🇨🇭 Global One SI - Prompt
EUROPE IS LOSING THREE WARS AT ONCE. 🇪🇺 The stablecoin war: 85% run on the dollar. The crypto war: regulated out by 2027. Now the AI war: 212,000 banking jobs gone by 2030. Morgan Stanley modeled it across 35 lenders. 10% of the workforce. 30% efficiency…
In europe, we already have the solution: VAT taxes on every service / product that the bot will produce
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🇨🇭 Global One SI - Prompt
🇺🇸 Treasury Secretary Scott Bessent says the U.S. Government has seized $1 billion of Iran's crypto: "Just outright grabbed the wallets. Some of them may be typing in right now and might not realize their wallet had been grabbed."
“And we are working with our allies all over Europe to grab villas and houses and properties. And this is money that's stolen from the Iranian people.”
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We can tell what is going here no matter what language it is in.
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🇨🇭 Global One SI - Prompt
Bill Maher: How the hell does the government afford to pay people a Universal Basic Income if AI takes away all the jobs? "How does the money get to the government if people aren't paying taxes?"
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You can just put a full AI supercomputer in ur pocket and it will print you money..
Nvidia dropped the DGX Spark
Your personal desk-sized beast that turns any idea into cash while normies beg for API credits.
This tiny Blackwell-powered monster delivers:
- 1 petaFLOP of AI performance,
- 128GB unified memory.
- And runs 200B parameter models locally.
No cloud bills.
No throttling.
Full control.
Endless ideas, endless money.
Nvidia dropped the DGX Spark
Your personal desk-sized beast that turns any idea into cash while normies beg for API credits.
This tiny Blackwell-powered monster delivers:
- 1 petaFLOP of AI performance,
- 128GB unified memory.
- And runs 200B parameter models locally.
No cloud bills.
No throttling.
Full control.
Endless ideas, endless money.
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🇨🇭 Global One SI - Prompt
"I feel like there are 3 asteroids heading for Earth." (SpaceX, Anthropic, OpenAI) "Can the market really support that much supply of new tech stocks all at the same time?"
SpaceX → Scheduled to go public on June 12th
OpenAI → Scheduled to go public as early as September
Anthropic → Scheduled to go public as early as November
OpenAI → Scheduled to go public as early as September
Anthropic → Scheduled to go public as early as November
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🇨🇭 Global One SI - Prompt
$KTOS Is quietly becoming one of the most interesting names in the autonomous systems and defense technology space. While much of the market remains focused on AI software, a massive parallel trend is developing around autonomous warfare, military drones…
Cathie Wood and Ark Invest bought 252,134 shares of Kratos Defense $KTOS
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🇨🇭 Global One SI - Prompt
A 3D printed drone built in someone’s bedroom just raised $1B and reportedly outflies DJI on every metric. No factory. No team. One printer, one AI chip, and a guy at a desk. Range over 100km. Battery that outlasts anything DJI ships. Optical tracking that…
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🇺🇸 The future of warfare is already here: Shane Smith visits African Lion 2026 to see AI-assisted weapons and autonomous armed vehicles in action.
Military leaders argue the technology can't be paused because adversaries won't wait.
Military leaders argue the technology can't be paused because adversaries won't wait.
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The ultimate holy grail of clean energy is closer than we think. A revolutionary new fusion reactor design is officially on track to connect directly to the power grid, bringing us a massive step closer to potentially powering the entire planet by 2030.
Unlike traditional nuclear plants, fusion generates boundless energy by mimicking the exact process that powers our sun, fusing atoms together instead of splitting them. This incredible technology produces zero greenhouse gases and leaves behind no long-lived radioactive waste, promising an era of completely clean, limitless electricity for everyone!
Unlike traditional nuclear plants, fusion generates boundless energy by mimicking the exact process that powers our sun, fusing atoms together instead of splitting them. This incredible technology produces zero greenhouse gases and leaves behind no long-lived radioactive waste, promising an era of completely clean, limitless electricity for everyone!
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🇨🇭 Global One SI - Prompt
The ultimate holy grail of clean energy is closer than we think. A revolutionary new fusion reactor design is officially on track to connect directly to the power grid, bringing us a massive step closer to potentially powering the entire planet by 2030. Unlike…
Once that happens then the Arab oil states will revert to their natural state,.....
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Nobody sees my posts anymore...
So this is the perfect opportunity for us to whisper, quietly together
So this is the perfect opportunity for us to whisper, quietly together
Goldman Sachs: "Token use by AI agents is expected to multiply 24 times by 2030"
AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.
A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop.
That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.
Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year.
The fight is now between agent productivity and token waste.
Earlier this month, Microsoft began revoking developer access to Claude Code, with plans to move them to its in-house Copilot Command Line Interface tool by June 30. The company has framed this as consolidating teams around its own tools, but the timing at the fiscal year’s end hints it may also be about lowering costs.
AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.
A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop.
That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.
Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year.
The fight is now between agent productivity and token waste.
Earlier this month, Microsoft began revoking developer access to Claude Code, with plans to move them to its in-house Copilot Command Line Interface tool by June 30. The company has framed this as consolidating teams around its own tools, but the timing at the fiscal year’s end hints it may also be about lowering costs.
🚨BREAKING: NVIDIA wants to turn your house into part of an AI data center.
Not metaphorically.
A startup backed by NVIDIA is installing GPU-powered compute nodes outside people's homes.
They look like oversized AC units.
But inside one box:
→ 16 NVIDIA Blackwell GPUs
→ 4 AMD EPYC server CPUs
→ 3TB RAM
→ 15kWh backup battery
Over $200,000 worth of AI hardware.
And it sits in your backyard.
Homeowners don't buy it.
Instead:
→ Installation is free
→ Electricity + internet costs are covered
→ A small monthly fee is paid to participate
→ The unused power capacity of your home becomes AI infrastructure
The idea is simple:
Most homes never use their full electrical capacity.
Span wants to aggregate thousands of homes into one giant distributed AI cluster.
Their claim:
8,000 homes could create the equivalent of a 100MW AI data center.
5× cheaper.
6× faster to deploy.
No waiting years for new grid connections.
No billion-dollar data center campuses.
Just thousands of suburban homes quietly running AI workloads.
The first 100-home pilot launches in 2026.
If it works, the next AI data center might not be built in the desert.
It might be built on your street. 👀
Not metaphorically.
A startup backed by NVIDIA is installing GPU-powered compute nodes outside people's homes.
They look like oversized AC units.
But inside one box:
→ 16 NVIDIA Blackwell GPUs
→ 4 AMD EPYC server CPUs
→ 3TB RAM
→ 15kWh backup battery
Over $200,000 worth of AI hardware.
And it sits in your backyard.
Homeowners don't buy it.
Instead:
→ Installation is free
→ Electricity + internet costs are covered
→ A small monthly fee is paid to participate
→ The unused power capacity of your home becomes AI infrastructure
The idea is simple:
Most homes never use their full electrical capacity.
Span wants to aggregate thousands of homes into one giant distributed AI cluster.
Their claim:
8,000 homes could create the equivalent of a 100MW AI data center.
5× cheaper.
6× faster to deploy.
No waiting years for new grid connections.
No billion-dollar data center campuses.
Just thousands of suburban homes quietly running AI workloads.
The first 100-home pilot launches in 2026.
If it works, the next AI data center might not be built in the desert.
It might be built on your street. 👀
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🇨🇭 Global One SI - Prompt
You can just put a full AI supercomputer in ur pocket and it will print you money.. Nvidia dropped the DGX Spark Your personal desk-sized beast that turns any idea into cash while normies beg for API credits. This tiny Blackwell-powered monster delivers:…
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This guy spent nearly $12,000 building an AI farm on his desk.
Most people won't understand why.
At first glance it looks like a few small boxes next to a laptop.
But those 4 Nvidia DGX Spark systems pack 512GB of unified memory and enough compute to run models that many gaming PCs can't even open.
And the math is surprisingly simple.
Many AI builders spend $1,500–$3,000 every month renting cloud GPUs for agents, fine-tunes, client work, and large models.
This setup turns that monthly bill into hardware he actually owns.
No hourly GPU charges.
No cloud bill growing in the background.
No margins disappearing every time an agent runs longer than expected.
Just a personal AI cluster sitting on a desk.
The crazy part?
Most people still think local AI means running a tiny model on a laptop.
Meanwhile, some builders are quietly bringing data-center-level compute home.
Save this post.
Home AI labs could become the new mining rigs.
Most people won't understand why.
At first glance it looks like a few small boxes next to a laptop.
But those 4 Nvidia DGX Spark systems pack 512GB of unified memory and enough compute to run models that many gaming PCs can't even open.
And the math is surprisingly simple.
Many AI builders spend $1,500–$3,000 every month renting cloud GPUs for agents, fine-tunes, client work, and large models.
This setup turns that monthly bill into hardware he actually owns.
No hourly GPU charges.
No cloud bill growing in the background.
No margins disappearing every time an agent runs longer than expected.
Just a personal AI cluster sitting on a desk.
The crazy part?
Most people still think local AI means running a tiny model on a laptop.
Meanwhile, some builders are quietly bringing data-center-level compute home.
Save this post.
Home AI labs could become the new mining rigs.
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McKinsey report: AI skills are getting hotter across Europe’s job market.
Nearly 1 in 5 European occupations now ask for AI-related skills, more than 3x the share seen in 2023.
The fastest-growing skill is not model building, but AI fluency, which means knowing how to prompt, manage, check, and apply AI systems inside real jobs.
Demand for AI fluency grew 5x, from work covering 1.9M employees in 2023 to 9.4M in 2025.
Demand for technical AI skills grew only 1.7x, which means Europe needs far more AI users than AI engineers.
The demand is still concentrated, with 75% coming from computer, management, and business-finance roles, but it is spreading into logistics, HR, compliance, and skilled trades.
Nearly 1 in 5 European occupations now ask for AI-related skills, more than 3x the share seen in 2023.
The fastest-growing skill is not model building, but AI fluency, which means knowing how to prompt, manage, check, and apply AI systems inside real jobs.
Demand for AI fluency grew 5x, from work covering 1.9M employees in 2023 to 9.4M in 2025.
Demand for technical AI skills grew only 1.7x, which means Europe needs far more AI users than AI engineers.
The demand is still concentrated, with 75% coming from computer, management, and business-finance roles, but it is spreading into logistics, HR, compliance, and skilled trades.
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Terence Tao: "We lived in a world with cognitive friction until very recently, where every task required us to use our brain.
So we didn't really think about it, we just thought this was the cost of doing something intellectual. But now we have AI and the other technologies that can bring these frictions down to zero."
Most research time is not spent having cinematic insights.
It is spent checking cases, chasing references, translating intuition into computation, testing a path, finding it false, and deciding whether the failure taught you anything.
AI changes the cost of that loop.
Terence Tao says that now he can try “crazier things,” and that makes so much difference. Because unconventional ideas are often not rejected by proof, but by inconvenience.
A mathematician may avoid a strange direction not because it is foolish, but because the bookkeeping, coding, or literature search needed to test it is too expensive for a hunch.
This is where cognitive friction becomes scientific friction.
Lowering it does not make taste, judgment, or proof disappear; it makes more weak signals cheap enough to inspect before they are abandoned.
AI is making hesitation less expensive, and that is often where discovery begins.
So we didn't really think about it, we just thought this was the cost of doing something intellectual. But now we have AI and the other technologies that can bring these frictions down to zero."
Most research time is not spent having cinematic insights.
It is spent checking cases, chasing references, translating intuition into computation, testing a path, finding it false, and deciding whether the failure taught you anything.
AI changes the cost of that loop.
Terence Tao says that now he can try “crazier things,” and that makes so much difference. Because unconventional ideas are often not rejected by proof, but by inconvenience.
A mathematician may avoid a strange direction not because it is foolish, but because the bookkeeping, coding, or literature search needed to test it is too expensive for a hunch.
This is where cognitive friction becomes scientific friction.
Lowering it does not make taste, judgment, or proof disappear; it makes more weak signals cheap enough to inspect before they are abandoned.
AI is making hesitation less expensive, and that is often where discovery begins.
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🇨🇭 Global One SI - Prompt
Smart Money Tracking on Polymarket: How to Copy the Best Traders and Steal Their Edge (+ Full Guide) While most retail traders chase headlines and emotions, smart traders quietly accumulate large positions based on data, probability, and proven edge This…
This GitHub repository uses the full 31-member GFS ensemble with smart probability conversion and an 8% edge filter to trade weather markets on Polymarket
It turns 31 forecasts into one calibrated probability, applies Laplace smoothing, and enters only when the edge is over 8%
The result is a clean, disciplined dual-platform bot that scans both Polymarket and Kalshi every 5 minutes
> How it works:
Open-Meteo gives all 31 ensemble runs of GFS in one free API request
Each member starts with slightly different initial conditions, and over 24–48 hours these small differences grow into noticeably different forecasts
The spread between members becomes your uncertainty signal.
> Converting 31 forecasts into one probability - 3 steps:
Step 1 - For each of the 31 members check if the condition is met:
condition_met = forecast_temp > threshold
Step 2 - Calculate the percentage of members where the condition is true:
p = count(condition_met) / 31
Example: 28 out of 31 members show above 70°F → model probability = 90%.
Step 3 - Apply Laplace smoothing on extreme values:
p_smooth = (count + 1) / (31 + 2)
This prevents 0% or 100% probabilities from small sample sizes
> The 8% threshold
The bot enters only when |p_ensemble − p_market| ≥ 0.08
This number is not random - it accounts for taker fees (~1.5–1.8%), slippage in weather markets (~1–2%), and model error (~2–3%)
Below 8% the costs usually eat the edge
> Dual-platform approach
The same GFS ensemble and probability logic is applied to both Polymarket and Kalshi every 5 minutes
Sometimes the same event is mispriced on one platform and fairly priced on the other
The bot catches both opportunities.What you can actually take for your own bot
> The most valuable part is the pipeline:
Open-Meteo GFS ensemble → percentage calculation → Laplace smoothing → comparison with market price → entry if edge ≥ your cost threshold.
You don’t need the whole bot. This logic alone is already a solid foundation for a serious weather trading system
Repo: github.com/suislanchez/polymarket-kalshi-weather-bot
It turns 31 forecasts into one calibrated probability, applies Laplace smoothing, and enters only when the edge is over 8%
The result is a clean, disciplined dual-platform bot that scans both Polymarket and Kalshi every 5 minutes
> How it works:
Open-Meteo gives all 31 ensemble runs of GFS in one free API request
Each member starts with slightly different initial conditions, and over 24–48 hours these small differences grow into noticeably different forecasts
The spread between members becomes your uncertainty signal.
> Converting 31 forecasts into one probability - 3 steps:
Step 1 - For each of the 31 members check if the condition is met:
condition_met = forecast_temp > threshold
Step 2 - Calculate the percentage of members where the condition is true:
p = count(condition_met) / 31
Example: 28 out of 31 members show above 70°F → model probability = 90%.
Step 3 - Apply Laplace smoothing on extreme values:
p_smooth = (count + 1) / (31 + 2)
This prevents 0% or 100% probabilities from small sample sizes
> The 8% threshold
The bot enters only when |p_ensemble − p_market| ≥ 0.08
This number is not random - it accounts for taker fees (~1.5–1.8%), slippage in weather markets (~1–2%), and model error (~2–3%)
Below 8% the costs usually eat the edge
> Dual-platform approach
The same GFS ensemble and probability logic is applied to both Polymarket and Kalshi every 5 minutes
Sometimes the same event is mispriced on one platform and fairly priced on the other
The bot catches both opportunities.What you can actually take for your own bot
> The most valuable part is the pipeline:
Open-Meteo GFS ensemble → percentage calculation → Laplace smoothing → comparison with market price → entry if edge ≥ your cost threshold.
You don’t need the whole bot. This logic alone is already a solid foundation for a serious weather trading system
Repo: github.com/suislanchez/polymarket-kalshi-weather-bot
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🇨🇭 Global One SI - Prompt
A new era of PC. Nvidia, Microsoft, and Arm are all teasing Nvidia’s new N1X laptop processors All eyes area now on Nvidia’s keynote at Computex on Sunday night.
Nvidia’s new ARM-based SOC is rumoured to feature:
- Up to 20 CPU cores
- Up to 6144 Blackwell CUDA cores
- Use a TSMC 3nm process
- Up to 128GB unified memory
This has the potential to be an M5 Max competitor for AI-developers.
- Up to 20 CPU cores
- Up to 6144 Blackwell CUDA cores
- Use a TSMC 3nm process
- Up to 128GB unified memory
This has the potential to be an M5 Max competitor for AI-developers.
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Microsoft open-sourced this AI quant investment platform 100% for free
Here's what it does (and how to get it for free):
https://qlib.readthedocs.io/en/
Here's what it does (and how to get it for free):
https://qlib.readthedocs.io/en/
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🇨🇭 Global One SI - Prompt
Microsoft open-sourced this AI quant investment platform 100% for free Here's what it does (and how to get it for free): https://qlib.readthedocs.io/en/
Introducing Qlib: An AI-oriented Quantitative Investment Platform
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