π¨π Global One SI - Prompt
Weather markets on Polymarket are full of easy money - if you know whose trades to follow Most people are just guessing on temperature outcomes while a small group of traders is consistently extracting edge using actual modeling and data Here's exactlyβ¦
6 traders made $470,000+ in profit trading weather markets on Polymarket - a niche most people completely ignore
These aren't lucky bets. Each of them built a repeatable edge around temperature data that the market consistently misprices
Here's the full ranking:
1. Russell110320
+$44K profit | 64.3% win rate | 150 trades
Trades global climate records, not city temps. Monthly anomaly ranges, "hottest on record" outcomes
Best trade: $32,475 β $42,759 on February global temp record
2. HondaCivic
+$55K profit | 84.2% win rate | 3,828 trades
The lowest-entry specialist on this list
Best trade: $37 β $15,182 on a Hong Kong temperature market
3. HenryTheAtmoPhD
+$56K profit | 36.5% win rate | 3,472 trades
Retired professor of atmospheric science. Pure domain expertise applied to city temperature markets in Seoul, NYC, London
Best trade: $13 β $1,586
4. BeefSlayer
+$62K profit | 68.2% win rate | 1,585 trades
US cities only - Seattle, Atlanta, Chicago, NYC. One of the cleanest risk/reward profiles in weather trading
Best trade: $6 β $2,990 on an Atlanta temperature market
5. VibeTrader
+$133K profit | 37.7% win rate | 5,114 trades
Dominates NYC and Miami daily temps while also running positions on Elon Musk tweet count markets
Biggest win: $21,300
6. Π‘oldMath
+$125K profit | 81.7% win rate | 6,575 trades
Not a manual trader. Runs an automated bot that scans real-time weather data 24/7 and fires when it finds even a tiny mispricing
Biggest win: $25 β $12,452 on Tokyo temp
All of these wallets are fully public. If you want to study how they actually enter positions - what price levels, which cities, which timing - their entire trade history is available on Parity
The patterns become obvious fast
These aren't lucky bets. Each of them built a repeatable edge around temperature data that the market consistently misprices
Here's the full ranking:
1. Russell110320
+$44K profit | 64.3% win rate | 150 trades
Trades global climate records, not city temps. Monthly anomaly ranges, "hottest on record" outcomes
Best trade: $32,475 β $42,759 on February global temp record
2. HondaCivic
+$55K profit | 84.2% win rate | 3,828 trades
The lowest-entry specialist on this list
Best trade: $37 β $15,182 on a Hong Kong temperature market
3. HenryTheAtmoPhD
+$56K profit | 36.5% win rate | 3,472 trades
Retired professor of atmospheric science. Pure domain expertise applied to city temperature markets in Seoul, NYC, London
Best trade: $13 β $1,586
4. BeefSlayer
+$62K profit | 68.2% win rate | 1,585 trades
US cities only - Seattle, Atlanta, Chicago, NYC. One of the cleanest risk/reward profiles in weather trading
Best trade: $6 β $2,990 on an Atlanta temperature market
5. VibeTrader
+$133K profit | 37.7% win rate | 5,114 trades
Dominates NYC and Miami daily temps while also running positions on Elon Musk tweet count markets
Biggest win: $21,300
6. Π‘oldMath
+$125K profit | 81.7% win rate | 6,575 trades
Not a manual trader. Runs an automated bot that scans real-time weather data 24/7 and fires when it finds even a tiny mispricing
Biggest win: $25 β $12,452 on Tokyo temp
All of these wallets are fully public. If you want to study how they actually enter positions - what price levels, which cities, which timing - their entire trade history is available on Parity
The patterns become obvious fast
β1
German robotics and autonomy companies are aiming for serious capital this year.
In March, Bloomberg said NEURA Robotics was in talks to raise ~β¬1B / $1.2B at a ~β¬4B valuation for its humanoid push.
Now it is reported SoftBank is in early talks to back Agile Robotsβ ~$800M round.
In May, Helsing was said to be nearing a ~$1.2B raise at an ~$18B valuation, while drone startup Stark was seeking β¬300M+ at a ~β¬2.5B valuation.
In March, Bloomberg said NEURA Robotics was in talks to raise ~β¬1B / $1.2B at a ~β¬4B valuation for its humanoid push.
Now it is reported SoftBank is in early talks to back Agile Robotsβ ~$800M round.
In May, Helsing was said to be nearing a ~$1.2B raise at an ~$18B valuation, while drone startup Stark was seeking β¬300M+ at a ~β¬2.5B valuation.
The bottleneck for mass-producing humanoids is not just AI.
It's the actuator and the supply base that sits underneath it.
Study these companies for each of the components.
- Japan dominates precision mechanical (reducers, bearings).
- Germany and Switzerland own high-end motion.
- China owns volume motors, reducers, and the upstream magnet supply.
Who am I missing?
It's the actuator and the supply base that sits underneath it.
Study these companies for each of the components.
- Japan dominates precision mechanical (reducers, bearings).
- Germany and Switzerland own high-end motion.
- China owns volume motors, reducers, and the upstream magnet supply.
Who am I missing?
The robotics race won't be won by legs.
Dancing, Kung Fu fighting robots capture mass attention. Hands unlock economic value.
Most deployed robots today use grippers, which work in structured industrial environments. The real world isn't structured.
Dexterous hands matter because manipulation in the wild requires:
- handling deformable objects
- adjusting grip force dynamically
- sensing slip before failure
- distinguishing texture, hardness, and temperature
- using tools designed for human hands
- operating safely beside people surviving millions of cycles at manufacturable cost
This is why dexterous hands are becoming one of the most important battlegrounds in physical AI.
Several enabling technologies are finally converging at once.
- Fingertip-scale multimodal tactile sensors
- Smaller high-torque actuators and frameless motors
- Micro harmonic reducers and precision transmission systems
- Flexible PCBs and dense embedded electronics
- Real-time force control and edge inference
- Lightweight composite materials
- Better imitation-learning and manipulation datasets
The hand is the missing interface layer between AI and the physical world.
Dancing, Kung Fu fighting robots capture mass attention. Hands unlock economic value.
Most deployed robots today use grippers, which work in structured industrial environments. The real world isn't structured.
Dexterous hands matter because manipulation in the wild requires:
- handling deformable objects
- adjusting grip force dynamically
- sensing slip before failure
- distinguishing texture, hardness, and temperature
- using tools designed for human hands
- operating safely beside people surviving millions of cycles at manufacturable cost
This is why dexterous hands are becoming one of the most important battlegrounds in physical AI.
Several enabling technologies are finally converging at once.
- Fingertip-scale multimodal tactile sensors
- Smaller high-torque actuators and frameless motors
- Micro harmonic reducers and precision transmission systems
- Flexible PCBs and dense embedded electronics
- Real-time force control and edge inference
- Lightweight composite materials
- Better imitation-learning and manipulation datasets
The hand is the missing interface layer between AI and the physical world.
A humanoid robot foot is basically a balance computer under pressure.
Inside one foot you can find the ankle interface compact actuator module sensing layer structural frame compliance layer and sole contact system working together in milliseconds.
>The actuator creates torque.
>The encoder tracks angle and velocity.
>The torque sensor estimates load.
>The frame carries impact from heel to toe.
>The elastomer layer absorbs shock.
>The sole pads read contact with the ground.
Walking looks simple from the outside.
Inside the foot every step is a fight between force friction timing and control.
Inside one foot you can find the ankle interface compact actuator module sensing layer structural frame compliance layer and sole contact system working together in milliseconds.
>The actuator creates torque.
>The encoder tracks angle and velocity.
>The torque sensor estimates load.
>The frame carries impact from heel to toe.
>The elastomer layer absorbs shock.
>The sole pads read contact with the ground.
Walking looks simple from the outside.
Inside the foot every step is a fight between force friction timing and control.
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Wuji Hand 2 a fully upgraded dexterous robotic hand designed for precision manipulation in embodied AI systems.
The company is positioning the hand for tasks where force alone is insufficient controlled grip object handling tactile adjustment and fine motor coordination.
In humanoid robotics the hand may become one of the hardest hardware layers to solve. Locomotion gets attention but real work happens when a robot has to touch hold adjus and recover without breaking the object.
Wuji Hand 2 is another sign that dexterous manipulation is becoming a serious race inside Physical AI.
The company is positioning the hand for tasks where force alone is insufficient controlled grip object handling tactile adjustment and fine motor coordination.
In humanoid robotics the hand may become one of the hardest hardware layers to solve. Locomotion gets attention but real work happens when a robot has to touch hold adjus and recover without breaking the object.
Wuji Hand 2 is another sign that dexterous manipulation is becoming a serious race inside Physical AI.
π1
π¨π Global One SI - Prompt
$HIMS/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
$HIMX This chart is telling a very interesting story.
Not because it's up 20% today.
Because it just completed a massive multi-month base breakout.
For almost a year:
β price went nowhere
β volume dried up
β expectations disappeared
Then suddenly:
β Volume exploded
β Buyers reclaimed the 200MA
β Momentum accelerated
β Resistance near $22 gets cleared
Now the market is starting to ask:
Why HIMX?
The answer may be tied to several themes heating up simultaneously:
β’ AI devices
β’ Edge AI
β’ Display drivers
β’ AR/VR applications
β’ Semiconductor demand
Technically this is no longer a bottoming chart.
It's becoming a momentum chart.
The longer $22-$23 converts into support...
the more traders will begin looking toward the next major zone above.
Sometimes the strongest trends start from the longest periods of neglect.
$HIMX is suddenly getting very difficult to ignore.
Not because it's up 20% today.
Because it just completed a massive multi-month base breakout.
For almost a year:
β price went nowhere
β volume dried up
β expectations disappeared
Then suddenly:
β Volume exploded
β Buyers reclaimed the 200MA
β Momentum accelerated
β Resistance near $22 gets cleared
Now the market is starting to ask:
Why HIMX?
The answer may be tied to several themes heating up simultaneously:
β’ AI devices
β’ Edge AI
β’ Display drivers
β’ AR/VR applications
β’ Semiconductor demand
Technically this is no longer a bottoming chart.
It's becoming a momentum chart.
The longer $22-$23 converts into support...
the more traders will begin looking toward the next major zone above.
Sometimes the strongest trends start from the longest periods of neglect.
$HIMX is suddenly getting very difficult to ignore.
π¨π Global One SI - Prompt
$NBIS/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Nebius Group $NBIS finalizes plans to invest over β¬8 billion ($8.7 billion) to build a massive 240MW AI computing factory in France.
The infrastructure project transforms a former Bridgestone manufacturing plant into a liquid cooled hub.
The infrastructure project transforms a former Bridgestone manufacturing plant into a liquid cooled hub.
π¨π Global One SI - Prompt
$LITE/USD H2 #Indicator #Signal #Trading #GlobalOneAI
Lumentum $LITE accelerates a major expansion of U.S. manufacturing lines for advanced indium phosphide optical chips.
The infrastructure scaling follows a strategic $2 billion investment and major purchase commitments from Nvidia.
The infrastructure scaling follows a strategic $2 billion investment and major purchase commitments from Nvidia.
Anthropic is expanding Claude Mythos Preview from about 50 Project Glasswing partners to about 200 vetted organizations.
This model is much closer to a cyber weapon detector than a normal coding assistant, since it can find weak spots in software and sometimes prove the attack path by building a working test exploit.
The select group is basically a defensive priority list. The list includes power, healthcare, water, communications, hardware, governments, nonprofits, and key software maintainers, with security checks before any group gets the model.
With this priority access Anthropic is trying to create a patching head start before similar AI-assisted exploit discovery becomes common across the industry.
Anthropic says partners have already found 10,000+ high- or critical-severity flaws.
Anthropic is still not making Mythos fully public because its own testing says the model can find subtle old bugs, chain small issues into bigger exploits, and help non-experts reach outcomes that previously required elite security skill.
The poin is that other top frontier models usually flag suspicious code, while Mythos can inspect a codebase, form a theory about a bug, test it in a sandbox, read the failure, adjust the plan, and repeat until it has proof.
This model is much closer to a cyber weapon detector than a normal coding assistant, since it can find weak spots in software and sometimes prove the attack path by building a working test exploit.
The select group is basically a defensive priority list. The list includes power, healthcare, water, communications, hardware, governments, nonprofits, and key software maintainers, with security checks before any group gets the model.
With this priority access Anthropic is trying to create a patching head start before similar AI-assisted exploit discovery becomes common across the industry.
Anthropic says partners have already found 10,000+ high- or critical-severity flaws.
Anthropic is still not making Mythos fully public because its own testing says the model can find subtle old bugs, chain small issues into bigger exploits, and help non-experts reach outcomes that previously required elite security skill.
The poin is that other top frontier models usually flag suspicious code, while Mythos can inspect a codebase, form a theory about a bug, test it in a sandbox, read the failure, adjust the plan, and repeat until it has proof.
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$ASML is the invisible backbone of AI and semiconductor revolution.
EUV (Extreme Ultraviolet lithography) for tiny transistors, DUV (Deep Ultraviolet lithography) for everything above.
Turns physicsβ limits into affordable, reliable chips.
EUV (Extreme Ultraviolet lithography) for tiny transistors, DUV (Deep Ultraviolet lithography) for everything above.
Turns physicsβ limits into affordable, reliable chips.
π¨π Global One SI - Prompt
$QMCO all set for a major move
$QMCO One of the more interesting under-the-radar AI infrastructure charts right now.
While everyone is chasing the names that already exploded...
$QMCO has quietly spent months repairing its structure.
Now:
- Higher lows continue forming
- Price reclaimed the 200MA
- Volume is improving
- Multi-month downtrend is breaking
- Momentum is accelerating
The really interesting part?
Quantum isn't being discussed as an AI winner nearly as much as names like:
$NVDA
$HPE
$SMCI
$DELL
Yet AI doesn't run on compute alone.
It needs:
β storage
β data management
β infrastructure
β high-performance workflows
That's where Quantum operates.
Technically this chart is approaching a major inflection point.
After more than a year of lower highs and declining momentum, buyers are finally challenging the long-term trendline.
Sometimes the biggest opportunities appear when a stock transitions from:
"forgotten"
to
"rediscovered."
$QMCO may be entering that phase.
#AI #DataStorage #Infrastructure #Stocks
While everyone is chasing the names that already exploded...
$QMCO has quietly spent months repairing its structure.
Now:
- Higher lows continue forming
- Price reclaimed the 200MA
- Volume is improving
- Multi-month downtrend is breaking
- Momentum is accelerating
The really interesting part?
Quantum isn't being discussed as an AI winner nearly as much as names like:
$NVDA
$HPE
$SMCI
$DELL
Yet AI doesn't run on compute alone.
It needs:
β storage
β data management
β infrastructure
β high-performance workflows
That's where Quantum operates.
Technically this chart is approaching a major inflection point.
After more than a year of lower highs and declining momentum, buyers are finally challenging the long-term trendline.
Sometimes the biggest opportunities appear when a stock transitions from:
"forgotten"
to
"rediscovered."
$QMCO may be entering that phase.
#AI #DataStorage #Infrastructure #Stocks
π€©1
πThis is big Local AI news! A new open-source Computer-Use LLM has just launched.
Holo 3.1 is H Companyβs (π«π·) new local computer-use agent model that beats Qwen3.5-397B, Kimi-K2.5, and Sonnet 4.6!
Since it is built for local deployment β
⬩ Runs fully on your machine (MacBook, Windows PC, DGX Spark, RTX Spark)
⬩ Based on Qwen architecture, specialized for GUI understanding & computer control
⬩ Optimized checkpoints: NVFP4, FP8 & Q4 GGUF (0.8B to 35B sizes)
⬩ Strong gains: 79.3% on AndroidWorld benchmark (35B model)
π» Comparison to Qwen3.5:
Holo 3.1 is fine-tuned specifically for computer-use agents (screen understanding, planning, clicking, navigation). Better at real GUI tasks than general-purpose Qwen3.5, especially when running locally.β‘
Holo 3.1 is H Companyβs (π«π·) new local computer-use agent model that beats Qwen3.5-397B, Kimi-K2.5, and Sonnet 4.6!
Since it is built for local deployment β
⬩ Runs fully on your machine (MacBook, Windows PC, DGX Spark, RTX Spark)
⬩ Based on Qwen architecture, specialized for GUI understanding & computer control
⬩ Optimized checkpoints: NVFP4, FP8 & Q4 GGUF (0.8B to 35B sizes)
⬩ Strong gains: 79.3% on AndroidWorld benchmark (35B model)
π» Comparison to Qwen3.5:
Holo 3.1 is fine-tuned specifically for computer-use agents (screen understanding, planning, clicking, navigation). Better at real GUI tasks than general-purpose Qwen3.5, especially when running locally.β‘
π¨π Global One SI - Prompt
Nvidia $NVDA DSX AI Factory Ecosystem
Nvidia's AI Factory Ecosystem
AI Clouds
$CRWV CoreWeave
$NBIS Nebius
$IREN IREN
AI Factory Software
$IBM Red Hat
Design and Construction
$CDNS Cadence
$J Jacobs
$PCOR Procore
$PTC PTC
$SIE Siemens
$SU.PA Schneider Electric
$DSY.PA Dassault Systèmes
Energy and Cooling
$VRT Vertiv
$GEV GE Vernova
$TT Trane Technologies
$ABBN ABB
$CAT Caterpillar
$ETN Eaton
$SU.PA Schneider Electric
$ENGI.PA ENGIE
Delta Electronics
Hitachi
Mitsubishi Electric
Infrastructure and Facilities
$DLR Digital Realty
$EQIX Equinix
$SIFY Sify
Compute Systems
$DELL Dell
$HPE HPE
$SMCI Supermicro
ASUS
Foxconn
Gigabyte
Lenovo
Pegatron
QCT
Wistron
Wiwynn
AI Clouds
$CRWV CoreWeave
$NBIS Nebius
$IREN IREN
AI Factory Software
$IBM Red Hat
Design and Construction
$CDNS Cadence
$J Jacobs
$PCOR Procore
$PTC PTC
$SIE Siemens
$SU.PA Schneider Electric
$DSY.PA Dassault Systèmes
Energy and Cooling
$VRT Vertiv
$GEV GE Vernova
$TT Trane Technologies
$ABBN ABB
$CAT Caterpillar
$ETN Eaton
$SU.PA Schneider Electric
$ENGI.PA ENGIE
Delta Electronics
Hitachi
Mitsubishi Electric
Infrastructure and Facilities
$DLR Digital Realty
$EQIX Equinix
$SIFY Sify
Compute Systems
$DELL Dell
$HPE HPE
$SMCI Supermicro
ASUS
Foxconn
Gigabyte
Lenovo
Pegatron
QCT
Wistron
Wiwynn
β1