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Santiago Valdarrama shares a framework for building AI agents that improve over time through three learning areas: model refinement, harness optimization, and context accumulation, emphasizing the importance of learning from user corrections.
A detailed blog post arguing that no font is truly web-safe, and recommending always including generic families like monospace, serif, or sans-serif in CSS font-family declarations. It criticizes excessive font stacking and advises trusting the system's default font selection.
The author shares five patterns that consistently kill AI agents: too many jobs per agent, no human-in-the-loop for destructive actions, unstructured outputs, no spend caps, and lack of uncertainty escalation paths. Practical guardrails and a checklist for reliable agent deployment are provided.
A practical guide to writing effective software design documents, outlining key components and best practices drawn from the author's experience at Google and Microsoft.
This article explores how Java development teams are establishing guardrails and best practices to manage the quality, security, and reliability of AI-generated code.
The article discusses varying approaches to writing specifications for AI coding agents and asks for community input on effective methods.
This tweet shares best practices for agent observability, covering metrics, logs, and traces to debug and optimize production AI agents.
Highlights the common disconnect between AI agents and human teams sharing the same source of truth, and how most current setups fail to achieve this.
A thread explaining the 5 core mental shifts needed to transition from traditional software engineering to agent engineering, emphasizing why conventional patterns like hard-coded routes and binary tests fail with AI agents.
The article proposes that organizations adopting AI coding agents should create a company-wide AGENTS.md file, similar to a human onboarding doc, to standardize agent behavior and context.
Many AI agent implementations fail because they treat agents like chatbots, relying on chat history for state rather than using deterministic data structures. The article advocates for separating reasoning (LLM), actions (tools), workflow progress (state machine), and external triggers (webhooks) to build reliable business agents.
A detailed guide to building a correct PyTorch training loop, highlighting common mistakes and proper ordering of operations.
This thread explains why AI builders should use loops instead of single prompts, emphasizing proper triggers, verification, and stop conditions to build reliable, cost-effective AI systems.
A blog post summarizing ten recent agentic RL frameworks and best practices, covering modular interfaces, trajectory structure, action masks, process rewards, advantage normalization, scalable rollouts, stability/exploration, and task curriculum.
An indie developer shares hard-earned lessons from over a year of experience, recommending building a WebPWA (React tech stack) first during the MVP stage to quickly validate requirements, then using React Native/Expo for the mobile app once users are acquired.
This tweet introduces the 9-step guide for Claude Code Dynamic Workflows, emphasizing structured loops and best practices for multi-agent workflows, including manual review, worktree isolation, and automatic rework, pointing out that this is the key to turning agent swarms from toys into productivity.
The article argues that human code reviewers should use AI to handle large diffs, and instead contribute their out-of-distribution knowledge and high-level context.
A comprehensive guide with 58K+ stars on best practices for using Claude Code, covering tips from Boris Cherny and community workflows to maximize agentic engineering.
This article shares practical experience of using Codex /goal mode for long-term unattended programming, including how to write effective prompts, using persistent project memory to prevent deviation, and key settings and precautions.
This Cloudflare playbook is designed for the AI coding era, organizing usage methods, common pitfalls, and AI coding workflows for each Cloudflare module, suitable as a reference guide for AI writing Cloudflare projects.