日刊 · 2026-05-29 — Glean

Fri, May 29, 2026 11picks

17:56

Claude Code Dynamic Workflows: A New Primitive That Moves Orchestration Into Code

Claude Code Dynamic Workflows：把编排逻辑搬进代码的新原语

Anthropic introduces Dynamic Workflows, a primitive that turns task orchestration into JavaScript scripts executed by a deterministic runtime. The script manages loops, branching, and intermediate results, so only the final answer enters the main Claude context—solving the bottleneck of context overflow and attention dilution when coordinating hundreds of parallel tasks. A deep dive into architecture, primitives, and execution model is paired with a real-world Bun-to-Rust migration (11 days, 750K lines, 99.8% test pass) and a personal 133-session analysis. Comparisons with n8n/Coze/Dify show Workflow's advantage: Turing-complete code offers more expressiveness than visual DAGs, and the orchestration can be generated on‑the‑fly by a model. It shines for codebase audits, large migrations, and adversarial verification but comes with high token costs and current preview limits. Target audience: engineers tackling massive, automated coding tasks.

x.com · 19 min · Agents · AI · Framework

14:47

Context Engineering Is Replacing Prompt Engineering. Here's How It Works

上下文工程正在替代提示工程：五层框架与实践指南

The author argues that prompt engineering is giving way to 'context engineering'—building the environment of information (identity, knowledge, memory, tools, processes) that enables a model to produce consistent results with minimal prompting. A five-layer framework is detailed, with practical steps for Claude users: set custom instructions, upload knowledge files, actively craft memory, connect MCP tools, and encode processes as Skills. The piece is opinionated and lacks empirical evidence but offers actionable guidance for those heavily using Claude.

x.com · 12 min · AI · Framework · LLM

09:11

Prompt → Context → Harness: The Three Paradigms of AI Engineering

从 Prompt 到 Context 再到 Harness：AI 工程的三次范式转移

AI engineering has undergone three paradigm shifts: from Prompt Engineering (2023–2024) to Context Engineering (2025), and now to Harness Engineering (early 2026). Harness Engineering combines evaluation feedback loops, architectural constraints, and memory governance. Anthropic’s evaluator agent turned a 20‑minute useless artifact into a 6‑hour complete game; OpenAI built a million‑line system with zero human‑written code in five months, enforcing architectural boundaries via CI/linters. Two academic papers fill the memory layer: (S)AGE uses Byzantine‑fault‑tolerant Proof of Experience consensus to double agent calibration accuracy; a longitudinal study shows that 3 lines of prompt plus memory matches 200 lines of expert prompt in performance, yet only the memory group improves over time. Essential for engineers building multi‑agent systems.

x.com · 3 min · Agents · AI · LLM

09:11

10x Your Claude Skills with Karpathy's Autoresearch Method

用 Karpathy 的 autoresearch 方法，将你的 Claude Skills 效果提升10倍

This article shows how to automatically improve Claude Skills using Karpathy's autoresearch method. It works by giving an agent a yes/no checklist and letting it iteratively test, tweak, and keep only beneficial changes. The author improved a landing page copy skill from 56% to 92% pass rate in four rounds, with visible changelogs. The method applies to any measurable task—define a checklist and let the agent run. Includes download links and practical examples for engineers building AI workflows.

x.com · 5 min · Agents · AI

09:11

ClickHouse 10 Best Practices

ClickHouse十大最佳实践：从主键到JOIN的深度优化指南

A ClickHouse solution architect shares 10 field-tested best practices derived from customer engagements, covering schema design, data types, partitioning, skipping indexes, JSON type, data ingestion, materialized views, system tables, ReplacingMergeTree, and JOIN optimization. Benchmarks on a 150M-row Amazon reviews dataset quantify the impact: proper ORDER BY reduces rows scanned by 347×, unnecessary partitioning slows queries by 46×, correct data types cut storage by 12% and double query speed, skipping indexes reduce scans by 80%, and dictionary lookups beat regular JOINs by nearly 3×. The article emphasizes understanding ClickHouse internals to achieve orders-of-magnitude improvements without hardware changes.

www.infoq.cn · 15 min · Database · Performance

09:10

Introducing React Best Practices: A Structured Repo for AI Agents

Vercel 发布 React 最佳实践仓库，面向 AI 编程代理优化

Vercel distills 10+ years of React and Next.js optimization into a structured repo with 40+ rules across 8 categories, ordered by impact from eliminating waterfalls to JavaScript micro-optimizations. Each rule includes code samples and impact ratings, and compiles into an AGENTS.md document consumable by AI coding agents.

vercel.com · 6 min · AI · Performance · React

09:10

ClickStack Observability: MCP Server, AI Notebooks, and ClickStack Cloud

ClickHouse 可观测性三连发布：MCP Server、AI Notebooks 与 ClickStack 云服务

At Open House, ClickHouse announced three major observability updates: ClickStack Cloud (serverless, managed, private preview), AI Notebooks (beta), and an open-source ClickStack MCP server. AI Notebooks replace linear chat with persistent, branchable investigation workspaces, exposing every query and step. The MCP server provides semantic investigative tools to external agents; internal benchmarks show 25% fewer tool calls, 2.5× consistency improvement, and 20% higher evaluation scores vs. raw SQL MCP. The server also supports bi‑directional orchestration: agents can create dashboards and persist results. The design philosophy is “bring your own agents,” with SQL as an escape hatch when pre‑built tools fall short. The post includes setup instructions and a demo. For infrastructure/SRE engineers evaluating ClickHouse-based observability.

clickhouse.com · 15 min · Agents · AI · Database

09:10

From OTel to Rotel: 4x Throughput Increase in PB-Scale Tracing

从 OTel 到 Rotel：每秒处理量提升 4 倍的 PB 级追踪系统

This article benchmarks OpenTelemetry data planes for writing trace spans to ClickHouse. On the same 8‑core host, Rotel achieves 3.7 million spans/sec (462k spans/core/sec), a >4× improvement over the OTel Collector. Gains come from three optimizations: binary encoding of JSON columns in RowBinary, moving deserialization to a shared thread pool to avoid tokio blocking and glibc allocator lock contention, and enabling fast LZ4 compression. The test also exposes silent data loss in the OTel Collector under backpressure. For engineers scaling large telemetry pipelines.

www.infoq.cn · 18 min · Database · Performance

09:10

A.I. Should Elevate Your Thinking, Not Replace It

工程思维的分水岭：你是在用 AI 提升层次，还是外包思考？

Software engineering is splitting into two groups: those who use AI to remove drudgery and invest in higher-level thinking, and those who outsource their reasoning to AI, simulating competence without building it. This 'outsourced thinking' is a new failure mode that erodes judgment over time. The real value of engineers lies in framing problems, making tradeoffs, and creating clarity—skills AI cannot own. Early-career engineers are especially at risk of skipping essential skill formation. Leadership must learn to differentiate polished output from genuine technical depth. The article argues that organizational health depends on recognizing this divide.

www.koshyjohn.com · 11 min · AI

09:10

Introducing ClickHouse Agent Skills

ClickHouse 发布 Agent Skills：28 条规则让 AI 助手学会 ClickHouse

ClickHouse has released official Agent Skills: an open-source set of 28 prioritized best-practice rules covering schema design, query optimization, and data ingestion, packaged using Anthropic's Agent Skills specification. Users can add them locally with `npx skills add clickhouse/agent-skills`. AI agents (e.g., Claude Code) automatically invoke these rules when appropriate, helping avoid common pitfalls like wrong ORDER BY, non-scalable JOINs, or missing materialized views. The Apache 2.0-licensed repo welcomes community contributions.

clickhouse.com · 3 min · Agents · AI · Database

09:10

Organizational Structure for AI-First in the Harness Era

Harness 时代 AI-First 的组织架构

A podcast interview with Creao's founders explores Harness Engineering—building self-healing, self-improving systems around LLMs. True AI-First companies restructure around AI as the primary producer: development cycles shrink from weeks to a day, product managers are dismantled, and cross-team alignment is automated. Junior engineers adapt faster than seniors; the future rewards architecture + product + marketing generalists. The 'Agent Economy' means content may be produced for AI consumers. A 25-person team rebuilt their architecture in two weeks. Full transcript available.

x.com · 2 min · Agents · AI · Framework

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