#supervised learning data value
385 related articles
·5 min
U.S. Treasury Warns of AI Bubble Risk: Systemic Concerns and Investment Rationality
The U.S. Treasury has issued a rare internal warning that the AI investment boom may be forming an asset bubble, with valuations detached from fundamentals and systemic risks mounting.
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·5 min
Claude Cowork Arrives on Mobile and Web, with Max Users Getting Early Access
Anthropic's AI collaboration platform Claude Cowork officially expands to mobile and web, breaking free from desktop-only limits. Max subscribers get early access, with other plans rolling out over the coming weeks.
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·6 min
Coze from Scratch: A Complete Guide to Building Multi-Agent Collaboration
A detailed guide to Coze's core features: cross-platform interoperability, the Skills system, multi-agent collaboration, and workflow building. Compare Coze and Dify to build practical AI apps with zero coding.
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·4 min
Python Learning Path for Absolute Beginners: A Science-Based Guide to Avoid Common Pitfalls
How can beginners learn Python without getting lost? This guide outlines a 3-stage learning path covering basics, advanced topics, and hands-on practice in web scraping, data analysis, and office automation.
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·7 min
Metacognitive Feedback: Using Reinforcement Learning to Help LLMs Accurately Express Uncertainty
LLMs are often overconfident and prone to hallucination. How can AI learn to say "I'm not sure"? This article explains the reinforcement learning approach with metacognitive feedback and how calibrating confidence boosts LLM trustworthiness.
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·7 min
The Birth of Claude Code: Design Philosophy from Internal Tool to AI Programming Assistant
An in-depth look at Claude Code's development and design philosophy: why the CLI form, how the "minimal scaffolding" architecture works, and how it balances safety with autonomy in AI programming.
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·7 min
GPT-5.6 Sol vs. Fable 5: Can Half the Price Match the Flagship?
OpenAI launches GPT-5.6 Sol at half the price of Anthropic's Fable 5. We compare both models on HealthBench, cybersecurity and more using hard system-card data, analyzing Sol's cost-effectiveness and shifts in AI power.
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·6 min
Deep Dive into GPT-5.6 Ultra's Sub-Agent Architecture and Ten Key AI Industry Trends
In-depth analysis of GPT-5.6 Ultra's sub-agent collaborative reasoning, the global rise of Chinese AI models, world-model evaluation gaps, and AI's real-world deployment challenges and bubble warnings.
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·3 min
Why Does Chain-of-Thought (CoT) Boost Reasoning? A Four-Layer Deep Dive
A four-layer breakdown of why Chain-of-Thought (CoT) boosts LLM reasoning: compute allocation, external working memory, pretraining pattern activation, and DeepSeek R1 RL evidence.
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·6 min
The Seven Core Components of Building Long-Running AI Agents
An in-depth look at the seven core components for building long-running AI agents: Goal, Evaluator, Verifier, Outer Loop, Orchestration, Observability, and Memory. Master this control system for reliable autonomous agents.
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Amazon MTurk Closes to New Customers: …
·4 min
Amazon MTurk Closes to New Customers: The End of the Crowdsourcing Era and the AI Revolution
Amazon MTurk stops accepting new customers after nearly 20 years. Explore its legacy in AI training and academic research, and how LLMs are reshaping the data annotation industry.
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·6 min
Alibaba Bans Claude Code: The Enterprise Security Red Line for AI Coding Tools
In-depth analysis of Alibaba's comprehensive internal ban on Claude Code: from the hidden-marker controversy and Anthropic's regional-restriction stance to five core questions of enterprise AI coding tool security admission.
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·6 min
Benchmarking as Data Activation: Giving Domain Data a Direction to Climb
How benchmarking transforms dormant domain data into an AI optimization engine. From healthcare to law to manufacturing, building vertical benchmarks activates proprietary data and builds a strategic moat.
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·3 min
Unsloth v0.1.45-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM
Unsloth v0.1.45-beta (PyPI: 2026.6.2) delivers 2x faster LLM fine-tuning and up to 70% VRAM reduction. Now at 67.9k GitHub stars, upgrade via pip install.
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·2 min
Zhipu GLM 5.2 Deep Dive: Can Open-Weight Models Challenge Top Closed-Source AI?
Zhipu GLM 5.2 review: open weights released within 24hrs, built for long-horizon Agent tasks. Strong benchmarks, standout writing & frontend design, at a fraction of closed-model pricing.
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·6 min
AI Characters Crashing Real-World Parties? The Boundaries of Virtual Socializing Are Vanishing
A tweet about an AI character "attending" a July 4th party reveals how virtual and real-world social boundaries are dissolving. A deep dive into AI socialization, anthropomorphism, and attention scarcity.
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·6 min
Not Everything Is Worth a Token: When to Use AI and When to Use Deterministic Code
Over-reliance on LLMs is an overlooked pitfall in AI development. Explore the hidden costs of Token economics, the boundaries between LLMs and deterministic code, and how hybrid architectures balance flexibility and reliability.
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·7 min
Agentic AI Reshapes Genomic Interpretation: A SOTA Case Study in Interstitial Lung Disease Diagnosis
How Agentic AI achieves SOTA performance in interstitial lung disease (ILD) genomic interpretation through autonomous planning, multi-step reasoning, and tool calling—and its clinical impact.
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·6 min
Does AI Adoption Actually Lead to More Hiring? Explaining This Counterintuitive Phenomenon
Growing data shows companies aggressively adopting AI are actually hiring more. This article explores the overlooked complex relationship between AI and employment through the Jevons Paradox, new job creation, and competitive divergence.
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Meta's Next-Gen Model Claims to Match …
·4 min
Meta's Next-Gen Model Claims to Match OpenAI's Flagship — Can Open Source Disrupt the AI Landscape?
Meta's Chief AI Scientist claims its next-gen LLM matches OpenAI's flagship. We break down the strategic intent, open vs. closed source dynamics, and what this means for the AI industry.
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