slim jim simons

@Vedankt @AgustinLebron3 hrt makes more than xtx with neural at this point and it's not close

@AgustinLebron3 XTX is the one I always think about with this, I know they r very deep learning heavy and have so many fucking gpus

Who's going to tell him.

@BeatzXBT - stop trading on a literal potato and buy moar RAM - stop thinking as % returns and think in dollar pnls (% returns only matter if capacity is >1B) - use claude code to bear skill issues good start and good luck!

so, been a while... back to making bad markets slightly less bad, and man i've missed it. this time its less stinky on the coins, more stinky on the exchange choice. the system is new, been working on it since ~mid feb and got it stable right before the start of this month.

@luke_pighetti @flyosity @kimmotamio $1m is pretty low by openai comp standards

@flyosity @kimmotamio but yeah i bet his tc is around $1M/yr

why is everyone saying OpenClaw was a $1B exit?

@fintechfrank largest???

The world’s largest trading firm is expanding its crypto team. Citadel Securities is hiring a crypto quantitative developer to build and optimize high-performance trading systems, according to a LinkedIn post

@haridigresses @Zerochurn @based16z > QQQ has entered the chat

@Zerochurn @based16z The moment your IRR beats Founders Fund or Iconiq or Lightspeed, we can talk.

@BenniKim you can be much faster for much less

You can run an algo on CME commodity futures with 350us tick to trade on a shoestring budget. 1/ +50 us to get the data, with a direct 10G link. $3k/month 2/ +80 us for the feed handler and orderbook feature generation (your code) 3/ +100 us for the decision logic (your code) 4/ +120 us for the pre-trade controls and order routing. $2k/month This is not particularly competitive, but just flagging that low-ish latency is now feasible at a much lower price point.

@BearOnWindows95 gay

Theres so much edge in PnL strategy for discretionary traders and I never hear anyone talk about. - knowing when to stop for the day - knowing when to press - understanding what PnL is possible for the current environment - Knowing when to expand targets and go for the home run - Size up or down when you are reading the market well - Sizing down when you are struggling - understanding how large winning days can affect you psychologically - the stress of losing days vs the stress of big winning days Figuring this out was the most difficult part of the journey for me.

@annanay A vs S is just horizon thing

How do MMs evolve into alpha-driven behemoths? It's fairly natural: >C tier: market making without an alpha signal How can I avoid getting picked off? Find alpha, make a model! >B tier: market making, avoiding bad trades based on an alpha signal. What if my alpha signal gets so good that I just want to position myself based on it? >A tier: trading on an alpha signal. Some of these trades might be liquidity-adding, but this isn't the main intention any more. >S tier: my alpha signal is too good. I have to avoid monetizing it too fast and leaking my intentions into the market before I can size up too much (Citadel is here).

@systematicls > Kaggle GMs. Physics PhD dropouts. Ex-poker pros. all of these resumes would get an interview lolz

ADVERSE SELECTION IN SMALL TRADING FIRMS Most small trading firms fail to scale because they can't solve a simple economics problem: If [BIG PODSHOP] offers $40mn + $5bn book for top talent, and you offer $1mn + $100mn book, you're priced out of the best traders. So who CAN you hire? 1. People who underpriced themselves -> they leave once they wise up 2. People who aren't worth your bid -> you get adversely selected The information asymmetry is brutal. Experienced hires always know more about their true probability of success than you do. This makes hiring experienced talent -EV for small firms. Big shops expand by hiring experienced talent to enter new markets because they can afford the ACTUAL best. You can't. The "best" you can afford is often no better than a fresh trader, sometimes worse because they're stuck in old patterns. So you pay a premium for "experience" with zero discovery advantage over a grad. This is why most small firms get trapped in their origin market and never achieve escape velocity. BUT, some firms are solving this brilliantly. Eqvilent is fishing from the Kaggle grandmaster pool for fresh trading talent. Think about the elegance: 1. Kaggle GMs have PROVEN ability to extract signal from noise 2. They've demonstrated creativity under constraints 3. They're unpriced 4. They have zero bad habits from legacy systems 5. Most importantly, they're SELECTED on the exact skill that matters They're not hiring "experienced traders" who may have gotten lucky. They're hiring people who've publicly demonstrated they can find alpha in datasets, repeatedly. This bypasses the adverse selection problem entirely. Instead of competing with [BIG PODSHOP] for proven PMs/traders, you're building a filtering mechanism that identifies raw talent BEFORE the market prices it correctly. The smartest small firms aren't trying to outbid the big shops. They're finding orthogonal talent pools where they have an information advantage. Kaggle GMs. Physics PhD dropouts. Ex-poker pros. People who've demonstrated the cognitive toolkit but haven't been absorbed by the pod economy yet. The lesson: You can't win by playing the same game as [BIG PODSHOP]. You win by finding talent pools they're not looking at and building the infrastructure to convert raw ability into edge.

@0xalpo your example is easily generalizable to "US interest rate in X days", which is most definitely something MMs already have models for (see option markets)

Take the market for "US interest rate in 17 days," or any variant Too small for trading firms to trade, even though it's ~trivial to arbitrage against US interest rate markets

There are markets where you can only make $1 even if you trade it to perfection Jane Street can never justify putting a quant on these markets But if you could make these markets for $0.10 of inference, you would

@KrisAbdelmessih @jasoncbuck not mag7, they're competing with openai, anthropic, and deepmind for the brightest quants. quite hard to do when the frontier labs are paying similar while also selling the vision of solving intelligence once and for all.

In response to this mornings letter @jasoncbuck texted me for my take on why prop firms are departing from historical anonymity and posting videos/content My guess on a long enough timeline is the TAM is huge and they need to keep attracting lots of talent

@evanjconrad "growing faster than cursor" is only impressive if your scale is comparable, which it is not

What SF Compute does. When you finance a GPU cluster, you need to get an "offtake" agreement. Basically, someone has to agree to rent the cluster from you, typically for a 3+ year period. If that agreement falls through (the person fails to pay), then the person who owns the cluster gets wiped out, and their lender ends up with a bunch of GPUs, rather than say, money. It really looks like the world is deploying more capital into the AI build out than any infrastructure project in the history of the world. You remember when people said there was going to be a Manhattan project for AI? The current build out is the size of 20 Manhattan projects. We’re so far past the Manhattan project it’s not even funny. This is the cost of a war. It would be really bad if that scale of capital was secured against offtake agreements (long term contracts) with application layer companies who turn around and sell to their customers on a month to month basis. If the AI SaaS has a bad few months, can the AI SaaS continue to front their compute bill? They could in CPUs, because in SaaS you might have a company with $20m in the bank, and has a $1m/year "CPU" bill. But in GPUs, you have a startup that raised $20m, but a $20m+/year compute bill. So a small shift in demand means lights out for your business, because the products are so levered. That works as long as you can plug the gaps with venture capital & high margins. But across the board, AI applications are lower margin than their SaaS counterparts, giving them less buffer to save them in a bad month. And even in a hot market, venture capital won't necessarily save you if you're running unprofitably with a massive liability. That’s the problem we solve. We let people buy long term contracts they can “exit”, by selling back. That lets them get liquidity in the most critical moments, ensuring they turn a profit rather than a loss on tight margins. In other words, we prevent a bubble. When we do that, it opens up blocks of compute for smaller use cases too, like academics or startups. When we started, we were "Junelark", a 2-person audio model company that bought too big of a cluster. We had bought 12 months, but could only afford 1 month. To avoid bankruptcy, we had to sublease the other 11 months by acting as GPU brokers. Our audio model company was forced to pivot or die because we didn't have liquidity. To make SF Compute, we split the company down the middle. One side of the house makes a billing company, a ledger, an order book, and a compliance program. The other side makes a systems engineering company. To make this work, you need to run the clusters. So we make the low level cloud stack that interacts with BMC (Redfish & IPMI), UFM, built a UEFI app that replicates PXE boot in weird environments, and a virtualization layer kind of like EC2. It’s a massively complex machine filled with nitty gritty challenges. Today, we’re growing faster than Cursor and we’re scaling to secure the risk of the largest infrastructure build out in the history of the world. We’re hiring across the board for rust programmers, systems engineers, and GTM, and we’d love for you to join us to prevent an AI bubble.

@ns123abc “top researchers” lolz

ex OpenAI top researchers are actively calling out serious OAI researchers to quit building the AI slop machine Lmao, its over

“why did a fat man drop off a bomb at our office?”

@MovieTimeDev calling it “sentiment arbitrage” is certainly one way to out yourself as a larp

scraping tiktok virality to frontrun political prediction markets.

@2500capital ambition decay. it happens.

When I started my fund in summer 2021, my goal was to establish a strong enough five year record that I could raise institutional money and have a “real fund.” Now that I’m four years into the venture, I’ve achieved the track record and lost the desire to raise institutional money. Has anyone else experienced this? Why do folks even bother seeking institutional money? Is this a shortfall of ambition on my part? Is it a triumph of wisdom over ego? Is it both? Some reflections: 1) It is a hell of a lot easier to both handle the logistics of a fund and generate outsized returns when you are running a small amount of money (we can debate whether that is under 10mm, under 25mm) 2) It feels really good to run the money “like it is your money” because a major portion of it is actually your money (for me ~25%) 3) It feels amazing to only have a couple of LPs and have them be close friends and allies who signed up wholeheartedly for the above 4) If you compound at 25-50% gross for many years, you end up making so much money on your own LP stake that the fees are really inconsequential by comparison. What am I giving up? 1) I guess some ego and status stuff? 2) A good year for me is measured in personal rewards in the seven rather than eight figures 3) I don’t get invited to conferences or get good service from providers. I don’t even have a “real” prime broker. I don’t know. It seems like a decent balance and I love the fact that I don’t have to pretend to be interested in the nonsense that institutional investors and allocators spout. Every time I read posts on here like the original thread from @orrdavid (no personal offense intended) about that game, I just think to myself that I’m glad I don’t have to pretend to take that stuff seriously.

@alyssakrejmas orrrr they are asking these questions to see if y’all have any common ground to riff on

My least favorite thing about SF is how people are constantly sizing you up see how much they can gain by becoming your “friend”. Assessing who you know, how you got into a party, where you’re from or where you live, as basis for whether or not you are worth even speaking to.

@rak_garg if you’re the child of a wealthy new yorker, the hamptons are your very exclusive summer camp. all your childhood homies, frenemies, and crushes are there every summer like clockwork. kinda brilliant.

Legitimately don't understand why NYC people spend $20k on a weekend in Montauk or the Hamptons just to get the same vibe as a very average backyard in San Mateo.

@nic_carter what makes you think capital will be worth anything?

everyone I know believes we have a few years max until the value of labor totally collapses and capital accretes to owners on a runaway loop - basically marx' worst nightmare/fantasy. this is the permanent underclass thing. and everyone I know subscribes to it