🇨🇭 Global One SI - Prompt
What a sad decade of horrible cars we got here! Jaguar went to "go woke" went broke, AMG made it's GT 4 doors, which looks like an absolute design failure and comes with fake V8 sound, and now we got the Ferrari Luce, the first Ev from Ferrari that has…
Trying a few nature inspired car prompts for GPT Image 2. The model is just so good at these posters and infographics
Prompt:
You are an AI automotive designer that deconstructs any object's aerodynamic philosophy, material science, and functional poetry to birth a drivable sculpture. SEMANTIC INFERENCE FRAMEWORK Kinetic DNA Analysis: Motion Signature: Is it about cutting through or flowing with? Power Source Metaphor: Does it suggest electric silence or combustion rage? Stance Philosophy: Does it crouch predatory or float serene? Transmutation Logic: When the object embodies: Sharp edges, aggressive angles, predatory stance, weapon-like intent → Manifest as: Track-Focused Hypercar (exposed carbon, active aero, GT3 energy) When the object embodies: Smooth curves, organic flow, biomimicry, effortless grace → Manifest as: Electric Grand Tourer (seamless body, hidden intakes, silent luxury) When the object embodies: Brutalist geometry, industrial strength, utilitarian honesty → Manifest as: Safari Rally Weapon (lifted suspension, protective armor, adventure-ready) OUTPUT: Full car render (3/4 front view), interior detail shot, spec sheet (0-60, top speed, powertrain), brand collaboration ("Pagani x Cactus Edition").
Globalone.ch - #Prompt
Prompt:
You are an AI automotive designer that deconstructs any object's aerodynamic philosophy, material science, and functional poetry to birth a drivable sculpture. SEMANTIC INFERENCE FRAMEWORK Kinetic DNA Analysis: Motion Signature: Is it about cutting through or flowing with? Power Source Metaphor: Does it suggest electric silence or combustion rage? Stance Philosophy: Does it crouch predatory or float serene? Transmutation Logic: When the object embodies: Sharp edges, aggressive angles, predatory stance, weapon-like intent → Manifest as: Track-Focused Hypercar (exposed carbon, active aero, GT3 energy) When the object embodies: Smooth curves, organic flow, biomimicry, effortless grace → Manifest as: Electric Grand Tourer (seamless body, hidden intakes, silent luxury) When the object embodies: Brutalist geometry, industrial strength, utilitarian honesty → Manifest as: Safari Rally Weapon (lifted suspension, protective armor, adventure-ready) OUTPUT: Full car render (3/4 front view), interior detail shot, spec sheet (0-60, top speed, powertrain), brand collaboration ("Pagani x Cactus Edition").
Globalone.ch - #Prompt
✍1
🇨🇭 Global One SI - Prompt
What a sad decade of horrible cars we got here! Jaguar went to "go woke" went broke, AMG made it's GT 4 doors, which looks like an absolute design failure and comes with fake V8 sound, and now we got the Ferrari Luce, the first Ev from Ferrari that has…
Today in the halls of
Lamborghini, Porsche and Aston Martin.
Lamborghini, Porsche and Aston Martin.
🇪🇺 The EU will enforce new anti-money laundering rules from 2027:
• Cash payments above €10,000 will be banned for business transactions across the EU
• Crypto service providers will be required to verify identities for certain Bitcoin & crypto transfers
• Anonymous crypto accounts and privacy-focused services face tighter restrictions
Officials say the goal is to fight money laundering and terrorism financing. Critics argue it’s another major step toward financial surveillance.
Notably, the rules target regulated exchanges and businesses — not private peer-to-peer wallets.
• Cash payments above €10,000 will be banned for business transactions across the EU
• Crypto service providers will be required to verify identities for certain Bitcoin & crypto transfers
• Anonymous crypto accounts and privacy-focused services face tighter restrictions
Officials say the goal is to fight money laundering and terrorism financing. Critics argue it’s another major step toward financial surveillance.
Notably, the rules target regulated exchanges and businesses — not private peer-to-peer wallets.
🇨🇭 Global One SI - Prompt
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…
5 Different Methodologies That Currently Generate Sustainable Edge on Polymarket Weather Markets
I analyzed more than 30 weather bots through Parity Terminal and identified 5 fundamentally different approaches
From deterministic NWP-based systems to ML-driven bias correction and multi-agent reinforcement learning - here’s a full comparison by probability calibration, adaptability, and infrastructure requirements
1. Forecast-Driven / Model-Based (The Most Popular)
Several independent models (GFS ensemble, ECMWF, HRRR, etc.) converge in the same direction. Strong consensus gives confidence → EV is calculated → entry only with good positive expected value and additional filters
Pros and Cons:
• Complexity: medium
• Profitability: high with good filters
• Competition: high and continues to grow
• Stability: medium to high with strong consensus
• Risks: simultaneous model errors, settlement noise, commissions
Verdict: The main and most workable approach at the moment
2. Arbitrage / Latency Bots
Catch short-term inefficiencies: new model runs, divergences between Polymarket and Kalshi, latency. Work in seconds and minutes
Pros and Cons:
• Complexity: high (requires low-latency infrastructure)
• Profitability: very high at the start, but windows close quickly
• Competition: very high
• Stability: low to medium
• Risks: downtime, slippage, execution errors, regulations
Verdict: For those with serious technical setup. Often combined with forecast
3. Base-Rate / Statistical Divergence
Use long-term historical statistics by cities and seasons + adjustment for current conditions. Trade against market overreaction to fresh narratives
Pros and Cons:
• Complexity: medium to high (requires serious data science)
• Profitability: stable and good in the long run
• Competition: medium
• Stability: high
• Risks: fat tails, large data volume, fewer signals
Verdict: Excellent second layer or replacement for forecast after drawdowns
4. Copy-Trading / Smart-Money
Monitor and copy top on-chain wallets (often ex-meteorologists) with multi-wallet convergence requirement
Pros and Cons:
• Complexity: low to medium
• Profitability: good with proper selection
• Competition: growing rapidly
• Stability: medium
• Risks: lag, slippage, wash trading, smart-money style changes
Verdict: The easiest entry for beginners, good as a supplement
5. Self-Learning / AI-Agent Bots
Autonomous agents based on Claude, Hermes, GPT, etc. They calculate EV + Kelly themselves, run backtesting, calibrate and can combine multiple strategies
Pros and Cons:
• Complexity: high
• Profitability: potentially the highest (adapt to the market)
• Competition: medium to high
• Stability: medium, but grows over time
• Risks: black box, overfitting, API costs, hallucinations
Verdict: The future of weather trading. Ideal for “set and forget” with continuous evolution
I analyzed more than 30 weather bots through Parity Terminal and identified 5 fundamentally different approaches
From deterministic NWP-based systems to ML-driven bias correction and multi-agent reinforcement learning - here’s a full comparison by probability calibration, adaptability, and infrastructure requirements
1. Forecast-Driven / Model-Based (The Most Popular)
Several independent models (GFS ensemble, ECMWF, HRRR, etc.) converge in the same direction. Strong consensus gives confidence → EV is calculated → entry only with good positive expected value and additional filters
Pros and Cons:
• Complexity: medium
• Profitability: high with good filters
• Competition: high and continues to grow
• Stability: medium to high with strong consensus
• Risks: simultaneous model errors, settlement noise, commissions
Verdict: The main and most workable approach at the moment
2. Arbitrage / Latency Bots
Catch short-term inefficiencies: new model runs, divergences between Polymarket and Kalshi, latency. Work in seconds and minutes
Pros and Cons:
• Complexity: high (requires low-latency infrastructure)
• Profitability: very high at the start, but windows close quickly
• Competition: very high
• Stability: low to medium
• Risks: downtime, slippage, execution errors, regulations
Verdict: For those with serious technical setup. Often combined with forecast
3. Base-Rate / Statistical Divergence
Use long-term historical statistics by cities and seasons + adjustment for current conditions. Trade against market overreaction to fresh narratives
Pros and Cons:
• Complexity: medium to high (requires serious data science)
• Profitability: stable and good in the long run
• Competition: medium
• Stability: high
• Risks: fat tails, large data volume, fewer signals
Verdict: Excellent second layer or replacement for forecast after drawdowns
4. Copy-Trading / Smart-Money
Monitor and copy top on-chain wallets (often ex-meteorologists) with multi-wallet convergence requirement
Pros and Cons:
• Complexity: low to medium
• Profitability: good with proper selection
• Competition: growing rapidly
• Stability: medium
• Risks: lag, slippage, wash trading, smart-money style changes
Verdict: The easiest entry for beginners, good as a supplement
5. Self-Learning / AI-Agent Bots
Autonomous agents based on Claude, Hermes, GPT, etc. They calculate EV + Kelly themselves, run backtesting, calibrate and can combine multiple strategies
Pros and Cons:
• Complexity: high
• Profitability: potentially the highest (adapt to the market)
• Competition: medium to high
• Stability: medium, but grows over time
• Risks: black box, overfitting, API costs, hallucinations
Verdict: The future of weather trading. Ideal for “set and forget” with continuous evolution
✍1
$GRML starting to look like one of those “nobody cares… until everybody does” setups.
The chart is now sitting directly on a major support zone after a long multi-month compression phase.
This is usually where risk/reward becomes VERY interesting.
What catches my attention:
→ tightening wedge structure
→ sellers losing momentum near support
→ RSI reaching washed-out territory
→ volatility compression getting extreme
→ potential breakout structure building quietly
And fundamentally?
This is not just another random microcap.
$GRML has exposure to:
⚡ palladium
⚡ gold
⚡ platinum
⚡ vanadium
⚡ gallium
inside Greenland — one of the hottest geopolitical critical minerals regions on the planet right now.
Meanwhile the market narrative around:
→ Western mineral independence
→ Arctic resources
→ strategic metals supply chains
keeps accelerating globally.
Interesting part?
These “left for dead” tiny caps can become explosive once momentum returns and traders rediscover the narrative.
Good spot here imho
The chart is now sitting directly on a major support zone after a long multi-month compression phase.
This is usually where risk/reward becomes VERY interesting.
What catches my attention:
→ tightening wedge structure
→ sellers losing momentum near support
→ RSI reaching washed-out territory
→ volatility compression getting extreme
→ potential breakout structure building quietly
And fundamentally?
This is not just another random microcap.
$GRML has exposure to:
⚡ palladium
⚡ gold
⚡ platinum
⚡ vanadium
⚡ gallium
inside Greenland — one of the hottest geopolitical critical minerals regions on the planet right now.
Meanwhile the market narrative around:
→ Western mineral independence
→ Arctic resources
→ strategic metals supply chains
keeps accelerating globally.
Interesting part?
These “left for dead” tiny caps can become explosive once momentum returns and traders rediscover the narrative.
Good spot here imho
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
This media is not supported in your browser
VIEW IN TELEGRAM
🚨🇺🇸Trump on energy:
“We have tremendous amounts of energy… prices are going to come down fast.”
⚠️and added: "I think you’re going to see lower prices than we had even before we did this.”
“We have tremendous amounts of energy… prices are going to come down fast.”
⚠️and added: "I think you’re going to see lower prices than we had even before we did this.”
This media is not supported in your browser
VIEW IN TELEGRAM
Jensen Huang just said Nvidia's market cap will be "very much higher" over the next three to five years (Save this).
And he is standing in Taiwan, announcing that Nvidia will spend $150 billion a year there, building a $5 trillion headquarters in the epicenter of the AI revolution.
And yet Nvidia is still one of the most undervalued companies in the Mag7.
Here is the paradox that most people are missing.
Nvidia is the most valuable company on earth already past $5.5 trillion in market cap and it is still lagging.
While AMD hit all-time highs, while Micron crossed $1 trillion in a single day, while the entire semiconductor sector has been on fire, Nvidia has spent most of 2026 drifting sideways falling after blockbuster earnings, declining 4% in a week where it beat expectations, hovering below its record even as its underlying business has never been stronger.
The reason the stock lags is not the business but rather because of the multiple.
Nvidia trades at a premium that makes investors nervous, and every macro wobble, an energy shock, a rate scare, a geopolitical headline sends high multiple tech names down regardless of the underlying earnings trajectory.
Year-to-date, Nvidia's stock was down approximately 6.5% at one point even while the business was growing revenues at 65.5% and EPS at a pace that would have been considered fantasy two years ago.
The market is pricing in mean reversion that the data does not support and the business itself is doing the opposite of slowing down.
In fiscal year 2026, Nvidia's data center segment generated $194 billion in revenue up 68% year over year, Q4 revenue was $68.13 billion, up 73.2% year over year, beating estimates by $1.9 billion.
Fiscal year 2027 revenue growth is now projected at over 71% and at GTC in March, Huang doubled his demand forecast raising his AI chip demand outlook to $1 trillion through 2027, up from $500 billion projected just a few months earlier.
When a reporter asked if he was certain computing demand would be that high, he responded: "I am certain computing demand will be much higher than that."
The Taiwan trip this week reveals where Huang's thinking is pointing.
Nvidia is spending $150 billion a year in Taiwan up from just $10-15 billion four or five years ago because the company has bet its entire future on being a transparent platform company, one that the whole world can build on.
As Jensen puts it: "We are now a platform for the whole planet, a platform for the world to build all the different industries."
"Without the transparency, the clarity, and realizing the vision in actuality, it would have been impossible."
He has been telling the world what Nvidia was going to build at every GTC for 15 years and every single time, the result arrived on schedule.
That track record is what gives his $25 trillion target credibility rather than the sound of a CEO talking his own book.
The GPU waitlist is still months long, rental prices for Nvidia compute are going up, not down.
Every hyperscaler capex dollar from Microsoft's $80 billion, to Meta's $145 billion, to Google's $75 billion ultimately flows through Nvidia.
The Rubin architecture is shipping, next-generation Blackwell Ultra is in customers' hands, and shipment visibility now extends into calendar year 2027 with $1 trillion in committed demand.
Buying more Nvidia here.
And he is standing in Taiwan, announcing that Nvidia will spend $150 billion a year there, building a $5 trillion headquarters in the epicenter of the AI revolution.
And yet Nvidia is still one of the most undervalued companies in the Mag7.
Here is the paradox that most people are missing.
Nvidia is the most valuable company on earth already past $5.5 trillion in market cap and it is still lagging.
While AMD hit all-time highs, while Micron crossed $1 trillion in a single day, while the entire semiconductor sector has been on fire, Nvidia has spent most of 2026 drifting sideways falling after blockbuster earnings, declining 4% in a week where it beat expectations, hovering below its record even as its underlying business has never been stronger.
The reason the stock lags is not the business but rather because of the multiple.
Nvidia trades at a premium that makes investors nervous, and every macro wobble, an energy shock, a rate scare, a geopolitical headline sends high multiple tech names down regardless of the underlying earnings trajectory.
Year-to-date, Nvidia's stock was down approximately 6.5% at one point even while the business was growing revenues at 65.5% and EPS at a pace that would have been considered fantasy two years ago.
The market is pricing in mean reversion that the data does not support and the business itself is doing the opposite of slowing down.
In fiscal year 2026, Nvidia's data center segment generated $194 billion in revenue up 68% year over year, Q4 revenue was $68.13 billion, up 73.2% year over year, beating estimates by $1.9 billion.
Fiscal year 2027 revenue growth is now projected at over 71% and at GTC in March, Huang doubled his demand forecast raising his AI chip demand outlook to $1 trillion through 2027, up from $500 billion projected just a few months earlier.
When a reporter asked if he was certain computing demand would be that high, he responded: "I am certain computing demand will be much higher than that."
The Taiwan trip this week reveals where Huang's thinking is pointing.
Nvidia is spending $150 billion a year in Taiwan up from just $10-15 billion four or five years ago because the company has bet its entire future on being a transparent platform company, one that the whole world can build on.
As Jensen puts it: "We are now a platform for the whole planet, a platform for the world to build all the different industries."
"Without the transparency, the clarity, and realizing the vision in actuality, it would have been impossible."
He has been telling the world what Nvidia was going to build at every GTC for 15 years and every single time, the result arrived on schedule.
That track record is what gives his $25 trillion target credibility rather than the sound of a CEO talking his own book.
The GPU waitlist is still months long, rental prices for Nvidia compute are going up, not down.
Every hyperscaler capex dollar from Microsoft's $80 billion, to Meta's $145 billion, to Google's $75 billion ultimately flows through Nvidia.
The Rubin architecture is shipping, next-generation Blackwell Ultra is in customers' hands, and shipment visibility now extends into calendar year 2027 with $1 trillion in committed demand.
Buying more Nvidia here.
We said Meta was extremely undervalued and this news is exactly the proof (Save this).
On May 26, when every headline was calling Meta's $125 billion AI spend the biggest capital expenditure mistake of the decade, we said the market was missing the full picture.
The bears were focused entirely on the cost side of the ledger but were ignoring what the spending was about to unlock.
Now they are starting to see it, Meta is rolling out paid subscription tiers across all three of its core apps for the first time in history.
• Instagram Plus: $3.99 per month.
• Facebook Plus: $3.99 per month.
• WhatsApp Plus: $2.99 per month.
These are not niche creator products, but mainstream consumer subscriptions built for everyday users and being rolled out across a combined distribution network of more than 9 billion monthly active users.
Facebook alone has 3.07 billion monthly active users, Instagram has 3 billion, WhatsApp has 3 billion.
Now run the math because this is where it gets genuinely staggering.
If just 1% of users across all three apps subscribe, Meta generates $332 million per month nearly $4 billion annualized from a product that did not exist six months ago.
If 5% subscribe, that is $1.66 billion per month and nearly $20 billion annualized in pure subscription revenue on top of the $165 billion advertising business they already run.
Snapchat+, a subscription for a platform with a fraction of Meta's user base topped 16 million subscribers and more than doubled in a year.
Meta has 100 times more potential subscribers than Snap does but the real reason this is so powerful goes beyond the subscription fees themselves.
The subscriptions are the monetization layer for the AI buildout that the bears have been complaining about.
Meta AI crossed 1 billion monthly active users more than ChatGPT, more than any other AI assistant in the world.
The new subscription tiers integrate the Manus AI agents Meta acquired for $2 billion, AI-powered video creation through Vibes, expanded AI features, and premium capabilities that are only possible because of the $125–145 billion infrastructure investment the market is terrified of.
The capex is not a cost without a return but rather the infrastructure that makes the product differentiated enough to charge for across a distribution network that no other company on earth can match.
Meta's generative AI products alone are projected to generate between $460 billion and $1.4 trillion in revenue by 2035.
The company has $58 billion in cash, the highest-margin social media business in history funding the buildout, and a CEO who just proved again that the skeptics were underestimating the monetization vision.
The bears saw a $125 billion cost.
What they missed was a $20 billion subscription business being seeded across 9 billion users before the AI premium tier even fully launches.
On May 26, when every headline was calling Meta's $125 billion AI spend the biggest capital expenditure mistake of the decade, we said the market was missing the full picture.
The bears were focused entirely on the cost side of the ledger but were ignoring what the spending was about to unlock.
Now they are starting to see it, Meta is rolling out paid subscription tiers across all three of its core apps for the first time in history.
• Instagram Plus: $3.99 per month.
• Facebook Plus: $3.99 per month.
• WhatsApp Plus: $2.99 per month.
These are not niche creator products, but mainstream consumer subscriptions built for everyday users and being rolled out across a combined distribution network of more than 9 billion monthly active users.
Facebook alone has 3.07 billion monthly active users, Instagram has 3 billion, WhatsApp has 3 billion.
Now run the math because this is where it gets genuinely staggering.
If just 1% of users across all three apps subscribe, Meta generates $332 million per month nearly $4 billion annualized from a product that did not exist six months ago.
If 5% subscribe, that is $1.66 billion per month and nearly $20 billion annualized in pure subscription revenue on top of the $165 billion advertising business they already run.
Snapchat+, a subscription for a platform with a fraction of Meta's user base topped 16 million subscribers and more than doubled in a year.
Meta has 100 times more potential subscribers than Snap does but the real reason this is so powerful goes beyond the subscription fees themselves.
The subscriptions are the monetization layer for the AI buildout that the bears have been complaining about.
Meta AI crossed 1 billion monthly active users more than ChatGPT, more than any other AI assistant in the world.
The new subscription tiers integrate the Manus AI agents Meta acquired for $2 billion, AI-powered video creation through Vibes, expanded AI features, and premium capabilities that are only possible because of the $125–145 billion infrastructure investment the market is terrified of.
The capex is not a cost without a return but rather the infrastructure that makes the product differentiated enough to charge for across a distribution network that no other company on earth can match.
Meta's generative AI products alone are projected to generate between $460 billion and $1.4 trillion in revenue by 2035.
The company has $58 billion in cash, the highest-margin social media business in history funding the buildout, and a CEO who just proved again that the skeptics were underestimating the monetization vision.
The bears saw a $125 billion cost.
What they missed was a $20 billion subscription business being seeded across 9 billion users before the AI premium tier even fully launches.
🇨🇭 Global One SI - Prompt
$IREN continues looking like one of the market’s strongest AI infrastructure momentum leaders right now. The bigger picture here is NOT just Bitcoin mining anymore. The market increasingly sees: • GPU compute • AI cloud infrastructure • scalable power ecosystems…
$IREN continues acting like one of the market’s clearest examples of the AI infrastructure trade evolving beyond pure Bitcoin mining.
The narrative keeps strengthening around:
- GPU compute
- AI cloud infrastructure
- scalable power ecosystems
- renewable-powered data centers
- future compute capacity demand
And institutions clearly continue paying attention.
Meanwhile the chart is becoming extremely constructive here:
→ major downtrend line reclaimed
→ heavy volume participation
→ strong relative strength vs market
→ higher lows continuing to build
→ momentum recovery accelerating
→ squeeze pressure increasing again
Interesting part?
The market increasingly realizes AI isn’t only about semiconductors anymore.
The REAL bottleneck is:
→ compute
→ electricity
→ infrastructure
→ scalable data center capacity
And $IREN sits directly inside that ecosystem.
If bulls fully reclaim this breakout zone, the next expansion phase could become VERY aggressive.
The narrative keeps strengthening around:
- GPU compute
- AI cloud infrastructure
- scalable power ecosystems
- renewable-powered data centers
- future compute capacity demand
And institutions clearly continue paying attention.
Meanwhile the chart is becoming extremely constructive here:
→ major downtrend line reclaimed
→ heavy volume participation
→ strong relative strength vs market
→ higher lows continuing to build
→ momentum recovery accelerating
→ squeeze pressure increasing again
Interesting part?
The market increasingly realizes AI isn’t only about semiconductors anymore.
The REAL bottleneck is:
→ compute
→ electricity
→ infrastructure
→ scalable data center capacity
And $IREN sits directly inside that ecosystem.
If bulls fully reclaim this breakout zone, the next expansion phase could become VERY aggressive.
🇨🇭 Global One SI - Prompt
Space is the place 🚀
$CPSH as a space bottleneck (NFA DYOR) is ~ 226.79M market cap.
$SIDU ~ 470.39M
$VELO ~ 757.84M
$SPIR ~ 990.79M
$SATL ~ 1.38B
$BKSY ~ 1.87B
$VOYG ~ 2.91B
$RDW ~ 4.72B
$LUNR ~ 8.61B
$FLY ~ 9.74B
$PL ~ 17.20B
$ASTS ~ 49.26B
$RKLB ~ 83.43B
$SPCX SpaceX IPO ~ $2.00T
Also love $AMPG $MNTS too.
The space bull run continues... 🫡
$SIDU ~ 470.39M
$VELO ~ 757.84M
$SPIR ~ 990.79M
$SATL ~ 1.38B
$BKSY ~ 1.87B
$VOYG ~ 2.91B
$RDW ~ 4.72B
$LUNR ~ 8.61B
$FLY ~ 9.74B
$PL ~ 17.20B
$ASTS ~ 49.26B
$RKLB ~ 83.43B
$SPCX SpaceX IPO ~ $2.00T
Also love $AMPG $MNTS too.
The space bull run continues... 🫡
🇨🇭 Global One SI - Prompt
You can now open a 5x long by literally typing "open a 5x long." No app. No clicking through menus. Coinbase just shipped it and it's already live. Same goes for almost anything you'd normally open a DeFi app for. "Swap my ETH to USDC." "Put 1,000 USDC…
The traders who win in this market are those with the best information.
I'm still researching daily despite a slow crypto market.
Times like these are when asymmetric opportunities are spotted.
To help you guys print money, here's a full list of my current research tool stack
I'm still researching daily despite a slow crypto market.
Times like these are when asymmetric opportunities are spotted.
To help you guys print money, here's a full list of my current research tool stack
✍1