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06:00

Build self-improving agent system with Fable 5 in 14 steps : loops, dynamic workflows, routines

用 Fable 5 搭建自我改进的代理系统：14 步指南

This article provides a detailed 14-step roadmap for building a self-improving agent system using Claude Fable 5. It moves Fable 5 from a prompt-and-close tool to a compounding system: using /goal and Outcomes for self-correcting loops, independent verifier sub-agents over self-critique, state files (STATE.md) and Skills for cross-session memory, and Dynamic Workflows and Routines for long-running autonomy. It includes a cost-capability matrix (Fable 5 for orchestration, Sonnet 4.6 for workers, Haiku 4.5 for graders) and guidance on handling the Mythos safety boundary. Suitable for AI engineers and system designers aiming to leverage Fable 5's days-long autonomous capability.

x.com · 28 min · Agent Engineering · Agents · AI Engineering · LLM · Loop Engineering
06:00

Getting started with loops in Claude Code

掌握Claude Code循环模式：从手动提示到自动任务编排

The Claude Code team defines four loop patterns (turn-based, goal-based, time-based, proactive) with trigger, stop criteria, use cases, and token management tips. Concrete commands like /goal, /loop, /schedule and a SKILL.md example show how to make agents iterate, self-verify, and compose primitives into automated workflows. A practical guide for developers exploring agent engineering.

x.com · 9 min · Agent Engineering · Claude Code · Context Engineering · Loop Engineering · Token-Optimization
06:00

Continual Learning for Agents: Eval, A/B, and Self-Improvement at Replit

不更新权重的持续学习：Replit Agent的评估与自改进实践

This article argues that continual learning for agents isn't limited to weight updates—agents using closed frontier models can improve via harness and context layers. Using Replit Agent as a case study, it details a three-layer evaluation system: ViBench, an offline benchmark for vibe coding that scores apps built from scratch against natural-language PRDs; online A/B tests to capture real user behavior; and Telescope, a trace analysis system that clusters failure patterns. These feed a self-improvement loop that automatically proposes patches (reviewed by engineers). A concrete example shows how a cold-start regression was detected, fixed, and shipped in one day. The piece is relevant for engineers building AI agents and evaluation infrastructure.

x.com · 16 min · Agent Architecture · Agents · AI Engineering · Context Engineering
01:37

Lessons from Building Claude Code: How We Use Skills

Claude Code 技能实战：Anthropic 内部数百条技能的经验与分类

Anthropic engineers share practical lessons from maintaining hundreds of Claude Code skills internally. The post categorizes skills into nine types—library reference, product verification, data fetching, business process automation, code scaffolding, code quality, CI/CD, runbooks, and infrastructure ops—and offers concrete writing tips: focus on non-obvious hints, build gotchas sections, use the filesystem for progressive disclosure, avoid over-constraint, design setup flows, treat the description field as a trigger for the model, store memory (logs, JSON, SQLite), and bundle scripts/libraries to reduce token waste. It also covers distribution strategies (in-repo vs. marketplace), skill composition, and usage measurement via PreToolUse hooks. A must-read for anyone building agent-centric workflows.

x.com · 15 min · Agent Engineering · Agent Skills · Anthropic · Claude Code · Context Engineering · Orchestration
01:36

Rewriting Bun in Rust: 535K Lines, 11 Days, 64 AI Agents

11天、64个AI Agent、535K行代码：Bun从Zig到Rust的史诗级重写

Bun's creator Jarred Sumner recounts how he used Anthropic's Claude Fable 5 to rewrite Bun's 535,496 lines of Zig into Rust in 11 days. The motivation: Zig's manual memory management caused numerous use-after-free, double-free, and memory leaks when mixed with JavaScriptCore's GC. Instead of an incremental port, he orchestrated 64 Claude agents in parallel using dynamic workflows and adversarial review. 100% of Bun's test suite (over 600k assertions) passed on all 6 platforms. The rewrite fixed 128 bugs, reduced memory usage by up to 90%, shrank the binary by ~20%, and improved throughput by 2-5%. The article details the workflow, common porting mistakes (e.g., debug_assert! side effects, slice overruns, comptime format differences), and how Rust's Drop systematically prevented memory leaks. A first-hand account of using cutting-edge AI to accomplish a year-long team project in less than two weeks.

bun.com · 65 min · Agent Engineering · AI Engineering · Code · Code Generation · Developer Tools