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Honeycomb's engineering team more than doubled peak daily merges from ~30 to ~74 using AI tools like Claude Code, with AI-attributed code rising to 82.6% by June 2026, while managing incidents proportionally. They share practices like continuous delivery, fast CI, and observability that amplified with AI.
Gergely Orosz tweets about a podcast with Dex Horthy on LLM context engineering, covering lessons like the dangers of shipping unread code and recognizing when an LLM session is trajectory-poisoned.
Replit AI head shares engineering practices for a production-grade Agent continual learning system, breaking improvements into three layers: Model, Harness, and Context, and building a closed loop of offline validation and online A/B testing, allowing the Agent to continuously improve without updating the model.
The author draws parallels between the early microservices hype and current multi-agent system hype, arguing that engineering practices—not better models—may be the key to reliable multi-agent systems.
This article compiles 10 high-value Skills essential for Codex, covering planning, code quality, frontend implementation, automated testing, CI/CD, security, and external tool integration, and provides installation methods and recommended combinations to help users improve development efficiency.
Google Principal Engineer Addy Osmani open-sourced agent-skills, which includes 24 AI programming skills, packaging engineering disciplines for use by AI agents to improve code quality and development efficiency.
An explanation of loop engineering for AI agents, emphasizing the separation of maker and checker agents, state persistence on disk, and exit conditions to avoid runaway costs.
A GitHub repository that packages production-grade engineering skills for AI coding agents, encoding senior engineer workflows and quality gates into slash commands like /spec, /plan, /build, etc., with setup instructions for Claude Code, Cursor, and other tools.
awesome-harness-engineering is a curated list of resources on AI agent harness engineering (context management, tool design, verification loops, memory systems, etc.) from companies like OpenAI, Anthropic, Microsoft, and Meta, aimed at helping developers build reliable agent frameworks.
Engineers across database, iOS, frontend, data engineering, and backend domains discuss how AI code generation shifts the hard part to verification and integration, requiring human judgment for subtle risks and architectural fit.
An article detailing the exact configuration and workflows that Anthropic's own engineers use with Claude Code, including parallel instances, CLAUDE.md patterns, writer/reviewer separation, skills folders, plugins, hooks, and batch operations.
Anthropic shares engineering best practices for designing, evaluating, and optimizing tools for AI agents, specifically utilizing the Model Context Protocol (MCP) and Claude Code to improve agent performance.