That's amazing!
FUSION PROGRESS: First private company generates electricity directly from fusion plasma 👀
A fusion milestone with huge implications.
wisconsin-based, Realta Fusion, became the first private fusion company to publicly demonstrate direct energy conversion, generating electricity directly from a controlled fusion plasma using its WHAM fusion device.
"Instead of converting fusion heat into steam to spin turbines, Realta's prototype captured the energy of charged particles and converted it directly into electricity, producing 100 volts and multiple amps, enough to power several lightbulbs."
why this matters?
Currently, traditional power plants lose much of their energy as heat, with steam turbines typically reaching ~33% efficiency.
Realta's direct energy conversion method is expected to achieve around 90% efficiency.
That could allow future fusion reactors to:
> Recycle power back into the reactor. > Increase overall plant output by 20-30%.
> Lower operating costs and improve commercial viability.
About 20% of the energy released in future deuterium-tritium fusion reactors, the fuel Realta plans to use in its commercial reactors, will be carried by charged helium nuclei (alpha particles).
Capturing that energy directly could make future fusion power plants significantly more efficient.
"We can take power from a plasma."
Kieran Furlong, Co-founder & CEO, Realta Fusion
He also added, "Harvesting electricity directly from the fusion reaction really helps with the economics of a reactor's design."
FUSION PROGRESS: First private company generates electricity directly from fusion plasma 👀
A fusion milestone with huge implications.
wisconsin-based, Realta Fusion, became the first private fusion company to publicly demonstrate direct energy conversion, generating electricity directly from a controlled fusion plasma using its WHAM fusion device.
"Instead of converting fusion heat into steam to spin turbines, Realta's prototype captured the energy of charged particles and converted it directly into electricity, producing 100 volts and multiple amps, enough to power several lightbulbs."
why this matters?
Currently, traditional power plants lose much of their energy as heat, with steam turbines typically reaching ~33% efficiency.
Realta's direct energy conversion method is expected to achieve around 90% efficiency.
That could allow future fusion reactors to:
> Recycle power back into the reactor. > Increase overall plant output by 20-30%.
> Lower operating costs and improve commercial viability.
About 20% of the energy released in future deuterium-tritium fusion reactors, the fuel Realta plans to use in its commercial reactors, will be carried by charged helium nuclei (alpha particles).
Capturing that energy directly could make future fusion power plants significantly more efficient.
"We can take power from a plasma."
Kieran Furlong, Co-founder & CEO, Realta Fusion
He also added, "Harvesting electricity directly from the fusion reaction really helps with the economics of a reactor's design."
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Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
President Trump says Freedom Fuel Network is lowering gas prices at 25 gas stations in the northeast US on July 3rd and "others should follow."
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🇨🇭 Global One SI - Prompt
Kelly vs Flat Sizing in Polymarket Weather - Who’s Winning in 2026 One of the most important (and rarely discussed) questions that separates profitable traders from everyone else >Kelly Sizing (Aggressive Approach) Position size is calculated based on…
How a Profitable Weather Bot Works on Polymarket: Full Pipeline from Forecast to Order (BOT PIPELINE)
Most people think weather bots require complex AI and tons of code
In reality, top bots run on a simple, understandable pipeline that you can build over a weekend
> Core Pipeline
1. Market Discovery
Scans active temperature bucket markets
2. Forecast Ingestion
Pulls 3–5 models (ECMWF, GEFS, HRRR, NWS, UKMO).
Data must be for the exact resolution station
3. Calibration
Raw models have systematic bias. Top bots correct each model per city and lead time using historical residuals
Simple decaying average bias correction already gives a noticeable boost
4. Probability Calculation
After calibration, converts the ensemble into probabilities for each bucket
5. Edge Detection
Compares calibrated model probability to market price. Only trades when edge ≥ 8%
6. Sizing & Execution
Fractional Kelly + hard caps, limit orders, circuit breakers
> About Paid Models
Many top accounts use not only free sources (Open-Meteo, NOAA) but also paid ones (Visual Crossing Pro, Tomorrow, proprietary AI models)
Paid sources often provide better short-range accuracy and cleaner station data
A 0.5–1.5°C difference on short horizons can significantly impact edge in narrow 1°C buckets
Good balance: free ensemble for baseline + 1–2 paid sources for final calibration
> Why This Works
- Models update on schedule → edge appears predictably
- Calibration removes systematic errors
- Automation lets you catch hundreds of micro-trades per day
Many think the model is good and calculate probabilities immediately
Winners first fix the bias, then calculate edge
If you want the next post - I can break down how to do simple and effective bias correction on historical data
Save this post!
Most people think weather bots require complex AI and tons of code
In reality, top bots run on a simple, understandable pipeline that you can build over a weekend
> Core Pipeline
1. Market Discovery
Scans active temperature bucket markets
2. Forecast Ingestion
Pulls 3–5 models (ECMWF, GEFS, HRRR, NWS, UKMO).
Data must be for the exact resolution station
3. Calibration
Raw models have systematic bias. Top bots correct each model per city and lead time using historical residuals
Simple decaying average bias correction already gives a noticeable boost
4. Probability Calculation
After calibration, converts the ensemble into probabilities for each bucket
5. Edge Detection
Compares calibrated model probability to market price. Only trades when edge ≥ 8%
6. Sizing & Execution
Fractional Kelly + hard caps, limit orders, circuit breakers
> About Paid Models
Many top accounts use not only free sources (Open-Meteo, NOAA) but also paid ones (Visual Crossing Pro, Tomorrow, proprietary AI models)
Paid sources often provide better short-range accuracy and cleaner station data
A 0.5–1.5°C difference on short horizons can significantly impact edge in narrow 1°C buckets
Good balance: free ensemble for baseline + 1–2 paid sources for final calibration
> Why This Works
- Models update on schedule → edge appears predictably
- Calibration removes systematic errors
- Automation lets you catch hundreds of micro-trades per day
Many think the model is good and calculate probabilities immediately
Winners first fix the bias, then calculate edge
If you want the next post - I can break down how to do simple and effective bias correction on historical data
Save this post!
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⚠️ This chart is really about separation!
At minimum, Anthropic can use Claude more intensely than anyone else.
Then small, rich companies can buy more API access than everyone else. Large companies buckle under cost burden.
Take that to the limit, and frontier models become a compounding business weapon. more AI → more output → competitive advatage .
At minimum, Anthropic can use Claude more intensely than anyone else.
Then small, rich companies can buy more API access than everyone else. Large companies buckle under cost burden.
Take that to the limit, and frontier models become a compounding business weapon. more AI → more output → competitive advatage .
🇨🇭 Global One SI - Prompt
We're all in the same boat with Mythos / Fable guys! Source: Trust me bro!
Feels like an end of era, ordinary people will probably never again get upgraded frontier models.
Fable 5’s return shows how safety routing can downgrade a frontier model.
Now we only permissioned intelligence.
The cost of putting a gatekeeper inside intelligence.
To note, that safeguard is not a simple refusal layer; it is a classifier that sends flagged Fable 5 requests to Opus 4.8.
Fable 5 came back, but the old promise did not.
End of an era. ☹️
Fable 5’s return shows how safety routing can downgrade a frontier model.
Now we only permissioned intelligence.
The cost of putting a gatekeeper inside intelligence.
To note, that safeguard is not a simple refusal layer; it is a classifier that sends flagged Fable 5 requests to Opus 4.8.
Fable 5 came back, but the old promise did not.
End of an era. ☹️
Anthropic is cooked the moment GPT-5.6 Sol becomes broadly available.
If OpenAI can offer similar or better intelligence at dramatically lower cost, then the competition shifts away from a pure model race and becomes an economics race.
In that context, OpenAI is clearly attacking inference cost hard now, reinforcing its strategic advantage.
As a result, you can’t compete with “smarter” if the other guy is both smarter and cheaper.
If OpenAI can offer similar or better intelligence at dramatically lower cost, then the competition shifts away from a pure model race and becomes an economics race.
In that context, OpenAI is clearly attacking inference cost hard now, reinforcing its strategic advantage.
As a result, you can’t compete with “smarter” if the other guy is both smarter and cheaper.
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Nobody cares about your truly opinion until your financial status is level above them
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June '26 LLM list (final)
Claude Fable 5 ✅ 🥇🐐
GLM 5.2 ✅ 🥇best open source
LFM2.5-230M ✅ Small CPU 🥇
Claude Mythos ✅ ...but not for you
Claude Sonnet-5 ✅
GPT-5.6 Sol ❌
Gemini 3.5 Pro ❌
Gemma 4 12B ✅
Grok 5 ❌
Grok 4.5 ❌
MiniMax M3 ✅ (weights released)
Nemotoron 3 Ultra ✅
Qwen3.7-Plus ✅
Qwen3.7-30B ❌ 🙏
Kimi K3.0 ❌
Kimi K2.7 Code✅
Nex-N2-Pro ✅
Hunyuan3 MoE ❌
Macron V1 Preview ✅
MAI Thinking-1 ✅
MAI Code-1-Falsh (coding model) → VSCode ✅
MAI Image-2.5 ✅
MAI Image-2.5-Flash ✅
MAI Transcribe-1.5 ✅
MAI Voice-2 ✅
MAI Voice-2-Flash ✅
Agents-A1 ✅
LongCat-2.0 (OwlAlpha) ✅
Ornith-1.0-35B ✅
Kog Laneformer 2B ✅
Sakana Fugu ✅
VibeThinker-3B ✅
Claude Fable 5 ✅ 🥇🐐
GLM 5.2 ✅ 🥇best open source
LFM2.5-230M ✅ Small CPU 🥇
Claude Mythos ✅ ...but not for you
Claude Sonnet-5 ✅
GPT-5.6 Sol ❌
Gemini 3.5 Pro ❌
Gemma 4 12B ✅
Grok 5 ❌
Grok 4.5 ❌
MiniMax M3 ✅ (weights released)
Nemotoron 3 Ultra ✅
Qwen3.7-Plus ✅
Qwen3.7-30B ❌ 🙏
Kimi K3.0 ❌
Kimi K2.7 Code✅
Nex-N2-Pro ✅
Hunyuan3 MoE ❌
Macron V1 Preview ✅
MAI Thinking-1 ✅
MAI Code-1-Falsh (coding model) → VSCode ✅
MAI Image-2.5 ✅
MAI Image-2.5-Flash ✅
MAI Transcribe-1.5 ✅
MAI Voice-2 ✅
MAI Voice-2-Flash ✅
Agents-A1 ✅
LongCat-2.0 (OwlAlpha) ✅
Ornith-1.0-35B ✅
Kog Laneformer 2B ✅
Sakana Fugu ✅
VibeThinker-3B ✅
The most expensive parts in humanoid robots are not the cameras.
The real hardware cost is hidden in the parts that move, carry load, survive impact and repeat the same motion thousands of times without failing.
Typical hardware cost range per humanoid robot:
• Dexterous hands
➝ $9K–$90K
The hardest part to make cheap. Each hand needs small actuators, tendons or linkages, tactile sensing, finger joints, wiring and control boards packed into a very small space.
• CNC metal frame
➝ $2K–$20K
The skeleton must hold motors, batteries, electronics and impact loads. Low volume machining makes this expensive, especially for torso, hip, shoulder and leg structures.
• LiDAR
➝ $1K–$15K
Used for mapping, navigation and obstacle detection. Cost depends on range, resolution, scan type and whether the robot needs outdoor reliability.
• Force-torque sensors
➝ $1K–$5K
Critical for balance, manipulation and safe contact. These sensors help the robot measure pressure through wrists, ankles or joints.
• Tactile sensors
➝ $500–$5K
Needed when a robot must grip soft, fragile or uneven objects. The cost rises fast when sensors cover fingers, palms or large skin-like surfaces.
• Actuator modules
➝ $300–$3K each
One of the biggest cost drivers. A humanoid can use dozens of actuators across legs, arms, waist, neck and hands. Torque, cooling, gearbox quality and control electronics change the price fast.
• Battery pack
➝ $500–$1.5K
The battery must deliver high current while staying compact. Weight is a major constraint because every extra kilogram makes the legs work harder.
• Compute / GPU
➝ $250–$2K
The robot needs onboard compute for vision, control, planning and sensor fusion. Higher autonomy requires more compute, better thermal design and more power.
• Harmonic drives
➝ $200–$2K each
Used where compact high torque is needed. They are expensive because precision, backlash and durability matter in knees, hips, shoulders and wrists.
• Power electronics
➝ $500–$5K
Motor drivers, converters, protection circuits and power distribution decide how stable the robot is under heavy motion.
• Wiring harness
➝ $300–$3K
Humanoids have cables moving through arms, legs, torso and neck. Bad routing means broken wires, noisy signals and hard maintenance.
• Precision encoders
➝ $50–$500 each
Every joint needs position feedback. Better encoders give smoother motion, better balance and more accurate manipulation.
A serious humanoid robot can still carry $35K–$180K+ in hardware before software, assembly, testing, repair stock, certification and support.
That is why the cheapest demo robot is not always the cheapest robot to deploy.
The real hardware cost is hidden in the parts that move, carry load, survive impact and repeat the same motion thousands of times without failing.
Typical hardware cost range per humanoid robot:
• Dexterous hands
➝ $9K–$90K
The hardest part to make cheap. Each hand needs small actuators, tendons or linkages, tactile sensing, finger joints, wiring and control boards packed into a very small space.
• CNC metal frame
➝ $2K–$20K
The skeleton must hold motors, batteries, electronics and impact loads. Low volume machining makes this expensive, especially for torso, hip, shoulder and leg structures.
• LiDAR
➝ $1K–$15K
Used for mapping, navigation and obstacle detection. Cost depends on range, resolution, scan type and whether the robot needs outdoor reliability.
• Force-torque sensors
➝ $1K–$5K
Critical for balance, manipulation and safe contact. These sensors help the robot measure pressure through wrists, ankles or joints.
• Tactile sensors
➝ $500–$5K
Needed when a robot must grip soft, fragile or uneven objects. The cost rises fast when sensors cover fingers, palms or large skin-like surfaces.
• Actuator modules
➝ $300–$3K each
One of the biggest cost drivers. A humanoid can use dozens of actuators across legs, arms, waist, neck and hands. Torque, cooling, gearbox quality and control electronics change the price fast.
• Battery pack
➝ $500–$1.5K
The battery must deliver high current while staying compact. Weight is a major constraint because every extra kilogram makes the legs work harder.
• Compute / GPU
➝ $250–$2K
The robot needs onboard compute for vision, control, planning and sensor fusion. Higher autonomy requires more compute, better thermal design and more power.
• Harmonic drives
➝ $200–$2K each
Used where compact high torque is needed. They are expensive because precision, backlash and durability matter in knees, hips, shoulders and wrists.
• Power electronics
➝ $500–$5K
Motor drivers, converters, protection circuits and power distribution decide how stable the robot is under heavy motion.
• Wiring harness
➝ $300–$3K
Humanoids have cables moving through arms, legs, torso and neck. Bad routing means broken wires, noisy signals and hard maintenance.
• Precision encoders
➝ $50–$500 each
Every joint needs position feedback. Better encoders give smoother motion, better balance and more accurate manipulation.
A serious humanoid robot can still carry $35K–$180K+ in hardware before software, assembly, testing, repair stock, certification and support.
That is why the cheapest demo robot is not always the cheapest robot to deploy.
Federal Reserve liabilities are spiking again and total assets held by the Federal Reserve have flattened, similar to February 2020
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President Trump says AI could be “much bigger than the internet” arguing that whoever leads the race will define the next era.
He added that the U.S. is currently “substantially No. 1” in AI.
He added that the U.S. is currently “substantially No. 1” in AI.
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President Trump says he will be ringing the stock market opening bells in the Oval Office on Tuesday.
This event is set to launch the "Trump Accounts" which are tax-advantaged investment accounts for children under 18, created under the One Big Beautiful Bill Act.
The U.S. Treasury deposits $1,000 for qualifying newborns, and families can add up to $5,000 annually. Funds generally remain locked in U.S. index funds until the child turns 18
This event is set to launch the "Trump Accounts" which are tax-advantaged investment accounts for children under 18, created under the One Big Beautiful Bill Act.
The U.S. Treasury deposits $1,000 for qualifying newborns, and families can add up to $5,000 annually. Funds generally remain locked in U.S. index funds until the child turns 18
🇨🇭 Global One SI - Prompt
How to invest in the AI/robotics gold rush (FULL GUIDE)
Anthropic LEAKS the optimal prompting structure for Fable 5.
This is how you save tokens while getting elite outputs
This is how you save tokens while getting elite outputs
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🇨🇭 Global One SI - Prompt
The most expensive parts in humanoid robots are not the cameras. The real hardware cost is hidden in the parts that move, carry load, survive impact and repeat the same motion thousands of times without failing. Typical hardware cost range per humanoid robot:…
The humanoid robot race will be decided inside the supply chain.
A robot body looks simple from the outside.
Inside every movement depends on a stack of expensive fragile and hard-to-scale components.
• Actuator modules
➝ Convert software commands into controlled movement
➝ Drive shoulders, hips, knees, elbows, ankles and wrists
➝ Bad actuators mean weak motion, heat issues and short service life
• Dexterous hands
➝ Decide what the robot can actually do
➝ Picking tools, opening doors, carrying objects and handling small parts all depend on the hand
➝ A humanoid without useful hands is mostly a walking demo
• Harmonic drives
➝ Give precision inside the joint
➝ Reduce backlash
➝ Help the robot move smoothly under load
• Battery packs
➝ Set runtime, weight and deployment time
➝ Every extra kilogram affects balance walking efficiency and payload
➝ Long runtime is one of the hardest problems for real work
• Compute / GPU
➝ Runs onboard AI, vision, planning and safety logic
➝ The robot has to react inside the body, not only in the cloud
➝ Latency matters when the robot is walking near people
• Cameras & sensors
➝ Give vision depth balance and force feedback
➝ The robot needs to see the floor, detect objects and understand contact
➝ No perception means no useful autonomy
• Torque motors
➝ Put power inside every joint
➝ Compact motors are critical for hips, knees, shoulders and wrists
➝ More torque with less heat means longer useful operation
• AI data & training
➝ Turns teleoperation, simulation and real failures into better behavior
➝ Walking, grasping and tool use need massive task data
➝ The robot learns from the stack behind it
The market will reward the companies that can control the full body stack:
• joints
• hands
• sensors
• batteries
• compute
• training data
• manufacturing
• repair network
A robot body looks simple from the outside.
Inside every movement depends on a stack of expensive fragile and hard-to-scale components.
• Actuator modules
➝ Convert software commands into controlled movement
➝ Drive shoulders, hips, knees, elbows, ankles and wrists
➝ Bad actuators mean weak motion, heat issues and short service life
• Dexterous hands
➝ Decide what the robot can actually do
➝ Picking tools, opening doors, carrying objects and handling small parts all depend on the hand
➝ A humanoid without useful hands is mostly a walking demo
• Harmonic drives
➝ Give precision inside the joint
➝ Reduce backlash
➝ Help the robot move smoothly under load
• Battery packs
➝ Set runtime, weight and deployment time
➝ Every extra kilogram affects balance walking efficiency and payload
➝ Long runtime is one of the hardest problems for real work
• Compute / GPU
➝ Runs onboard AI, vision, planning and safety logic
➝ The robot has to react inside the body, not only in the cloud
➝ Latency matters when the robot is walking near people
• Cameras & sensors
➝ Give vision depth balance and force feedback
➝ The robot needs to see the floor, detect objects and understand contact
➝ No perception means no useful autonomy
• Torque motors
➝ Put power inside every joint
➝ Compact motors are critical for hips, knees, shoulders and wrists
➝ More torque with less heat means longer useful operation
• AI data & training
➝ Turns teleoperation, simulation and real failures into better behavior
➝ Walking, grasping and tool use need massive task data
➝ The robot learns from the stack behind it
The market will reward the companies that can control the full body stack:
• joints
• hands
• sensors
• batteries
• compute
• training data
• manufacturing
• repair network