#critical thinking AI
1231 related articles
·7 min
The Revenge of the Philosophy Majors in the AI Era: Why Critical Thinking Has Become the New Scarce Resource
As generative AI sweeps the workplace, once-marginalized philosophy and humanities are being revalued. This article explores why critical thinking, ethical judgment, and questioning are the new scarce competencies in the AI era.
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Tech Frontiers·2 min
Claude Opus 4.8 Thinking Effort Calibration Explained: A Critical Optimization Direction for AI Reasoning Models
Anthropic releases Claude Opus 4.8 with optimized thinking effort calibration. This article explains what it is, why it matters for AI reasoning models, and its impact on industry competition.
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·4 min
Getting Started with AI Agent Development: A Complete Guide from Concept to Practice
A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.
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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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·2 min
Complete Tutorial: Installing MCP Connector in WorkBuddy and Syncing Skills Across AI Workstations
Step-by-step guide to installing MCP connector in WorkBuddy and syncing Skills across Cursor and other AI workstations via AI conversation.
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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 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 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
How Complaining Erodes Your Mind: Understanding the Self-Reinforcing Nature of Attention
Habitual complaining trains your brain to find more negativity, creating a vicious cycle. Learn about the self-reinforcing nature of attention and practical ways to break free from negative loops.
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·3 min
The Depth Perception Challenge for Transparent Objects: How LingBot-Depth Breaks Through with Masked Depth Modeling
Depth perception for transparent and reflective objects has long been a core challenge in robotic grasping. LingBot-Depth uses masked depth modeling to turn sensor failure into supervisory signals, inferring glass depth from RGB context.
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·2 min
The Invisible Prompt Trap: How a Professor Caught 32 Students Cheating with AI in One Move
A professor embedded invisible prompts in assignments, catching 32 of 35 students using AI to cheat. Learn how this prompt injection trap works and what it means for education.
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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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·2 min
How Complaining Erodes Your Mind: Understanding the Self-Reinforcing Nature of Attention
Habitual complaining trains your brain to find more negativity, creating a vicious cycle. Learn how the self-reinforcing nature of attention works and practical ways to break free from negative loops.
Read more →
·3 min
DeepSeek Ignites Open Source vs. Closed Source Debate in Silicon Valley, Splitting Tech Giants into Rival Camps
DeepSeek's open source model shakes Silicon Valley. OpenAI defends closed source while Microsoft, NVIDIA, and Meta back open ecosystems. Analysis of the AI open/closed source debate, Apple-Micron chip tensions, and AI-driven historical disinformation.
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