Javier Blas
“I have begun confidential talks with the French president on European nuclear deterrence" -- Chancellor Friedrich Merz
If would be surreal if Germany was to get a nuclear bomb -- or put itself under a French nuclear umbrella -- before it re-start its nuclear power stations.
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“I have begun confidential talks with the French president on European nuclear deterrence" -- Chancellor Friedrich Merz
If would be surreal if Germany was to get a nuclear bomb -- or put itself under a French nuclear umbrella -- before it re-start its nuclear power stations.
tweet
Offshore
Photo
Dimitry Nakhla | Babylon Capital®
Triple Frond Partners Q4 25’ 13F (Dataroma)
Top 5 holdings: $MSFT $TDG $ASML $GOOG $AMZN
Top Buys: $TDG $TRU $CCS $LYV
Top Sales: $GOOG $LRCX https://t.co/ijZo9ps9Zi
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Triple Frond Partners Q4 25’ 13F (Dataroma)
Top 5 holdings: $MSFT $TDG $ASML $GOOG $AMZN
Top Buys: $TDG $TRU $CCS $LYV
Top Sales: $GOOG $LRCX https://t.co/ijZo9ps9Zi
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Offshore
Video
DAIR.AI
RT @omarsar0: Excited to present the LLM-Council skill.
Initial idea by Karpathy. I just packaged it as a skill.
You can easily spin up a council of LLMs or agents via @FireworksAI_HQ.
Watch how the new GLM-5 model "deliberates" on other LLMs' thoughts on the big question, "Can LLMs reason?"
Things worth paying attention to:
New open models like GLM-5 have surprisingly improved on complex reasoning and long-running agentic tasks.
The AskUserQuestion tool in Claude Code came in handy to select the council and chairperson.
As @karpathy puts it, it's a really interesting way to get different perspectives from LLMs, which can lead to better decision-making on whatever task you are working on.
You can use it for other agentic coding use cases, like evaluation, tool building, designing, and research.
tweet
RT @omarsar0: Excited to present the LLM-Council skill.
Initial idea by Karpathy. I just packaged it as a skill.
You can easily spin up a council of LLMs or agents via @FireworksAI_HQ.
Watch how the new GLM-5 model "deliberates" on other LLMs' thoughts on the big question, "Can LLMs reason?"
Things worth paying attention to:
New open models like GLM-5 have surprisingly improved on complex reasoning and long-running agentic tasks.
The AskUserQuestion tool in Claude Code came in handy to select the council and chairperson.
As @karpathy puts it, it's a really interesting way to get different perspectives from LLMs, which can lead to better decision-making on whatever task you are working on.
You can use it for other agentic coding use cases, like evaluation, tool building, designing, and research.
tweet
Offshore
Photo
God of Prompt
CLAUDE IS SO COOKED THIS TIME
China just dropped Kimi K2.5, the best open model for OpenClaw(ClawdBot)
It's on par with Claude Opus4.5,
but 8x CHEAPER!!!
It's currently the #1 most used model for OpenClaw and the #1 most used model overall on OpenRouter!
Here's everything you should know:
tweet
CLAUDE IS SO COOKED THIS TIME
China just dropped Kimi K2.5, the best open model for OpenClaw(ClawdBot)
It's on par with Claude Opus4.5,
but 8x CHEAPER!!!
It's currently the #1 most used model for OpenClaw and the #1 most used model overall on OpenRouter!
Here's everything you should know:
tweet
Brady Long
RT @thisguyknowsai: The only thing that can kill your startup is you.
And it will most likely be because you spent too much time comparing yourself to the next guy or girl.
tweet
RT @thisguyknowsai: The only thing that can kill your startup is you.
And it will most likely be because you spent too much time comparing yourself to the next guy or girl.
tweet
Offshore
Photo
Benjamin Hernandez😎
$BROS: Caffeinated Growth is Scaling
Dutch Bros ($BROS) pops +18% pre-market. Revenue surged 29% in Q4 and the 2026 guidance of $2B+ suggests a massive retail expansion. While peers struggle, $BROS is scaling without friction.
My watchlist is live.
$IREN $OPEN $RKLB $ASTS $F https://t.co/1Rtr44s8wt
tweet
$BROS: Caffeinated Growth is Scaling
Dutch Bros ($BROS) pops +18% pre-market. Revenue surged 29% in Q4 and the 2026 guidance of $2B+ suggests a massive retail expansion. While peers struggle, $BROS is scaling without friction.
My watchlist is live.
$IREN $OPEN $RKLB $ASTS $F https://t.co/1Rtr44s8wt
tweet
Offshore
Photo
Lumida Wealth Management
Nick Leeson was 28 years old.
Worked in the basement of Barings Bank's Singapore office.
Lost $1.4 billion in unauthorized trades.
Destroyed a 233-year-old bank in 3 years.
Britain's oldest merchant bank. Financed the Napoleonic Wars. Survived two World Wars.
Couldn't survive one trader in Singapore.
tweet
Nick Leeson was 28 years old.
Worked in the basement of Barings Bank's Singapore office.
Lost $1.4 billion in unauthorized trades.
Destroyed a 233-year-old bank in 3 years.
Britain's oldest merchant bank. Financed the Napoleonic Wars. Survived two World Wars.
Couldn't survive one trader in Singapore.
tweet
Offshore
Photo
Wasteland Capital
Inflation ice cold at +2.4%, 🥶 like the winter storm, even in the BLS numbers (Truflation is at +0.7%).
Shelter, healthcare, electricity & restaurants still big drivers annually, while energy saw a big drop (except piped gas!). Transports jumped m/m. Whataboutthem tariffs, huh? https://t.co/Pq8N1tTlVE
tweet
Inflation ice cold at +2.4%, 🥶 like the winter storm, even in the BLS numbers (Truflation is at +0.7%).
Shelter, healthcare, electricity & restaurants still big drivers annually, while energy saw a big drop (except piped gas!). Transports jumped m/m. Whataboutthem tariffs, huh? https://t.co/Pq8N1tTlVE
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