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Moon Dev
Just added 2 more openclaws to my swarm
Mac minis are becoming hard to find https://t.co/xjcVQd9w7U
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Just added 2 more openclaws to my swarm
Mac minis are becoming hard to find https://t.co/xjcVQd9w7U
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Javier Blas
US Energy Secretary Chris Wright tells me he sees Venezuelan production up 30-40% by year-end from current level (that's ~270,000-360,000 b/d extra).
Last month, I wrote this @Opinion column suggesting there're "low hanging oil barrels" in Venezuela ⬇️⬇️
https://t.co/ptdquUaohr
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US Energy Secretary Chris Wright tells me he sees Venezuelan production up 30-40% by year-end from current level (that's ~270,000-360,000 b/d extra).
Last month, I wrote this @Opinion column suggesting there're "low hanging oil barrels" in Venezuela ⬇️⬇️
https://t.co/ptdquUaohr
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Dimitry Nakhla | Babylon Capital®
Moody’s $MCO Q4 25’ Report🗓️
✅ REV: $1.89B (+13% YoY)
✅ EPS: $3.64 (+39% YoY) https://t.co/CNJFpAduw1
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Moody’s $MCO Q4 25’ Report🗓️
✅ REV: $1.89B (+13% YoY)
✅ EPS: $3.64 (+39% YoY) https://t.co/CNJFpAduw1
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God of Prompt
RT @godofprompt: 🚨 Holy shit… Stanford just published a paper that questions whether we even need humans to study humans.
The title sounds like a joke:
“This human study did not involve human subjects.”
But it’s dead serious.
The researchers are asking a controversial question:
Can LLM simulations count as behavioral evidence?
Here’s the core idea.
Instead of recruiting thousands of participants, running surveys, and waiting weeks for results, they simulate people using large language models.
Not generic prompts.
But structured simulations where the model is assigned demographic traits, preferences, beliefs, and contextual constraints.
Then they test whether the simulated responses statistically match real-world human data.
And disturbingly… they often do.
Across multiple behavioral tasks, the LLM-generated “participants” reproduced known human patterns:
• Established psychological biases
• Preference distributions
• Decision-making trends
• Even demographic splits
Not perfectly. Not universally.
But far closer than most people would expect.
The key contribution of the paper isn’t “LLMs are human.”
It’s validation.
They systematically compare simulated outputs to ground-truth human datasets and evaluate alignment using statistical benchmarks.
When the distributions match, the simulation isn’t just storytelling.
It becomes empirical evidence.
That’s the uncomfortable shift.
If a sufficiently constrained LLM simulation reproduces real behavioral patterns, does it become a legitimate experimental proxy?
Because if the answer is yes, this changes everything:
• Behavioral economics
• Political science
• Market research
• Policy testing
• UX experimentation
You could prototype social interventions before deploying them in the real world.
You could stress-test messaging strategies across simulated demographics.
You could explore rare edge-case populations without recruitment bottlenecks.
But here’s where Stanford is careful.
The models don’t “understand” humans.
They reflect training data patterns.
They can amplify biases.
They can collapse under distribution shift.
And they can simulate plausibility without causality.
So the paper doesn’t claim replacement.
It argues for calibration.
LLM simulations can be useful behavioral instruments if validated against real data and bounded within known limits.
That’s the distinction.
Not synthetic humans.
Synthetic behavioral priors.
The wild part?
This paper forces academia to confront something bigger:
If large models encode large-scale behavioral regularities from the internet, they become compressed maps of human tendencies.
Not minds.
Maps.
And maps can be useful.
We’re moving from “AI as text generator” to “AI as behavioral simulator.”
The ethics, methodology, and epistemology implications are massive.
Because once simulation becomes statistically reliable, the bottleneck in social science shifts from data collection to model alignment.
And that might be the real revolution hidden in this paper.
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RT @godofprompt: 🚨 Holy shit… Stanford just published a paper that questions whether we even need humans to study humans.
The title sounds like a joke:
“This human study did not involve human subjects.”
But it’s dead serious.
The researchers are asking a controversial question:
Can LLM simulations count as behavioral evidence?
Here’s the core idea.
Instead of recruiting thousands of participants, running surveys, and waiting weeks for results, they simulate people using large language models.
Not generic prompts.
But structured simulations where the model is assigned demographic traits, preferences, beliefs, and contextual constraints.
Then they test whether the simulated responses statistically match real-world human data.
And disturbingly… they often do.
Across multiple behavioral tasks, the LLM-generated “participants” reproduced known human patterns:
• Established psychological biases
• Preference distributions
• Decision-making trends
• Even demographic splits
Not perfectly. Not universally.
But far closer than most people would expect.
The key contribution of the paper isn’t “LLMs are human.”
It’s validation.
They systematically compare simulated outputs to ground-truth human datasets and evaluate alignment using statistical benchmarks.
When the distributions match, the simulation isn’t just storytelling.
It becomes empirical evidence.
That’s the uncomfortable shift.
If a sufficiently constrained LLM simulation reproduces real behavioral patterns, does it become a legitimate experimental proxy?
Because if the answer is yes, this changes everything:
• Behavioral economics
• Political science
• Market research
• Policy testing
• UX experimentation
You could prototype social interventions before deploying them in the real world.
You could stress-test messaging strategies across simulated demographics.
You could explore rare edge-case populations without recruitment bottlenecks.
But here’s where Stanford is careful.
The models don’t “understand” humans.
They reflect training data patterns.
They can amplify biases.
They can collapse under distribution shift.
And they can simulate plausibility without causality.
So the paper doesn’t claim replacement.
It argues for calibration.
LLM simulations can be useful behavioral instruments if validated against real data and bounded within known limits.
That’s the distinction.
Not synthetic humans.
Synthetic behavioral priors.
The wild part?
This paper forces academia to confront something bigger:
If large models encode large-scale behavioral regularities from the internet, they become compressed maps of human tendencies.
Not minds.
Maps.
And maps can be useful.
We’re moving from “AI as text generator” to “AI as behavioral simulator.”
The ethics, methodology, and epistemology implications are massive.
Because once simulation becomes statistically reliable, the bottleneck in social science shifts from data collection to model alignment.
And that might be the real revolution hidden in this paper.
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DAIR.AI
RT @omarsar0: // From Vibe Coding to Agentic Engineering //
GLM-5 is a foundation model designed to transition from vibe coding to agentic engineering.
The model introduces novel asynchronous agent RL algorithms that enable learning from complex, long-horizon interactions. It also adopts DSA to reduce computational costs while preserving long-context performance.
The key contribution is an asynchronous RL infrastructure that decouples generation from training, allowing the model to learn from extended agentic workflows rather than short isolated tasks.
GLM-5 demonstrates strong performance on standard benchmarks and surpasses previous baselines in handling end-to-end software engineering challenges.
Paper: https://t.co/pl50bRSXVR
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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RT @omarsar0: // From Vibe Coding to Agentic Engineering //
GLM-5 is a foundation model designed to transition from vibe coding to agentic engineering.
The model introduces novel asynchronous agent RL algorithms that enable learning from complex, long-horizon interactions. It also adopts DSA to reduce computational costs while preserving long-context performance.
The key contribution is an asynchronous RL infrastructure that decouples generation from training, allowing the model to learn from extended agentic workflows rather than short isolated tasks.
GLM-5 demonstrates strong performance on standard benchmarks and surpasses previous baselines in handling end-to-end software engineering challenges.
Paper: https://t.co/pl50bRSXVR
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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DAIR.AI
RT @dair_ai: A paper worth paying close attention to.
It presents Lossless Context Management (LCM), which reframes how agents handle long contexts.
It outperforms Claude Code on long-context tasks.
Recursive Language Models give the model full autonomy to write its own memory scripts. LCM takes that power back, handing it to a deterministic engine that compresses old messages into a hierarchical DAG while keeping lossless pointers to every original. Less expressive in theory, far more reliable in practice.
The results:
Their agent (Volt, on Opus 4.6) beats Claude Code at *every* context length from 32K to 1M tokens on the OOLONG benchmark. +29.2 points average improvement versus Claude Code's +24.7. The gap widens at longer contexts.
The implication is one we keep relearning from software engineering history: how you manage what the model sees may matter more than giving the model tools to manage it itself. Every agent framework shipping with "let the model figure it out" memory strategies may be building on the wrong abstraction entirely.
Paper: https://t.co/LtqS7pzmP4
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
tweet
RT @dair_ai: A paper worth paying close attention to.
It presents Lossless Context Management (LCM), which reframes how agents handle long contexts.
It outperforms Claude Code on long-context tasks.
Recursive Language Models give the model full autonomy to write its own memory scripts. LCM takes that power back, handing it to a deterministic engine that compresses old messages into a hierarchical DAG while keeping lossless pointers to every original. Less expressive in theory, far more reliable in practice.
The results:
Their agent (Volt, on Opus 4.6) beats Claude Code at *every* context length from 32K to 1M tokens on the OOLONG benchmark. +29.2 points average improvement versus Claude Code's +24.7. The gap widens at longer contexts.
The implication is one we keep relearning from software engineering history: how you manage what the model sees may matter more than giving the model tools to manage it itself. Every agent framework shipping with "let the model figure it out" memory strategies may be building on the wrong abstraction entirely.
Paper: https://t.co/LtqS7pzmP4
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
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Dimitry Nakhla | Babylon Capital®
RT @patrick_oshag: This is my second conversation with @JoshuaKushner.
Josh started Thrive in 2011 and the firm now manages ~$50 billion. We cover the iconic investments that defined it: Instagram, Stripe, GitHub, and spend a lot of time on OpenAI. He explains how Thrive thinks about investing today and the three categories they're currently focused on.
Josh also talks about how he built the firm – why they keep the team so small, why concentration is core to what they do, and what he's learned from A24 about enabling artists to create their best work.
Throughout the conversation, Josh shares the personal stories that shaped him, from his grandmother surviving the Holocaust to lessons from Stan Druckenmiller and Jon Winkelried at formative moments in Thrive's history.
Enjoy!
https://t.co/B0ZMk6Oydo
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RT @patrick_oshag: This is my second conversation with @JoshuaKushner.
Josh started Thrive in 2011 and the firm now manages ~$50 billion. We cover the iconic investments that defined it: Instagram, Stripe, GitHub, and spend a lot of time on OpenAI. He explains how Thrive thinks about investing today and the three categories they're currently focused on.
Josh also talks about how he built the firm – why they keep the team so small, why concentration is core to what they do, and what he's learned from A24 about enabling artists to create their best work.
Throughout the conversation, Josh shares the personal stories that shaped him, from his grandmother surviving the Holocaust to lessons from Stan Druckenmiller and Jon Winkelried at formative moments in Thrive's history.
Enjoy!
https://t.co/B0ZMk6Oydo
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Offshore
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: Daniel Loeb has bought & completely sold $META four separate times over the past decade.
𝙃𝙖𝙙 𝙝𝙚 𝙨𝙞𝙢𝙥𝙡𝙮 𝙝𝙚𝙡𝙙 his original 3.75M shares, that stake would be worth roughly $2.40B today — nearly 1/3 of Third Point’s reported current assets (latest Q4 ’25 13F).
___
No — this is not a knock on Loeb.
He’s far smarter & more successful than me.
𝐁𝐮𝐭 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐚𝐧 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐥𝐞𝐬𝐬𝐨𝐧 𝐡𝐞𝐫𝐞:
For those of us fortunate enough to own a truly exceptional business, the hardest (yet often most profitable) strategy can be:
𝐃𝐨𝐢𝐧𝐠 𝐧𝐨𝐭𝐡𝐢𝐧𝐠.
As Charlie Munger said:
“𝘐𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘪𝘴 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘧𝘪𝘯𝘥 𝘢 𝘧𝘦𝘸 𝘨𝘳𝘦𝘢𝘵 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘴𝘪𝘵 𝘰𝘯 𝘺𝘰𝘶𝘳 𝘢𝘴𝘴.”
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RT @DimitryNakhla: Daniel Loeb has bought & completely sold $META four separate times over the past decade.
𝙃𝙖𝙙 𝙝𝙚 𝙨𝙞𝙢𝙥𝙡𝙮 𝙝𝙚𝙡𝙙 his original 3.75M shares, that stake would be worth roughly $2.40B today — nearly 1/3 of Third Point’s reported current assets (latest Q4 ’25 13F).
___
No — this is not a knock on Loeb.
He’s far smarter & more successful than me.
𝐁𝐮𝐭 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐚𝐧 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐥𝐞𝐬𝐬𝐨𝐧 𝐡𝐞𝐫𝐞:
For those of us fortunate enough to own a truly exceptional business, the hardest (yet often most profitable) strategy can be:
𝐃𝐨𝐢𝐧𝐠 𝐧𝐨𝐭𝐡𝐢𝐧𝐠.
As Charlie Munger said:
“𝘐𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘪𝘴 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘧𝘪𝘯𝘥 𝘢 𝘧𝘦𝘸 𝘨𝘳𝘦𝘢𝘵 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘴𝘪𝘵 𝘰𝘯 𝘺𝘰𝘶𝘳 𝘢𝘴𝘴.”
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