Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Wall Street's worst nightmare isn't regulation. It's a GitHub repo. Kronos. An AI trained from scratch on 12 billion candles from 45 exchanges, and it reads charts as a native language. The numbers: - 93% better at ranking price forecasts than the best time-series model that exists - Zero-shot... show more

AlphaCartel

4,934 subscribers

128,651 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии Финансы

Anya Rossi• Live Now

Private livecam show

Комментарии: 14

Фото профиля Mbelli

Mbelli2 месяцев назад

@aibullss Lost to both ewma and garch on every test I ran

Фото профиля Martin Vivier

Martin Vivier2 месяцев назад

@aibullss I tried the same kind of system on Grok with MT5 and was quite pleased. It’s on demo now for 2 weeks. Needs some adjustment as we go along. But it’s better than other bots I tried. I like it’s entry levels

Фото профиля Anki

Anki2 месяцев назад

@aibullss Every few weeks, some peddles this shit for engagement. Its engagement slop, doesn't actually work for anything

$Фото профиля ¯\_(ツ)_/¯$

¯\_(ツ)_/¯2 месяцев назад

@aibullss backtested the shit out of kronos result: no value

Фото профиля ᶜᵃᵐᵉᶫ

ᶜᵃᵐᵉᶫ2 месяцев назад

@aibullss It simply doesn’t work. I spent way too long trying

Фото профиля LevelUpWorld

LevelUpWorld2 месяцев назад

@aibullss The ….. 😂😂😂

Фото профиля 晚晚

晚晚2 месяцев назад

@aibullss GitHub repo成华尔街噩梦，这标题够狠

Фото профиля openQuant

openQuant2 месяцев назад

@aibullss Model is bullshit. Quants do not use garbage like this.

Фото профиля StormFX

StormFX2 месяцев назад

@aibullss 🤯Intriguing! AI like Kronos could really shake up the financial world.

Фото профиля jquant

jquant2 месяцев назад

@aibullss I threw this into the bag of features I evaluate in backtests. Not very useful in my experiments, especially for more recent data that's out-of-sample.

Фото профиля Jurly

Jurly2 месяцев назад

@aibullss the benchmark design and leakage controls matter more than the headline percentage. market data makes clean evaluation notoriously difficult.

Фото профиля Sophie Namir

Sophie Namir2 месяцев назад

@aibullss 12 billion candles from 45 exchanges and they still led with 'bookmark before I delete this'

Фото профиля AI Mastery Guide

AI Mastery Guide2 месяцев назад

@aibullss Zero-shot forecasting on assets it's never seen is the part that gets me.

Фото профиля RamonAvocado

RamonAvocado2 месяцев назад

@aibullss Lol, how it works?

Похожие видео

Claude Code + /argusdesk skill from Bloomberg & Goldman Sachs & BlackRock repos is unreal for trading and research... here's how it runs your own 24/7 AI hedge fund: • Bloomberg repo: finds which stocks actually move yours and how many days later, not just correlation • Goldman Sachs repo: measures your risk with their own Quant math: volatility, drawdown, beta, RSI • BlackRock repo: their optimizer tunes a trading rule on real peers from iShares • Tests the rule on data it has never seen, with Monte Carlo checks to see if it was just luck • Tsinghua's Kronos model, trained on 12B+ candles from 45 exchanges, forecasts next 5 days • Paper-trades it on Alpaca (only if you say yes ofc) • Free, runs on your laptop, no terminal subscription send to Claude:

Claude Code + /argusdesk skill from Bloomberg & Goldman Sachs & BlackRock repos is unreal for trading and research... here's how it runs your own 24/7 AI hedge fund: • Bloomberg repo: finds which stocks actually move yours and how many days later, not just correlation • Goldman Sachs repo: measures your risk with their own Quant math: volatility, drawdown, beta, RSI • BlackRock repo: their optimizer tunes a trading rule on real peers from iShares • Tests the rule on data it has never seen, with Monte Carlo checks to see if it was just luck • Tsinghua's Kronos model, trained on 12B+ candles from 45 exchanges, forecasts next 5 days • Paper-trades it on Alpaca (only if you say yes ofc) • Free, runs on your laptop, no terminal subscription send to Claude:

bodila

15,560 просмотров • 4 дней назад

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,910 просмотров • 3 месяцев назад

I told Claude to predict the next BTC candle using Kronos. didn't touch anything. +$4,200 overnight. Kronos isn't another LLM wrapper. it's a GPT-style model trained on 12 billion candles from 45 exchanges. Binance, NYSE, NASDAQ, LSE. it reads OHLC the way GPT reads English. > words = candle patterns > sentences = OHLC sequences > the corpus = every trade ever printed > the edge is simple: P(next | history) = softmax(W · h_t) same math GPT uses to predict the next word, except the "word" is the next candle. no twitter sentiment. no blog scraping. no hallucinated confidence. just statistics over 12B real price moves. I plugged Kronos into a 5-min BTC Polymarket bot. input: last 50 candles output: probability next candle closes up trades only when |p_model - p_market| > 0.04 72 hours: $300 → $4,500 ChatGPT looks at a chart and pattern-matches against an article it read. Kronos looks at the chart and remembers 12 billion of them. 87% of Polymarket is exit liquidity. one of these models is actually trading the market. 21.8k stars, free, MIT license. Tsinghua University release.

I told Claude to predict the next BTC candle using Kronos. didn't touch anything. +$4,200 overnight. Kronos isn't another LLM wrapper. it's a GPT-style model trained on 12 billion candles from 45 exchanges. Binance, NYSE, NASDAQ, LSE. it reads OHLC the way GPT reads English. > words = candle patterns > sentences = OHLC sequences > the corpus = every trade ever printed > the edge is simple: P(next | history) = softmax(W · h_t) same math GPT uses to predict the next word, except the "word" is the next candle. no twitter sentiment. no blog scraping. no hallucinated confidence. just statistics over 12B real price moves. I plugged Kronos into a 5-min BTC Polymarket bot. input: last 50 candles output: probability next candle closes up trades only when |p_model - p_market| > 0.04 72 hours: $300 → $4,500 ChatGPT looks at a chart and pattern-matches against an article it read. Kronos looks at the chart and remembers 12 billion of them. 87% of Polymarket is exit liquidity. one of these models is actually trading the market. 21.8k stars, free, MIT license. Tsinghua University release.

RetroChainer

94,716 просмотров • 6 месяцев назад

What the f*ck is this GitHub repo, 33k+ stars in less than a month. It's called OpenHuman, and the newest feature is why it's exploding, Super Context. > The moment you open a new chat, it runs a context pass first > Gathers what's relevant about you, your screen, your work > Then answers turn one like it's turn ten, already grounded It lives at the OS level, speaks in your voice across your whole system, connects to 118 apps you already use. Runs locally, never leaves your machine, and keeps learning you the longer you use it. It was #31 trending on GitHub for ten days straight and now it's the #1 repo on GitHub. > Repo:

What the f*ck is this GitHub repo, 33k+ stars in less than a month. It's called OpenHuman, and the newest feature is why it's exploding, Super Context. > The moment you open a new chat, it runs a context pass first > Gathers what's relevant about you, your screen, your work > Then answers turn one like it's turn ten, already grounded It lives at the OS level, speaks in your voice across your whole system, connects to 118 apps you already use. Runs locally, never leaves your machine, and keeps learning you the longer you use it. It was #31 trending on GitHub for ten days straight and now it's the #1 repo on GitHub. > Repo:

Defileo🔮

208,158 просмотров • 4 месяцев назад

$Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.$

Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.

George Noble

71,531 просмотров • 4 месяцев назад

these 2 free GitHub repos should honestly be illegal in trading - they just made a $30K/year terminal obsolete hedge funds pay $30K a year per seat for a Bloomberg terminal - someone rebuilt one with 2 GitHub repos and made every chart draw 100 possible futures for 100% FREE the repos are REA and Kronos. REA hit #1 on GitHub and has 200k stars. Kronos was trained on 12 BILLION candles, the paper got into AAAI 2026, and it has 41k stars first understand how it works: > REA lets your AI agent see how any app works, down to the binary, and rebuild it from scratch > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > in the paper's benchmarks it beat the best rival model's forecasts by 93% > put them together and you get a Bloomberg-style terminal where every chart forecasts itself here is how to build it yourself for FREE: 1. open Claude Code and connect REA as an MCP. it maps how the terminal works: quotes, charts, news, screeners 2. ask Claude to rebuild every panel from scratch. equities, FX, commodities, crypto, live news feed 3. pull the data from free public APIs. Binance gives you any coin, any timeframe, no account 4. clone and load Kronos-small. 24.7M params, it runs on a normal laptop, no GPU 5. feed it the last 512 candles and ask for the next 24, exactly like an LLM finishing a sentence 6. sample 100 futures, not one guess, and draw all of them right on the chart. the spread shows how risky the next move really is 7. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 8. backtest it walk-forward with real fees and paper trade it before you trust a single path i won't say anything more:

these 2 free GitHub repos should honestly be illegal in trading - they just made a $30K/year terminal obsolete hedge funds pay $30K a year per seat for a Bloomberg terminal - someone rebuilt one with 2 GitHub repos and made every chart draw 100 possible futures for 100% FREE the repos are REA and Kronos. REA hit #1 on GitHub and has 200k stars. Kronos was trained on 12 BILLION candles, the paper got into AAAI 2026, and it has 41k stars first understand how it works: > REA lets your AI agent see how any app works, down to the binary, and rebuild it from scratch > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > in the paper's benchmarks it beat the best rival model's forecasts by 93% > put them together and you get a Bloomberg-style terminal where every chart forecasts itself here is how to build it yourself for FREE: 1. open Claude Code and connect REA as an MCP. it maps how the terminal works: quotes, charts, news, screeners 2. ask Claude to rebuild every panel from scratch. equities, FX, commodities, crypto, live news feed 3. pull the data from free public APIs. Binance gives you any coin, any timeframe, no account 4. clone and load Kronos-small. 24.7M params, it runs on a normal laptop, no GPU 5. feed it the last 512 candles and ask for the next 24, exactly like an LLM finishing a sentence 6. sample 100 futures, not one guess, and draw all of them right on the chart. the spread shows how risky the next move really is 7. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 8. backtest it walk-forward with real fees and paper trade it before you trust a single path i won't say anything more:

Сarm1ne

49,274 просмотров • 7 дней назад

NVIDIA just removed one of the biggest friction points in Voice AI. PersonaPlex-7B is an open-source, full-duplex conversational model. Free, open source (MIT), with open model weights on Hugging Face 🤗 Links to repo and weights in 🧵↓ The traditional ASR → LLM → TTS pipeline forces rigid turn-taking. It’s efficient, but it never feels natural. PersonaPlex-7B changes that. This NVIDIA model can listen and speak at the same time. It runs directly on continuous audio tokens with a dual-stream transformer, generating text and audio in parallel instead of passing control between components. That unlocks: → instant back-channel responses → interruptions that feel human → real conversational rhythm Persona control is fully zero-shot! If you’re building low-latency assistants or support agents, this is a big step forward 🔥

NVIDIA just removed one of the biggest friction points in Voice AI. PersonaPlex-7B is an open-source, full-duplex conversational model. Free, open source (MIT), with open model weights on Hugging Face 🤗 Links to repo and weights in 🧵↓ The traditional ASR → LLM → TTS pipeline forces rigid turn-taking. It’s efficient, but it never feels natural. PersonaPlex-7B changes that. This NVIDIA model can listen and speak at the same time. It runs directly on continuous audio tokens with a dual-stream transformer, generating text and audio in parallel instead of passing control between components. That unlocks: → instant back-channel responses → interruptions that feel human → real conversational rhythm Persona control is fully zero-shot! If you’re building low-latency assistants or support agents, this is a big step forward 🔥

Charly Wargnier ♨️

566,596 просмотров • 9 месяцев назад

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

cvxv666

83,333 просмотров • 2 месяцев назад

Google launched a brand new AI tool. It's called CodeWiki, and it might be the biggest upgrade GitHub has had in years. And all you do is paste your GitHub repo in, and it turns your entire project into an interactive guide. It also generates diagrams, explanations, walkthroughs, everything you could ever want, and even a chatbot that knows the code better than anyone else. So you never have to dig through a giant repo again wondering what does this do

Prasenjit

839,879 просмотров • 8 месяцев назад

Trump on the racist Obama post: A staffer posted, I knew, it was all about, if you take a look at it and see the whole thing, it was a small section at the end…I liked the beginning, I saw it and passed it on, but probably nobody reviewed the end of it. Somebody slipped and missed a very small part. I didn't do it, by the way, it was done by someone else. This was a retruth. It was not done by us.

Trump on the racist Obama post: A staffer posted, I knew, it was all about, if you take a look at it and see the whole thing, it was a small section at the end…I liked the beginning, I saw it and passed it on, but probably nobody reviewed the end of it. Somebody slipped and missed a very small part. I didn't do it, by the way, it was done by someone else. This was a retruth. It was not done by us.

Republicans against Trump

1,641,467 просмотров • 8 месяцев назад

#WATCH | Bengaluru, Karnataka: Co-founder, Sarvam AI, Pratyush Kumar says, "... It's been three years now. We started off building on top of the Llama model, which was the best open model at that time. Sarvam was the only company outside, or maybe in Asia, that contributed to the Llama 3 Series. We understood how to add language skills into models, and Llama 3 was reasonable in Indian languages. When the first set of GPUs arrived in India, we pre-trained from scratch a 2-billion model and worked with Infosys to make it useful for IT services. Then we recognised the importance of scaling up reinforcement learning and built a reasoning model called Sarvam M on top of Mistral's small model. Over the last year, we pre-trained from scratch in India on 4,000 Hopper Series GPUs a 100-billion-plus-parameter model..."

#WATCH | Bengaluru, Karnataka: Co-founder, Sarvam AI, Pratyush Kumar says, "... It's been three years now. We started off building on top of the Llama model, which was the best open model at that time. Sarvam was the only company outside, or maybe in Asia, that contributed to the Llama 3 Series. We understood how to add language skills into models, and Llama 3 was reasonable in Indian languages. When the first set of GPUs arrived in India, we pre-trained from scratch a 2-billion model and worked with Infosys to make it useful for IT services. Then we recognised the importance of scaling up reinforcement learning and built a reasoning model called Sarvam M on top of Mistral's small model. Over the last year, we pre-trained from scratch in India on 4,000 Hopper Series GPUs a 100-billion-plus-parameter model..."

ANI

163,169 просмотров • 3 месяцев назад

Agaram 2.5B is out. It's a Tamil-first language model we trained from scratch at Murai Labs , our small non-profit lab. As far as we know, it's the first Tamil model trained from scratch that can call tools. It chats in Tamil, Tanglish and English, translates, and can call tools. We're releasing the base model, chat model and GGUF files under Apache-2.0. You can download it and run it offline on your own laptop. The first chat model had a four-day public preview. Seeing where it fell short with real users helped us decide what to work on. We rebuilt the fine-tuning around what we saw, using 222,662 records. Thank you to everyone who spent time with that early version. The video walks through the model. You can try it here: நன்றி 🙏

Agaram 2.5B is out. It's a Tamil-first language model we trained from scratch at Murai Labs , our small non-profit lab. As far as we know, it's the first Tamil model trained from scratch that can call tools. It chats in Tamil, Tanglish and English, translates, and can call tools. We're releasing the base model, chat model and GGUF files under Apache-2.0. You can download it and run it offline on your own laptop. The first chat model had a four-day public preview. Seeing where it fell short with real users helped us decide what to work on. We rebuilt the fine-tuning around what we saw, using 222,662 records. Thank you to everyone who spent time with that early version. The video walks through the model. You can try it here: நன்றி 🙏

Ramchand Kumaresan

41,492 просмотров • 9 часов назад

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.

sopersone

48,482 просмотров • 1 месяц назад

Dario Amodei was asked whether open source will eventually gut Anthropic's business. He didn't defend the moat. He didn't argue closed beats open. He said the whole question is a red herring. That is the reframe. And it flips how the industry keeps scoring this race. The conventional narrative is inherited from the last era of tech: open source wins because anyone can read the code, anyone improves it, contributions stack, and eventually the free thing catches the paid thing. Investors have a full lexicon for it. Commoditization. Which layer captures the value. Everyone repeats it. Amodei says the analogy breaks at the root. It's called open weights, not open source, for a reason: you can't see inside the model. So the thing that actually made open source powerful elsewhere, many people reading and additively improving shared code, never transfers. You just get a large file of numbers. Now here's where it gets interesting. The second engine isn't ideology. It's infrastructure. Free isn't free. Someone still has to host it. These are big models, and they're hard to run inference on. Someone has to make that fast. And the capabilities people assume only open weights unlock fine tuning, steering, inspecting activations labs are increasingly serving on their own clouds anyway. When DeepSeek shipped, he says he never asked whether it was open. He asked one thing: is it a good model, and is it better than us. That's the only axis he competes on. He even inverts the usual edge. Coming from outside that investor lexicon, he thinks knowing none of it lets him predict this better than the people fluent in it. He is not defending closed models. He is saying the scoreboard everyone is watching measures the wrong thing. The uncomfortable question if the free model still needs someone to run it, was the moat ever the weights, or always the machine underneath ?

Dario Amodei was asked whether open source will eventually gut Anthropic's business. He didn't defend the moat. He didn't argue closed beats open. He said the whole question is a red herring. That is the reframe. And it flips how the industry keeps scoring this race. The conventional narrative is inherited from the last era of tech: open source wins because anyone can read the code, anyone improves it, contributions stack, and eventually the free thing catches the paid thing. Investors have a full lexicon for it. Commoditization. Which layer captures the value. Everyone repeats it. Amodei says the analogy breaks at the root. It's called open weights, not open source, for a reason: you can't see inside the model. So the thing that actually made open source powerful elsewhere, many people reading and additively improving shared code, never transfers. You just get a large file of numbers. Now here's where it gets interesting. The second engine isn't ideology. It's infrastructure. Free isn't free. Someone still has to host it. These are big models, and they're hard to run inference on. Someone has to make that fast. And the capabilities people assume only open weights unlock fine tuning, steering, inspecting activations labs are increasingly serving on their own clouds anyway. When DeepSeek shipped, he says he never asked whether it was open. He asked one thing: is it a good model, and is it better than us. That's the only axis he competes on. He even inverts the usual edge. Coming from outside that investor lexicon, he thinks knowing none of it lets him predict this better than the people fluent in it. He is not defending closed models. He is saying the scoreboard everyone is watching measures the wrong thing. The uncomfortable question if the free model still needs someone to run it, was the moat ever the weights, or always the machine underneath ?

Vikram M

58,688 просмотров • 3 месяцев назад

this is the most BROKEN github repo in trading right now real trading firms pay analysts, researchers, traders and risk managers millions a year - someone replaced the whole floor with AI agents that argue with each other before every single trade, and put it on GitHub for 100% FREE it's called TradingAgents. the paper is on arXiv, the repo has 112k stars and it hit #1 on GitHub trending first understand how it works: > 4 AI analysts read the market: fundamentals, news, sentiment, technicals > a BULL agent and a BEAR agent debate the trade > a trader agent turns the debate into a decision > a risk team (aggressive / neutral / conservative) attacks that decision > a portfolio manager approves it or kills it > it's a full trading firm. it just runs on your laptop here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. add your Anthropic API key. Opus 5.5 as the deep-thinking model, Sonnet 5.5 as the fast one 3. type tradingagents in the terminal and pick a ticker 4. watch the 4 analysts write their reports in real time 5. read the bull vs bear debate. this is where the edge is - you see both sides before any money moves 6. check the risk team's vote. if the conservative agent flags it, ask yourself why 7. let the portfolio manager approve or reject, then plot every call on your chart 8. backtest it on past dates and paper trade it for a few weeks before you trust a single call i won't say anything more:

this is the most BROKEN github repo in trading right now real trading firms pay analysts, researchers, traders and risk managers millions a year - someone replaced the whole floor with AI agents that argue with each other before every single trade, and put it on GitHub for 100% FREE it's called TradingAgents. the paper is on arXiv, the repo has 112k stars and it hit #1 on GitHub trending first understand how it works: > 4 AI analysts read the market: fundamentals, news, sentiment, technicals > a BULL agent and a BEAR agent debate the trade > a trader agent turns the debate into a decision > a risk team (aggressive / neutral / conservative) attacks that decision > a portfolio manager approves it or kills it > it's a full trading firm. it just runs on your laptop here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. add your Anthropic API key. Opus 5.5 as the deep-thinking model, Sonnet 5.5 as the fast one 3. type tradingagents in the terminal and pick a ticker 4. watch the 4 analysts write their reports in real time 5. read the bull vs bear debate. this is where the edge is - you see both sides before any money moves 6. check the risk team's vote. if the conservative agent flags it, ask yourself why 7. let the portfolio manager approve or reject, then plot every call on your chart 8. backtest it on past dates and paper trade it for a few weeks before you trust a single call i won't say anything more:

Сarm1ne

149,763 просмотров • 6 дней назад

elon musk grabbed the source code openai open-sourced by accident, rewrote it in rust over a weekend, and shipped it as a free coding agent that does everything $200/mo chatgpt pro does. why pay $200 to openai and $200 to claude when this runs for $8 the swarm above is one weekend of exactly that: thousands of agents pouring through four endpoints, three paid seats billing $1.80 a task while the free fork bills $0. musk co-founded openai, walked out, and when they left codex on github under a permissive license, he forked it, stamped grok on it, and gave it away what the free version does that the $200 seat charges for: the agent · openai's own engine -> it reads your repo, writes patches, runs your tests, and loops until they pass, exactly like codex -> because under the hood it is codex, just faster and free. you are paying $200 for the paid skin of a tool now sitting on github the license · apache-2.0, un-revocable -> free to use, free to fork, free to ship inside your own product with zero strings -> openai cannot pull it back. musk made sure the license is the kind that never expires the switch · one line, no new tools -> point it at any openai-compatible or claude-compatible endpoint, including an $8 kimi backend -> same terminal, same workflow, gpt-5.6 and opus 5 just quietly lose the seat the bill · $400 down to $8 -> chatgpt pro plus claude max is $400 a month. the free agent plus an $8 kimi key does the same daily work -> that is a 98% cut, built out of openai's own source code, handed to you by the guy suing them here is the part they will fight me on: openai did not lose this to a better model, they lost it to their own license and an enemy with a weekend free. the $200 was never the tool, it was the toll, and musk just put openai's own logo on the road around it drop your $400/mo ai stack to $8. the run above is openai's own agent, rewritten free, doing the job it bills $200 a month for. the full breakdown is in the article below

starmex

111,834 просмотров • 2 месяцев назад

📢 Inference is ON 😎 Run any AI model on dedicated GPU hardware, starting today. The GPU is yours while you're on it, the full price is displayed before you commit, and the model is whatever you bring from Hugging Face. Wild concept, we know! 👇

📢 Inference is ON 😎 Run any AI model on dedicated GPU hardware, starting today. The GPU is yours while you're on it, the full price is displayed before you commit, and the model is whatever you bring from Hugging Face. Wild concept, we know! 👇

Ocean Network

459,411 просмотров • 1 месяц назад

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.

RetroChainer

16,506 просмотров • 3 месяцев назад

Gavin Baker and Antonio Gracias — Tesla board member — just confirmed what no AI investor has publicly priced yet. Tesla is the most advanced AI in the world. Not OpenAI. Not Anthropic. Not Google DeepMind. Here's why: Tesla's autopilot runs on an A4 chip — significantly inferior to Nvidia's H100. It runs at the edge with no cloud access. It's doing the single most complex task a human does: driving a car in real time. And it does it at 20 watts of power. The human brain runs on 20 to 30 watts. Every frontier model from OpenAI, Anthropic, Google — trained on hundreds of megawatts over months. Tesla just matched biological efficiency at superhuman performance. Baker's exact words: "This is the first time you've gotten superhuman AI at the same power consumption as the human mind." The frontier isn't the data center. It's the dashboard. Not financial advice.

Gavin Baker and Antonio Gracias — Tesla board member — just confirmed what no AI investor has publicly priced yet. Tesla is the most advanced AI in the world. Not OpenAI. Not Anthropic. Not Google DeepMind. Here's why: Tesla's autopilot runs on an A4 chip — significantly inferior to Nvidia's H100. It runs at the edge with no cloud access. It's doing the single most complex task a human does: driving a car in real time. And it does it at 20 watts of power. The human brain runs on 20 to 30 watts. Every frontier model from OpenAI, Anthropic, Google — trained on hundreds of megawatts over months. Tesla just matched biological efficiency at superhuman performance. Baker's exact words: "This is the first time you've gotten superhuman AI at the same power consumption as the human mind." The frontier isn't the data center. It's the dashboard. Not financial advice.

stark0xbt

86,961 просмотров • 13 дней назад

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 просмотров • 2 месяцев назад