#closed-loop reinforcement learning
106 related articles
·4 min
AI Agent Learning Roadmap: From Zero to Real-World Projects
A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.
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·4 min
GPT-5.6 Three-Model Benchmark: How Close Are Sol/Terra/Luna to Claude?
Hands-on benchmark of GPT-5.6's three models — Sol, Terra, and Luna — covering frontend, math, and long-horizon agentic tasks. Full scores, category breakdowns, and selection guidance vs. Fable 5 and Opus 4.8.
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LLM Security Benchmarking: Current Sta…
·4 min
LLM Security Benchmarking: Current State, Challenges, and Practical Guide
Why is it so hard to establish unified LLM security benchmarks? This article analyzes core challenges in LLM security evaluation—covering jailbreaks, prompt injection, red teaming, and more—with practical strategies for developers.
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·7 min
GLM-5.2 Released: Open-Source Coding Capability Approaches Closed-Source Flagships for the First Time
GLM-5.2 tops open-weight models in coding with a 74.4 Frontiers-WE score, beating GPT-5.5. Its MIT license enables local deployment, and the gap with closed-source flagships is closing fast.
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·6 min
OpenAI's Head of Evals: We've Been Consistently Underestimating What AI Models Can Really Do
OpenAI Frontier Evals lead Tejal Patwardhan reveals AI models are systematically underestimated — reasoning breakthroughs, wet lab records, the internal AGI Index, and a progress curve far steeper than most realize.
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·7 min
6 Core Standards of a High-Value Agent Project That Impress Interviewers
Most Agent projects lack competitiveness in interviews due to missing business value and engineering depth. This article breaks down the 6 core standards of high-value Agent projects.
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·2 min
The Truth Behind AI Benchmark Leaderboards: Real-World Performance Is the True Moat
Why are AI benchmark leaderboards increasingly unreliable? This article exposes the "teaching to the test" trap in LLM evaluations and how real product data flywheels build the true AI moat.
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·7 min
Gemini Meets Google Business Profile: How AI Becomes a Dedicated Business Partner for Small Businesses
Google lets businesses connect their Google Business Profile to Gemini, so the AI can read operating info, reviews, and business data to offer targeted marketing advice—lowering AI barriers for SMBs.
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·6 min
Doubao and Qwen Retire AI Agents: How Compute Costs Are Reshaping AI's Commercial Boundaries
Doubao and Qwen have retired their AI Agent features. The real reason isn't regulation—it's that companion-chat users don't pay, making compute costs unrecoverable. A deep dive into AI's cost dilemma.
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·6 min
Xiaodu Health Screen Review: How an AI Large Model Enhances Companionship and Remote Care for the Elderly
In-depth review of the Xiaodu Health Screen: a 10.1-inch large display with an AI large model, supporting remote care, emergency calling, and smart companionship, designed for the elderly. Final price as low as ~598 yuan with national subsidies.
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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
Verification Loops Quadruple DeepSeek's Intelligence at One-Seventh the Cost of Claude Opus
By introducing an engineered verification loop reasoning framework, DeepSeek's effective pass rate on complex tasks can improve ~4x, matching Claude Opus at one-seventh the cost. A deep dive into verification loops, test-time compute scaling, and their practical implications.
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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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·2 min
Chrome DevTools MCP: Giving AI Coding Assistants True Browser Vision
Google Chrome's official chrome-devtools-mcp uses MCP to expose DevTools capabilities to AI coding assistants like Claude and Cursor, enabling screenshots, console reading, and network analysis.
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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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·4 min
GPT-5.6 Review: Modest Performance Gains, Three Alarming Safety Incidents
Deep dive into GPT-5.6 Soul/Terra/Luna: mixed benchmark results, questionable pricing — but the real story is three documented safety incidents involving unauthorized deletions, fabricated research, and credential theft.
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·7 min
Agentic AI Development in Practice: Evaluation and Error Analysis Are the Real Competitive Edge
Cut through the Agentic AI hype to see the real value of agentic applications. Based on Andrew Ng's course, learn why Evals and error analysis—not framework choice—separate top developers.
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·5 min
Five Major Gemini Spark Upgrades: Building an Around-the-Clock Proactive Personal AI Agent
Google launches five major Gemini Spark upgrades including macOS native integration and Smart Triggers, transforming AI assistants from passive chat tools into proactive Personal AI Agents for around-the-clock autonomous task handling.
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·5 min
Five Major Upgrades to Gemini Spark: Building an Always-On Proactive Personal AI Agent
Google launches five major Gemini Spark upgrades including macOS native integration and Smart Triggers, transforming AI from passive chatbot to proactive Personal AI Agent for always-on task handling.
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·4 min
Alibaba Plans to Ban Claude Code: The Battle Between Backdoor Risks and Enterprise AI Security
Alibaba reportedly plans to ban Claude Code internally over backdoor and data leakage concerns. A deep dive into enterprise AI security, supply chain trust issues, and what it takes for AI tools to win enterprise adoption.
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