🇨🇭 Global One SI - Prompt
$POET/USD H2 #Indicator #Signal #Trading #GlobalOneAI
$POET is doing exactly what strong stocks do.
Not pulling back.
Not breaking down.
Not giving shorts any relief.
After yesterday's surge, price is simply compressing beneath resistance while buyers continue stepping in.
That's usually how momentum builds.
A clean move through $15.50 could attract a whole new wave of attention.
Breakout appears to be gaining legs.
Not pulling back.
Not breaking down.
Not giving shorts any relief.
After yesterday's surge, price is simply compressing beneath resistance while buyers continue stepping in.
That's usually how momentum builds.
A clean move through $15.50 could attract a whole new wave of attention.
Breakout appears to be gaining legs.
$UP
Breakout.
Consolidation.
Higher highs.
Higher lows.
That's the sequence bulls want to see.
The stock continues absorbing supply near the highs rather than giving back gains.
No heavy distribution.
No major rejection.
Momentum appears alive and well.
If buyers remain in control, this consolidation could simply be the pause before the next expansion leg.
$20.00 this month?
Breakout.
Consolidation.
Higher highs.
Higher lows.
That's the sequence bulls want to see.
The stock continues absorbing supply near the highs rather than giving back gains.
No heavy distribution.
No major rejection.
Momentum appears alive and well.
If buyers remain in control, this consolidation could simply be the pause before the next expansion leg.
$20.00 this month?
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Humanoid Sales Champion
Unitree just announced that its G1 humanoid robot has reached a cumulative production milestone of 11,000 units.
This number does not include other humanoid models or wheeled-base humanoid robots.
Notably, Unitree also passed its listing application on China’s STAR Market yesterday and plans to raise 4.2 billion RMB (~$620 million).
Unitree just announced that its G1 humanoid robot has reached a cumulative production milestone of 11,000 units.
This number does not include other humanoid models or wheeled-base humanoid robots.
Notably, Unitree also passed its listing application on China’s STAR Market yesterday and plans to raise 4.2 billion RMB (~$620 million).
🇨🇭 Global One SI - Prompt
If you're still not using /goal inside of Codex, you're missing out big time. I recently shared this insanely powerful /goal prompt that lets your AI agents work autonomously for 8+ hours straight. If you haven't tried it yet, you'll want to save this &…
The most powerful thing you can do in Codex.
[/goal] prompt + GPT-5.5 + Extra High reasoning + Full access
Every single time I use this setup, I'm completely blown away.
If you think AI is useless, give this a try, and your mind will change instantly.
[/goal] prompt + GPT-5.5 + Extra High reasoning + Full access
Every single time I use this setup, I'm completely blown away.
If you think AI is useless, give this a try, and your mind will change instantly.
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🇨🇭 Global One SI - Prompt
These 30 Claude Code slash commands will make you 10x more productive inside Claude Code. If you're using Claude regularly, you'll want to save these:
The one prompt that builds a second-brain database in Obsidian.
One of the most powerful AI workflows you could build right now:
One of the most powerful AI workflows you could build right now:
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🇨🇭 Global One SI - Prompt
The one prompt that builds a second-brain database in Obsidian. One of the most powerful AI workflows you could build right now:
Become a master Claude Code prompt engineer in <60 seconds:
(my ultimate Claude Code prompting guide)
(my ultimate Claude Code prompting guide)
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Alphabet to raise $80B from share sales to fund AI spending splurge, Berkshire to invest $10B
Very bullish news, as it shows again the limiting factor is no longer demand, models, or products, but compute supply.
For years, the cleanest story was excess cash, buybacks, and software-like returns; now the story is closer to railroads, power grids, and fabs, where whoever can finance the bottleneck may own the toll road.
Berkshire’s $10B placement gives the raise a stamp of patient capital.
Alphabet is not simply selling $80B of common stock straight into the market tomorrow for AI capex. It is using a mix of underwritten stock, mandatory convertible preferred, Berkshire private placement, and a $40B ATM program, with about $30B of that ATM tied to employee equity tax mechanics.
Very bullish news, as it shows again the limiting factor is no longer demand, models, or products, but compute supply.
For years, the cleanest story was excess cash, buybacks, and software-like returns; now the story is closer to railroads, power grids, and fabs, where whoever can finance the bottleneck may own the toll road.
Berkshire’s $10B placement gives the raise a stamp of patient capital.
Alphabet is not simply selling $80B of common stock straight into the market tomorrow for AI capex. It is using a mix of underwritten stock, mandatory convertible preferred, Berkshire private placement, and a $40B ATM program, with about $30B of that ATM tied to employee equity tax mechanics.
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New video of Qualcomm CEO Cristiano Amon: AI will require “gazillions” of tokens.
Because, Agentic AI will consume dramatically more tokens because it performs autonomous tasks, uses multiple systems, and interacts with tools.
AI demand will grow hugely when software starts letting agents act, not just answer.
A chatbot spends tokens on language; an agent spends tokens on deciding, checking, calling tools, reading outputs, revising plans, and coordinating with other software.
Today a single human-AI exchange may be large, a reasoning task may be much larger, but we are already entering the agentic era, where an autonomous workflow can become exponentially larger still because the model is no longer producing one response.
It is running a process.
When a SaaS product redesigns itself around human-agent interaction, every task can become a chain of hidden micro-decisions, and each micro-decision consumes context, memory, tool calls, verification, and output tokens.
Another point is people hear “more tokens” and think only of bigger data centers, when the deeper change is economic: software usage may stop being measured mainly by clicks, seats, or sessions.
It will be measured by how much machine reasoning/tokens gets spent on behalf of each user.
Because, Agentic AI will consume dramatically more tokens because it performs autonomous tasks, uses multiple systems, and interacts with tools.
AI demand will grow hugely when software starts letting agents act, not just answer.
A chatbot spends tokens on language; an agent spends tokens on deciding, checking, calling tools, reading outputs, revising plans, and coordinating with other software.
Today a single human-AI exchange may be large, a reasoning task may be much larger, but we are already entering the agentic era, where an autonomous workflow can become exponentially larger still because the model is no longer producing one response.
It is running a process.
When a SaaS product redesigns itself around human-agent interaction, every task can become a chain of hidden micro-decisions, and each micro-decision consumes context, memory, tool calls, verification, and output tokens.
Another point is people hear “more tokens” and think only of bigger data centers, when the deeper change is economic: software usage may stop being measured mainly by clicks, seats, or sessions.
It will be measured by how much machine reasoning/tokens gets spent on behalf of each user.
🇨🇭 Global One SI - Prompt
Alphabet to raise $80B from share sales to fund AI spending splurge, Berkshire to invest $10B Very bullish news, as it shows again the limiting factor is no longer demand, models, or products, but compute supply. For years, the cleanest story was excess…
In roughly 28 years, Google/Alphabet has tapped primary equity capital only seven times; the 2026 announced $80B raise is more than 10x all prior identified primary raises combined.
SK hynix just said AI memory demand is now so large that it will double wafer capacity within 5 years, yet still expects supply to stay tight until 2030.
A wafer is the round silicon starting plate that becomes thousands of memory chips, so doubling wafer capacity basically means SK hynix is trying to expand the physical base of its chip output, not just run current lines harder.
AI supply is increasingly constrained by the physical rhythm of memory manufacturing, where wafers, packaging, yields, and supply agreements move far slower than GPU roadmaps.
The pressure comes from HBM (High-bandwidth memor), the stacked memory used beside Nvidia GPUs.
HBM is hard to scale because it needs advanced DRAM, stacking, packaging, testing, and close work with GPU designers, which is why SK hynix is working with Nvidia and TSMC on HBM4 base dies.
---
The global memory market.
The global memory market has 2 main layers: DRAM, which includes the memory used next to CPUs and AI GPUs, and NAND flash, which is the storage inside SSDs, phones, and data centers.
In DRAM, the market is extremely concentrated, with Samsung at 38.5%, SK hynix at 28.8%, and Micron at 22.4% in 1Q26, meaning the top 3 control about 90% of global DRAM revenue.
In HBM, which is a premium submarket inside DRAM, the AI-specific memory used beside Nvidia GPUs, SK hynix is the market leader, with 58% share in 1Q26, while Samsung and Micron each had 21%.
HBM, or High Bandwidth Memory, is a special form of DRAM built for extreme data movement.
The difference is physical design.
Normal DRAM chips usually sit on memory modules or near the processor, and data moves through relatively narrower connections.
HBM stacks multiple DRAM dies vertically and places them very close to the GPU through advanced packaging, which creates a much wider data path.
That wider path gives AI chips much higher memory bandwidth, meaning the GPU can receive data faster instead of sitting idle.
A wafer is the round silicon starting plate that becomes thousands of memory chips, so doubling wafer capacity basically means SK hynix is trying to expand the physical base of its chip output, not just run current lines harder.
AI supply is increasingly constrained by the physical rhythm of memory manufacturing, where wafers, packaging, yields, and supply agreements move far slower than GPU roadmaps.
The pressure comes from HBM (High-bandwidth memor), the stacked memory used beside Nvidia GPUs.
HBM is hard to scale because it needs advanced DRAM, stacking, packaging, testing, and close work with GPU designers, which is why SK hynix is working with Nvidia and TSMC on HBM4 base dies.
---
The global memory market.
The global memory market has 2 main layers: DRAM, which includes the memory used next to CPUs and AI GPUs, and NAND flash, which is the storage inside SSDs, phones, and data centers.
In DRAM, the market is extremely concentrated, with Samsung at 38.5%, SK hynix at 28.8%, and Micron at 22.4% in 1Q26, meaning the top 3 control about 90% of global DRAM revenue.
In HBM, which is a premium submarket inside DRAM, the AI-specific memory used beside Nvidia GPUs, SK hynix is the market leader, with 58% share in 1Q26, while Samsung and Micron each had 21%.
HBM, or High Bandwidth Memory, is a special form of DRAM built for extreme data movement.
The difference is physical design.
Normal DRAM chips usually sit on memory modules or near the processor, and data moves through relatively narrower connections.
HBM stacks multiple DRAM dies vertically and places them very close to the GPU through advanced packaging, which creates a much wider data path.
That wider path gives AI chips much higher memory bandwidth, meaning the GPU can receive data faster instead of sitting idle.
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Jensen Huang gathered SK Hynix, Samsung, LG and Naver in Taipei.
Korea's importance is growing to Nvidia’s stack. which supply memory, devices, factories, software, and large-scale demand for robotics.
Korean tech shares rallied on optimism over dinner
Korea's importance is growing to Nvidia’s stack. which supply memory, devices, factories, software, and large-scale demand for robotics.
Korean tech shares rallied on optimism over dinner
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