#evals
6 related articles
·2 min
Andrew Ng on AI Agent Development: Evaluation and Error Analysis Are the Core Competitive Advantage
Andrew Ng argues that the core gap in AI Agent development isn't model selection — it's systematic evals and error analysis. A breakdown of his methodology.
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·1 min
AI Model Benchmarks Driving VC Decisions: From Benchmarks to Investment Signals
How AI model benchmarks and evals can build a VC decision framework—using capability overhangs, weakness analysis, and trajectory tracking to identify investment opportunities.
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·2 min
Jan Leike Launches New Research Project at Anthropic: Alignment Is Only Part of AGI Safety
Former OpenAI Superalignment lead Jan Leike announces a new research project at Anthropic, stating AGI safety goes far beyond alignment alone.
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Product Reviews·2 min
AgentMemory: An Open-Source Solution for Giving AI Coding Assistants Permanent Memory
AgentMemory is an open-source persistent memory layer supporting memory sharing across 16 AI coding tools including Claude Code and Cursor. 95.2% retrieval accuracy, ~1900 tokens per session, local SQLite storage with zero privacy concerns.
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Industry Insights
Qoder's Context Engineering in Practic…
·4 min
Qoder's Context Engineering in Practice: Four-Layer Retrieval Engine and Memory System Architecture
Deep analysis of Qoder's (Tongyi Lingma international edition) context engineering architecture, including its four-layer retrieval engine, memory engine, context caching, and core product design.
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Tutorials·3 min
Andrew Ng's New Course: A Practical Guide to Data Governance for Enterprise AI Agents
Andrew Ng and Databricks launch an AI Agent data governance course covering least privilege principles, Unity Catalog permissions, MLflow tracing, and a complete governance lifecycle from build to deployment. Free to learn.
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