日刊 · 2026-05-27 — Glean

Wed, May 27, 2026 11picks

17:41

The Future Of Software Engineering with Anthropic

Anthropic 圆桌：软件工程的未来

A summary of a roundtable on the future of software engineering, featuring leaders from Stripe, NVIDIA, Microsoft, and others. Key insights: closed-loop development creates compounding gains; test-first is the new default; human code review is fading; comments are written for AI readability; long-horizon tasks remain unsolved; developer tooling is being displaced first; hiring values experimentation over raw skill; human-authored context files help, agent-authored ones can hurt. Candid trade-offs and real-world practices are shared.

www.akashbajwa.co · 12 min · Agents · AI · LLM

17:41

5 Agent Skill Design Patterns Every ADK Developer Should Know

5 种 ADK 开发者必备的 Agent Skill 设计模式

With SKILL.md format standardised across 30+ agent tools, the real challenge is content design. This article distills five recurring patterns from ecosystem-wide practices: Tool Wrapper (on-demand library context), Generator (template fill-in for consistent output), Reviewer (checklist scoring by severity), Inversion (agent-led interview before acting), and Pipeline (strict multi-step with gate conditions). Each pattern includes working ADK code, helping developers build reliable agents.

x.com · 13 min · Agents · AI · Framework

17:41

Your Best Prompt Is a Well-Defined User Story

你的最佳提示就是一份定义清晰的用户故事

In the age of agentic development, user story quality directly impacts AI output. The article argues teams should invest more time in breaking down stories and writing clear acceptance criteria rather than just estimating story points. A well-defined story includes three parts: Context, Acceptance Criteria, and Technical Hypothesis. Story point estimation is valuable only when forecasting or surfacing team misalignment is needed; otherwise it can be skipped. A good story acts as a good prompt, accelerating development cycles. Relevant for engineering teams using agile/Scrum.

spin.atomicobject.com · 7 min · Agents · LLM

17:40

Dreaming, Outcomes, and Multiagent Orchestration in Claude Managed Agents

Claude Managed Agents 推出「梦境」、成果评估与多智能体编排

Anthropic launches Dreaming in research preview for Claude Managed Agents: a scheduled process that reviews past sessions and memory to extract patterns, enabling agents to self-improve. Outcomes let developers define rubrics with a separate grader for self-correcting work; internal benchmarks show +10pp task success, +8.4% on docx, +10.1% on pptx. Multiagent orchestration allows a lead agent to decompose tasks to specialist subagents running in parallel with shared filesystem and traceability. Case studies include Harvey (6x completion rate improvement), Netflix (parallel log analysis), Spiral (writing quality via outcomes), and Wisedocs (50% faster document reviews). For engineers building autonomous AI agent systems.

claude.com · 6 min · Agents · LLM

16:35

ByteDance TRAE AI Coding Manuals: Context Engineering as Moat

字节TRAE AI编程手册精读：上下文是护城河

A distilled summary of ByteDance TRAE team's 20 internal AI coding practice manuals. The core argument is that the bottleneck in AI coding efficiency is not model capability but context engineering. The article details six methodologies: Context Engineering, Skills, Spec Coding, Rules, MCP, and Agentic Coding, backed by experimental data (e.g., 32 real bug fixes: 100% success with Skills vs 59% without). Suitable for frontline developers, tech leads, and engineering managers.

x.com · 14 min · Agents · AI · LLM

15:23

From Scratch: Build Automated Claude Code Workflows with Hooks

从 0 开始：用 Hooks 打造自动化 Claude Code 工作流

A tutorial on using Claude Code Hooks to automate shell commands at lifecycle events, replacing unreliable prompt instructions. Covers 5 key events (PostToolUse, PreToolUse, etc.), 3 hook types (command/prompt/agent), and config file structure. Provides 5 ready-to-use examples: desktop notification, auto-formatting, file protection, context recovery after compaction, and commit message linting. Exit code 2 blocks dangerous actions and feeds stderr back to Claude. For developers seeking reliable Claude Code workflows.

x.com · 10 min · Agents · AI

15:12

Claude Code in Large Codebases: Best Practices and Getting Started

Claude Code 在大代码库中的工作方式：最佳实践与入门指南

This article covers how Claude Code navigates large codebases using agentic search instead of RAG indexing, avoiding stale index issues but requiring good context configuration. It details the 'harness' ecosystem around the model—CLAUDE.md, Hooks, Skills, Plugins, MCP servers, LSP integration, and subagents—and presents three configuration patterns from successful deployments: making the codebase navigable, maintaining CLAUDE.md as models evolve, and assigning ownership for rollout. A practical guide for teams adopting Claude Code at scale.

claude.com · 19 min · Agents · AI

15:08

Why Your “AI-First” Strategy Is Probably Wrong

为什么你的“AI 优先”战略可能错了

The CTO of an agent platform shares their journey of rebuilding the entire engineering workflow around AI: 99% of production code is written by AI, shipping features within a day. The article critiques the superficial “AI-assisted” approach and introduces “harness engineering,” detailing their tech stack, self-healing feedback loop, and the new engineer roles of Architect and Operator. Real-world results include 3–8 deployments per day. Valuable for teams and CTOs seeking genuine AI integration.

x.com · 19 min · Agents · AI

14:54

Using Claude Code: The unreasonable effectiveness of HTML

HTML 的超常效力：用 Claude Code 摆脱 Markdown

Thariq Shihipar argues for using HTML instead of Markdown when working with Claude Code. HTML can represent tables, SVG, designs, and interactions—far denser information than Markdown. HTML docs are more readable, shareable, and can include interactive elements. Claude Code can pull context from codebases, Slack, git history to generate rich HTML reports, prototypes, and review interfaces. Concrete use cases cover planning, code review, design, reporting, and custom editing tools, with reusable prompt examples. For developers seeking to make Claude Code outputs more engaging and actionable.

claude.com · 12 min · AI · LLM

14:32

Claude Can Do All of This. Most People Have No Idea.

Claude 隐藏功能全指南：17 个你不知道的用法

This guide covers 17 hidden Claude features: persistent memory via Projects, interactive app building with Artifacts, step-by-step reasoning in Adaptive Thinking, long-term user profiling with Memory, role-based prompts, a browser agent (Claude in Chrome), desktop file-system access (Cowork), scheduled tasks, installable skills, CLAUDE.md project rules, terminal coding with Claude Code, visual design with Claude Design, and 90% cost reduction through Prompt Caching. Each includes where to find it and a ready-to-use prompt.

x.com · 11 min · AI

14:18

How to Actually Use Claude. 18 steps that unlock 100% of its potential

Claude 实战指南：18 个步骤解锁全部潜力

This guide provides 18 actionable steps to fully leverage Claude. It covers setting up Projects and Custom Instructions for persistent context, shifting your mindset to treat Claude as a thinking partner rather than a search engine, and using advanced techniques like style cloning, Extended Thinking, and token-saving prompts. Ready-to-use templates are included for Feynman-style learning, travel planning, expense analysis, and business idea stress-testing. A key insight: simply specifying output length can cut token usage by 40-60%. Aimed at users who want to go beyond basic Q&A and make Claude work for them.

x.com · 10 min · AI · LLM

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