Illiquid
This tweet had 95 impressions and four likes. Ofc, the stock is up 124% since then.
tweet
This tweet had 95 impressions and four likes. Ofc, the stock is up 124% since then.
AI Mechatec (6227.T) has an order backlog of $284M, exceeding its entire market cap ($187M) by roughly 50%. 🧐🤏 tweet
X (formerly Twitter)
Jaraxle (@hyperuniformity) on X
AI Mechatec (6227.T) has an order backlog of $284M, exceeding its entire market cap ($187M) by roughly 50%. 🧐🤏
Offshore
Photo
Jukan
Just tried Claude Cowork now… and I seriously regret not using it sooner. This thing is actually insane/crazy good. https://t.co/dGZKoB0gZ2
tweet
Just tried Claude Cowork now… and I seriously regret not using it sooner. This thing is actually insane/crazy good. https://t.co/dGZKoB0gZ2
tweet
Offshore
Video
Michael Fritzell (Asian Century Stocks)
RT @tradebutwhy: "if all the news is great and the stock is not acting well, get out, which is a pretty simple thing that for some reason most analysts don’t know"
- Stanley Druckenmiller
tweet
RT @tradebutwhy: "if all the news is great and the stock is not acting well, get out, which is a pretty simple thing that for some reason most analysts don’t know"
- Stanley Druckenmiller
Cant this guy just shut the fuck up
https://t.co/FTxYefISI6 tweet
Offshore
Photo
Michael Fritzell (Asian Century Stocks)
RT @neilksethi: BoA's Hartnett (Flow Show) on Japan:
Japan yen & Nikkei price correlation just flipped positive 1st time since 2005 (Chart 2)… nothing says “secular bull” more than FX up, stocks up (see Japan '82-'90, Germany '85-'95, China '00-'08);
but short-term Japan yen adds to crypto, silver, PE, software, energy, unwind pain; can’t have disorderly yen surge now (i.e., JPY below 145)… hits global liquidity & has always coincided with global deleveraging.
tweet
RT @neilksethi: BoA's Hartnett (Flow Show) on Japan:
Japan yen & Nikkei price correlation just flipped positive 1st time since 2005 (Chart 2)… nothing says “secular bull” more than FX up, stocks up (see Japan '82-'90, Germany '85-'95, China '00-'08);
but short-term Japan yen adds to crypto, silver, PE, software, energy, unwind pain; can’t have disorderly yen surge now (i.e., JPY below 145)… hits global liquidity & has always coincided with global deleveraging.
tweet
Offshore
Photo
Javier Blas
CHART OF THE DAY: Among the world's top oil consumers, a curious trend.
The 2nd largest consumption drop last year ocurred in Saudi Arabia, where demand fell ~60,000 b/d (only South Korea saw a larger drop). The reason? Gas is displacing oil in electricity generation.
1/2 https://t.co/MzJRLq2NRW
tweet
CHART OF THE DAY: Among the world's top oil consumers, a curious trend.
The 2nd largest consumption drop last year ocurred in Saudi Arabia, where demand fell ~60,000 b/d (only South Korea saw a larger drop). The reason? Gas is displacing oil in electricity generation.
1/2 https://t.co/MzJRLq2NRW
tweet
Michael Fritzell (Asian Century Stocks)
RT @DrDavidKass: 9 out of the 10 largest companies (market capitalization) are down year-to-date. Only Taiwan Semiconductor is up (+20.6%).
tweet
RT @DrDavidKass: 9 out of the 10 largest companies (market capitalization) are down year-to-date. Only Taiwan Semiconductor is up (+20.6%).
tweet
Offshore
Photo
Michael Fritzell (Asian Century Stocks)
RT @bhaddhadon: $4691.T Washington Hotels
No Chinese tourists. No problem.
Healthy domestic biz and domestic leisure demand.
FY26 Operating profit forecast: 3.76Bn
Market cap: 18.6 Bn https://t.co/m0RrZCa7nx
tweet
RT @bhaddhadon: $4691.T Washington Hotels
No Chinese tourists. No problem.
Healthy domestic biz and domestic leisure demand.
FY26 Operating profit forecast: 3.76Bn
Market cap: 18.6 Bn https://t.co/m0RrZCa7nx
$4691.T Washington Hotels
Occupancy rate and revPar looks great 2Q into FY2026.
Already meets 70% of FY net income guidance.
Likely there would be an upward revision down the road. https://t.co/voLHIuNrjm tweet
Offshore
Photo
God of Prompt
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."
And it quietly exposes why 99% of AI agents will fail in the real world.
Here’s the paper:
Most “AI agents” today aren’t agents.
They’re glorified task runners.
You give them a goal.
They break it into steps.
They call tools.
They return an output.
That’s not delegation.
That’s automation with better marketing.
Google’s paper makes a brutal point:
Delegation isn’t just splitting tasks.
It’s transferring authority, responsibility, accountability, and trust across agents dynamically.
And almost no current system does this.
Here’s what they argue real delegation actually requires:
1. Dynamic assessment
Before assigning a task, an agent must evaluate:
- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility
Not just “who has the tool?”
But: “Who should be trusted with this specific task under these constraints?”
That’s a massive shift.
2. Adaptive execution
If the delegatee underperforms…
You don’t wait for failure.
You reassign mid-execution.
Switch agents.
Escalate to a human.
Restructure the task graph.
Current agents are brittle.
Real agents need recovery logic.
3. Structural transparency
Today’s AI-to-AI delegation is opaque.
If something fails, you don’t know:
- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?
The paper proposes enforced auditability and verifiable completion.
In other words:
Agents must prove what they did.
Not just say they did it.
4. Trust calibration
This is huge.
Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.
Delegation must align trust with actual capability.
Too much trust = catastrophe.
Too little trust = wasted potential.
5. Systemic resilience
This is the part nobody is talking about.
If every agent delegates to the same high-performing model…
You create a monoculture.
One failure.
System-wide collapse.
Efficiency without redundancy = fragility.
Google explicitly warns about cascading failures in agentic economies.
That’s not sci-fi.
That’s distributed systems reality.
The paper also breaks down:
- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models
This isn’t a toy-agent paper.
It’s an operating system blueprint for the “agentic web.”
The core idea:
Delegation must be a protocol.
Not a prompt.
Right now, most “multi-agent systems” are:
Agent A → Agent B → Agent C
With zero formal responsibility structure.
In a real delegation framework:
• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable
That’s enterprise-grade infrastructure.
And we don’t have it yet.
The most important line in the paper?
Automation is not just about what AI can do.
It’s about what AI *should* do.
That distinction will decide:
- which startups survive
- which enterprises scale
- which ai deployments implode
We’re entering the phase where:
Prompt engineering → Agent engineering → Delegation engineering.
The companies that figure out intelligent delegation protocols first will build:
• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms
Everyone else will ship brittle demos.
This paper isn’t flashy.
No benchmarks.
No model release.
No hype numbers.
Just a 42-page warning:
If we don’t build adaptive, accountable delegation frameworks…
The agentic web collapses under its own complexity.
And honestly?
They’re probably right. tweet
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."
And it quietly exposes why 99% of AI agents will fail in the real world.
Here’s the paper:
Most “AI agents” today aren’t agents.
They’re glorified task runners.
You give them a goal.
They break it into steps.
They call tools.
They return an output.
That’s not delegation.
That’s automation with better marketing.
Google’s paper makes a brutal point:
Delegation isn’t just splitting tasks.
It’s transferring authority, responsibility, accountability, and trust across agents dynamically.
And almost no current system does this.
Here’s what they argue real delegation actually requires:
1. Dynamic assessment
Before assigning a task, an agent must evaluate:
- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility
Not just “who has the tool?”
But: “Who should be trusted with this specific task under these constraints?”
That’s a massive shift.
2. Adaptive execution
If the delegatee underperforms…
You don’t wait for failure.
You reassign mid-execution.
Switch agents.
Escalate to a human.
Restructure the task graph.
Current agents are brittle.
Real agents need recovery logic.
3. Structural transparency
Today’s AI-to-AI delegation is opaque.
If something fails, you don’t know:
- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?
The paper proposes enforced auditability and verifiable completion.
In other words:
Agents must prove what they did.
Not just say they did it.
4. Trust calibration
This is huge.
Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.
Delegation must align trust with actual capability.
Too much trust = catastrophe.
Too little trust = wasted potential.
5. Systemic resilience
This is the part nobody is talking about.
If every agent delegates to the same high-performing model…
You create a monoculture.
One failure.
System-wide collapse.
Efficiency without redundancy = fragility.
Google explicitly warns about cascading failures in agentic economies.
That’s not sci-fi.
That’s distributed systems reality.
The paper also breaks down:
- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models
This isn’t a toy-agent paper.
It’s an operating system blueprint for the “agentic web.”
The core idea:
Delegation must be a protocol.
Not a prompt.
Right now, most “multi-agent systems” are:
Agent A → Agent B → Agent C
With zero formal responsibility structure.
In a real delegation framework:
• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable
That’s enterprise-grade infrastructure.
And we don’t have it yet.
The most important line in the paper?
Automation is not just about what AI can do.
It’s about what AI *should* do.
That distinction will decide:
- which startups survive
- which enterprises scale
- which ai deployments implode
We’re entering the phase where:
Prompt engineering → Agent engineering → Delegation engineering.
The companies that figure out intelligent delegation protocols first will build:
• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms
Everyone else will ship brittle demos.
This paper isn’t flashy.
No benchmarks.
No model release.
No hype numbers.
Just a 42-page warning:
If we don’t build adaptive, accountable delegation frameworks…
The agentic web collapses under its own complexity.
And honestly?
They’re probably right. tweet
Michael Fritzell (Asian Century Stocks)
RT @firstadopter: Nvidia's Parakeet transcription models are remarkably good, almost like magic in speed and accuracy. How did they do this?
tweet
RT @firstadopter: Nvidia's Parakeet transcription models are remarkably good, almost like magic in speed and accuracy. How did they do this?
tweet
Offshore
Photo
Michael Fritzell (Asian Century Stocks)
RT @pitdesi: Interesting look at how an ultra-wealthy family spends money-
$72k on landscaping, $67k in wine/liquor for 2 months,
$154M in cash earning ~nothing
$484M loan against art at 1.43% interest! https://t.co/St9jPFeNvI
tweet
RT @pitdesi: Interesting look at how an ultra-wealthy family spends money-
$72k on landscaping, $67k in wine/liquor for 2 months,
$154M in cash earning ~nothing
$484M loan against art at 1.43% interest! https://t.co/St9jPFeNvI
Documents reveal the granular details of the billionaire Leon Black’s net worth, from 69 bank accounts to a $484 million loan backed by his art collection https://t.co/AGHQGAZGO9 tweet