#sycophantic agent
7 related articles
·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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·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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·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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·3 min
OpenAI Researcher: Spec Documents Are the Real Code
OpenAI researcher Sean Grove argues code is only 10-20% of value—spec documents are the real source code. From Vibe Coding insights to model alignment, why writing specs will be the scarcest skill in the AI era.
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Industry Insights·1 min
6 Months After AI Coding: The Bottleneck Shifted from Writing Code to Supervision and Specification
After 6 months of AI coding tools, a 20-person team found bottlenecks shifted from writing code to supervision and specification. Real lessons on review overload, sycophantic agents, and why heavyweight docs are back.
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