πŸ‡¨πŸ‡­ Global One SI - Prompt
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πŸŽ—GLOBAL ONE is a live super intelligence (SI) built on real-time dataβ€”designed for decisions. Currently focused on global financial markets

t.me/Prompts_SI @Global_one - Software/Developer/Programmer - Globalone.ch - Prompt - AI - Super Intelligence
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Real answer is inflation masked the economic decline

Like 2008, there's a moment where the public suddenly realizes the reason oil is declining is not because of the Strait being open

It's because growth has collapsed & the economy is toast

Trump's peace deal is to avoid blame
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Did the AI bubble pop for Cisco $CSCO ?
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Lexar prepares Thor 7600 DDR5 modules produced with chips by ChangXin Memory Technologies (CXMT) in China
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We've never seen a semiconductor bubble like this before in history.

Did the $SOX bubble just pop?
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πŸ‡¨πŸ‡­ Global One SI - Prompt
Absolutely 100% correct. Brent Johnson said it brilliantly yesterday, "The U.S. is transitioning from a Democratic Republic to a 'Technocratic' Empire the same way the Roman Republic transitioned to the Roman Empire." That's why this global conflict will…
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- Iran didn't control the Strait of Hormuz, now it does
- Iran's oil was sanctioned, now it isn't
- Iran was not building a nuclear weapon, now it will
- US bases in the Gulf were assets, now they're liabilities
- Inflation was declining, now it's rising
- No more wars became WW3
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πŸ‡¨πŸ‡­ Global One SI - Prompt
Memory stocks are crashing in the as Samsung's revenue miss sparks a sector wide selloff. $MU down -8%, wiping $96 billion in market cap. $SNDK down -12%, wiping $33 billion in market cap. $INTC down -10%, wiping $47 billion in market cap. $WDC down -10%…
Holy shit, Micron's EVP and Chief People Officer April Arnzen just sold 1/3rd of her $MU shares.

Since April 2026 she has liquidated over 1/2 of her shares.

These are the people who know mass layoffs are coming because their department budgets get slashed first.
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Africa’s Mining Map Is Also an Infrastructure Map πŸŒβ›οΈ

Africa holds some of the world’s most important mineral deposits

But many mining operations are concentrated in regions with serious infrastructure and environmental challenges

The opportunity is massive

But the bottlenecks are just as important

πŸ”Ή Roads
πŸ”Ή Railways
πŸ”Ή Ports
πŸ”Ή Power supply
πŸ”Ή Water access
πŸ”Ή Environmental permitting
πŸ”Ή Security and logistics

Africa is not short of minerals

The real challenge is turning geological potential into reliable supply chains

The next commodity cycle will not only reward countries with resources

It will reward countries that can build the infrastructure to move them
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Boomers after realizing the war is continuing and may cause a financial crisis that will help their property prices rise while the younger generation suffers
πŸ‡¨πŸ‡­ Global One SI - Prompt
Me and bros crafting up the perfect strategy to beat the market and escape the matrix
Microsoft made ChatGPT's accuracy jump from 41% to 80% without touching a single parameter.

The system is called SkillOpt.

Most people think AI agents improve by writing better prompts or fine-tuning the model itself. But fine-tuning is rigid, slow, and expensive. And "prompt engineering" is just guessing.

Microsoft’s approach treats the AI’s skill set, the actual text document that tells it how to solve a problem, as a living, breathing model that learns from its own failures.

Here is how it works:

1. Rollouts: The agent attempts tasks. It captures its own successes and failures.

2. Optimizer Model: A separate, small model analyzes those results and makes atomic edits to the skill document (Add, Delete, or Replace).

3. Validation Gate: The new skill is only "accepted" if it strictly improves performance on a held-out set of tasks.

It is essentially "Gradient Descent" for natural language.

And the results are staggering:

Across six major benchmarks, SkillOpt outperformed every human-written skill and every one-shot LLM approach.

On some tasks, accuracy skyrocketed. On the ALFWorld benchmark, one model jumped from 70% to 85% accuracy. In direct chat scenarios, it boosted accuracy by over 23 points.

The best part?

There is zero inference-time overhead.

You spend the compute optimizing the skill once. Then, the agent runs with that hyper-optimized playbook forever.

The playbook effectively "trains" itself through feedback.

If you want an agent that actually gets better at its job every single day, without you having to touch a single line of code, this is how it’s done.

github.com/microsoft/SkillOpt
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