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Michael Fritzell (Asian Century Stocks)
Google Trends seems to be broken, presumably due to traffic from generative AI bots. Any fix? https://t.co/pufXKRSvxV
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Google Trends seems to be broken, presumably due to traffic from generative AI bots. Any fix? https://t.co/pufXKRSvxV
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Michael Fritzell (Asian Century Stocks)
RT @kos_data: 🇨🇳 Xi net approval rating in Europe:
Top fans:
🇷🇸 Serbia: 22
🇲🇩 Moldova: 15
🇬🇷 Greece: 8
Top haters:
🇸🇪 Sweden: -81
🇺🇦 Ukraine: -69
🇻🇦 Pope Leo XIV net approval rating in Europe:
Top fans:
🇭🇷 Croatia: 67
🇵🇱 Poland: 46
🇮🇪 Ireland: 44
Top haters:
🇹🇷 Turkey: -31
🇷🇸 Serbia: -19
🇮🇱 Netanyahu net approval rating in Europe:
Netanyahu only fans:
🇺🇦 Ukraine: 2
Top haters:
🇧🇦 Bosnia: -80
🇳🇴 Norway: -75
🇸🇪 Sweden: -71
🇮🇳 Modi net approval rating in Europe:
Top fans:
🇬🇷 Greece: 16
🇲🇩 Moldova: 12
🇷🇸 Serbia: 6
Top haters:
🇺🇦 Ukraine: -35
🇩🇰 Denmark: -34
🇱🇹 Lithuania: -32
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RT @kos_data: 🇨🇳 Xi net approval rating in Europe:
Top fans:
🇷🇸 Serbia: 22
🇲🇩 Moldova: 15
🇬🇷 Greece: 8
Top haters:
🇸🇪 Sweden: -81
🇺🇦 Ukraine: -69
🇻🇦 Pope Leo XIV net approval rating in Europe:
Top fans:
🇭🇷 Croatia: 67
🇵🇱 Poland: 46
🇮🇪 Ireland: 44
Top haters:
🇹🇷 Turkey: -31
🇷🇸 Serbia: -19
🇮🇱 Netanyahu net approval rating in Europe:
Netanyahu only fans:
🇺🇦 Ukraine: 2
Top haters:
🇧🇦 Bosnia: -80
🇳🇴 Norway: -75
🇸🇪 Sweden: -71
🇮🇳 Modi net approval rating in Europe:
Top fans:
🇬🇷 Greece: 16
🇲🇩 Moldova: 12
🇷🇸 Serbia: 6
Top haters:
🇺🇦 Ukraine: -35
🇩🇰 Denmark: -34
🇱🇹 Lithuania: -32
🇺🇸 US President Trump net approval rating in Europe:
Top fans:
🇽🇰 Kosovo: 27
🇷🇴 Romania: 11
🇲🇩 Moldova: 10
🇲🇰 N.Macedonia: 2
Top haters:
🇩🇰 Denmark: -84
🇸🇪 Sweden: -80
🇳🇴 Norway: -79
(Poll: Gallup International) https://t.co/XCAXSStNBt tweet
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DAIR.AI
RT @dair_ai: What if you could get multi-agent performance from a single model?
Multi-agent debate systems are powerful. Multiple LLMs can critique each other's reasoning, catch errors, and converge on better answers.
However, the cost scales linearly with the number of agents. Five agents means 5x the compute. Twenty agents means 20x and so on.
But the intelligence gained from debate doesn't have to stay locked behind a compute wall.
This new research introduces AgentArk, a framework that distills the reasoning capabilities of multi-agent debate into a single LLM through trajectory extraction and targeted fine-tuning.
This work addresses an important problem: multi-agent systems are effective but expensive at inference time. AgentArk moves that cost to training time, letting a single model carry the reasoning depth of an entire agent team.
The key idea: run multi-agent debate offline to generate high-quality reasoning traces, then train a smaller model to internalize those patterns.
Five agents debate, one student learns.
AgentArk tests three distillation methods. RSFT uses supervised fine-tuning on correct trajectories. DA filters for diverse reasoning paths. PAD, their strongest method, preserves the full structure of multi-agent deliberation, capturing how agents verify intermediate steps and localize errors.
The results across 120 experiments:
> PAD achieves a 4.8% average gain over single-agent baselines, with in-domain improvements reaching up to 30%. On reasoning quality metrics,
> PAD scores highest in intermediate verification (4.07 vs 2.41 baseline) and reasoning coherence (3.96 vs 1.88 baseline).
>The distilled models also transfer: trained on math, they improve on TruthfulQA with ROUGE-L jumping from 0.613 to 0.657.
Scaling from Qwen3-32B teachers down to Qwen3-0.6B students, the framework holds up. Even sub-billion parameter models absorb meaningful reasoning improvements from multi-agent debate.
Paper: https://t.co/cyPTig221s
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
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RT @dair_ai: What if you could get multi-agent performance from a single model?
Multi-agent debate systems are powerful. Multiple LLMs can critique each other's reasoning, catch errors, and converge on better answers.
However, the cost scales linearly with the number of agents. Five agents means 5x the compute. Twenty agents means 20x and so on.
But the intelligence gained from debate doesn't have to stay locked behind a compute wall.
This new research introduces AgentArk, a framework that distills the reasoning capabilities of multi-agent debate into a single LLM through trajectory extraction and targeted fine-tuning.
This work addresses an important problem: multi-agent systems are effective but expensive at inference time. AgentArk moves that cost to training time, letting a single model carry the reasoning depth of an entire agent team.
The key idea: run multi-agent debate offline to generate high-quality reasoning traces, then train a smaller model to internalize those patterns.
Five agents debate, one student learns.
AgentArk tests three distillation methods. RSFT uses supervised fine-tuning on correct trajectories. DA filters for diverse reasoning paths. PAD, their strongest method, preserves the full structure of multi-agent deliberation, capturing how agents verify intermediate steps and localize errors.
The results across 120 experiments:
> PAD achieves a 4.8% average gain over single-agent baselines, with in-domain improvements reaching up to 30%. On reasoning quality metrics,
> PAD scores highest in intermediate verification (4.07 vs 2.41 baseline) and reasoning coherence (3.96 vs 1.88 baseline).
>The distilled models also transfer: trained on math, they improve on TruthfulQA with ROUGE-L jumping from 0.613 to 0.657.
Scaling from Qwen3-32B teachers down to Qwen3-0.6B students, the framework holds up. Even sub-billion parameter models absorb meaningful reasoning improvements from multi-agent debate.
Paper: https://t.co/cyPTig221s
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
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DAIR.AI
RT @omarsar0: This is a great read if you are building complex applications with Claude Code and Codex.
Most AI coding agents can generate a frontend.
But building a real full-stack application is a completely different story.
The gap between generating a landing page and shipping a working app with a functional backend, database, and API layer remains wide.
Most coding agents default to mock data, fake endpoints, and frontend-only implementations.
But real-world web development requires all three layers working together.
This new research introduces FullStack-Agent, a multi-agent system designed for end-to-end full-stack web development with two key innovations: Development-Oriented Testing and Repository Back-Translation.
The system uses three specialized agents. A Planning Agent generates structured frontend and backend designs in JSON. A Backend Coding Agent implements server logic with a dedicated debugging tool that sends HTTP requests and validates responses. A Frontend Coding Agent builds the UI against real backend APIs with a tool that monitors terminal and browser console errors dynamically.
Development-Oriented Testing validates code during generation, not after. Each agent gets real-time execution feedback, catching integration failures as they happen rather than at the end of a long generation chain.
Repository Back-Translation solves the training data problem. An information-gathering agent reads real open-source repositories and extracts development patterns. A trajectory agent then reproduces those repositories from scratch given only the extracted plans, generating high-quality training examples grounded in real codebases.
The results on their FullStack-Bench (647 frontend, 604 backend, 389 database test cases): FullStack-Dev achieves 64.7% frontend accuracy, 77.8% backend accuracy, and 77.9% database accuracy. That's an 8.7%, 38.2%, and 15.9% improvement over the strongest baseline respectively.
After training a Qwen3-Coder-30B model on 2K crawled and 8K augmented trajectories, frontend accuracy improved by 9.7% and backend accuracy by 9.5% in just two rounds.
The bottleneck in AI-assisted web development isn't frontend generation. It's building functional backends and databases that actually work together. FullStack-Agent closes that gap with execution-grounded testing and real-world training data.
Paper: https://t.co/b2g041Pvrb
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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RT @omarsar0: This is a great read if you are building complex applications with Claude Code and Codex.
Most AI coding agents can generate a frontend.
But building a real full-stack application is a completely different story.
The gap between generating a landing page and shipping a working app with a functional backend, database, and API layer remains wide.
Most coding agents default to mock data, fake endpoints, and frontend-only implementations.
But real-world web development requires all three layers working together.
This new research introduces FullStack-Agent, a multi-agent system designed for end-to-end full-stack web development with two key innovations: Development-Oriented Testing and Repository Back-Translation.
The system uses three specialized agents. A Planning Agent generates structured frontend and backend designs in JSON. A Backend Coding Agent implements server logic with a dedicated debugging tool that sends HTTP requests and validates responses. A Frontend Coding Agent builds the UI against real backend APIs with a tool that monitors terminal and browser console errors dynamically.
Development-Oriented Testing validates code during generation, not after. Each agent gets real-time execution feedback, catching integration failures as they happen rather than at the end of a long generation chain.
Repository Back-Translation solves the training data problem. An information-gathering agent reads real open-source repositories and extracts development patterns. A trajectory agent then reproduces those repositories from scratch given only the extracted plans, generating high-quality training examples grounded in real codebases.
The results on their FullStack-Bench (647 frontend, 604 backend, 389 database test cases): FullStack-Dev achieves 64.7% frontend accuracy, 77.8% backend accuracy, and 77.9% database accuracy. That's an 8.7%, 38.2%, and 15.9% improvement over the strongest baseline respectively.
After training a Qwen3-Coder-30B model on 2K crawled and 8K augmented trajectories, frontend accuracy improved by 9.7% and backend accuracy by 9.5% in just two rounds.
The bottleneck in AI-assisted web development isn't frontend generation. It's building functional backends and databases that actually work together. FullStack-Agent closes that gap with execution-grounded testing and real-world training data.
Paper: https://t.co/b2g041Pvrb
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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Michael Fritzell (Asian Century Stocks)
RT @ai: TSMC is going to manufacture advanced AI semiconductors in Japan. This is a big deal for supply chain diversification. For decades, cutting-edge chip fabrication was concentrated in Taiwan. Now TSMC is building serious capacity in Japan, Arizona, and Germany.
https://t.co/EHng9bVPd6
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RT @ai: TSMC is going to manufacture advanced AI semiconductors in Japan. This is a big deal for supply chain diversification. For decades, cutting-edge chip fabrication was concentrated in Taiwan. Now TSMC is building serious capacity in Japan, Arizona, and Germany.
https://t.co/EHng9bVPd6
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Michael Fritzell (Asian Century Stocks)
RT @origoinvest: Korea discount still in full force despite 121% stock market run-up https://t.co/PWUwvwnqSL
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RT @origoinvest: Korea discount still in full force despite 121% stock market run-up https://t.co/PWUwvwnqSL
@DanielSLoeb1 makes a strong case for SK Square currently trading at a 47% discount to a NAV where its primary asset is SK Hynix
3 ways of winning here beyond your typical NAV discount story:
1) DDRAM supercycle
2) Explicit NAV discount reduction targets
3) Compound gains through margin driven buybacks
Haven't been involved but this looks very compelling
$402340.KS $000660.KS tweet
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God of Prompt
RT @godofprompt: Prompting is foundational infrastructure, not a surface-level trick - it only appears to die because success means integration. https://t.co/KidFQLFwnO
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RT @godofprompt: Prompting is foundational infrastructure, not a surface-level trick - it only appears to die because success means integration. https://t.co/KidFQLFwnO
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