Two fundamental macroeconomic and technological trends make $DAIO an indispensable gatekeeper for the future of the tech industry
The Paradigm Shift to UFS Storage:
Legacy flash memory standards are rapidly disappearing. Modern connected vehicles, autonomous driving suites (ADAS), and Edge AI devices require massive Universal Flash Storage (UFS) capacities, handling gigabytes of data instead of megabytes. Competitors hit physical speed limits trying to flash these volumes. Powered by patented VerifyBoosttechnology (delivering verification speeds up to 750 MB/s), Data I/O's LumenX and LumenX2 architectures are the only platforms capable of flashing these enormous data volumes without slowing down automated factory assembly lines.
Regulatory Cyber-Security Mandates:
Global regulatory bodies, such as strict EU automotive cybersecurity directives, now legally compel manufacturers to secure hardware devices directly at the factory root level. Because cryptographic keys can no longer be safely injected later via post-production software updates, they must be embedded securely during the initial chip flashing phase (Security Provisioning).
$DAIO is the only market provider that has seamlessly merged this complex cryptographic identity injection directly into high-speed, high-volume automated manufacturing lines.
The Paradigm Shift to UFS Storage:
Legacy flash memory standards are rapidly disappearing. Modern connected vehicles, autonomous driving suites (ADAS), and Edge AI devices require massive Universal Flash Storage (UFS) capacities, handling gigabytes of data instead of megabytes. Competitors hit physical speed limits trying to flash these volumes. Powered by patented VerifyBoosttechnology (delivering verification speeds up to 750 MB/s), Data I/O's LumenX and LumenX2 architectures are the only platforms capable of flashing these enormous data volumes without slowing down automated factory assembly lines.
Regulatory Cyber-Security Mandates:
Global regulatory bodies, such as strict EU automotive cybersecurity directives, now legally compel manufacturers to secure hardware devices directly at the factory root level. Because cryptographic keys can no longer be safely injected later via post-production software updates, they must be embedded securely during the initial chip flashing phase (Security Provisioning).
$DAIO is the only market provider that has seamlessly merged this complex cryptographic identity injection directly into high-speed, high-volume automated manufacturing lines.
Yes, great time for $INFQ, $LAES and $WKEY.
„Full migration to quantum-resistant encryption protocols for high-value targets by 2031.“
„Full migration to quantum-resistant encryption protocols for high-value targets by 2031.“
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Google just lost the AI race.
Seedance 2.0 in 4k is insane, and it's genuinely 10x better than Google's Omni video gen model.
Seedance 2.0 can now produce 30-second clips in 4K.
Fable moment for AI video generation.
Seedance 2.0 in 4k is insane, and it's genuinely 10x better than Google's Omni video gen model.
Seedance 2.0 can now produce 30-second clips in 4K.
Fable moment for AI video generation.
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This humanoid robot has been working since 2023!
Continuous loop, while being streamed.
Everyone's talking about Figure robots, but this Digit bot is crazy impressive.
Continuous loop, while being streamed.
Everyone's talking about Figure robots, but this Digit bot is crazy impressive.
Global Robotics Snapshot
Robotics is already a numbers story.
4.66M industrial robots are now in service worldwide.
Factory Robots
➤ 542K new industrial robots installed
➤ $16.7B installation value
China
➤ 295K new robots installed
➤ 54% global share
➤ 2M industrial robots in operation
Service Robots
➤ 200K professional units sold
➤ 102.9K logistics robots
➤ 20M consumer robots
Humanoids
➤ 16K installed in 2025
➤ 80%+ in China
➤ Unitree shipped 5.5K units
The robot economy is not waiting for humanoids.
Factories already run on robots.
Logistics is scaling fast.
Medical robots are growing.
China is building the largest installed base.
Humanoids are still early but the production race has started.
Robotics is already a numbers story.
4.66M industrial robots are now in service worldwide.
Factory Robots
➤ 542K new industrial robots installed
➤ $16.7B installation value
China
➤ 295K new robots installed
➤ 54% global share
➤ 2M industrial robots in operation
Service Robots
➤ 200K professional units sold
➤ 102.9K logistics robots
➤ 20M consumer robots
Humanoids
➤ 16K installed in 2025
➤ 80%+ in China
➤ Unitree shipped 5.5K units
The robot economy is not waiting for humanoids.
Factories already run on robots.
Logistics is scaling fast.
Medical robots are growing.
China is building the largest installed base.
Humanoids are still early but the production race has started.
Claude just entered the robodog lab.
Anthropic Project Fetch Phase Two shows Claude Opus 4.7 using Claude Code to connect to a robot dog video camera lidar sensor and write control code.
On the shared tasks Claude finished in 12 min 7 sec.
Team Claude took 264 min.
Claude still failed the hard part: precise autonomous ball retrieval.
1. The goal
Test whether Claude can use Claude Code to program a robot dog in a classic offline robotics setup.
2. The tasks
Claude had to help with:
Connecting the robodog video camera
Connecting the lidar sensor
Writing control code
Tracking the robot’s movement
Detecting a beach ball
Trying to bring the ball back automatically
3. The strongest result
On the tasks completed by humans in the first phase Claude Opus 4.7 was much faster.
Anthropic gives these numbers:
Team Claude-less: 361 min
Team Claude: 181 min
Claude Opus 4.7 alone: 9 min 35 sec
Across 5 tasks Claude Opus 4.7 finished in an average of 12 min 7 sec compared with 264 min for Team Claude.
4. The important limitation
Claude did not solve the hardest robotics part: pushing and retrieving the ball with precise autonomous control.
Anthropic clearly says this does not mean LLMs have solved robotics.
5. Why it matters
This is not a demo of a perfect autonomous robot.
It is a signal that models like Claude can start using existing physical tools through code camera lidar and robot control.
Anthropic describes this as an early step toward physical agentic AI.
But it is a clear signal for Physical AI.
Claude Anthropic Project Fetch Claude Opus 4.7 Claude Code AI agents robotics robodog robot dog quadruped robot autonomous robot lidar computer vision embodied AI robot control Physical AI
anthropic.com/research/project-fetch-phase-two
Anthropic Project Fetch Phase Two shows Claude Opus 4.7 using Claude Code to connect to a robot dog video camera lidar sensor and write control code.
On the shared tasks Claude finished in 12 min 7 sec.
Team Claude took 264 min.
Claude still failed the hard part: precise autonomous ball retrieval.
1. The goal
Test whether Claude can use Claude Code to program a robot dog in a classic offline robotics setup.
2. The tasks
Claude had to help with:
Connecting the robodog video camera
Connecting the lidar sensor
Writing control code
Tracking the robot’s movement
Detecting a beach ball
Trying to bring the ball back automatically
3. The strongest result
On the tasks completed by humans in the first phase Claude Opus 4.7 was much faster.
Anthropic gives these numbers:
Team Claude-less: 361 min
Team Claude: 181 min
Claude Opus 4.7 alone: 9 min 35 sec
Across 5 tasks Claude Opus 4.7 finished in an average of 12 min 7 sec compared with 264 min for Team Claude.
4. The important limitation
Claude did not solve the hardest robotics part: pushing and retrieving the ball with precise autonomous control.
Anthropic clearly says this does not mean LLMs have solved robotics.
5. Why it matters
This is not a demo of a perfect autonomous robot.
It is a signal that models like Claude can start using existing physical tools through code camera lidar and robot control.
Anthropic describes this as an early step toward physical agentic AI.
But it is a clear signal for Physical AI.
Claude Anthropic Project Fetch Claude Opus 4.7 Claude Code AI agents robotics robodog robot dog quadruped robot autonomous robot lidar computer vision embodied AI robot control Physical AI
anthropic.com/research/project-fetch-phase-two
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🇨🇭 Global One SI - Prompt
How to get the most out of Codex (OpenAI's hidden advice for unlocking maximum power).
If you're tired of overpaying for AI, you need to read this.
GLM-5,2 was recently launched, and it's the best cost-per-intelligence AI on the market.
I'm personally running a "barbell" approach with this new model to maximise both intelligence & cost.
Full GLM-5.2 guide
GLM-5,2 was recently launched, and it's the best cost-per-intelligence AI on the market.
I'm personally running a "barbell" approach with this new model to maximise both intelligence & cost.
Full GLM-5.2 guide
✍1
GLM-5.2 is the best cost-per-intelligence model on the market right now.
~80% cheaper than Claude, with ~95% of the intelligence of Opus 4.8.
Insane value.
Most people don't realize how huge a leap this really is.
GLM-5.2: $5.80 per million tokens, ~95% of frontier intelligence
GPT-5.5: ~$9.70, 92% intelligence
Opus 4.8: ~$23, 98% intelligence
Fable 5: ~$30, 100% intelligence
Plot it on a cost-vs-intelligence chart, and GLM-5.2 sits exactly where you want to be: high intelligence, low cost.
This is the first open-source model to hit frontier-level performance at a fraction of the price.
However, I'm still using Claude and other frontier models regularly with a "barbell" approach (which most people should be doing).
My barbell strategy:
Opus 4.8 for the initial 10% planning.
GLM-5.2 for the cheap 80% (grunt work, first drafts, repetitive tasks).
Opus 4.8 for the last-step verification.
~80% cheaper than Claude, with ~95% of the intelligence of Opus 4.8.
Insane value.
Most people don't realize how huge a leap this really is.
GLM-5.2: $5.80 per million tokens, ~95% of frontier intelligence
GPT-5.5: ~$9.70, 92% intelligence
Opus 4.8: ~$23, 98% intelligence
Fable 5: ~$30, 100% intelligence
Plot it on a cost-vs-intelligence chart, and GLM-5.2 sits exactly where you want to be: high intelligence, low cost.
This is the first open-source model to hit frontier-level performance at a fraction of the price.
However, I'm still using Claude and other frontier models regularly with a "barbell" approach (which most people should be doing).
My barbell strategy:
Opus 4.8 for the initial 10% planning.
GLM-5.2 for the cheap 80% (grunt work, first drafts, repetitive tasks).
Opus 4.8 for the last-step verification.
🇨🇭 Global One SI - Prompt
If you're tired of overpaying for AI, you need to read this. GLM-5,2 was recently launched, and it's the best cost-per-intelligence AI on the market. I'm personally running a "barbell" approach with this new model to maximise both intelligence & cost. Full…
Stop prompting GLM-5.2 wrong.
This model is NOT a general conversational LLM like Claude/GPT.
You need to prompt it completely differently for the best outputs.
This guide teaches you exactly how
This model is NOT a general conversational LLM like Claude/GPT.
You need to prompt it completely differently for the best outputs.
This guide teaches you exactly how
✍1
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When you're worrying about college exams but then realize you're a trader
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When Trump is tweeting about crypto but me and bro haven't placed a trade yet
$SIVE has quietly positioned itself as an indispensable monopoly supplier for the critical light source powering the future roadmaps of $AAPL, $NVDA, $AEVA, and #BostonDynamics.
By controlling the production of ultra-precise Indium Phosphide (InP) Distributed Feedback (DFB) laser arrays, $SIVE owns the technological bottleneck for next-generation consumer health wearables and autonomous physical artificial intelligence.
The most imminent validation of this thesis lies within the Frequency Modulated Continuous Wave (FMCW) 4D-LiDAR ecosystem.
Unlike traditional Time-of-Flight (ToF) sensors that rely on simple light pulses, FMCW technology requires a continuous, ultra-stable, and perfectly modulated laser beam to measure depth and instantaneous velocity simultaneously.
$SIVE is the global leader in manufacturing laser engines with the required sub-megahertz linewidth stability.
$SIVE multi-year development partnership with $AEVA has officially reached its commercial inflection point.
The company recently confirmed that a strategic automotive and sensing customer, heavily correlated with $AEVA, will transition into high-volume serial production in the fourth quarter of 2026.
This single production ramp is contractually projected to generate between $53 million and $138 million in cumulative revenue over its lifecycle, completely eclipsing $SIVE historic annual revenues.
This automotive win acts as a Trojan horse into the broader $NVDA and Boston Dynamics Physical AI ecosystems.
$AEVA 4D-LiDAR is deeply integrated into the $NVDA DRIVE Hyperion platform, which $NVDA pushes as the standardized autonomous architecture for global robotaxi and vehicle fleets.
Furthermore, this sensor technology bridges directly into humanoid robotics via LG Innotek, which maintains a multi-million-dollar alliance with $AEVA to manufacture complete vision-sensing modules.
Given LG Innotek’s recent landmark agreement to supply the advanced sensor platform for Boston Dynamics new, fully electric Atlashumanoid robot, $SIVE lasers are positioned to become the literal eyes of physical AI.
Because humanoids require a 360-degree situational awareness, each robot utilizes multiple laser arrays.
As these platforms are trained and deployed via $NVDA Isaac and Omniverse platforms, $SIVE scales exponentially with the volume of the entire smart-factory and robotics revolution.
Simultaneously, $SIVE holds the golden key to the most anticipated breakthrough in mass consumer healthcare: non-invasive blood glucose and multi-biomarker monitoring for wearables.
Recent updates to $SIVE official technical documentation highlighting "optical sensing modules for wearables" serve as a definitive, confidentiality-restricted signal pointing toward $AAPL.
To accurately isolate glucose molecules through human skin without drawing blood, a sensor cannot rely on standard infrared LEDs; it requires a hyper-dense, multi-wavelength laser setup to eliminate background blood noise.
$SIVE has perfected a proprietary architecture capable of driving up to 135 distinct wavelengths on a single, micro-scaled chip.
With $AAPL shipping between 38 million and 50 million Apple Watches annually, even a minor per-unit licensing or supply fee for this custom laser engine creates a multi-hundred-million-dollar high-margin revenue stream.
By fusing the contractually backed Q4 2026 automotive ramp with $AEVA and $NVDA, the structural expansion into Boston Dynamics' robotics, and the massive volume optionality of $AAPL consumer health roadmap, $SIVE has secured a multi-front monopoly on tomorrow's tech.
As the company prepares for its upcoming US dual-listing on the Nasdaq New York, the massive discrepancy between $SIVE small market capitalization and its near-term $800 million opportunity pipeline represents a generational buying opportunity for growth investors.
By controlling the production of ultra-precise Indium Phosphide (InP) Distributed Feedback (DFB) laser arrays, $SIVE owns the technological bottleneck for next-generation consumer health wearables and autonomous physical artificial intelligence.
The most imminent validation of this thesis lies within the Frequency Modulated Continuous Wave (FMCW) 4D-LiDAR ecosystem.
Unlike traditional Time-of-Flight (ToF) sensors that rely on simple light pulses, FMCW technology requires a continuous, ultra-stable, and perfectly modulated laser beam to measure depth and instantaneous velocity simultaneously.
$SIVE is the global leader in manufacturing laser engines with the required sub-megahertz linewidth stability.
$SIVE multi-year development partnership with $AEVA has officially reached its commercial inflection point.
The company recently confirmed that a strategic automotive and sensing customer, heavily correlated with $AEVA, will transition into high-volume serial production in the fourth quarter of 2026.
This single production ramp is contractually projected to generate between $53 million and $138 million in cumulative revenue over its lifecycle, completely eclipsing $SIVE historic annual revenues.
This automotive win acts as a Trojan horse into the broader $NVDA and Boston Dynamics Physical AI ecosystems.
$AEVA 4D-LiDAR is deeply integrated into the $NVDA DRIVE Hyperion platform, which $NVDA pushes as the standardized autonomous architecture for global robotaxi and vehicle fleets.
Furthermore, this sensor technology bridges directly into humanoid robotics via LG Innotek, which maintains a multi-million-dollar alliance with $AEVA to manufacture complete vision-sensing modules.
Given LG Innotek’s recent landmark agreement to supply the advanced sensor platform for Boston Dynamics new, fully electric Atlashumanoid robot, $SIVE lasers are positioned to become the literal eyes of physical AI.
Because humanoids require a 360-degree situational awareness, each robot utilizes multiple laser arrays.
As these platforms are trained and deployed via $NVDA Isaac and Omniverse platforms, $SIVE scales exponentially with the volume of the entire smart-factory and robotics revolution.
Simultaneously, $SIVE holds the golden key to the most anticipated breakthrough in mass consumer healthcare: non-invasive blood glucose and multi-biomarker monitoring for wearables.
Recent updates to $SIVE official technical documentation highlighting "optical sensing modules for wearables" serve as a definitive, confidentiality-restricted signal pointing toward $AAPL.
To accurately isolate glucose molecules through human skin without drawing blood, a sensor cannot rely on standard infrared LEDs; it requires a hyper-dense, multi-wavelength laser setup to eliminate background blood noise.
$SIVE has perfected a proprietary architecture capable of driving up to 135 distinct wavelengths on a single, micro-scaled chip.
With $AAPL shipping between 38 million and 50 million Apple Watches annually, even a minor per-unit licensing or supply fee for this custom laser engine creates a multi-hundred-million-dollar high-margin revenue stream.
By fusing the contractually backed Q4 2026 automotive ramp with $AEVA and $NVDA, the structural expansion into Boston Dynamics' robotics, and the massive volume optionality of $AAPL consumer health roadmap, $SIVE has secured a multi-front monopoly on tomorrow's tech.
As the company prepares for its upcoming US dual-listing on the Nasdaq New York, the massive discrepancy between $SIVE small market capitalization and its near-term $800 million opportunity pipeline represents a generational buying opportunity for growth investors.
👀1
🇨🇭 Global One SI - Prompt
#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" 😉
#WTI If we compare oil prices directly to 2008, the technicals point to roughly the 2nd week of August.
If you don't remember, the stock market actually began rolling over mid-August, while the crash didn't occur until the beginning of September.
What does this mean?
Well that part is still unclear. The market indices are not in a descending pattern of lower lows and lower highs like 2008.
So what we have right now is oil similar to August 2008 and the markets similar to February 2020.
If you don't remember, the stock market actually began rolling over mid-August, while the crash didn't occur until the beginning of September.
What does this mean?
Well that part is still unclear. The market indices are not in a descending pattern of lower lows and lower highs like 2008.
So what we have right now is oil similar to August 2008 and the markets similar to February 2020.
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
🚨Trump ordered a DOJ investigation into Big Oil over gasoline prices that stay high while crude drops.
His warning: "Gasoline prices better start going down a lot faster than what I'm seeing."
The driver is political.
High pump prices are hurting him in the polls, and he wants them down now.
Pump prices shoot up fast when crude rises. They drift down slowly when crude falls.
Refiners and retailers pocket the lag, widening margins on the way down.
This isn't new, drivers have noticed it for years andow a president is naming it.
For refiners like PSX, VLO, and MPC, the risk is margin compression if the lag gets regulated away.
Watch crack spreads into summer. If political pressure forces faster pass-through, refining margins are the line item that gets squeezed.
His warning: "Gasoline prices better start going down a lot faster than what I'm seeing."
The driver is political.
High pump prices are hurting him in the polls, and he wants them down now.
Pump prices shoot up fast when crude rises. They drift down slowly when crude falls.
Refiners and retailers pocket the lag, widening margins on the way down.
This isn't new, drivers have noticed it for years andow a president is naming it.
For refiners like PSX, VLO, and MPC, the risk is margin compression if the lag gets regulated away.
Watch crack spreads into summer. If political pressure forces faster pass-through, refining margins are the line item that gets squeezed.
🇨🇭 Global One SI - Prompt
Claude when it comes to killing startups
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When they're ready to pop this bubble, I still believe it will be a cyber-attack specifically targeted at BlackRock.
That will be the excuse for pension funds to be locked in on the way down.
They'll blame Iran but obviously we know who the real "attacker" will be.
That will be the excuse for pension funds to be locked in on the way down.
They'll blame Iran but obviously we know who the real "attacker" will be.
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