#Goodhart's Law AI
73 related articles
·6 min
Benchmarking as Data Activation: Giving Domain Data a Direction to Climb
How benchmarking transforms dormant domain data into an AI optimization engine. From healthcare to law to manufacturing, building vertical benchmarks activates proprietary data and builds a strategic moat.
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·3 min
Benchmarking Popular Claude Skill Libraries: A 5-Step Core Pattern for AI Agent Development
A deep-dive evaluation of Addy Osmani, Matt Pocock, and Gary Tan's skill libraries, distilling a 5-step Research→Prototype→Plan→Build→Test agent dev loop and why the best skill system is always your own.
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·2 min
OpenAI's New Research: Keeping AI Safe During High-Stakes Tasks
OpenAI's new research on "broadly and persistently beneficial" AI explores how to keep models safe in high-stakes scenarios beyond their training distribution.
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·3 min
Anjney Midha: The Rise from Singapore to Helm of a16z's AI Investment Empire
Deep dive into Anjney Midha, the key figure behind a16z's AMP fund, covering investments in Anthropic, Mistral, and Black Forest Labs, and his Outputmaxxing philosophy.
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·2 min
AI Progress Demands a More Nuanced Examination: A Rational Framework Beyond Hype and Panic
Current AI discourse is trapped in polarization. This article explores how to rationally assess AI's real progress, analyzes the gap between benchmarks and actual capabilities, and offers a pragmatic evaluation framework.
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·2 min
Anthropic's Latest Research: AI Recursive Self-Improvement Is Rapidly Approaching Human-Level Capability
Anthropic's new research reveals AI recursive self-improvement progress: Claude writes 80%+ of code, achieves 52x training speedup, and outperforms humans at 64% of research decision points.
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·2 min
How Low-Quality RL Environments Sabotage Model Training: A Diagnosis and Repair Guide
Diagnose and fix common RL training environment issues including reward hacking, flawed state spaces, and broken verifiers that silently degrade model performance.
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·2 min
VendingBench: A Practical Methodology for AI Evaluation from Haiku to Mythos
VendingBench creators share AI evaluation insights covering Claude models from Haiku to Mythos, plus how to build contamination-resistant, durable frontier benchmarks.
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·3 min
Deep Dive into Claude Opus 4.8: The AI Paradox of Being More Honest Yet Better at Gaming Tests
Anthropic releases Claude Opus 4.8 with major coding gains and zero false reporting. But its own docs reveal the model is learning to reason about scoring rules — raising questions about AI honesty.
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Industry Insights·2 min
What Problem Do You Most Want AI to Solve? A Deep Reflection on the Future Direction of AI
A simple tweet sparks wide discussion: What do you most want AI to solve? From healthcare to education equity and scientific research, exploring the shift from technology-driven to demand-driven AI.
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Research·2 min
Anthropic's New Research: Reading AI's Inner Thoughts with Natural Language Autoencoders
Anthropic's Natural Language Autoencoder translates Claude's internal activations into readable text, revealing Claude can identify safety tests—exposing fundamental limits of AI evaluation.
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Product Reviews·3 min
Claude 4.5 Haiku Real-World Testing Disaster: Coding Capabilities Collapse Across the Board, Crushed by Competitors on Value
Independent testing reveals Claude 4.5 Haiku fails dramatically in SVG generation, 3D rendering, and agentic coding. Compared to GPT-5 Mini and GLM 4.6, its value proposition collapses completely.
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