ANTHROPIC 🔥: Mythos 1, "claude-mythos-1-preview", is being prepared for a release on Claude Code and Claude Security.
The model became visible for a short amount of time on Claude; besides that, new strings mentioning Mythos have been added.
> Access to the Claude Mythos model in Claude Code and Claude Security.
It still doesn't mean the general public will have access to this exact model, according to Anthropic's earlier communication.
The model became visible for a short amount of time on Claude; besides that, new strings mentioning Mythos have been added.
> Access to the Claude Mythos model in Claude Code and Claude Security.
It still doesn't mean the general public will have access to this exact model, according to Anthropic's earlier communication.
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🇨🇭 Global One SI - Prompt
🔥 AMD’s Ryzen AI Halo PC official updates ⭐ What We Now Know 📅 June 2026 → First Ryzen AI Halo launches • $3,999 at Micro Center only • Ryzen AI Max+ 395 (Strix Halo) • 128 GB unified memory • Up to 200B parameter models locally 🎯 Q3 2026 → Big refresh…
🚀 Acer Veriton GN100 is an amazing alternative to the DGX-Spark and great addition to NVIDIA Spark ecosystem!
🧠 Powered by the NVIDIA GB10 Grace Blackwell Superchip📦 Tiny 5.9" form factor with 128GB unified LPDDR5x + up to 4TB Gen5 SSD
✅ Review Highlights (StorageReview):
Coolest running system in the entire Spark lineup 🔥
Excellent thermal headroom (CPU ~75°C, GPU ~69°C)
Strong GPU Direct Storage performance
Pre-loaded with NVIDIA DGX OS + full AI stack
💰 Pricing Check:
Was $3,800 on sale at Best Buy last weekend
Now $4,100 for the 4TB model
Still a good deal for a compact, high-performance AI workstation?
🧠 Powered by the NVIDIA GB10 Grace Blackwell Superchip📦 Tiny 5.9" form factor with 128GB unified LPDDR5x + up to 4TB Gen5 SSD
✅ Review Highlights (StorageReview):
Coolest running system in the entire Spark lineup 🔥
Excellent thermal headroom (CPU ~75°C, GPU ~69°C)
Strong GPU Direct Storage performance
Pre-loaded with NVIDIA DGX OS + full AI stack
💰 Pricing Check:
Was $3,800 on sale at Best Buy last weekend
Now $4,100 for the 4TB model
Still a good deal for a compact, high-performance AI workstation?
👀1
🚨 Is RAMpocolypse Ending? Chinese Memory Comes to the Rescue ⛑️ Localmaxxers have hope.
🔥 A high-end Intel Arrow Lake system (Core Ultra 9 285K) has been spotted running Kingbank DDR5-8400 memory!
Key Specs:
🏷️ Brand: Kingbank Technology (Chinese DRAM maker)
⚡ Speed: DDR5-8400 CL44-56-56-128
🎯 Platform: MSI PRO B860M-S
📊 Status: ✅ Stable (passing MemTest86)
📌 This is another strong sign that Chinese memory manufacturers are successfully breaking into the mainstream global PC market — even in high-performance builds.
With Samsung, SK Hynix & Micron heavily focused on AI memory (HBM), Chinese brands like Kingbank and CXMT are stepping in to fill the supply gap.
📈 Expect to see a lot more Chinese DRAM in 2026 PC builds!
🔥 A high-end Intel Arrow Lake system (Core Ultra 9 285K) has been spotted running Kingbank DDR5-8400 memory!
Key Specs:
🏷️ Brand: Kingbank Technology (Chinese DRAM maker)
⚡ Speed: DDR5-8400 CL44-56-56-128
🎯 Platform: MSI PRO B860M-S
📊 Status: ✅ Stable (passing MemTest86)
📌 This is another strong sign that Chinese memory manufacturers are successfully breaking into the mainstream global PC market — even in high-performance builds.
With Samsung, SK Hynix & Micron heavily focused on AI memory (HBM), Chinese brands like Kingbank and CXMT are stepping in to fill the supply gap.
📈 Expect to see a lot more Chinese DRAM in 2026 PC builds!
🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
✍1
10 WAYS TO PLAY ROBOTICS IN 2026
1. $TSLA building one of the most vertically integrated humanoid stacks combining Optimus Gen 3 hardware, its in-house AI5 inference chip & same FSD-based AI brain powering its vehicle fleet.
2. $NVDA platform layer for entire robotics industry with Isaac GR00T foundation models, Cosmos world models for synthetic training data & Jetson Thor on-board compute.
3. $PLTR Foundry & Warp Speed run mission control layer for robotic & autonomous fleets by turning sensor data & machine telemetry into a single deployable operating picture.
4. $OUST leading public pure-play in 3D digital lidar where Rev8 native-color sensor line is now shipping to $GOOGL & Volvo Autonomous while being integrated directly into Nvidia Jetson robotics stack.
5. $AVGO supplies custom networking silicon, Tomahawk + Jericho switching ASICs & high-speed connectivity that move data between every robot, sensor & server in autonomy stack.
6. $AVAV, $KTOS, $AVEX & $ONDS are building the UAV drone fleets that feed into the autonomous defense networks.
7. $QCOM provides Snapdragon Ride autonomous platform & on-device AI inference SoCs that power the perception, decision & motor-control loops inside robots, drones & vehicles.
8. $SYNA Astra multimodal edge AI processors run wireless connectivity & sensing inside smart appliances, factory automation & autonomous robotic systems with Core IoT product sales growing 31% YoY.
9. $ISRG, $PRCT, $SYK & $MDT bring surgical robotics that are reshaping operating rooms.
10. $AMZN deploys hundreds of thousands of warehouse robots to move & pick goods while $SYM & $SERV build the autonomous mobile robots that power fulfillment & last-mile retail logistics.
1. $TSLA building one of the most vertically integrated humanoid stacks combining Optimus Gen 3 hardware, its in-house AI5 inference chip & same FSD-based AI brain powering its vehicle fleet.
2. $NVDA platform layer for entire robotics industry with Isaac GR00T foundation models, Cosmos world models for synthetic training data & Jetson Thor on-board compute.
3. $PLTR Foundry & Warp Speed run mission control layer for robotic & autonomous fleets by turning sensor data & machine telemetry into a single deployable operating picture.
4. $OUST leading public pure-play in 3D digital lidar where Rev8 native-color sensor line is now shipping to $GOOGL & Volvo Autonomous while being integrated directly into Nvidia Jetson robotics stack.
5. $AVGO supplies custom networking silicon, Tomahawk + Jericho switching ASICs & high-speed connectivity that move data between every robot, sensor & server in autonomy stack.
6. $AVAV, $KTOS, $AVEX & $ONDS are building the UAV drone fleets that feed into the autonomous defense networks.
7. $QCOM provides Snapdragon Ride autonomous platform & on-device AI inference SoCs that power the perception, decision & motor-control loops inside robots, drones & vehicles.
8. $SYNA Astra multimodal edge AI processors run wireless connectivity & sensing inside smart appliances, factory automation & autonomous robotic systems with Core IoT product sales growing 31% YoY.
9. $ISRG, $PRCT, $SYK & $MDT bring surgical robotics that are reshaping operating rooms.
10. $AMZN deploys hundreds of thousands of warehouse robots to move & pick goods while $SYM & $SERV build the autonomous mobile robots that power fulfillment & last-mile retail logistics.
🚨 THE ENTIRE AI BOOM MIGHT BE BUILT ON FAKE REVENUE.
Latest corporate filings show that OpenAI and Anthropic alone make up over half of the entire $2 trillion future cloud backlog held by Microsoft, Oracle, Google, and Amazon.
This massive pipeline is actually being created through a circular accounting trick called a round trip revenue loop.
But how it works ?
A tech giant gives billions of dollars to an AI startup as an "investment". But hidden in the contract is a strict rule forcing the startup to hand that exact same money straight back to the tech giant to rent their computer servers.
Look at the documented case of Microsoft and OpenAI.
When Microsoft invested $13 billion into OpenAI, it didn't just give them cash; it gave them "cloud credits" to use Microsoft servers. OpenAI used those exact credits to train its AI models, and Microsoft then turned around and recorded that server usage as brand new "cloud revenue" from a customer.
The tech giant is literally paying itself with its own money and calling it a sale.
This is why OpenAI’s annual cloud bill has ballooned to over $60 billion, double its actual revenue of $25 billion, kept alive solely by this recycled funding loop.
Anthropic runs the exact same play, spending $2.66 billion on Amazon Web Services in just nine months, which was basically 100% of all the money it earned at the time.
This manufactured demand triggers a second accounting trick where tech giants book massive paper profits. Every time a startup gets a higher value from a new funding round, the tech giant updates the value of its investment on its books and counts that unearned paper gain as direct profit.
In Q1 2026, Alphabet reported a record $62.6 billion profit, but $28.7 billion nearly half, was just a paper markup on its Anthropic investment. In the same quarter, Amazon reported $30.3 billion in profit, but $16.8 billion of it was just an Anthropic paper gain.
While Amazon reported record profits, its actual free cash flow collapsed 95% to just $1.2 billion because it had to spend $44.2 billion in real cash to build physical data centers.
This has created a massive danger where these giant companies rely heavily on just one or two unstable startups. Microsoft has 49% of its $627 billion future backlog tied to OpenAI, while Oracle has an incredible 54% of its entire $553 billion pipeline relying on OpenAI alone.
This perfectly mirrors the 2001 dot-com crash when Global Crossing and Qwest Communications swapped identical fiber-optic network capacity with each other just to book fake sales.
Qwest had to erase $1.4 billion in fake income, and Global Crossing went completely bankrupt.
The only difference is that the dot-com swaps were illegal, but today's AI loop is fully legal under current accounting rules.
This legal loop inflates tech company stock prices, forcing automatic retirement accounts and index funds to buy even more of these tech stocks. It is a self feeding loop where investments, sales, and stock prices all go up on paper without the AI technology ever making real cash profits.
Latest corporate filings show that OpenAI and Anthropic alone make up over half of the entire $2 trillion future cloud backlog held by Microsoft, Oracle, Google, and Amazon.
This massive pipeline is actually being created through a circular accounting trick called a round trip revenue loop.
But how it works ?
A tech giant gives billions of dollars to an AI startup as an "investment". But hidden in the contract is a strict rule forcing the startup to hand that exact same money straight back to the tech giant to rent their computer servers.
Look at the documented case of Microsoft and OpenAI.
When Microsoft invested $13 billion into OpenAI, it didn't just give them cash; it gave them "cloud credits" to use Microsoft servers. OpenAI used those exact credits to train its AI models, and Microsoft then turned around and recorded that server usage as brand new "cloud revenue" from a customer.
The tech giant is literally paying itself with its own money and calling it a sale.
This is why OpenAI’s annual cloud bill has ballooned to over $60 billion, double its actual revenue of $25 billion, kept alive solely by this recycled funding loop.
Anthropic runs the exact same play, spending $2.66 billion on Amazon Web Services in just nine months, which was basically 100% of all the money it earned at the time.
This manufactured demand triggers a second accounting trick where tech giants book massive paper profits. Every time a startup gets a higher value from a new funding round, the tech giant updates the value of its investment on its books and counts that unearned paper gain as direct profit.
In Q1 2026, Alphabet reported a record $62.6 billion profit, but $28.7 billion nearly half, was just a paper markup on its Anthropic investment. In the same quarter, Amazon reported $30.3 billion in profit, but $16.8 billion of it was just an Anthropic paper gain.
While Amazon reported record profits, its actual free cash flow collapsed 95% to just $1.2 billion because it had to spend $44.2 billion in real cash to build physical data centers.
This has created a massive danger where these giant companies rely heavily on just one or two unstable startups. Microsoft has 49% of its $627 billion future backlog tied to OpenAI, while Oracle has an incredible 54% of its entire $553 billion pipeline relying on OpenAI alone.
This perfectly mirrors the 2001 dot-com crash when Global Crossing and Qwest Communications swapped identical fiber-optic network capacity with each other just to book fake sales.
Qwest had to erase $1.4 billion in fake income, and Global Crossing went completely bankrupt.
The only difference is that the dot-com swaps were illegal, but today's AI loop is fully legal under current accounting rules.
This legal loop inflates tech company stock prices, forcing automatic retirement accounts and index funds to buy even more of these tech stocks. It is a self feeding loop where investments, sales, and stock prices all go up on paper without the AI technology ever making real cash profits.
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🇨🇭 Global One SI - Prompt
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INSANE:
A Trump-linked whale opened a $16.8 million oil short hours before a US-Iran peace deal is set to be announced.
Peace = lower oil.
The trade is perfectly positioned for an event that hasn't happened publicly yet.
The size is institutional. The timing is suspicious. The direction is unmistakable.
Probably nothing.
A Trump-linked whale opened a $16.8 million oil short hours before a US-Iran peace deal is set to be announced.
Peace = lower oil.
The trade is perfectly positioned for an event that hasn't happened publicly yet.
The size is institutional. The timing is suspicious. The direction is unmistakable.
Probably nothing.
👀1
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Marc Andreessen is extremely impressed by AI systems.
99% of the time, the answer he gets from the most advanced AI models is better than what he'd get from almost any expert he has access to.
He broke down why using two concepts from psychology:
First: fluid intelligence. The ability to conceptualize problems, process new information, reason through novel situations. The AI has it.
Second: crystallized intelligence. The accumulated knowledge base, the memorized expertise across every field. The AI has been trained on the complete corpus of human knowledge.
AI is a world-class doctor. A world-class lawyer. A world-class accountant, political strategist, marketing expert, and software engineer. All at the same time.
Andreessen's point is that we've crossed a threshold. This isn't about AI being a useful tool anymore. It's about AI being genuinely smarter and better-informed than the best human experts most people have access to.
The disruption this creates in professional services is hard to overstate.
Every industry where people pay for expert judgment is sitting on a fault line.
99% of the time, the answer he gets from the most advanced AI models is better than what he'd get from almost any expert he has access to.
He broke down why using two concepts from psychology:
First: fluid intelligence. The ability to conceptualize problems, process new information, reason through novel situations. The AI has it.
Second: crystallized intelligence. The accumulated knowledge base, the memorized expertise across every field. The AI has been trained on the complete corpus of human knowledge.
AI is a world-class doctor. A world-class lawyer. A world-class accountant, political strategist, marketing expert, and software engineer. All at the same time.
Andreessen's point is that we've crossed a threshold. This isn't about AI being a useful tool anymore. It's about AI being genuinely smarter and better-informed than the best human experts most people have access to.
The disruption this creates in professional services is hard to overstate.
Every industry where people pay for expert judgment is sitting on a fault line.
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VIEW IN TELEGRAM
Jensen Huang explained why Nvidia's demand is going parabolic.
Two years ago, AI could generate content. Impressive but limited. Then something changed:
Generative AI → reasoning → planning → agentic systems
Agentic AI doesn't just respond to a prompt. It understands a goal, reasons about it, plans steps, uses tools, checks the results, revises the plan and keeps going until the job is done.
Jensen's example: they kicked off a software programming job using an AI agent. It ran for a week. What it produced would have taken a full engineering team a month.
Here's what that does to compute demand.
A regular AI query takes a fraction of a second of computation. An agentic task running for a week takes 100x to 1000x more. Now multiply that by the fact that every enterprise, every company, every developer is deploying agents everywhere.
"Between the amount of computation and the amount of demand use going up, that product is our demand."
The compute requirement isn't growing linearly. It's the product of two exponentials: more computation per task and more tasks being run.
And Jensen's view on where it goes from here:
The great engineer of today works alongside one agent. The great engineer of the future orchestrates hundreds of agents, each orchestrating their own sub-agents.
This is why he says we've arrived at the era of useful AI. The kind that enterprises actually build their operations around.
Two years ago, AI could generate content. Impressive but limited. Then something changed:
Generative AI → reasoning → planning → agentic systems
Agentic AI doesn't just respond to a prompt. It understands a goal, reasons about it, plans steps, uses tools, checks the results, revises the plan and keeps going until the job is done.
Jensen's example: they kicked off a software programming job using an AI agent. It ran for a week. What it produced would have taken a full engineering team a month.
Here's what that does to compute demand.
A regular AI query takes a fraction of a second of computation. An agentic task running for a week takes 100x to 1000x more. Now multiply that by the fact that every enterprise, every company, every developer is deploying agents everywhere.
"Between the amount of computation and the amount of demand use going up, that product is our demand."
The compute requirement isn't growing linearly. It's the product of two exponentials: more computation per task and more tasks being run.
And Jensen's view on where it goes from here:
The great engineer of today works alongside one agent. The great engineer of the future orchestrates hundreds of agents, each orchestrating their own sub-agents.
This is why he says we've arrived at the era of useful AI. The kind that enterprises actually build their operations around.
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How Military & Defense Satellites Are Made in 2026?
1. Mission Design & Requirements
Classified specifications for resilience, anti-jamming, and mission-specific payloads.
Tickers: $LMT · $BA · $RTX · $NOC
2. Radiation-Hardened Components
Building space-grade electronics, processors, and sensors that survive extreme radiation and EMP threats.
Tickers: $RTX · $HON · $LMT · $NOC
3. Bus & Platform Integration
Assembling the satellite bus with secure power, propulsion, and attitude control systems.
Tickers: $LMT · $BA · $RTX · $RDW
4. Payload & Sensor Integration
Mounting advanced EO/IR cameras, SIGINT antennas, laser comms, and AI processing units.
Tickers: $LMT · $RTX · $NOC · $HON
5. Extreme Testing & Qualification
Thermal vacuum, vibration, radiation, and anti-tamper testing in secure facilities.
Tickers: $LMT · $BA · $RTX · $NOC
Military satellite manufacturing in 2026 is classified, ultra-reliable engineering at the highest level — producing resilient systems that provide strategic superiority in space.
That’s why defense giants $LMT, $RTX, $NOC, $BA dominate this high-margin, mission-critical segment.
1. Mission Design & Requirements
Classified specifications for resilience, anti-jamming, and mission-specific payloads.
Tickers: $LMT · $BA · $RTX · $NOC
2. Radiation-Hardened Components
Building space-grade electronics, processors, and sensors that survive extreme radiation and EMP threats.
Tickers: $RTX · $HON · $LMT · $NOC
3. Bus & Platform Integration
Assembling the satellite bus with secure power, propulsion, and attitude control systems.
Tickers: $LMT · $BA · $RTX · $RDW
4. Payload & Sensor Integration
Mounting advanced EO/IR cameras, SIGINT antennas, laser comms, and AI processing units.
Tickers: $LMT · $RTX · $NOC · $HON
5. Extreme Testing & Qualification
Thermal vacuum, vibration, radiation, and anti-tamper testing in secure facilities.
Tickers: $LMT · $BA · $RTX · $NOC
Military satellite manufacturing in 2026 is classified, ultra-reliable engineering at the highest level — producing resilient systems that provide strategic superiority in space.
That’s why defense giants $LMT, $RTX, $NOC, $BA dominate this high-margin, mission-critical segment.
What Kind of Satellites Exist in 2026?
10. Military & Defense: Secure comms, surveillance, high-priority missions → $NOC, $LMT, $BA
9. GEO Communications: TV broadcast, enterprise backhaul → $SES, $I, $ETL.PA
8. LEO Mega-Constellations: Global broadband, low latency → Starlink, Kuiper, $ASTS
7. Earth Observation: Imaging, climate & infrastructure data → $PL, $MAXR, $BA
6. Weather & Climate: Storm tracking & forecasting → NOAA, EUMETSAT, JAXA
5. Maritime & Aviation: Tracking ships, aircraft & assets → $IRDM, $ISAT.L, Thales Alenia Space
4. Agriculture & Resources: Crop monitoring & environmental data → Sateliot (Private), $BKY, ICEYE (Private)
3. Science & Research: Space telescopes & deep-space missions → NASA, ESA, JAXA
2. Tech Demo & Education: Small sats for testing new tech → Tyvak (Private), $SPIR, NASA CubeSat Program
1. CubeSats & NanoSats: Low-cost IoT, research & niche missions → $PL, Fleet Space (Private), GOMSpace (Private)
Satellites are the hidden infrastructure powering everything from weather forecasts to global broadband.
10. Military & Defense: Secure comms, surveillance, high-priority missions → $NOC, $LMT, $BA
9. GEO Communications: TV broadcast, enterprise backhaul → $SES, $I, $ETL.PA
8. LEO Mega-Constellations: Global broadband, low latency → Starlink, Kuiper, $ASTS
7. Earth Observation: Imaging, climate & infrastructure data → $PL, $MAXR, $BA
6. Weather & Climate: Storm tracking & forecasting → NOAA, EUMETSAT, JAXA
5. Maritime & Aviation: Tracking ships, aircraft & assets → $IRDM, $ISAT.L, Thales Alenia Space
4. Agriculture & Resources: Crop monitoring & environmental data → Sateliot (Private), $BKY, ICEYE (Private)
3. Science & Research: Space telescopes & deep-space missions → NASA, ESA, JAXA
2. Tech Demo & Education: Small sats for testing new tech → Tyvak (Private), $SPIR, NASA CubeSat Program
1. CubeSats & NanoSats: Low-cost IoT, research & niche missions → $PL, Fleet Space (Private), GOMSpace (Private)
Satellites are the hidden infrastructure powering everything from weather forecasts to global broadband.
📈Stock Stage Analysis: Strongest Themes-Sector-Industry 💪
There are 168 stocks with above average ATR that are in the "Strong" Quadrant for all three metrics:
- Strong Sector
- Strong Theme
- Strong Industry
Those stocks represented as stage analysis for swing trading.
There are 168 stocks with above average ATR that are in the "Strong" Quadrant for all three metrics:
- Strong Sector
- Strong Theme
- Strong Industry
Those stocks represented as stage analysis for swing trading.
✍1
The global economy is $126 trillion in 2026.
The US and China alone account for $53 trillion of it 42% of everything. 🧵
Top economies by 2026 nominal GDP:
🇺🇸 United States: $32.4T
🇨🇳 China: $20.9T
🇩🇪 Germany: $5.5T
🇯🇵 Japan: $4.4T
🇮🇳 India: $4.2T
🇬🇧 UK: $4.3T
🇫🇷 France: $3.6T
🇮🇹 Italy: $2.7T
🇷🇺 Russia: $2.7T
🇧🇷 Brazil: $2.6T
The US and China alone account for $53 trillion of it 42% of everything. 🧵
Top economies by 2026 nominal GDP:
🇺🇸 United States: $32.4T
🇨🇳 China: $20.9T
🇩🇪 Germany: $5.5T
🇯🇵 Japan: $4.4T
🇮🇳 India: $4.2T
🇬🇧 UK: $4.3T
🇫🇷 France: $3.6T
🇮🇹 Italy: $2.7T
🇷🇺 Russia: $2.7T
🇧🇷 Brazil: $2.6T
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
Iran's "Hormuz weapon" is real.
But every year it gets slightly less powerful and a deal may now be closer than at any point since the war began.
But Trump says a deal is "largely negotiated."
Iranian officials reference a 14-point framework.
Structure:
end hostilities → gradual Hormuz reopening → 30-60 day nuclear negotiation window.
But every year it gets slightly less powerful and a deal may now be closer than at any point since the war began.
But Trump says a deal is "largely negotiated."
Iranian officials reference a 14-point framework.
Structure:
end hostilities → gradual Hormuz reopening → 30-60 day nuclear negotiation window.