this quant paper is f*cking insane it's 151 Trading Strategies by Zura Kakushadze and Juan Andrés Serur, 361 pages, 550+ formulas, R source code for backtesting, released free specifically to break down the info silos in quant finance. the crazy part is the range it covers: stocks, options, futures, crypto, volatility treated as its own asset class, even weather and tax arbitrage strategies, all in one place. closest thing to a quant desk's internal playbook that's ever gone public for free. bookmark it before it disappears.

this paper is f*cking insane a team out of NASA and MIT built a stripped-down trend and momentum system for gold futures, then tested it strictly out of sample. the numbers: 2.88 Sharpe, 0.52% max drawdown, 43% annualized return at a 15% vol target, all net of costs and market impact. across 2,793 trading days the strategy ran near-zero beta to gold, with estimated capacity approaching $1 billion. the crazy part is the signal itself isn't fancy at all, the real edge comes from turning weak predictability into disciplined sizing, exits, and execution. bookmark it before this thread gets buried.

this Stanford paper is f*cking insane it's about as close to a real HFT desk as I've ever seen go public. 14 pages, top-tier signal combination, a full statistical framework. the same framework I broke down in 11 steps in the article below: serial demeaning, cross-sectional normalization, residual weighting, empirical Kelly. the crazy part is most people call the market direction right and still lose money, simply because they never ran into these 14 pages. read the paper first, then read the article. bookmark it before this thread gets buried.

this quant paper is f*cking insane it's basically the 12-step checklist hedge funds run through before they ever pull the trigger on a trade. every step, every formula, full Python code, all of it laid out. drop it into Claude and you'll have a working trading system by tonight. the crazy part is most people are trading blind while this entire framework has been sitting out in the open the whole time. full breakdown in the article below. bookmark it before this thread gets buried.

I STILL CANNOT F**KING UNDERSTAND WHY PEOPLE ARE NOT USING THIS AI STACK Grok Bot finds the opportunities. The agents do everything in between. And somehow this setup is generating $11,000+ a month. Here's the workflow: 1. Scout finds stories and formats already getting attention. 2. Scriptwriter turns them into short-form scripts. 3. Art Director decides what every shot should look like. 4. Producer generates the visuals with Picsart. 5. AI adds voice + captions. 6. Auditor reviews the finished videos and kills anything that feels repetitive. 7. Publisher sends the survivors to Shorts, TikTok and Reels. 8. Analyst reads the numbers and tells the system what to stop doing. The crazy part isn't the generation. It's the filtering. 42 videos made every week. 30 published. 12 killed. The system would rather destroy a bad video than waste distribution on it. That's why this works without: filming a camera an editor a face I spend roughly 2 hours a week touching the system. $11,000+ a month from a stack that most people could probably build with the same tools sitting in front of them. The tools aren't the moat. The workflow is. I mapped the entire thing - agents, prompts, setup, costs and the exact production flow.

I STILL DON'T UNDERSTAND WHY PEOPLE ARE PAYING EDITORS $500+ FOR THIS my entire setup runs without a camera, editor or filming. here's the workflow: → grok finds formats already pulling millions of views → i strip the video down to its structure → rebuild it around a completely different topic → generate the visuals → AI voice + captions → publish everywhere → kill anything that doesn't hold attention the trick isn't making AI videos. it's finding a format that already proved it works. one format can become 20 completely different videos without copying the original. that's where it gets stupid. no camera. no editor. no filming. no face. i use @Picsart for the visual generation because it removes the most time-consuming part. the exact prompts + workflow are here ↓

this paper is f*cking insane a quant paper combined a Hidden Markov Model with reinforcement learning to shift portfolio allocation on the fly as market regimes change. the numbers: it beat SPY on risk-adjusted returns, with shallower drawdowns across 2004-2025. the crazy part is it's not trying to forecast the market at all, it figures out what regime it's in first, then decides the allocation from there. bookmark it before this thread gets buried.

this quant paper is f*cking insane it's a 51-page guide from an MIT Sloan Business Club member covering probability, stats, market making, and real interview questions from Jane Street, Citadel, Two Sigma and more, all for free. the crazy part is no professor put this together, it started as one student's own interview prep notes before it turned into a full guide to help others break into quant finance. bookmark it before it disappears.

this Stanford paper is f*cking insane they just compressed the entire hedge fund playbook into a 17-page pdf. Stanford put out the complete Hidden Markov Model framework that quants at firms like Jane Street and Two Sigma are known to run, and released it for free. the crazy part is this isn't some watered-down summary, it's the actual mechanics behind models these desks keep locked up internally. most people assume this stuff never leaves institutional walls. this one hands you the framework directly, no gatekeeping. bookmark it and read before someone takes it down.

this quant paper is f*cking insane a former Morgan Stanley quant who went on to launch his own hedge fund just put the whole thing out there for free. 224 pages, and it's not theory, it's the actual winning algo strategies with every line of code included. the crazy part is stuff this detailed almost never makes it past a fund's walls, let alone gets released to the public. if quant trading has been on your radar but you didn't know where to start, this is the entry point. bookmark it now before it disappears.

this Anthropic paper is f*cking insane it's a 13-page breakdown of 5 memory layers that cut token costs by 90% and make an agent actually learn. 1. WORKING MEMORY: what it sees now the context window, everything the agent has in front of it right now. when it fills up, old context gets lost. most agents live here, then we wonder why they keep failing. 2. EPISODIC MEMORY: what happened the full history of interactions, with dates and times. the agent remembers the deployment broke Tuesday at 3am because of a typo in the migration script. you never explain it twice. 3. SEMANTIC MEMORY: what it knows facts, entities, and relationships stored in a knowledge graph. "the user prefers TypeScript" lives here, and it doesn't vanish when the session ends. 4. PROCEDURAL MEMORY: how to do things the agent tries 3 approaches, one works. that method becomes a reusable skill. next time it goes straight to what worked. 5. FORGETTING: what it should erase an agent that never forgets ends up stacking contradictions. old preferences override the new ones. you move cities and it's still recommending restaurants from where you used to live. remembering matters. knowing what to forget matters just as much. the result? → Mem0 dropped from 26,000 tokens per query to 1,800 → Snowflake added an ontology layer and got 20% more accuracy with 39% fewer tool calls memory pays for itself from day one. this 13-page pdf is the difference between a chatbot and an agent that actually learns. don't skip it ↓

this paper is f*cking insane Chinese researchers just published one with a brutal title: "The End of Software Engineering." their argument: in traditional software, code carries pre-written human logic. in agentic software, the AI agent is the software, code just gets generated, run, and thrown away on the fly by an LLM reasoning loop. they map it out in three eras: on-prem software you install, then SaaS hosted in the cloud, now Agent-as-a-Service. the crazy part is what actually gets transferred this time. the first shifts moved complexity off the user's plate. this one transfers the decision-making itself. human devs can only hold so much state in their head before things break. LLM agents don't just code faster, they handle architectural complexity that scales past what a person can track. so the job flips: you stop writing implementation line by line and start setting goals, designing how agents coordinate, and auditing what they produce. bookmark this before it gets buried.