------------------------- 🚨 RT + reply "FREEHAND" and we'll send you the AI upskilling guide that's already helped 750+ displaced workers move into more secure, higher-paying AI-native roles through Freehand's Transition Bootcamp.

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André
André@oracles·
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Peter
Peter@peterwong_xyz·
@oracles That’s an iced out Patek philippa
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André
André@oracles·
fully AI generated image - it’s becoming really hard to recognize it
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Lauris
Lauris@lzminsky·
you can't call yourself a man if you haven't gone through the most important rite of passage there is (losing 1m+ in the span of a few hours to a week). if you've lost that much, then every single number will seem smaller super healthy for having a risk on attitude
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André
André@oracles·
Can we bring the AI slop button to X? @nikitabier Thanks for your attention to this matter.
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André
André@oracles·
it they call you the next warren buffet, it’s over
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goldenlabubuwatch
goldenlabubuwatch@pandawatch88·
The last thing you see before becoming fully aware of your situation
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André
André@oracles·
we all grew up in different places but we share the same trenches
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P2P.org
P2P.org@P2Pvalidator·
We broke down what Q2 actually built, what it sets up for H2, and how our own validators performed against the network. x.com/i/article/2082…
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