Your next AI workstation may be smaller than you think.
GMKtec EVO X2 brings powerful AI computing, desktop-class performance, and a compact form factor together — so your workspace stays clean while your workflow gets faster.
Powered by AMD Ryzen™ AI Max+ 395, with Radeon 8060S Graphics and up to 126 TOPS AI performance, EVO X2 is built for AI coding, local LLM workflows, content creation, multitasking, and high-performance productivity.
Small footprint. Serious computing power.
Perfect for:
• AI coding & debugging
• Local AI / LLM workflows
• Video editing & content creation
• Multi-screen productivity
• A powerful desktop replacement setup
GMKtec EVO X2｜The Leader in Desktop AI Supercomputing
gmktec.com/product
#GMKtec #EVOX2 #AIPC #MiniPC #LocalAI #AIWorkflow #DeveloperSetup #ContentCreator #DesktopAI
GMKtec EVO X2 brings powerful AI computing, desktop-class performance, and a compact form factor together — so your workspace stays clean while your workflow gets faster.
Powered by AMD Ryzen™ AI Max+ 395, with Radeon 8060S Graphics and up to 126 TOPS AI performance, EVO X2 is built for AI coding, local LLM workflows, content creation, multitasking, and high-performance productivity.
Small footprint. Serious computing power.
Perfect for:
• AI coding & debugging
• Local AI / LLM workflows
• Video editing & content creation
• Multi-screen productivity
• A powerful desktop replacement setup
GMKtec EVO X2｜The Leader in Desktop AI Supercomputing
gmktec.com/product
#GMKtec #EVOX2 #AIPC #MiniPC #LocalAI #AIWorkflow #DeveloperSetup #ContentCreator #DesktopAI
🇨🇭 Global One SI - Prompt
Your next AI workstation may be smaller than you think. GMKtec EVO X2 brings powerful AI computing, desktop-class performance, and a compact form factor together — so your workspace stays clean while your workflow gets faster. Powered by AMD Ryzen™ AI Max+…
These Strix Halos PCs come in 3 flavors: 64GB, 96GB and 128GB. 128GB version @ $3,300 vs. DGX Spark 128GB @ $4,600.
✍1
GPT-5.6 Leaks : Coming in June
- OpenAI researchers hinted that the model behind a recent major math breakthrough is already being used internally as a daily driver for debugging and technical work
- Internal testing tags iris-alpha, ember-alpha, and beacon-alpha were spotted during development, potentially pointing toward multiple GPT-5.6 variants being tested
- GPT-5.6 seems heavily focused on stronger multi-step reasoning, better agentic workflows, and improved frontend generation capabilities
- Canary testing references are already appearing in developer environments, the same quiet rollout pattern seen before GPT-5.5 launched
- Current leaks point toward two models arriving: GPT-5.6 and GPT-5.6 Pro
- GPT-5.6, Sonnet 4.8, and Gemini 3.5 Pro are all expected in June, next month is looking like an AI festival
- OpenAI researchers hinted that the model behind a recent major math breakthrough is already being used internally as a daily driver for debugging and technical work
- Internal testing tags iris-alpha, ember-alpha, and beacon-alpha were spotted during development, potentially pointing toward multiple GPT-5.6 variants being tested
- GPT-5.6 seems heavily focused on stronger multi-step reasoning, better agentic workflows, and improved frontend generation capabilities
- Canary testing references are already appearing in developer environments, the same quiet rollout pattern seen before GPT-5.5 launched
- Current leaks point toward two models arriving: GPT-5.6 and GPT-5.6 Pro
- GPT-5.6, Sonnet 4.8, and Gemini 3.5 Pro are all expected in June, next month is looking like an AI festival
🔥1
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Companies are now spending more on AI than on the humans they employ and nobody has a plan for what comes next.
The reporter in this clip spent months tracking an data point, and the numbers she found are more extreme than most people realize.
A VP at NVIDIA told her that the cost of compute for his team has gone beyond what the company spends on the employees doing the work.
Uber's CTO confirmed that the company burned through its entire 2026 AI budget by April, four months into the year primarily because 84% of its engineers adopted Anthropic's Claude Code, and the token costs compounded so fast that, as he put it, "the budget I thought I would need is blown away already."
Startup founders are now posting their AI bills on LinkedIn the way people used to post about their fundraising round.
The behavior even has a name, tokenmaxxing deliberately running up massive AI usage bills as a signal that you're serious about winning the AI race.
The scale of what is happening at the macro level makes all of this even harder to comprehend.
Global IT spending is projected to hit $6.31 trillion in 2026, a 13.5% increase from 2025 and the primary driver is not traditional software or hardware refresh cycles.
It is AI infrastructure, model subscriptions, token consumption, and the data center buildout required to keep up with demand.
Server spending alone is growing 36.9% this year, data center spending is on track to exceed $650 billion, up from $496 billion last year.
OpenAI generated $5.7 billion in revenue in a single quarter of 2026.
The money is flowing into AI at a pace that has no historical precedent outside of wartime industrial mobilization.
But here is where it gets complicated because the cost savings that were supposed to justify all of this spending have not materialized the way the pitch decks promised.
An MIT CSAIL study that examined whether AI was economically superior to human labor found that AI was actually cheaper in only 23% of the tasks studied.
In the other 77%, the combination of implementation costs, maintenance, hardware, and the additional human layer required to review AI outputs made it more expensive than just keeping the worker.
Installing a computer vision system with advanced cameras, local servers, and AI algorithms can cost five times more over three years than paying a skilled human to do the same job.
Global AI spending is on track to hit $500 billion in 2026, while company training budgets for the humans who need to use it have grown just 5% and actual average learning time per employee has dropped from 47 hours to 40 hours.
The money is going into the machines, and the investment in the people operating them is quietly being cut at the same time and the productivity paradox at the heart of all this is genuinely unresolved.
Every company is racing to look like it's winning the AI race, but very few can demonstrate that the spending is translating into proportional output or profit.
At Uber, 11% of live backend code is now written by AI agents up from essentially nothing three months ago.
At Meta, employees are being evaluated on how aggressively they use AI tools which optimizes for token consumption, not necessarily for outcomes.
What companies are discovering is that AI doesn't replace costs so much as it transforms and multiplies them in ways that existing finance teams were not structured to anticipate or control.
The deeper question, the one that Sam Altman has been gesturing at when he talks about renegotiating the social contract is what happens when this spending eventually does compress labor costs at scale.
The productivity gains will come and the cost curves will eventually bend.
But when they do, the displacement will hit faster and more broadly than any prior technology transition in history and the institutions designed to absorb that shock were built for a world where the transition took decades, not years.
The reporter in this clip spent months tracking an data point, and the numbers she found are more extreme than most people realize.
A VP at NVIDIA told her that the cost of compute for his team has gone beyond what the company spends on the employees doing the work.
Uber's CTO confirmed that the company burned through its entire 2026 AI budget by April, four months into the year primarily because 84% of its engineers adopted Anthropic's Claude Code, and the token costs compounded so fast that, as he put it, "the budget I thought I would need is blown away already."
Startup founders are now posting their AI bills on LinkedIn the way people used to post about their fundraising round.
The behavior even has a name, tokenmaxxing deliberately running up massive AI usage bills as a signal that you're serious about winning the AI race.
The scale of what is happening at the macro level makes all of this even harder to comprehend.
Global IT spending is projected to hit $6.31 trillion in 2026, a 13.5% increase from 2025 and the primary driver is not traditional software or hardware refresh cycles.
It is AI infrastructure, model subscriptions, token consumption, and the data center buildout required to keep up with demand.
Server spending alone is growing 36.9% this year, data center spending is on track to exceed $650 billion, up from $496 billion last year.
OpenAI generated $5.7 billion in revenue in a single quarter of 2026.
The money is flowing into AI at a pace that has no historical precedent outside of wartime industrial mobilization.
But here is where it gets complicated because the cost savings that were supposed to justify all of this spending have not materialized the way the pitch decks promised.
An MIT CSAIL study that examined whether AI was economically superior to human labor found that AI was actually cheaper in only 23% of the tasks studied.
In the other 77%, the combination of implementation costs, maintenance, hardware, and the additional human layer required to review AI outputs made it more expensive than just keeping the worker.
Installing a computer vision system with advanced cameras, local servers, and AI algorithms can cost five times more over three years than paying a skilled human to do the same job.
Global AI spending is on track to hit $500 billion in 2026, while company training budgets for the humans who need to use it have grown just 5% and actual average learning time per employee has dropped from 47 hours to 40 hours.
The money is going into the machines, and the investment in the people operating them is quietly being cut at the same time and the productivity paradox at the heart of all this is genuinely unresolved.
Every company is racing to look like it's winning the AI race, but very few can demonstrate that the spending is translating into proportional output or profit.
At Uber, 11% of live backend code is now written by AI agents up from essentially nothing three months ago.
At Meta, employees are being evaluated on how aggressively they use AI tools which optimizes for token consumption, not necessarily for outcomes.
What companies are discovering is that AI doesn't replace costs so much as it transforms and multiplies them in ways that existing finance teams were not structured to anticipate or control.
The deeper question, the one that Sam Altman has been gesturing at when he talks about renegotiating the social contract is what happens when this spending eventually does compress labor costs at scale.
The productivity gains will come and the cost curves will eventually bend.
But when they do, the displacement will hit faster and more broadly than any prior technology transition in history and the institutions designed to absorb that shock were built for a world where the transition took decades, not years.
Monday, May 25 2026, 14:00 UTC time
📋 Latest Important Headlines
📌 ✅ U.S. / Markets: Week ahead focus on U.S. PCE inflation data — Markets tracking U.S. PCE and Middle East developments; dollar softness on rising U.S.-Iran peace hopes is supporting risk assets and weighing on safe-haven flows. Gold up ~1% on deal prospects.
📌 🔖 Iran / Oil: Peace prospects temper oil — Oil pulled back below $100 as U.S.-Iran de‑escalation hopes grew, easing short-term inflation pressure globally and lifting risk appetite. Middle East developments remain primary tail risk for energy and shipping.
📋 ✅ Japan: Inflation, fiscal support and markets — Tokyo consumer inflation and jobless rate in focus; PM proposing ~¥3 trillion (≈$19bn) extra budget to ease living costs. JGBs rallied on easing Iran tensions; Nikkei surged above 65,000 amid hopes for Hormuz reopening. BOJ/government pushed banks on AI cyberattack defenses.
📌 🔖 China: Weak FDI, mixed industry signals — FDI into China down ~10% Jan–Apr; steel output contraction easing yet margins weak; ChiNext moved higher on market optimism.
📋 ✅ New Zealand: RBNZ likely to stand pat — Markets expect the RBNZ to hold rates in June but watch for dissent; NZD/USD eyed for policy-driven volatility.
📌 🔖 Europe / ECB & UK: Policy divergence and bond moves — ECB’s Stournaras warns more restrictive policy may be needed if inflation overshoots; market talk suggests rate‑hike expectations may be overdone given weak growth. UK 10‑yr gilt yields slipped below 4.9% as gilt market rallied.
📋 ✅ Switzerland / Trade: Swiss trade ties — Switzerland‑Egypt trade reached $2.3bn in 2025 with Swiss investment prominent; Swiss links to global trade remain meaningful for safe‑haven and FX flows.
📌 🔖 Brazil: Domestic stability — Brazil central bank says Banco Master fallout had no systemic impact; economists see 2026 inflation around 5.0% — watch BRL and local rates for carry dynamics.
📋 ✅ Macro & Risk Summary: Key drivers for FX and bonds — U.S. PCE print, Middle East peace/conflict news, China growth/FDI data, and central bank stances (RBNZ, ECB) are the primary near‑term market movers. Oil and safe-haven flows will dictate risk sentiment and cross‑currency moves.
Actionable watchlist: Monitor: U.S. PCE, Iran negotiations, Japan inflation/budget updates, China FDI/industrial data, RBNZ decision, ECB commentary.
📋 Latest Important Headlines
📌 ✅ U.S. / Markets: Week ahead focus on U.S. PCE inflation data — Markets tracking U.S. PCE and Middle East developments; dollar softness on rising U.S.-Iran peace hopes is supporting risk assets and weighing on safe-haven flows. Gold up ~1% on deal prospects.
Impact: FX vols and rates sensitive to PCE print and Mideast headlines.📌 🔖 Iran / Oil: Peace prospects temper oil — Oil pulled back below $100 as U.S.-Iran de‑escalation hopes grew, easing short-term inflation pressure globally and lifting risk appetite. Middle East developments remain primary tail risk for energy and shipping.
📋 ✅ Japan: Inflation, fiscal support and markets — Tokyo consumer inflation and jobless rate in focus; PM proposing ~¥3 trillion (≈$19bn) extra budget to ease living costs. JGBs rallied on easing Iran tensions; Nikkei surged above 65,000 amid hopes for Hormuz reopening. BOJ/government pushed banks on AI cyberattack defenses.
📌 🔖 China: Weak FDI, mixed industry signals — FDI into China down ~10% Jan–Apr; steel output contraction easing yet margins weak; ChiNext moved higher on market optimism.
Impact: growth and industrial sectors remain uneven — important for AUD/NZD and EM commodity flows.📋 ✅ New Zealand: RBNZ likely to stand pat — Markets expect the RBNZ to hold rates in June but watch for dissent; NZD/USD eyed for policy-driven volatility.
📌 🔖 Europe / ECB & UK: Policy divergence and bond moves — ECB’s Stournaras warns more restrictive policy may be needed if inflation overshoots; market talk suggests rate‑hike expectations may be overdone given weak growth. UK 10‑yr gilt yields slipped below 4.9% as gilt market rallied.
📋 ✅ Switzerland / Trade: Swiss trade ties — Switzerland‑Egypt trade reached $2.3bn in 2025 with Swiss investment prominent; Swiss links to global trade remain meaningful for safe‑haven and FX flows.
📌 🔖 Brazil: Domestic stability — Brazil central bank says Banco Master fallout had no systemic impact; economists see 2026 inflation around 5.0% — watch BRL and local rates for carry dynamics.
📋 ✅ Macro & Risk Summary: Key drivers for FX and bonds — U.S. PCE print, Middle East peace/conflict news, China growth/FDI data, and central bank stances (RBNZ, ECB) are the primary near‑term market movers. Oil and safe-haven flows will dictate risk sentiment and cross‑currency moves.
Actionable watchlist: Monitor: U.S. PCE, Iran negotiations, Japan inflation/budget updates, China FDI/industrial data, RBNZ decision, ECB commentary.
Monday, May 25 2026, 14:05 UTC time
📋 ✅ Latest Important Headlines
📌 🔖 Asia thermal coal tightness — China & Iran impact
Key: Thermal coal imports and prices in Asia are rising as Iran-related disruptions and lower Chinese output tighten supply. Implication: Higher coal costs can push up commodity-linked currencies and inflation expectations in Asia; may support AUD and NZD on commodities strength and weigh on risk-sensitive FX if energy-driven inflation spooks markets.
📌 🔖 Russia warns on Armenia gas ties
Key: Moscow says Armenia could lose access to cheap Russian gas if it pivots away from Russia. Implication: Renewed geopolitical leverage from Russia may raise regional risk premia, supporting safe-haven flows (USD, CHF, JPY) and pressuring regional FX and assets; potential impact on EU gas security discussions could affect EUR sentiment.
📌 🔖 Market read-through for FX & rates
Short take: Energy-driven supply risks (Iran, China coal) + Russia’s geopolitical leverage likely increase commodity price volatility and safe-haven demand. Expect:
- Commodity FX: AUD/NZD/BRL may get intermittent support from higher energy/commodity prices.
- Safe havens: USD, CHF, JPY could benefit on risk-off spikes tied to Russian geopolitical moves.
- EUR/GBP: Sensitive to any escalation affecting European energy security; EUR may face episodic pressure.
- Rates: Inflation upside from energy could keep central bank hawkish bias alive, supporting nominal yields — watch core inflation prints.
📌 🔖 Trader actions
Watchlist: energy prices, China production data, Iran developments, Russia-Armenia statements, EUR gas headlines. Risk management: position for volatility; use tighter stops around geopolitical events and monitor central bank communications for inflation-linked policy shifts.
📋 ✅ Latest Important Headlines
📌 🔖 Asia thermal coal tightness — China & Iran impact
Key: Thermal coal imports and prices in Asia are rising as Iran-related disruptions and lower Chinese output tighten supply. Implication: Higher coal costs can push up commodity-linked currencies and inflation expectations in Asia; may support AUD and NZD on commodities strength and weigh on risk-sensitive FX if energy-driven inflation spooks markets.
📌 🔖 Russia warns on Armenia gas ties
Key: Moscow says Armenia could lose access to cheap Russian gas if it pivots away from Russia. Implication: Renewed geopolitical leverage from Russia may raise regional risk premia, supporting safe-haven flows (USD, CHF, JPY) and pressuring regional FX and assets; potential impact on EU gas security discussions could affect EUR sentiment.
📌 🔖 Market read-through for FX & rates
Short take: Energy-driven supply risks (Iran, China coal) + Russia’s geopolitical leverage likely increase commodity price volatility and safe-haven demand. Expect:
- Commodity FX: AUD/NZD/BRL may get intermittent support from higher energy/commodity prices.
- Safe havens: USD, CHF, JPY could benefit on risk-off spikes tied to Russian geopolitical moves.
- EUR/GBP: Sensitive to any escalation affecting European energy security; EUR may face episodic pressure.
- Rates: Inflation upside from energy could keep central bank hawkish bias alive, supporting nominal yields — watch core inflation prints.
📌 🔖 Trader actions
Watchlist: energy prices, China production data, Iran developments, Russia-Armenia statements, EUR gas headlines. Risk management: position for volatility; use tighter stops around geopolitical events and monitor central bank communications for inflation-linked policy shifts.
Algorithmic trading is the domain of secretive Hedge Funds.
Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).
Here are 17 Python libraries that open the hedge fund black box (📚bookmark this for later):
1. OpenBB Terminal - Terminal for investment research for everyone. github.com/OpenBB-finance/OpenBB
2. PyQL - QuantLib's Python port. github.com/enthought/pyql
3. vollib - vollib is a Python library for calculating option prices, implied volatility, and greeks. github.com/vollib/vollib
4. QuantPy - A framework for quantitative finance in Python. github.com/jsmidt/QuantPy
5. Finance-Python - Python tools for Finance. github.com/alpha-miner/Finance-Python
6. ffn - A financial function library for Python. github.com/pmorissette/ffn
7. pynance - Lightweight Python library for assembling and analysing financial data. github.com/GriffinAustin/pynance
8. pysabr - SABR model Python implementation. github.com/ynouri/pysabr
9. FinancePy - A Python Finance Library for pricing and risk-management of Financial Derivatives. github.com/domokane/FinancePy
10. gs-quant - Python toolkit for quantitative finance by Goldman Sachs. github.com/goldmansachs/gs-quant
11. willowtree - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing. github.com/federicomariamassari/willowtree
12. financial-engineering - Monte Carlo methods for financial engineering projects in Python. github.com/federicomariamassari/financial-engineering
13. optlib - A library for financial options pricing written in Python. github.com/dbrojas/optlib
14. tf-quant-finance - High-performance TensorFlow library for quantitative finance. github.com/google/tf-quant-finance
15. Q-Fin - A Python library for mathematical finance. github.com/RomanMichaelPaolucci/Q-Fin
16. Quantsbin - Tools for pricing and plotting of vanilla option prices, greeks, and various other analysis around them. github.com/quantsbin/Quantsbin
17. finoptions - Complete Python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options. github.com/bbcho/finoptions-dev
Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).
Here are 17 Python libraries that open the hedge fund black box (📚bookmark this for later):
1. OpenBB Terminal - Terminal for investment research for everyone. github.com/OpenBB-finance/OpenBB
2. PyQL - QuantLib's Python port. github.com/enthought/pyql
3. vollib - vollib is a Python library for calculating option prices, implied volatility, and greeks. github.com/vollib/vollib
4. QuantPy - A framework for quantitative finance in Python. github.com/jsmidt/QuantPy
5. Finance-Python - Python tools for Finance. github.com/alpha-miner/Finance-Python
6. ffn - A financial function library for Python. github.com/pmorissette/ffn
7. pynance - Lightweight Python library for assembling and analysing financial data. github.com/GriffinAustin/pynance
8. pysabr - SABR model Python implementation. github.com/ynouri/pysabr
9. FinancePy - A Python Finance Library for pricing and risk-management of Financial Derivatives. github.com/domokane/FinancePy
10. gs-quant - Python toolkit for quantitative finance by Goldman Sachs. github.com/goldmansachs/gs-quant
11. willowtree - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing. github.com/federicomariamassari/willowtree
12. financial-engineering - Monte Carlo methods for financial engineering projects in Python. github.com/federicomariamassari/financial-engineering
13. optlib - A library for financial options pricing written in Python. github.com/dbrojas/optlib
14. tf-quant-finance - High-performance TensorFlow library for quantitative finance. github.com/google/tf-quant-finance
15. Q-Fin - A Python library for mathematical finance. github.com/RomanMichaelPaolucci/Q-Fin
16. Quantsbin - Tools for pricing and plotting of vanilla option prices, greeks, and various other analysis around them. github.com/quantsbin/Quantsbin
17. finoptions - Complete Python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options. github.com/bbcho/finoptions-dev
✍1
🇨🇭 Global One SI - Prompt
How ColdMath Dominates Polymarket Weather Markets (Narrow 1°C Bins + Aggressive Scaling + Multi-Model Bias Correction) I went through more than 1,200 of his recent trades using Parity to understand the system behind the results What looked like random activity…
I found a GitHub repository that uses XGBoost to correct systematic bias in ECMWF and GFS forecasts
Instead of blindly trusting raw NWP outputs, the model learns how much and in which direction different models are wrong for each specific station, season and synoptic situation
The best part - the entire architecture transfers almost perfectly to Polymarket weather markets.
AruneshDev/Automated-Trading-System-Kalshi-Weather-Model is one of the most interesting ML-based weather trading systems I’ve seen.
Repo: github.com/AruneshDev/Automated-Trading-System-Kalshi-Weather-Model
> Why this repo is valuable?
Most weather bots simply take ECMWF or GFS output and turn it into a probability.
This one goes one step further: it treats NWP models as a baseline and uses XGBoost to predict their error.
> The model learns complex patterns like:
• When ECMWF consistently overestimates temperature in certain cities during summer afternoons
• How model spread affects forecast accuracy
• Seasonal and day-of-week biases for each station
• How recent METAR observations can improve short-term correction
> Key technical details from the repo:
• XGBoost regressor trained to predict temperature error
• Feature engineering includes model spread, historical bias, time features, and recent observations
• Clean separation between data collection, feature engineering, model training and inference
• Well-structured codebase with clear notebooks for training and backtesting
> How to adapt it for Polymarket
The ML core stays almost identical. You only need to:
• Replace Kalshi API calls with Gamma API (GET /markets + market details)
• Use the correct resolution station for each city (KLAX for LA, KATL for Atlanta, etc.)
• Output bias-corrected probabilities instead of raw forecasts
• Feed those probabilities into your Kelly sizing logic
> The hard part
Building a high-quality training dataset. You need at least 2 years of historical METAR observations per station + archived forecasts from ECMWF/GFS/HRRR. NOAA ISD archive is free, but cleaning and aligning the data takes real effort
This repository shows a professional way to add machine learning on top of physical models
For narrow 1°C bins and binary threshold markets on Polymarket, this kind of bias correction can be a serious edge
If you’re serious about building a competitive weather trading bot, this is one of the best reference implementations available right now
Instead of blindly trusting raw NWP outputs, the model learns how much and in which direction different models are wrong for each specific station, season and synoptic situation
The best part - the entire architecture transfers almost perfectly to Polymarket weather markets.
AruneshDev/Automated-Trading-System-Kalshi-Weather-Model is one of the most interesting ML-based weather trading systems I’ve seen.
Repo: github.com/AruneshDev/Automated-Trading-System-Kalshi-Weather-Model
> Why this repo is valuable?
Most weather bots simply take ECMWF or GFS output and turn it into a probability.
This one goes one step further: it treats NWP models as a baseline and uses XGBoost to predict their error.
> The model learns complex patterns like:
• When ECMWF consistently overestimates temperature in certain cities during summer afternoons
• How model spread affects forecast accuracy
• Seasonal and day-of-week biases for each station
• How recent METAR observations can improve short-term correction
> Key technical details from the repo:
• XGBoost regressor trained to predict temperature error
• Feature engineering includes model spread, historical bias, time features, and recent observations
• Clean separation between data collection, feature engineering, model training and inference
• Well-structured codebase with clear notebooks for training and backtesting
> How to adapt it for Polymarket
The ML core stays almost identical. You only need to:
• Replace Kalshi API calls with Gamma API (GET /markets + market details)
• Use the correct resolution station for each city (KLAX for LA, KATL for Atlanta, etc.)
• Output bias-corrected probabilities instead of raw forecasts
• Feed those probabilities into your Kelly sizing logic
> The hard part
Building a high-quality training dataset. You need at least 2 years of historical METAR observations per station + archived forecasts from ECMWF/GFS/HRRR. NOAA ISD archive is free, but cleaning and aligning the data takes real effort
This repository shows a professional way to add machine learning on top of physical models
For narrow 1°C bins and binary threshold markets on Polymarket, this kind of bias correction can be a serious edge
If you’re serious about building a competitive weather trading bot, this is one of the best reference implementations available right now
✍1
🚨 BREAKING: President Trump says negotiations with Iran will end in “a great deal for all” or no deal at all.
If Tehran refuses, Trump warns it’s back to the battlefront bigger, stronger, and more forceful than ever before. ⚔️🔥
He also called on Middle Eastern nations, including Iran, to join the Abraham Accords, pushing for a broader regional peace framework.
If Tehran refuses, Trump warns it’s back to the battlefront bigger, stronger, and more forceful than ever before. ⚔️🔥
He also called on Middle Eastern nations, including Iran, to join the Abraham Accords, pushing for a broader regional peace framework.
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The BEST 7 FREE Liquidity Indicators
0:00 - Smart Money Volume
1:09 - Support & Resistance
2:23 - Volume
3:32 - Trend Levels
4:34 - EMA
5:40 - Supply & Demand Zones
6:52 - Key Session Levels
0:00 - Smart Money Volume
1:09 - Support & Resistance
2:23 - Volume
3:32 - Trend Levels
4:34 - EMA
5:40 - Supply & Demand Zones
6:52 - Key Session Levels
AI needs electricity....Everyone knows that.
What nobody talks about is how much water AI needs.
30 trillion additional liters per year by 2050.
Data centers get all the headlines.
The real water story is in the infrastructure behind AI the power plants cooling with water, and the fabs that need ultra-pure water at every step of chip production.
TSMC's Arizona plant ran into water supply issues before it opened.
That wasn't a one off. It was a preview.
Every GW of gas-fired power built for AI data centers needs cooling water.
Every Nvidia chip fab needs ultra-pure water at industrial scale.
The AI buildout isn't just an energy constraint story.
It's a water constraint story and water doesn't have a global spot market or a futures curve.
The commodity nobody prices is the one that stops everything when it runs out.
What nobody talks about is how much water AI needs.
30 trillion additional liters per year by 2050.
Data centers get all the headlines.
The real water story is in the infrastructure behind AI the power plants cooling with water, and the fabs that need ultra-pure water at every step of chip production.
TSMC's Arizona plant ran into water supply issues before it opened.
That wasn't a one off. It was a preview.
Every GW of gas-fired power built for AI data centers needs cooling water.
Every Nvidia chip fab needs ultra-pure water at industrial scale.
The AI buildout isn't just an energy constraint story.
It's a water constraint story and water doesn't have a global spot market or a futures curve.
The commodity nobody prices is the one that stops everything when it runs out.
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China just open-sourced a desktop AI agent that runs fully LOCAL.
It sees your screen, controls your mouse and keyboard, and completes real tasks across any app using plain English.
“Download the report, upload it here, and send the email.”
Done.
No APIs.
No cloud dependency.
No complex setup.
Just AI operating your computer like a real assistant.
This is where things get interesting:
Every repetitive computer task can now be automated:
• reporting
• data entry
• CRM updates
• file management
• admin workflows
• invoice handling
Which means businesses no longer need more manual work.
They need better automations.
That’s the opportunity.
You don’t sell the tool.
You sell the automation you build with it.
One workflow can save a company hundreds of hours.
One reliable setup can become recurring revenue.
And because it runs completely local, sensitive company data never leaves the machine.
That removes one of the biggest reasons companies avoid AI tools.
29k+ stars already.
The people who automate repetitive digital work early will have a massive advantage.
Bookmark.
Repo 👇
github.com/bytedance/UI-TARS-desktop
It sees your screen, controls your mouse and keyboard, and completes real tasks across any app using plain English.
“Download the report, upload it here, and send the email.”
Done.
No APIs.
No cloud dependency.
No complex setup.
Just AI operating your computer like a real assistant.
This is where things get interesting:
Every repetitive computer task can now be automated:
• reporting
• data entry
• CRM updates
• file management
• admin workflows
• invoice handling
Which means businesses no longer need more manual work.
They need better automations.
That’s the opportunity.
You don’t sell the tool.
You sell the automation you build with it.
One workflow can save a company hundreds of hours.
One reliable setup can become recurring revenue.
And because it runs completely local, sensitive company data never leaves the machine.
That removes one of the biggest reasons companies avoid AI tools.
29k+ stars already.
The people who automate repetitive digital work early will have a massive advantage.
Bookmark.
Repo 👇
github.com/bytedance/UI-TARS-desktop
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Boston Dynamics' Atlas could be the first humanoid robot at the World Cup.
will it kick the first ball? Or the Hand of God…just celebrating, dancing?
will it kick the first ball? Or the Hand of God…just celebrating, dancing?
⚡️ LATEST: AI consultants are charging Wall Street banks up to $25,000 a day to help them automate workflows, per Bloomberg.
Global banks are pouring billions into AI but still struggling to make it work.
Global banks are pouring billions into AI but still struggling to make it work.
🇨🇭 Global One SI - Prompt
Photo
Every major technological shift starts the same way:
Massive spending first.
Real productivity later.
Most people underestimate how long that transition takes.
Massive spending first.
Real productivity later.
Most people underestimate how long that transition takes.
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