Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

a 19 year old girl just coded a quant trading bot from a research paper. Codex did 90% of the work drop PDF into Codex. ask it to act like a quant researcher. get working strategy code strategy: perps DEX funding carry. market-neutral. collect the spread between longs and... show more

NO1ennn

1,404 subscribers

46,468 Aufrufe • vor 3 Monaten •via X (Twitter)

Bildung Wissenschaft & Technologie Finanzen

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

Gipp 🦅

55,610 Aufrufe • vor 2 Monaten

Quant funds don’t go live after a lucky streak. They go live after their strategy survives thousands of simulations. You? You probably went live after 2–3 green trades in a row. That’s not a discipline issue. That’s an access issue. Before a single dollar touches the market, real quant desks run: → Multi-asset robustness checks across timeframes → Monte Carlo simulations across thousands of market conditions → Stress tests to expose exactly where the strategy breaks This used to require $25K/year terminals and entire research teams. Now it doesn’t. Horizon put that entire workflow into a single chat interface. Describe your strategy in plain English. It builds it. Tests it. Breaks it. Shows you where it survives — and where it doesn’t. Before you ever go live. The edge was never intelligence. It was access. Now you have it. 👇 Join early. Get on the waitlist. Stay ahead. Save this.

Quant funds don’t go live after a lucky streak. They go live after their strategy survives thousands of simulations. You? You probably went live after 2–3 green trades in a row. That’s not a discipline issue. That’s an access issue. Before a single dollar touches the market, real quant desks run: → Multi-asset robustness checks across timeframes → Monte Carlo simulations across thousands of market conditions → Stress tests to expose exactly where the strategy breaks This used to require $25K/year terminals and entire research teams. Now it doesn’t. Horizon put that entire workflow into a single chat interface. Describe your strategy in plain English. It builds it. Tests it. Breaks it. Shows you where it survives — and where it doesn’t. Before you ever go live. The edge was never intelligence. It was access. Now you have it. 👇 Join early. Get on the waitlist. Stay ahead. Save this.

Marry Evan

56,965 Aufrufe • vor 4 Monaten

Ken Griffin built Citadel into $67B fund and hires PhDs out of Stanford and MIT. He watched agentic AI do their month work in an afternoon and admitted it sent him home depressed. The interesting bit is what he was describing. A six-part research loop. Find, code, test, deploy, autopsy, learn. Five of those six are done. Free. Available tonight. Prompt to backtested strategy in 12 seconds. Prompt to live broker in 90. The Greenwich quant doing that same work costs $650,000 a year and clears one hypothesis a month. The sixth exists nowhere. Not at Citadel, not at Man Group, not in any repo. It's the part where the machine remembers what already failed. Every losing test dies in a log file. The next agent starts blind. Negative results are the most valuable data in quant research and literally nobody stores them. This loop broken down in my article below. Read it before someone builds the last one and stops explaining how.

Ken Griffin built Citadel into $67B fund and hires PhDs out of Stanford and MIT. He watched agentic AI do their month work in an afternoon and admitted it sent him home depressed. The interesting bit is what he was describing. A six-part research loop. Find, code, test, deploy, autopsy, learn. Five of those six are done. Free. Available tonight. Prompt to backtested strategy in 12 seconds. Prompt to live broker in 90. The Greenwich quant doing that same work costs $650,000 a year and clears one hypothesis a month. The sixth exists nowhere. Not at Citadel, not at Man Group, not in any repo. It's the part where the machine remembers what already failed. Every losing test dies in a log file. The next agent starts blind. Negative results are the most valuable data in quant research and literally nobody stores them. This loop broken down in my article below. Read it before someone builds the last one and stops explaining how.

cvxv666

131,163 Aufrufe • vor 29 Tagen

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

Muhammad Ayan

40,052 Aufrufe • vor 2 Monaten

This trader turned $76 into +$4,304 with a single bet on the weather market! Account: His PnL in March: -$1,000. His PnL now: +$12,000. Best trades: $76 -> $4,304 $19 -> $1,370 $52 -> $1,016 Main strategy: buying low-probability outcomes. This approach can lead to 50 losses in a row, but weather markets are full of inefficiencies. That’s why he sticks to his strategy, and in the end it pays off. With this approach, win rate doesn’t matter. One successful 1¢ trade can generate huge profit even if the previous 49 trades were losses. Simple math. The lower the probability of the outcome, the smaller his position size. Perfect use of the Kelly Criterion allows him to avoid going negative from incorrect sizing. Result: +$10,000 in just one month! To profit from this strategy, just set up copy trading on his account:

This trader turned $76 into +$4,304 with a single bet on the weather market! Account: His PnL in March: -$1,000. His PnL now: +$12,000. Best trades: $76 -> $4,304 $19 -> $1,370 $52 -> $1,016 Main strategy: buying low-probability outcomes. This approach can lead to 50 losses in a row, but weather markets are full of inefficiencies. That’s why he sticks to his strategy, and in the end it pays off. With this approach, win rate doesn’t matter. One successful 1¢ trade can generate huge profit even if the previous 49 trades were losses. Simple math. The lower the probability of the outcome, the smaller his position size. Perfect use of the Kelly Criterion allows him to avoid going negative from incorrect sizing. Result: +$10,000 in just one month! To profit from this strategy, just set up copy trading on his account:

me_cool_off

12,213 Aufrufe • vor 4 Monaten

Turn any paper into running code. Just swap arxiv → autoarxiv in the paper url. That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies, broken paths, environment config, and hardware assumptions. From there it designs a minimal reproduction. That means a smaller model, fewer steps, and a single GPU instead of a cluster, scaled down just enough to test whether the headline claim holds. The whole run is live and fully logged. Loss curves, metrics, and training progress are all observable as it happens. What comes back is a clean signal on whether the minimal run matches the paper's reported result, plus an estimate of what a full replication would cost in compute and time. A lot of research code dies in setup before anyone verifies a single number. This moves reproduction from a weekend of debugging to a url change. Pick a paper and try it now. video credits: alphaXiv

Turn any paper into running code. Just swap arxiv → autoarxiv in the paper url. That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies, broken paths, environment config, and hardware assumptions. From there it designs a minimal reproduction. That means a smaller model, fewer steps, and a single GPU instead of a cluster, scaled down just enough to test whether the headline claim holds. The whole run is live and fully logged. Loss curves, metrics, and training progress are all observable as it happens. What comes back is a clean signal on whether the minimal run matches the paper's reported result, plus an estimate of what a full replication would cost in compute and time. A lot of research code dies in setup before anyone verifies a single number. This moves reproduction from a weekend of debugging to a url change. Pick a paper and try it now. video credits: alphaXiv

Akshay 🚀

46,032 Aufrufe • vor 2 Monaten

a portfolio manager running $8.4 billion once paid a quant researcher $410,000 a year to answer one question: “can you tell me which breakouts are real before we risk the money?” the researcher started with an idea traders had repeated for decades a breakout is stronger when the tape becomes “twitchy” everyone understood what that looked like nobody could define it precisely enough to code so he measured the speed at which trades forced price to update not raw volume because one million orders hitting the same price can create activity without moving the market the difference was enormous during lunch, price changed around 4 times per second near the closing bell, it jumped to almost 70 a 17.5x increase in the speed of price discovery then he built the test: breakout = 5-minute close beyond the previous 30-minute high or low signal = normalized price-update speed success = profit barrier reached before the equal-distance loss barrier now imagine the fund puts $25 million behind each breakout basket filtering out one false 0.8% move protects roughly $200,000 that is why the manager did not care whether the chart looked convincing he cared whether “twitchy” could be turned into a number most traders protect their intuition from being tested quants turn it into code and make it fight for capital and this is exactly the skill firms pay six figures for: taking a vague market idea, defining it mathematically, then testing whether it deserves real money I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓

a portfolio manager running $8.4 billion once paid a quant researcher $410,000 a year to answer one question: “can you tell me which breakouts are real before we risk the money?” the researcher started with an idea traders had repeated for decades a breakout is stronger when the tape becomes “twitchy” everyone understood what that looked like nobody could define it precisely enough to code so he measured the speed at which trades forced price to update not raw volume because one million orders hitting the same price can create activity without moving the market the difference was enormous during lunch, price changed around 4 times per second near the closing bell, it jumped to almost 70 a 17.5x increase in the speed of price discovery then he built the test: breakout = 5-minute close beyond the previous 30-minute high or low signal = normalized price-update speed success = profit barrier reached before the equal-distance loss barrier now imagine the fund puts $25 million behind each breakout basket filtering out one false 0.8% move protects roughly $200,000 that is why the manager did not care whether the chart looked convincing he cared whether “twitchy” could be turned into a number most traders protect their intuition from being tested quants turn it into code and make it fight for capital and this is exactly the skill firms pay six figures for: taking a vague market idea, defining it mathematically, then testing whether it deserves real money I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓

Sammy

54,858 Aufrufe • vor 1 Monat

"You cannot walk from point A to B inside OpenAI without hearing Codex get mentioned." Here's my new episode with Rohan Varma (Codex PM) on how he uses Codex as his everything app to: → Prototype designs with Image Gen → Turn repeated work into automations → Turn project context into live Codex Sites → Use one Codex thread to manage others Some quotes from Rohan: "[Ask Codex] for something that seems 10x more ridiculous than you ever think it could do. And it probably can do 90% of it." "Over the course of the day, I'm probably doing 3-4x more than I would have without Codex." "For me, every time I do anything I basically think, could Codex have done this?" 📌 Watch now: Thanks to our sponsors: Wispr Flow: 4x faster than typing with your voice Riverside: All-in-one AI studio for podcasts and video

"You cannot walk from point A to B inside OpenAI without hearing Codex get mentioned." Here's my new episode with Rohan Varma (Codex PM) on how he uses Codex as his everything app to: → Prototype designs with Image Gen → Turn repeated work into automations → Turn project context into live Codex Sites → Use one Codex thread to manage others Some quotes from Rohan: "[Ask Codex] for something that seems 10x more ridiculous than you ever think it could do. And it probably can do 90% of it." "Over the course of the day, I'm probably doing 3-4x more than I would have without Codex." "For me, every time I do anything I basically think, could Codex have done this?" 📌 Watch now: Thanks to our sponsors: Wispr Flow: 4x faster than typing with your voice Riverside: All-in-one AI studio for podcasts and video

Peter Yang

141,629 Aufrufe • vor 2 Monaten

This bot started with tiny $5 orders - and somehow pulled in over $200,000 on the 5-minute market. In just one week, it’s already cleared more than +$212,578 And the biggest single win? Only $745. But the wild part isn’t the size of any one trade - it’s how those tiny positions stack up into such an aggressive profit curve. From the outside, it looks like it’s running three simple plays: - Uses $5 to grab 1¢ outcomes - Exits early before expiration to lock in profit - Scales up to $250 on 25¢–50¢ positions Then pushes further with $500 into 50¢ outcomes. So this doesn’t look like a typical “elite” trading bot squeezing every edge with precision. It feels more like something is off inside Polymarket - creating these exaggerated, almost unnatural returns. And yet, right now, this account sits at #2 in weekly crypto profits. If it’s really hitting profitable trades this consistently - that’s not just a bot. That’s basically a money printer.

This bot started with tiny $5 orders - and somehow pulled in over $200,000 on the 5-minute market. In just one week, it’s already cleared more than +$212,578 And the biggest single win? Only $745. But the wild part isn’t the size of any one trade - it’s how those tiny positions stack up into such an aggressive profit curve. From the outside, it looks like it’s running three simple plays: - Uses $5 to grab 1¢ outcomes - Exits early before expiration to lock in profit - Scales up to $250 on 25¢–50¢ positions Then pushes further with $500 into 50¢ outcomes. So this doesn’t look like a typical “elite” trading bot squeezing every edge with precision. It feels more like something is off inside Polymarket - creating these exaggerated, almost unnatural returns. And yet, right now, this account sits at #2 in weekly crypto profits. If it’s really hitting profitable trades this consistently - that’s not just a bot. That’s basically a money printer.

Dekos

19,539 Aufrufe • vor 4 Monaten

The current market environment on Bitget is perfectly suited for running a Futures Grid bot, as strong trends combined with high volatility create more profit opportunities. This setup allows traders on Bitget to benefit from both upward and downward price movements through automated buy-sell execution. A smart grid strategy helps capture consistent gains while reducing emotional trading and saving time. By copying a proven Bitget Futures Grid configuration, you can start trading efficiently without complex setup or constant chart watching. Click my link to instantly copy my grid strategy and get started in just one tap. Let automation work for you and take advantage of current market momentum today. 🔗 Check out the bot copy-trading link and start now 💪 #Bitget #FuturesGrid #Bot #TradingBot

The current market environment on Bitget is perfectly suited for running a Futures Grid bot, as strong trends combined with high volatility create more profit opportunities. This setup allows traders on Bitget to benefit from both upward and downward price movements through automated buy-sell execution. A smart grid strategy helps capture consistent gains while reducing emotional trading and saving time. By copying a proven Bitget Futures Grid configuration, you can start trading efficiently without complex setup or constant chart watching. Click my link to instantly copy my grid strategy and get started in just one tap. Let automation work for you and take advantage of current market momentum today. 🔗 Check out the bot copy-trading link and start now 💪 #Bitget #FuturesGrid #Bot #TradingBot

Ben_NFT

22,823 Aufrufe • vor 7 Monaten

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

cvxv666

93,826 Aufrufe • vor 2 Monaten

We aren't ready for this next generation of agentic engineers. 100k Github stars in 24 hours (claw-code), Yeachan Heo (Bellman) has 3 to 5 pro accounts and ships software from telegram and discord. Those who accuse these guys of AI slop haven't done their homework because the real story is wild and vastly mis-understood. Yeachan has a background in quant trading and developed agentic systems to help with research (and says that LLMs aren't good for trading). He uses the term "agentic runtime" and uses CS principles to treat skills like pointers in memory. oh-my-codex was used to make the clean room clone of Claude Code...in 2 hours...on a plane...over text!! He developed this orchestration layer for Codex and it is powerful. It covers the entire SWE workflow like pipelines, persistent memory/state MCP servers, and extensible hooks. They aren't burning tokens for the sake of burning. I highly encourage you to look at the oh my codex repo and start extracting some of the ideas Bellman uses to ship software.

We aren't ready for this next generation of agentic engineers. 100k Github stars in 24 hours (claw-code), Yeachan Heo (Bellman) has 3 to 5 pro accounts and ships software from telegram and discord. Those who accuse these guys of AI slop haven't done their homework because the real story is wild and vastly mis-understood. Yeachan has a background in quant trading and developed agentic systems to help with research (and says that LLMs aren't good for trading). He uses the term "agentic runtime" and uses CS principles to treat skills like pointers in memory. oh-my-codex was used to make the clean room clone of Claude Code...in 2 hours...on a plane...over text!! He developed this orchestration layer for Codex and it is powerful. It covers the entire SWE workflow like pipelines, persistent memory/state MCP servers, and extensible hooks. They aren't burning tokens for the sake of burning. I highly encourage you to look at the oh my codex repo and start extracting some of the ideas Bellman uses to ship software.

Ray Fernando

59,092 Aufrufe • vor 4 Monaten

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.

Rossst.03

699,772 Aufrufe • vor 14 Tagen

This trader reportedly made $90,000 in one day after using Claude Fable 5 to test 600 strategies in 48 hours. He was not smarter than Wall Street. He simply killed bad ideas thousands of times faster. His old backtesting system needed nearly a week to evaluate one strategy. Claude Fable 5 reportedly reduced the same process to around two minutes, letting him test hundreds of ideas over a single weekend. The results were brutal. 597 strategies failed. Only 3 survived. Those three were then deployed into live Polymarket trading and reportedly generated $90,000 in one day. The real edge was not discovering one brilliant strategy. It was eliminating almost every weak strategy before real money touched the market. His process started with a simple rule: “When the order book leans 70/30 during the final 90 seconds, buy the UP side.” Fable 5 then replayed that rule tick by tick across thousands of settled markets, including real order books, fills, liquidity and slippage. Two minutes later, the system could show whether the idea had any chance of surviving. The three successful strategies now reportedly run through MiroFish, use Kelly-based position sizing and only enter when simulated expectations diverge from the live market. That is what makes AI quant trading powerful. It does not instantly turn someone into a trading genius. It gives them far more attempts to test ideas, reject weak setups and identify the few patterns that remain profitable after realistic simulation. A traditional fund might test 20 strategies in a quarter. He tested 600 in one weekend. The advantage was not better intuition. It was faster elimination.

This trader reportedly made $90,000 in one day after using Claude Fable 5 to test 600 strategies in 48 hours. He was not smarter than Wall Street. He simply killed bad ideas thousands of times faster. His old backtesting system needed nearly a week to evaluate one strategy. Claude Fable 5 reportedly reduced the same process to around two minutes, letting him test hundreds of ideas over a single weekend. The results were brutal. 597 strategies failed. Only 3 survived. Those three were then deployed into live Polymarket trading and reportedly generated $90,000 in one day. The real edge was not discovering one brilliant strategy. It was eliminating almost every weak strategy before real money touched the market. His process started with a simple rule: “When the order book leans 70/30 during the final 90 seconds, buy the UP side.” Fable 5 then replayed that rule tick by tick across thousands of settled markets, including real order books, fills, liquidity and slippage. Two minutes later, the system could show whether the idea had any chance of surviving. The three successful strategies now reportedly run through MiroFish, use Kelly-based position sizing and only enter when simulated expectations diverge from the live market. That is what makes AI quant trading powerful. It does not instantly turn someone into a trading genius. It gives them far more attempts to test ideas, reject weak setups and identify the few patterns that remain profitable after realistic simulation. A traditional fund might test 20 strategies in a quarter. He tested 600 in one weekend. The advantage was not better intuition. It was faster elimination.

Secta

84,871 Aufrufe • vor 1 Monat

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Dan Shipper 📧

55,561 Aufrufe • vor 4 Monaten

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 Aufrufe • vor 2 Monaten

This trader used Claude to build a Quant Bot and made +$38,385 on Polymarket Since May 11, this wallet has been averaging about $1,200 in profit per day while doing almost 15 trades per hour 11,859 predictions with a 53% win rate in 32 days This trader’s Polymarket account: He started with a $5.2K deposit and is now up roughly +733% ROI The bot strategy is simple: > it keeps targeting markets where the board is still behind the move > gets in before the odds fully catch up then reuses the same setup across a long stream of entries > one trade does not carry the result, the PnL comes from repeating the same small edge until it compounds Most profitable trades: $1,022 → $3,993 (+$2,971, +290.65%) $36 → $2,358 (+$2,322, +6,416.77%) $16 → $2,327 (+$2,310, +13,672.04%) What stands out here is not one lucky trade It is a repeatable short-window process that keeps turning delayed pricing into steady profit

This trader used Claude to build a Quant Bot and made +$38,385 on Polymarket Since May 11, this wallet has been averaging about $1,200 in profit per day while doing almost 15 trades per hour 11,859 predictions with a 53% win rate in 32 days This trader’s Polymarket account: He started with a $5.2K deposit and is now up roughly +733% ROI The bot strategy is simple: > it keeps targeting markets where the board is still behind the move > gets in before the odds fully catch up then reuses the same setup across a long stream of entries > one trade does not carry the result, the PnL comes from repeating the same small edge until it compounds Most profitable trades: $1,022 → $3,993 (+$2,971, +290.65%) $36 → $2,358 (+$2,322, +6,416.77%) $16 → $2,327 (+$2,310, +13,672.04%) What stands out here is not one lucky trade It is a repeatable short-window process that keeps turning delayed pricing into steady profit

Daniro

42,417 Aufrufe • vor 2 Monaten

A 33-year-old quant from Hong Kong built a perfect BTC price simulation engine with a swarm of agents. In a single trade, he turned $2,000 into $166,000. 7,500% profit. All proof onchain - every single one of his trades is publicly visible on Polymarket. His wallet: Humans can’t process dozens of gigabytes of data in seconds - but Claude can. Closed order book feeds from CEXs, live data from OTC trading desks, Pyth oracles - AI agent gathers all of it in one iteration. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $369,000 all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. On April 24, he was only one who knew the market was wrong. He entered a trade with just a 1.3% implied probability of execution. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Want the full guide to build the same bot? Save the post and read the article. Want to start copying his trades right now? Use this TG copy-trading bot (US users can actually trade here, unlike the main Polymarket site):

A 33-year-old quant from Hong Kong built a perfect BTC price simulation engine with a swarm of agents. In a single trade, he turned $2,000 into $166,000. 7,500% profit. All proof onchain - every single one of his trades is publicly visible on Polymarket. His wallet: Humans can’t process dozens of gigabytes of data in seconds - but Claude can. Closed order book feeds from CEXs, live data from OTC trading desks, Pyth oracles - AI agent gathers all of it in one iteration. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $369,000 all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. On April 24, he was only one who knew the market was wrong. He entered a trade with just a 1.3% implied probability of execution. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Want the full guide to build the same bot? Save the post and read the article. Want to start copying his trades right now? Use this TG copy-trading bot (US users can actually trade here, unlike the main Polymarket site):

cvxv666

83,208 Aufrufe • vor 3 Monaten

This trader used Claude to build a Quant Bot and made +$61,370 on Polymarket Since Mar 27, this wallet has been averaging roughly $820 in daily profit and a little over 20 trades per hour 36,611 predictions with a 50% win rate in 75 days This trader’s Polymarket account: The first deposit was $5.4K From that starting balance, the bot is up roughly +1,136% ROI Quant strategy is simple: > It does not wait around for one huge move > It keeps entering where short-term pricing is still behind the tape > Runs the same setup again and again across a large number of entries > Each trade on its own is not massive, but repetition is what builds the result Most profitable trades: $604 → $1,923 (+$1,319 +218.48%) $1,083 → $2,112 (+$1,029 +95%) $114 → $1,132 (+$1,018 +892.74%) It is a repeatable short-window process that keeps turning small pricing mistakes into steady PnL

This trader used Claude to build a Quant Bot and made +$61,370 on Polymarket Since Mar 27, this wallet has been averaging roughly $820 in daily profit and a little over 20 trades per hour 36,611 predictions with a 50% win rate in 75 days This trader’s Polymarket account: The first deposit was $5.4K From that starting balance, the bot is up roughly +1,136% ROI Quant strategy is simple: > It does not wait around for one huge move > It keeps entering where short-term pricing is still behind the tape > Runs the same setup again and again across a large number of entries > Each trade on its own is not massive, but repetition is what builds the result Most profitable trades: $604 → $1,923 (+$1,319 +218.48%) $1,083 → $2,112 (+$1,029 +95%) $114 → $1,132 (+$1,018 +892.74%) It is a repeatable short-window process that keeps turning small pricing mistakes into steady PnL

Daniro

11,958 Aufrufe • vor 2 Monaten

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 Aufrufe • vor 1 Jahr