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%).
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RT @DrDavidKass: 9 out of the 10 largest companies (market capitalization) are down year-to-date. Only Taiwan Semiconductor is up (+20.6%).
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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
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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
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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?
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RT @firstadopter: Nvidia's Parakeet transcription models are remarkably good, almost like magic in speed and accuracy. How did they do this?
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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
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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
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Brady Long
RT @thisguyknowsai: The era of “prompt and wait for a response” seems to be over.
As soon as I saw this I went immediately to Hugging Face to try it out. Nuts.
https://t.co/JlERquiIcQ
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RT @thisguyknowsai: The era of “prompt and wait for a response” seems to be over.
As soon as I saw this I went immediately to Hugging Face to try it out. Nuts.
https://t.co/JlERquiIcQ
MiniCPM-o 4.5: Seeing, Listening, and Speaking — All at Once. 👁️👂🗣️
✨Beyond traditional turn-taking, we’ve built a Native Full-Duplex engine that allows a 9B model to see, listen, and speak in one concurrent, non-blocking stream.
Watch how it masters real-world complexity in real-time:
🔔 Proactive Auditory Interaction: Interrupts itself to alert you when it hears a "Ding!" while reading cards.
🎨 Temporal Flow Tracking: Follows your pen in real-time, narrating and "mind-reading" your drawing as you sketch.
🍎 Omni-Perception: Scans groceries & identifies prices on the fly.
✨Why it’s a category-leader:
📌Performance: Surpasses GPT-4o and Gemini 2.0 Pro on OpenCompass (Avg. 77.6).
📌Architecture: End-to-end fusion of SigLip2, Whisper, and CosyVoice2 on a Qwen3-8B base.
📌Efficiency: Full-duplex live streaming now runs locally on PCs via llama.cpp-omni.
The era of "Wait-and-Response" AI is over. Proactive, real-time intelligence is now open-source.
🚀Experience it on Hugging Face: 🔗https://t.co/KzzgiGYhVr
#MiniCPM #Omnimodal #FullDuplex #EdgeAI #OpenSource #ComputerVision tweet
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The Transcript
RT @TheTranscript_: $ABNB CEO: "For the full year 2026, we expect year-over-year revenue growth to accelerate to low double digits with an ambition to grow even faster than that." https://t.co/g7P7M1w8bX
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RT @TheTranscript_: $ABNB CEO: "For the full year 2026, we expect year-over-year revenue growth to accelerate to low double digits with an ambition to grow even faster than that." https://t.co/g7P7M1w8bX
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Brady Long
R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
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R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
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God of Prompt
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
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RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
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Brady Long
RT @thisguyknowsai: this is funny, but it also made me think about how fast user expectations have shifted
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RT @thisguyknowsai: this is funny, but it also made me think about how fast user expectations have shifted
AI might forget. Thine does not. Keep remembering. https://t.co/GIL4Pv5qjs tweet