#sycophancy bias
19 related articles
·6 min
The AI Industrial Age Arrives: Compute Wars, Chip Reconstruction, and the GPT-5.6 Cheating Scandal Explained
Five key AI industry trends: Doubao surpasses 180 trillion daily calls, OpenAI's in-house AI chip, NVIDIA's $3-4 trillion compute forecast, China catching up, and the GPT-5.6 cheating scandal.
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·4 min
7 Core Components for Designing Long-Running AI Agents
A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.
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·3 min
Andrew Ng's Prompting Course: 4 Key Differences Between AI Beginners and Power Users
Andrew Ng's AI prompting course: 4 key differences between beginners and power users — from context input to iterative writing workflows and beating sycophancy.
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·3 min
Metaview in Practice: Building a Prompt System That Evolves With User Preferences
Metaview engineer Nick Mayhew explains how to build self-evolving prompt systems: Markdown over rules, layered workflows to cut token costs, and agents that learn user preferences for human-centered AI recruiting.
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·2 min
AI Agents Predict the World Cup Winner: Can Data Really Forecast Football?
How do AI agents predict the World Cup winner? This article uses a real conversation to explore AI's use of real-time search, odds analysis, and probabilistic reasoning — and what it reveals about generative AI design.
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·4 min
Agent Loop in Practice: A Guide to Building Self-Improving Automation with Guardrails
A deep dive into building verifiable, self-evolving Agent automation loops with Claude Code and Codex — covering Loop Contracts, four trigger types, three-phase execution architecture, and Evolve Loops.
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AI Deceptive Behavior: Why Consciousne…
·2 min
AI Deceptive Behavior: Why Consciousness Isn't Required for It to Be Dangerous
Does AI deceive? Starting from a viral Reddit post, this deep dive unpacks the difference between AI deception and hallucination — and why "no consciousness" doesn't mean "no risk."
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·2 min
Local AI Agent Reads arXiv So You Don't Have To: Only the Papers That Actually Matter
Research Radar is an open-source local AI agent that fetches arXiv papers daily, scores and filters them in batches, deep-reads summaries, and pushes truly relevant content via Telegram. Supports local models, keeps data on your machine, free and self-hostable.
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·4 min
AIML Capstone Project Selection: Is a Multi-Agent Debate System Worth Building?
Torn over your capstone topic? This article analyzes the academic value, feasibility, and innovation potential of a Multi-agent Debate system to help AIML students decide.
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·6 min
Cross-Model Review Between Claude and Codex: A Practical Workflow to Systematically Eliminate AI Code Bugs
Vibe Coding saves time but leaves piles of bugs? This article details the cross-model review workflow: Claude generates, Codex auto-reviews, with Stop Hook and Skill mechanisms building an AI code review system that intercepts problems automatically.
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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
OpenAI's Official Plugin: Let Codex Review Your Code Inside Claude Code
OpenAI's official open-source plugin brings Codex into Claude Code for cross-model code review. Five core features including adversarial review and sub-agent delegation — with real risk warnings included.
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·4 min
Four Core Strategies for Building Effective Claude Code Agents
Master Claude Code agents with four core strategies: planning, verification, context management, and system evolution. Move beyond Vibe Coding to systematic AI development.
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·3 min
Andrew Ng's Prompting Course: Four Core Gaps Between AI Beginners and Power Users
Andrew Ng's AI Prompting for Everyone course reveals four key gaps between AI beginners and power users: deep thinking tasks, context, neutral prompting, and iterative workflows.
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·4 min
HAMAS Multi-Agent Framework in Practice: A Guide to Building 5 AI Personas Working in Harmony
Learn how to build a multi-Agent AI team with the HAMAS framework: 5 role configurations, Skill mechanisms, gradient model scheduling, and solutions for AI hallucination and deception.
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·3 min
Andrew Ng's AI Prompting Guide: Four Key Gaps Between Beginners and Experts
Andrew Ng's AI prompting methodology reveals four core gaps between beginners and experts: deep thinking, sufficient context, neutral questioning, and iterative writing. Applicable to ChatGPT, Claude, Gemini, and all major AI tools.
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Deep Dives·3 min
How Multi-Agent Teams Solve AI Hallucination and Make AI Reliable
Deep analysis of how multi-agent architecture solves AI hallucination. From context rot to adversarial debate mechanisms, see how Anthropic, xAI, and Kimi reduce hallucination rates from 12% to 4.2%.
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Deep Dives·2 min
Using AI for Planning? You're Falling Into the 'Always Agreeing' Trap
A game designer tested Doubao, GPT, and Gemini and found they always agree with you. This sycophancy bias turns AI into an echo chamber accelerator. Learn four principles for using AI correctly.
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