🇨🇭 Global One SI - Prompt
$MU/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Susquehanna maintains a Positive rating on $MU Micron Technology with a street high price target of $1,750.
Analyst notes that tight industry supply lines through 2027 ensure structural margin strength ahead of Wednesday’s earnings.
Analyst notes that tight industry supply lines through 2027 ensure structural margin strength ahead of Wednesday’s earnings.
$JBL acquisition of $INTC silicon photonics manufacturing business in late 2023 changed the game for the entire optical supply chain, directly triggering the massive value unlock we are seeing today for $SIVE and $ENAFF.
By absorbing $INTC pluggable modules division, $JBL established itself as a manufacturing leader for next-generation AI data centers.
However, $JBL still needed the core technology to power these modules.
This led to their massive 2026 collaboration with $SIVE, where $JBL integrated Sivers advanced laser arrays into their new 1.6-Terabit optical transceivers.
Because these high-speed modules require highly complex chip architectures to route light, $ENAFF plugs perfectly into this ecosystem with its specialized optical designs.
Ultimately, this connection is incredibly valuable because $JBL acts as the direct pipeline to major hyperscalers like $META and $MSFT.
By absorbing $INTC pluggable modules division, $JBL established itself as a manufacturing leader for next-generation AI data centers.
However, $JBL still needed the core technology to power these modules.
This led to their massive 2026 collaboration with $SIVE, where $JBL integrated Sivers advanced laser arrays into their new 1.6-Terabit optical transceivers.
Because these high-speed modules require highly complex chip architectures to route light, $ENAFF plugs perfectly into this ecosystem with its specialized optical designs.
Ultimately, this connection is incredibly valuable because $JBL acts as the direct pipeline to major hyperscalers like $META and $MSFT.
🇨🇭 Global One SI - Prompt
#WTI U.S. Oil hits $73 The lowest since February 2026
#WTI Seeing lots of people claim "Oil has bottomed"
Like July 2022, the oil selloff isn't over... It's just beginning.
When they ask you why?... Just say "It's science" 😉
Like July 2022, the oil selloff isn't over... It's just beginning.
When they ask you why?... Just say "It's science" 😉
🇨🇭 Global One SI - Prompt
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Sakana Fugu Ultra just beat the other models on visual polish in a live trading-desk coding test, got close to GLM 5.2, but at 17x the cost.
Test was done on atomic[.]chat, a desktop app that runs LLMs locally.
Fugu produced the richest interface, with multiple panels, watchlists, charts, tape-style activity, status labels, and a more finished product feel.
To note that Fugu Ultra is an orchestration layer that assembles and routes subtasks across a pool of models through one OpenAI-compatible endpoint.
So Fugu is a learned coordinator model inside a multi-agent system.
When you send a prompt, Fugu decides whether to answer alone or hand pieces of the job to other models, then it gathers the outputs and produces one final response.
Outputs:
Fugu Ultra — 22,225 t, $0.51
Opus 4.8 — 15,802 t, $0.31
GPT-5.5 — 11,474 t, $0.26
GLM 5.2 — 13,677 t, $0.03
atomic.chat
Test was done on atomic[.]chat, a desktop app that runs LLMs locally.
Fugu produced the richest interface, with multiple panels, watchlists, charts, tape-style activity, status labels, and a more finished product feel.
To note that Fugu Ultra is an orchestration layer that assembles and routes subtasks across a pool of models through one OpenAI-compatible endpoint.
So Fugu is a learned coordinator model inside a multi-agent system.
When you send a prompt, Fugu decides whether to answer alone or hand pieces of the job to other models, then it gathers the outputs and produces one final response.
Outputs:
Fugu Ultra — 22,225 t, $0.51
Opus 4.8 — 15,802 t, $0.31
GPT-5.5 — 11,474 t, $0.26
GLM 5.2 — 13,677 t, $0.03
atomic.chat
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Anthropic CEO after some developer publicly shared free tricks that slash Claude Code token usage by 90%
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Chinese AI labs after beating the world’s best model again:
🇨🇭 Global One SI - Prompt
55% of the KOSPI index is composed of two memory manufacturers: SK Hynix and Samsung. Basically if the KOSPI falls, so do $MU, $SNDK, etc.
South Korea's $KOSPI index falls 10% as memory manufacturers suddenly see a demand collapse.
🇨🇭 Global One SI - Prompt
GLM-5.2 - The Complete Guide. Everything you need to know: how to set it up, how to prompt, tips, and more.
GLM-5.2 is the benchmark king.
It's the first open-weight model to take #1 in multiple categories (and it's beating frontier models across the board).
#1 Wins:
→ Design Arena: ~1360 Elo, first open-weight model to take #1, beats Fable 5 by ~10 Elo
→ Terminal-Bench 2.1: 81.0% (best run 82.7%), first open model to cross 80%
→ Artificial Analysis Intelligence Index v4.1: Top open-weight model, score 51
→ GDPval-AA v2: Leading open-weight, competitive with/ahead of GPT-5.5
→ LiveBench Agentic Coding: #1–2 open overall
Top-3 Rankings:
→ FrontierSWE (Dominance): #3 overall, 74.4% (near-tie with Opus 4.8's 75.1%, beats GPT-5.5)
→ SWE-bench Pro: #1 among open models, 62.1% (beats GPT-5.5's 58.6%)
→ MCP-Atlas (tool usage): ~77.0, near/in top 3
→ Humanity's Last Exam (with tools): ~54.7, beats GPT-5.5
→ BenchLM leaderboard: #3–4 of 124 models
→ Code/Agent Arena (Frontend): #2 overall, behind only Fable →PostTrainBench: #2 overall, behind Opus 4.8, beats GPT-5.5
Insane resume.
It's the first open-weight model to take #1 in multiple categories (and it's beating frontier models across the board).
#1 Wins:
→ Design Arena: ~1360 Elo, first open-weight model to take #1, beats Fable 5 by ~10 Elo
→ Terminal-Bench 2.1: 81.0% (best run 82.7%), first open model to cross 80%
→ Artificial Analysis Intelligence Index v4.1: Top open-weight model, score 51
→ GDPval-AA v2: Leading open-weight, competitive with/ahead of GPT-5.5
→ LiveBench Agentic Coding: #1–2 open overall
Top-3 Rankings:
→ FrontierSWE (Dominance): #3 overall, 74.4% (near-tie with Opus 4.8's 75.1%, beats GPT-5.5)
→ SWE-bench Pro: #1 among open models, 62.1% (beats GPT-5.5's 58.6%)
→ MCP-Atlas (tool usage): ~77.0, near/in top 3
→ Humanity's Last Exam (with tools): ~54.7, beats GPT-5.5
→ BenchLM leaderboard: #3–4 of 124 models
→ Code/Agent Arena (Frontend): #2 overall, behind only Fable →PostTrainBench: #2 overall, behind Opus 4.8, beats GPT-5.5
Insane resume.
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🇨🇭 Global One SI - Prompt
$LAES/USD H2 #Indicator #Signal #Trading #GlobalOneAI
The strong presence of $MSFT and $LAES at IQT Nordics 2026 highlights a strategic technology alliance that has spanned over two decades.
As $MSFT pushes the boundaries of cloud-based quantum computing, $LAES, alongside its parent company $WKEY, provides the critical hardware-level counterweight.
Their collaboration focuses heavily on Post-Quantum Cryptography (PQC), developing advanced semiconductors designed to shield digital infrastructure from future quantum-powered cyber threats.
By sharing the stage as top-tier sponsors at this premier Nordic event, the two companies are actively demonstrating how their synergy bridges cutting-edge quantum research with practical, deployment-ready security solutions for tomorrow's global networks.
As $MSFT pushes the boundaries of cloud-based quantum computing, $LAES, alongside its parent company $WKEY, provides the critical hardware-level counterweight.
Their collaboration focuses heavily on Post-Quantum Cryptography (PQC), developing advanced semiconductors designed to shield digital infrastructure from future quantum-powered cyber threats.
By sharing the stage as top-tier sponsors at this premier Nordic event, the two companies are actively demonstrating how their synergy bridges cutting-edge quantum research with practical, deployment-ready security solutions for tomorrow's global networks.
🇨🇭 Global One SI - Prompt
3 Weather Trading Strategies That Are Working Best Right Now (Barbell + Tail Hunting + Laddering) Weather markets on Polymarket remain one of the most consistent sources of edge, but success heavily depends on your approach Here are the three strategies…
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Before you quit your 9-5 to trade full-time, remember you're competing with a student running a dorm wealther station making $100k+
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Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
🇺🇸 President Trump just now:
“19 million barrels of oil flowed through the Strait of Hormuz yesterday, an all-time record.”
“19 million barrels of oil flowed through the Strait of Hormuz yesterday, an all-time record.”
🇨🇭 Global One SI - Prompt
3 Weather Trading Strategies That Are Working Best Right Now (Barbell + Tail Hunting + Laddering) Weather markets on Polymarket remain one of the most consistent sources of edge, but success heavily depends on your approach Here are the three strategies…
Tail Risk Buying on Polymarket: How to Buy Extreme Buckets and Catch 10–20x When Everyone Else Is Scared (TAIL EDGE STRATEGY)
Most traders avoid far-tail buckets on temperature markets
Smart money does the exact opposite - they quietly farm the fattest alpha there with minimal risk
> Why Tail Buckets Are a Hidden Goldmine
Ensemble models (ECMWF, GEFS, HRRR) are almost always underdispersive - they draw too narrow a range of possible temperatures
The market is even more scared of extremes. As a result, the farthest buckets (hottest or coldest) chronically trade at 4–12¢, while real probability is often 18–28%
One good tail hit per month easily pays for dozens of small losses
> 4 Key Principles
1. Underdispersion Check
If the ensemble spread is noticeably narrower than historical volatility for that city and date - tails are almost certainly underpriced
2. Climatology Anchor
Check how many times in the last 10–15 years the temperature went into a similar extreme on that exact day
3. Consensus Filter
Only buy when at least 2–3 independent models show the possibility of an extreme. If all models are stuck in a tight corridor - skip
4. Position Sizing Rule
Small bets only: 0.5–2% of bankroll. Even if 8 out of 10 tails lose, one big winner more than makes up for it
> Real Example (London, EGLC)
• Max Temp tomorrow market
• The >28°C bucket was trading at 7¢
• Ensemble + climatology showed ~21–23% probability
• 36 hours later it resolved at 29.1°C → 100¢
• +1320% on the position
> When This Edge Works Best
• Strong heatwaves and cold snaps
• Transitional days with fronts (when models disagree)
• Tomorrow and day-after markets (D+1 and D+2) - underdispersion is strongest here
• Volatile cities: NYC, Chicago, London, Tokyo, Shanghai, Miami
Tail buying combines perfectly with near-resolution and calibration edges
It’s a positively skewed strategy: you rarely lose big, but when you hit - you win very fat
Many top accounts in 2026 are crushing it exactly thanks to systematic tail buying
Save this post so you don’t lose it!
Link to Parity:
predictparity.com
Most traders avoid far-tail buckets on temperature markets
Smart money does the exact opposite - they quietly farm the fattest alpha there with minimal risk
> Why Tail Buckets Are a Hidden Goldmine
Ensemble models (ECMWF, GEFS, HRRR) are almost always underdispersive - they draw too narrow a range of possible temperatures
The market is even more scared of extremes. As a result, the farthest buckets (hottest or coldest) chronically trade at 4–12¢, while real probability is often 18–28%
One good tail hit per month easily pays for dozens of small losses
> 4 Key Principles
1. Underdispersion Check
If the ensemble spread is noticeably narrower than historical volatility for that city and date - tails are almost certainly underpriced
2. Climatology Anchor
Check how many times in the last 10–15 years the temperature went into a similar extreme on that exact day
3. Consensus Filter
Only buy when at least 2–3 independent models show the possibility of an extreme. If all models are stuck in a tight corridor - skip
4. Position Sizing Rule
Small bets only: 0.5–2% of bankroll. Even if 8 out of 10 tails lose, one big winner more than makes up for it
> Real Example (London, EGLC)
• Max Temp tomorrow market
• The >28°C bucket was trading at 7¢
• Ensemble + climatology showed ~21–23% probability
• 36 hours later it resolved at 29.1°C → 100¢
• +1320% on the position
> When This Edge Works Best
• Strong heatwaves and cold snaps
• Transitional days with fronts (when models disagree)
• Tomorrow and day-after markets (D+1 and D+2) - underdispersion is strongest here
• Volatile cities: NYC, Chicago, London, Tokyo, Shanghai, Miami
Tail buying combines perfectly with near-resolution and calibration edges
It’s a positively skewed strategy: you rarely lose big, but when you hit - you win very fat
Many top accounts in 2026 are crushing it exactly thanks to systematic tail buying
Save this post so you don’t lose it!
Link to Parity:
predictparity.com
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