🇨🇭 Global One SI - Prompt
An autonomous weather trading bot powered by the Hermes Agent. It gets temperature data from multiple sources and uses a Gaussian Bucket strategy: For example, if the forecast is 72F, it estimates that the final temperature will be between 71F and 73F.…
Multi-Model Weighting on Polymarket Weather: How to Properly Weight ECMWF, GEFS and HRRR for Maximum Edge
Most traders take a simple average of all models and think that’s good enough
Top accounts weight models differently - depending on city, lead time, and season
This is one of the most underrated ways to improve your probabilities
> Why Blind Average Performs Poorly
- ECMWF is often stronger on 48–72h horizon
- HRRR / GEFS shine on short horizons (0–36h)
- The same model can have very different skill in different cities
- Summer and winter biases change
Simple averaging smooths out strengths and amplifies weaknesses
> How to Do Weighting Properly
Start simple and scale up:
1. Basic approach (good for starters)
Build a static table: for each city and horizon assign higher weight to the historically strongest model (e.g. HRRR for D+0 in US cities, ECMWF for D+2)
2. Better approach (recommended)
Use dynamic weighting based on recent performance - last 30–60 days accuracy per city. The model that performed best lately gets more weight
3. Pro level
Implement stacking or Bayesian weighting - a small meta-model that learns the best combination on the fly
Even moving from equal weights to a simple per-city table already gives a noticeable lift in probability accuracy
> Real-World Impact
On narrow 1–2°C buckets, proper weighting can shift a bucket’s fair probability by 6–12%
That directly means more +EV trades and higher win-rate.Many consistently profitable bots use weighted ensembles instead of raw averages
If you’re still giving equal weight to every model - you’re leaving one of the cleanest repeatable edges on the table
Most traders take a simple average of all models and think that’s good enough
Top accounts weight models differently - depending on city, lead time, and season
This is one of the most underrated ways to improve your probabilities
> Why Blind Average Performs Poorly
- ECMWF is often stronger on 48–72h horizon
- HRRR / GEFS shine on short horizons (0–36h)
- The same model can have very different skill in different cities
- Summer and winter biases change
Simple averaging smooths out strengths and amplifies weaknesses
> How to Do Weighting Properly
Start simple and scale up:
1. Basic approach (good for starters)
Build a static table: for each city and horizon assign higher weight to the historically strongest model (e.g. HRRR for D+0 in US cities, ECMWF for D+2)
2. Better approach (recommended)
Use dynamic weighting based on recent performance - last 30–60 days accuracy per city. The model that performed best lately gets more weight
3. Pro level
Implement stacking or Bayesian weighting - a small meta-model that learns the best combination on the fly
Even moving from equal weights to a simple per-city table already gives a noticeable lift in probability accuracy
> Real-World Impact
On narrow 1–2°C buckets, proper weighting can shift a bucket’s fair probability by 6–12%
That directly means more +EV trades and higher win-rate.Many consistently profitable bots use weighted ensembles instead of raw averages
If you’re still giving equal weight to every model - you’re leaving one of the cleanest repeatable edges on the table
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Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
The document includes new measures against countries that continue to purchase Russian oil and gas.
According to senators, the bill will be formally presented in the near future.
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▪️Waller, Chairman of the Federal Reserve:
- The main risk for the Federal Reserve is now high inflation.
- The risks have completely changed – the labor market is stabilizing, while inflation is accelerating.
- The Federal Reserve's forecasts on interest rates may be useful, but only when applied flexibly. Otherwise, they limit the actions of the regulator.
▪️Williams, Chairman of the Federal Reserve:
- Inflation is still too high, however, the decline in energy prices improves the short-term outlook.
- Oil prices are likely near their peak and could fall in the next 6-12 months.
- The current Federal Reserve policy is in the right position. Further decisions will depend on the data.
- The decision on interest rates in July has not yet been made, and the analysis of the situation is still to be conducted.
❗️Market expectations (interest rate #FederalReserve):
- July 29: PAUSE.
- September 16: Increase by 25 basis points to 3.75-4.00%.
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🇨🇭 Global One SI - Prompt
SK Hynix $SKHY is picking the perfect time to go public (July 10) on U.S. exchanges. If they're following the typical pattern, this bounce will be the complacency stage of the memory cycle now that the parabolic has been broken.
SK Hynix $SKHYV rallied 13% above the US IPO price to $168.
To give you a rough idea what Monday should look like in South Korea, I've added the candlestick the 13% gain from today.
To give you a rough idea what Monday should look like in South Korea, I've added the candlestick the 13% gain from today.
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🇨🇭 Global One SI - Prompt
Nasdaq futures are holding. Almost as if it's magic... 😉
Nasdaq $NDX Update: Looks like we're setting up perfectly for a big jump in indices next week, lining up with a big earnings week.
If this happens as expected then it should be a huge gap up on Monday, with markets remaining largely flat for the rest of the week. That will suck retail in so they can sell.
Friendly reminder, the parabolic is now going back in time.
If this happens as expected then it should be a huge gap up on Monday, with markets remaining largely flat for the rest of the week. That will suck retail in so they can sell.
Friendly reminder, the parabolic is now going back in time.
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🇨🇭 Global One SI - Prompt
We've never seen a semiconductor bubble like this before in history. Did the $SOX bubble just pop?
Sandisk $SNDK and Philadelphia Semiconductor Index $SOX are both bouncing right on schedule.
If the parabolics are truly broken then this is the complacency stage, also known as the "return to normal" trap.
Ironically the $SOX appears to have formed a head-and-shoulders pattern
If the parabolics are truly broken then this is the complacency stage, also known as the "return to normal" trap.
Ironically the $SOX appears to have formed a head-and-shoulders pattern
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🇨🇭 Global One SI - Prompt
One of the most powerful deep reasoning prompts to send to GPT-5.6 Sol right now: (kills hallucinations, triggers deep reasoning, deep research & more)
Hermes Agent just killed the $20/month AI subscription model (and for good reason).
You're paying monthly, hitting usage caps, and your agent forgets you every single session.
Here's how to cut your AI costs by 80%+ in under 60 seconds using Hermes:
Step 1. Download Hermes Desktop
Skip the terminal entirely.
Go to hermes-agent.nousresearch.com download the desktop app.
Step 2. Pick a budget model
This is where the savings happen.
Inside the app, swap your default model to DeepSeek V4 Flash (it costs $0.14 per million input tokens and $0.28 per million output tokens).
The same workload on Claude Opus costs roughly 30x more.
Step 3. Switch from gateway to CLI
Running Hermes through messaging apps like Telegram or Discord sends 15,000 to 20,000 tokens of tool definitions per request.
The CLI sends 6,000 to 8,000. Same output. 2-3x cheaper per request.
Step 4. Enable the learning loop
Toggle on persistent memory and skill generation.
After 20+ self-created skills, Hermes completes similar future tasks 40% faster, meaning 40% fewer tokens burned.
Costs drop the longer you run it.
Step 5. Set your VPS and forget it
A Hetzner CX22 runs at around $4/month. Your agent runs 24/7, hibernates when idle, and costs nearly nothing between tasks. OpenHosst
Step 6. Test it
Send a research task, build a workflow, anything. Watch it run for cents instead of dollars.
That's it.
A full-budget Hermes setup costs $5 to $10 per month, all-in.
Save this and actually set this up.
You're paying monthly, hitting usage caps, and your agent forgets you every single session.
Here's how to cut your AI costs by 80%+ in under 60 seconds using Hermes:
Step 1. Download Hermes Desktop
Skip the terminal entirely.
Go to hermes-agent.nousresearch.com download the desktop app.
Step 2. Pick a budget model
This is where the savings happen.
Inside the app, swap your default model to DeepSeek V4 Flash (it costs $0.14 per million input tokens and $0.28 per million output tokens).
The same workload on Claude Opus costs roughly 30x more.
Step 3. Switch from gateway to CLI
Running Hermes through messaging apps like Telegram or Discord sends 15,000 to 20,000 tokens of tool definitions per request.
The CLI sends 6,000 to 8,000. Same output. 2-3x cheaper per request.
Step 4. Enable the learning loop
Toggle on persistent memory and skill generation.
After 20+ self-created skills, Hermes completes similar future tasks 40% faster, meaning 40% fewer tokens burned.
Costs drop the longer you run it.
Step 5. Set your VPS and forget it
A Hetzner CX22 runs at around $4/month. Your agent runs 24/7, hibernates when idle, and costs nearly nothing between tasks. OpenHosst
Step 6. Test it
Send a research task, build a workflow, anything. Watch it run for cents instead of dollars.
That's it.
A full-budget Hermes setup costs $5 to $10 per month, all-in.
Save this and actually set this up.
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JPMorgan spent 8 years manipulating precious metals markets.
Gold, silver, the whole thing.
They got caught.
They paid $920 million in fines.
And no one went to jail.
Now, that number sounds huge, right?
$920 million.
People see that and go, "wow, they really got punished."
But hold on.
Let me show you how this actually works.
During those same 8 years of manipulation? JPMorgan made $50 billion in profit.
The "punishment" was roughly 1.8 cents on every dollar they made doing the thing they weren't supposed to do.
That's not a penalty. that's a licensing fee.
And this is the part that nobody wants to say out loud: when the fine is smaller than the profit, the fine becomes part of the business model.
You do the math, you pay the fine, you keep the rest.
That's it.
I've seen this pattern over and over again.
It's not unique to jpmorgan.
It's how the game is structured at the top.
The rules exist, but they apply differently depending on how big your balance sheet is.
Not saying every banker is corrupt.
That's not the point.
The point is the system itself creates a situation where breaking the rules and paying the fine is simply… more profitable than following the rules.
And here's what gets me: regular people, people like you watching this right now, they get financially squeezed by the exact markets these institutions manipulate.
Your savings, your retirement account, your cost of living.
All of it connected to the same system.
But most people have no idea how any of this works.
They see the headline, they feel vaguely angry, and then they move on.
Because nobody ever explained the mechanics.
So here's my honest advice: stop waiting for the news to explain this to you.
they won't.
Start learning how these institutions actually work, from the beginning, from first principles, so you can see the whole picture for yourself.
Gold, silver, the whole thing.
They got caught.
They paid $920 million in fines.
And no one went to jail.
Now, that number sounds huge, right?
$920 million.
People see that and go, "wow, they really got punished."
But hold on.
Let me show you how this actually works.
During those same 8 years of manipulation? JPMorgan made $50 billion in profit.
The "punishment" was roughly 1.8 cents on every dollar they made doing the thing they weren't supposed to do.
That's not a penalty. that's a licensing fee.
And this is the part that nobody wants to say out loud: when the fine is smaller than the profit, the fine becomes part of the business model.
You do the math, you pay the fine, you keep the rest.
That's it.
I've seen this pattern over and over again.
It's not unique to jpmorgan.
It's how the game is structured at the top.
The rules exist, but they apply differently depending on how big your balance sheet is.
Not saying every banker is corrupt.
That's not the point.
The point is the system itself creates a situation where breaking the rules and paying the fine is simply… more profitable than following the rules.
And here's what gets me: regular people, people like you watching this right now, they get financially squeezed by the exact markets these institutions manipulate.
Your savings, your retirement account, your cost of living.
All of it connected to the same system.
But most people have no idea how any of this works.
They see the headline, they feel vaguely angry, and then they move on.
Because nobody ever explained the mechanics.
So here's my honest advice: stop waiting for the news to explain this to you.
they won't.
Start learning how these institutions actually work, from the beginning, from first principles, so you can see the whole picture for yourself.
✍2
16 AI subsectors that could 10x over the next decade.
(shortlisted from my market research - save this):
Core Infrastructure & Hardware Layer:
• AI Semiconductors (accelerators, custom ASICs, advanced packaging)
• Physical AI & Robotics (especially humanoid robots)
• AI Cloud & Data Center Infrastructure (compute clusters, power, cooling, networking)
AI Software & Intelligence Layer
• Generative AI (foundation models + multimodal generation: text, video, audio, 3D)
• Agent-focused companies (autonomous task execution, multi-agent workflows)
• Computer Vision, 3D Perception & Spatial AI
• Synthetic Data Generation & AI-native data platforms
High-Growth Apps
• Cybersecurity (autonomous detection & response)
• AI Drug Discovery, Biotech & Precision Medicine
• Autonomous Vehicles, Robotaxis & Transportation AI
• AI for Energy, Grid Optimization & Climate Tech
• AI in Finance (algo trading, fraud, personalization, risk)
Emerging Layers
• MLOps / LLMOps & Enterprise AI Platforms
• AI Safety, Alignment, Evaluation & Governance Tools
• Next-Gen AI Hardware (neuromorphic, analog, optical computing)
• World Models & Simulation AI (for training robots, AVs, and planning systems)
AI is about to explode across many industries - save this list.
(shortlisted from my market research - save this):
Core Infrastructure & Hardware Layer:
• AI Semiconductors (accelerators, custom ASICs, advanced packaging)
• Physical AI & Robotics (especially humanoid robots)
• AI Cloud & Data Center Infrastructure (compute clusters, power, cooling, networking)
AI Software & Intelligence Layer
• Generative AI (foundation models + multimodal generation: text, video, audio, 3D)
• Agent-focused companies (autonomous task execution, multi-agent workflows)
• Computer Vision, 3D Perception & Spatial AI
• Synthetic Data Generation & AI-native data platforms
High-Growth Apps
• Cybersecurity (autonomous detection & response)
• AI Drug Discovery, Biotech & Precision Medicine
• Autonomous Vehicles, Robotaxis & Transportation AI
• AI for Energy, Grid Optimization & Climate Tech
• AI in Finance (algo trading, fraud, personalization, risk)
Emerging Layers
• MLOps / LLMOps & Enterprise AI Platforms
• AI Safety, Alignment, Evaluation & Governance Tools
• Next-Gen AI Hardware (neuromorphic, analog, optical computing)
• World Models & Simulation AI (for training robots, AVs, and planning systems)
AI is about to explode across many industries - save this list.
✍1
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
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Iran warned it would retaliate if its energy infrastructure was hit again.
Hours later, a massive fire breaks out at the Poldokhtar refinery in Lorestan.
That timing is either a brutal coincidence or exactly the trigger Iran said it was waiting for.
If this was a strike:
The warning becomes a promise. Israeli and Gulf energy infrastructure just became fair game again.
If it wasn’t:
Iran’s own domestic energy system is now failing on its own, on top of everything else the mines, the sanctions, the strikes on Kharg Island.
Either way this isn’t a good sign.
Hours later, a massive fire breaks out at the Poldokhtar refinery in Lorestan.
That timing is either a brutal coincidence or exactly the trigger Iran said it was waiting for.
If this was a strike:
The warning becomes a promise. Israeli and Gulf energy infrastructure just became fair game again.
If it wasn’t:
Iran’s own domestic energy system is now failing on its own, on top of everything else the mines, the sanctions, the strikes on Kharg Island.
Either way this isn’t a good sign.
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🇨🇭 Global One SI - Prompt
The railroad bubble of 1873 sounds like the AI bubble of 2026, doesn't it? Same taglines, same promises, same private credit, same Rothschilds, same result?
Everyone is comparing the AI bubble to the 1873 railroad bubble and 2000 fiber optical bubble, but that's totally incorrect.
Even though most of the railroad and fiber optic companies went bankrupt, those bubbles built infrastructure that benefitted the US economy for decades to come. It reduced shipping / communication costs and boosted economic growth.
Fundamentally the AI buildout is the complete opposite. It becomes worthless every 3 years due to outdated hardware. There is no economic model where suddenly this buildout becomes profitable because of the high cost to actually operate the data centers.
If anything AI is like the printing press. You can produce a vast amount of media content (slop) in very little time, but at a high cost.
What is the same with 1873 and 2000 is the debt. However, when this debt bubble pops that doesn't reduce the cost to operate the data centers, if anything it actually increases it because there will be less customers able to afford it.
Outside of media, the only potential customer for the operational cost of the infrastructure is a government funded surveillance state... and that's likely the purpose of all this.
Even though most of the railroad and fiber optic companies went bankrupt, those bubbles built infrastructure that benefitted the US economy for decades to come. It reduced shipping / communication costs and boosted economic growth.
Fundamentally the AI buildout is the complete opposite. It becomes worthless every 3 years due to outdated hardware. There is no economic model where suddenly this buildout becomes profitable because of the high cost to actually operate the data centers.
If anything AI is like the printing press. You can produce a vast amount of media content (slop) in very little time, but at a high cost.
What is the same with 1873 and 2000 is the debt. However, when this debt bubble pops that doesn't reduce the cost to operate the data centers, if anything it actually increases it because there will be less customers able to afford it.
Outside of media, the only potential customer for the operational cost of the infrastructure is a government funded surveillance state... and that's likely the purpose of all this.
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🚨 One Tech Worker Has Lost Their Job Every 100 Seconds in 2026
According to TrueUp, 165,940 tech workers have been laid off across 449 layoff events so far in 2026, despite record AI spending across the industry.
An average of 864 layoffs every day, compared to 245,953 employees averaging 674 per day throughout all of 2025.
At the current pace, 2026 is on track to surpass 2025 in total tech layoffs.
The biggest workforce reductions include Oracle (30,000), Amazon (16,000), Dell (11,000), Meta (8,000) and Microsoft (4,800).
March was the worst month of the year, with 50,389 employees impacted.
Despite strong financial performance, many companies are restructuring their workforces to accelerate AI adoption
At the same time, hiring remains focused on high-demand areas such as AI, cloud infrastructure and advanced engineering, while reducing traditional roles.
According to TrueUp, 165,940 tech workers have been laid off across 449 layoff events so far in 2026, despite record AI spending across the industry.
An average of 864 layoffs every day, compared to 245,953 employees averaging 674 per day throughout all of 2025.
At the current pace, 2026 is on track to surpass 2025 in total tech layoffs.
The biggest workforce reductions include Oracle (30,000), Amazon (16,000), Dell (11,000), Meta (8,000) and Microsoft (4,800).
March was the worst month of the year, with 50,389 employees impacted.
Despite strong financial performance, many companies are restructuring their workforces to accelerate AI adoption
At the same time, hiring remains focused on high-demand areas such as AI, cloud infrastructure and advanced engineering, while reducing traditional roles.
🇨🇭 Global One SI - Prompt
Hermes Agent just killed the $20/month AI subscription model (and for good reason). You're paying monthly, hitting usage caps, and your agent forgets you every single session. Here's how to cut your AI costs by 80%+ in under 60 seconds using Hermes: Step…
What I'm about to reveal will completely blow your mind.
I used Claude Fable 5 to build a trading bot that prints money.
This article walks you through the entire process of building a profitable trading strategy with AI.
No other creator is talking about this - pure sauce
I used Claude Fable 5 to build a trading bot that prints money.
This article walks you through the entire process of building a profitable trading strategy with AI.
No other creator is talking about this - pure sauce
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🇨🇭 Global One SI - Prompt
What I'm about to reveal will completely blow your mind. I used Claude Fable 5 to build a trading bot that prints money. This article walks you through the entire process of building a profitable trading strategy with AI. No other creator is talking about…
What we are trying to do here is extract a set of rules that we can then turn into Pine Script code, and backtest on TradingView.
The exact rules you extract can vary, but in general, I'm looking for:
Entry triggers
Stop loss triggers
Exit plan
Position sizing rules
The good thing about Fable is that it's extremely in-depth, and it's why it's my preferred model for complex workflows like this.
The exact rules you extract can vary, but in general, I'm looking for:
Entry triggers
Stop loss triggers
Exit plan
Position sizing rules
The good thing about Fable is that it's extremely in-depth, and it's why it's my preferred model for complex workflows like this.
🇨🇭 Global One SI - Prompt
What I'm about to reveal will completely blow your mind. I used Claude Fable 5 to build a trading bot that prints money. This article walks you through the entire process of building a profitable trading strategy with AI. No other creator is talking about…
Some notes on building winning strategies
The strategy you use may vary depending on market conditions (i.e., in trending markets, a momentum strategy is probably best)
Test your strategies on multiple pairs and asset classes. A momentum strategy may absolutely crush it on equities, but completely suck on something like crypto. Don't give up if your strategy fails on one pair.
In your Claude projects, you can attach .MD files to explain your exact trading parameters for extra context
Don't be afraid to go back and forth with Claude. AI is exceptionally good now, and it can fix all your coding errors.
The strategy you use may vary depending on market conditions (i.e., in trending markets, a momentum strategy is probably best)
Test your strategies on multiple pairs and asset classes. A momentum strategy may absolutely crush it on equities, but completely suck on something like crypto. Don't give up if your strategy fails on one pair.
In your Claude projects, you can attach .MD files to explain your exact trading parameters for extra context
Don't be afraid to go back and forth with Claude. AI is exceptionally good now, and it can fix all your coding errors.
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