#Chain of Thought
1487 related articles
·3 min
Chain of Thought (CoT) Explained: Teaching AI to Reason Step by Step
A deep dive into Chain of Thought (CoT) prompting: from prompt construction to reasoning chain generation, revealing the three mechanisms behind AI's improved reasoning. Covers math, medical, and financial use cases.
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Latent Reasoning: The Next-Generation …
·3 min
Latent Reasoning: The Next-Generation AI Reasoning Paradigm Beyond Chain of Thought
Is CoT really AI 'thinking'? This deep dive covers latent reasoning's rise — Coconut, HRM, BDH — and the core trade-offs between interpretability, efficiency, and governance in high-stakes AI.
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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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·2 min
Google Hides Gemini's Chain of Thought: Why This AI Transparency Rollback Is Sparking Controversy
Google now hides Gemini's thinking process by default, preventing users from verifying reasoning logic and search behavior. We analyze why this transparency rollback matters and how it compares to ChatGPT and Claude.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·3 min
Three Essential Hard Skills Traditional Product Managers Need to Transition into AI PM
How can traditional product managers transition to AI PM? This article analyzes the essential differences and details three must-have skills: AI product cognition, advanced Prompt engineering, and large model technical logic.
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·3 min
AI Not Performing Well? Start by Getting These Simple but Often Overlooked Things Right
When your AI system underperforms, the problem is often not the model or algorithm — it's basic work like data cleaning, prompt writing, and evaluation that hasn't been done right.
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·3 min
Google Launches Gemini 4 Pre-training: Its Most Ambitious AI Model Training Yet
Google DeepMind announces Gemini 4 pre-training has begun, calling it their most ambitious training yet. A deep dive into its technical direction, compute scale, multimodal breakthroughs, and competitive impact.
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·3 min
AI Research Automation: More Like Data Cleaning Than Inventing the Transformer
AI research automation will look more like data cleaning than inventing the Transformer. Explore how automating 60%-80% of repetitive research work reshapes the AI research paradigm.
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·3 min
AI Research Automation: More Like Data Cleaning Than Inventing the Transformer
Why AI research automation looks more like data cleaning than inventing the Transformer. Exploring the value of automating 60%-80% of repetitive research work and how human-AI collaboration reshapes the research paradigm.
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·3 min
AI Not Working Well? Start by Getting These Simple but Often Overlooked Things Right
When AI systems underperform, the problem often isn't the model or algorithm — it's that basics like data cleaning, prompt writing, and evaluation aren't done right. Learn the simple fixes that matter most.
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·2 min
Will AI End Human History? The Deep Anxiety Behind Technological Optimism
A viral Reddit post asks: will AI end human history? This article analyzes the blind spots of tech accelerationism, the governance mismatch, and how to rationally navigate AI transformation.
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·2 min
Office Snacks Reveal Corporate Health Culture: The Management Truth Hidden in Your Break Room
Office break room snack choices reveal a company's true attitude toward employee health. From candy-filled kitchens to thoughtful nutrition, this article analyzes the cost logic, cultural signals, and practical insights for managers and job seekers.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy to Help You Fully Understand
Large models aren't search engines — they're more like super compressors. This article explains how LLMs compress data to learn semantic patterns, and explores the phenomenon of intelligent emergence.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Poolside Laguna Model Launch: Can Silicon Valley's Open-Source Push Catch Up to China's AI?
Poolside launches Laguna open-weight model after 18 months of silence, pitting 118B parameters against Kimi K3's 2.8 trillion. Can Silicon Valley's open-source push close the gap with Chinese AI?
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·3 min
AI Weekly: Sandbox Escape Sparks Safety Legislation, Kimi Open-Source Triggers Geopolitical Dynamics
Deep analysis of five key AI events this week: OpenAI sandbox escape driving safety legislation, Kimi K3 open-source sparking geopolitical debate, Gemini Flash full rollout, Anthropic's $1.5B copyright settlement, and Chinese models' mobile expansion.
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·3 min
Fields Medal Winner Joins OpenAI: A Triple Signal of Talent, Capital, and Capability
Fields Medal winner Jacob Tsimerman joins OpenAI's safety team on award day, saying math careers won't survive. NVIDIA finances a $250B data center. Kimi K3 opens a 2.8T-parameter model.
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