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06:00

The new rules of context engineering for Claude 5 models

Claude 5 上下文工程新规则:删掉 80% 系统提示,让模型凭判断力工作

Anthropic shares updated best practices for context engineering with Claude 5 models (Opus 5, Fable 5). Key finding: previous system prompts and constraints were over-restrictive; newer models have better judgment, allowing significant simplification. They removed 80% of Claude Code's system prompt with no measurable loss on coding evals. Article contrasts old practices (give rules, give examples, put everything upfront, repeat yourself, memory in CLAUDE.md, simple specs) with new ones (let model use judgment, design interfaces, progressive disclosure, simple tool descriptions, auto-memory, rich references). Recommendations: keep CLAUDE.md lightweight focusing on repo-specific gotchas; use Skills as on-demand lightweight guides; use progressive disclosure to avoid context bloat; prefer code references over descriptions. Includes introduction of `claude doctor` command for automatic simplification. For engineers using Claude Code or building Claude-based agents.

06:00

Introducing Claude Opus 5: Near-Frontier Performance at Half the Cost

Claude Opus 5 发布:接近 Fable 5 性能,成本减半

Anthropic releases Claude Opus 5, approaching the frontier intelligence of Fable 5 at half the cost. It achieves state-of-the-art results on coding and knowledge work benchmarks, e.g., Frontier-Bench v0.1 (more than double Opus 4.8 performance) and ARC-AGI 3 (3x score over next best). However, it remains behind Mythos 5 on cybersecurity tasks. The model offers tunable effort settings for cost-performance trade-offs. Customer reports highlight gains in software engineering, finance, and legal work. Alignment improves, but cybersecurity capabilities are intentionally limited. Safety classifiers intervene ~85% less than Fable 5. Pricing same as Opus 4.8, with fast mode available.

www.anthropic.com · 20 min · AI Engineering · Anthropic · Cost Optimization · LLM · Performance
06:00

AI Agent Engineering in Depth: From Principles to Production

AI Agent 工程实践全覆盖:从原理到生产的开源书

An open-source book providing a comprehensive guide to AI Agent engineering, from fundamental principles to production practices. Written by Li Bojie, it follows the core formula 'Agent = LLM + Context + Tools' across 10 chapters, covering context engineering, memory, tool use, coding agents, evaluation, post-training, continuous evolution, multimodal, and multi-agent collaboration. Includes 92 hands-on experiments (70+ runnable) on MCP, RAG, RL, etc. Ideal for engineers and researchers building production AI agents.

github.com · 15 min · Agents · AI Engineering · Context Engineering · LLM · Mcp