by geoffrey-huntley
Understanding how coding agents work—tool calls, inferencing loops, the basic primitives—is now baseline knowledge for software engineering interviews, not optional curiosity.
by indydevdan
Thread-based engineering provides a mental framework for measuring improvement with AI agents—scale by running more threads, longer threads, thicker threads, and fewer human checkpoints.
by simon-willison
Claude Cowork repackages Claude Code's powerful agentic capabilities for general audiences through accessible design rather than technical innovation—a pragmatic approach to unlock untapped value.
by anthropic
Official documentation on Skills—markdown files that extend Claude Code with specialized knowledge and workflows, triggered automatically based on semantic matching.
by geoffrey-huntley
A hands-on Go workshop that builds a coding agent incrementally through six files, each adding one capability - proving agents need only simple primitives composed in a loop.
The Ralph Wiggum technique: a bash loop that runs AI agents autonomously, resetting context each iteration to stay in the 'smart zone'
by geoffrey-huntley
Software development now costs $10.42/hour when running Ralph loops—the key is deterministically malicking the array, starting with a screwdriver before grabbing the jackhammer.
by covibes
Multi-agent validation prevents self-deception in autonomous coding—the validator didn't write the code, so it can't lie about tests.
by peter-steinberger
A self-hosted Claude-powered assistant that runs across messaging platforms like WhatsApp, Telegram, and Slack—turning natural language into automated workflows for email, calendars, and home automation.
by worldofai
Claude Code 2.1 ships a massive feature set—skill hot-reloading, forked sub-agents, Chrome browser integration, LSP support, and async agents transform the terminal into a multi-threaded agent orchestration platform.
by alexander-opalic
Claude Code 2.1 collapses the distinction between skills, slash commands, and subagents—skills can now fork into isolated contexts and specify their own model, making them the single abstraction for agent orchestration.
by alexander-opalic
Claude Code 2.1 transforms skills from specialized knowledge containers into a unified automation abstraction—hot-reloading, forked contexts, lifecycle hooks, and agent specification make skills the default choice for extending Claude Code.
by visual-studio-code
Agent Skills are portable instruction folders that load on demand, transforming AI agents from general assistants into domain-specific experts through scripts, examples, and specialized workflows.
by humanlayer-team
Systematic context management—through frequent intentional compaction and a Research-Plan-Implement workflow—enables productive AI-assisted development in complex production codebases.
by langchain
Context engineering—filling the context window with the right information at each step—determines agent performance more than model choice or complex frameworks.
by jediah-katz
Coding agents perform better when they pull context on demand rather than receiving everything upfront—files serve as a simple, future-proof abstraction for this dynamic retrieval.
by ryan-carson, geoffrey-huntley
Ralph ships code while you sleep—a bash loop that pipes prompts into your AI agent, picks tasks from a JSON backlog, runs tests, commits passing code, and repeats until done.
by ryan-carson
Ralph solves the context window limitation by breaking work into independent iterations—each Amp session gets a fresh context, implements one story, and commits before the next iteration begins.
by repomirrorhq
Running Claude Code in headless loops can port entire codebases overnight—the key is simple prompts, one goal per iteration, and letting agents self-terminate when stuck.
by developers-digest
Claude Code can become a self-correcting system by capturing mistakes in CLAUDE.md—each error becomes a permanent lesson that compounds over time.
by dex-horthy
The Ralph Wiggum Technique evolved from a novel idea to a mainstream development methodology in under a year—early adopters learned that poor specs doom loops and iteration beats over-planning.
by thariq-shihipar
Bash is the most powerful agent tool. The Claude Agent SDK packages Claude Code's battle-tested patterns—tools, file system, skills, sandboxing—for building coding and non-coding agents alike.
by burke-holland
AI coding agents can replace developers for many tasks—Claude Opus 4.5's self-correction capabilities make human code review increasingly optional.
by geoffrey-huntley, dex-horthy
The official Anthropic Ralph plugin differs fundamentally from the original technique: outer orchestrators with full context resets produce deterministic outcomes, while inner-loop plugins with auto-compaction lose critical context.
by alexander-opalic
Ralph is a bash loop that feeds prompts to AI coding agents repeatedly—the key is one goal per context window, deliberate context allocation, and robust feedback loops.
by philipp-schmid
As AI models converge in benchmark performance, the infrastructure managing them—Agent Harnesses—becomes the competitive differentiator for building reliable, multi-day workflows.
The Pragmatic Engineer Podcast
Chip Huyen explains how AI engineering differs from ML engineering, walking through the practical developmental path from prompts to RAG to fine-tuning.
by chip-huyen
A practitioner's guide to building applications on foundation models, covering prompt engineering, RAG, finetuning, agents, and evaluation.
by aicodeking
Vibe Kanban is an open-source tool that lets you orchestrate multiple AI coding agents from a visual Kanban board, treating coding tasks as asynchronous jobs rather than blocking conversations.
by cian-clarke
BMAD's spec-driven methodology beats pure prompting for production-ready AI development because it forces clarity before code and catches requirements gaps before they become technical debt.
The Diary of a CEO • Guest: tristan-harris
Tristan Harris warns that AI companies are racing to build a 'digital god' that could automate all human cognitive labor, with insiders believing this will happen within 2-10 years while publicly downplaying the risks.
by mo-gawdat, steven-bartlett
Mo Gawdat, former Chief Business Officer at Google X, warns that AI represents humanity's greatest existential challenge—bigger than climate change—and outlines his 'three inevitables' for why we're approaching a point of no return.
by dex-horthy
A practical framework for getting AI coding agents to work reliably in brownfield codebases through context engineering, intentional compaction, and the Research-Plan-Implement workflow.
by upro-vi
Community discussion on Ralph Wiggum loop trustworthiness—skeptics question validation limits while practitioners share multi-iteration success stories.
by jesse-vincent
A skills library for Claude Code that provides structured workflows for AI-assisted development, including brainstorming, implementation planning, TDD, and debugging.
by varin-nair
Frontier models cap out at 1-2 million tokens, yet enterprise codebases span several million. Factory's solution: a five-layer context stack that delivers the right information at the right time.
by alexander-opalic
A comprehensive breakdown of Claude Code's extensibility layers—MCP servers, CLAUDE.md files, slash commands, subagents, hooks, and skills—explaining when to use each component.
by antonio-gulli
A practical guide presenting 21 design patterns for building AI agents, covering prompt chaining, tool use, multi-agent collaboration, and self-correction techniques with examples in LangChain, CrewAI, and Google ADK.
by mohammad-ghassemi
Michigan State University lecture covering three agentic design patterns—prompt chaining, routing, and reflection—with practical LangChain and LangGraph implementations.
Patterns and practices for building autonomous AI agents with prompt chaining, routing, and reflection
by lee-robinson
Lee Robinson reflects on how coding agents have surpassed his coding ability and shares four recommendations for software engineers adapting to 2026.
by andrej-karpathy, dwarkesh-patel
Karpathy argues we're building 'ghosts' that imitate internet documents rather than evolved animals, and that practical AI agents will take a decade—not a year—to fully mature.
by steve-yegge
A distributed, git-backed graph issue tracker designed for AI coding agents, replacing markdown task lists with structured, dependency-aware workflows.
by maham-codes
A practical guide to building AI agents using prompt chaining and basic primitives instead of heavy frameworks.
by mark-anthony-cianfrani
A first-principles guide to building AI agents in TypeScript, covering LLM integration, conversation memory, tool calling, and agentic loops.
by erik-schluntz, barry-zhang
Anthropic's guide to building agentic LLM systems, advocating for simple composable patterns over complex frameworks.
by john-lindquist
Claude Code operates as a programmable AI platform with isolation, extensibility, and automation as first-class features—not a traditional CLI assistant.
by worldofai
Step-by-step walkthrough for installing and using the Ralph Loop plugin with Claude Code, enabling autonomous iterative development through a stop-hook mechanism.
by daniel-avila-arias
Hedgineer built a knowledge distribution system using Claude Skills that automatically applies institutional expertise without requiring users to invoke specific commands.
by luke-parker
Replace interactive AI chat with structured execution loops—invest heavily in planning, dump full context each iteration, and let verification backpressure catch errors before they compound.
by yacine-mahdid
Prompt chaining breaks complex LLM tasks into sequential steps, each with a specific job and structured input/output, trading latency for reliability.
by humanlayer-team
A manifesto for building production-grade LLM agents, arguing that effective agents combine mostly deterministic software with strategic LLM decision-making rather than naive 'loop until solved' patterns.
by simon-willison
Simon Willison's comprehensive year-in-review analyzing how reasoning models, autonomous agents, and Chinese AI competition fundamentally reshaped the landscape in 2025.
A curated map connecting key resources for understanding AI agents and context engineering
by nate-b-jones
Traditional front-end engineering (hand-coding pages from Figma designs) is being replaced by composability—designing primitives, schemas, and contracts that let AI, PMs, and designers ship dynamic interfaces without reinventing the wheel.
by geoffrey-huntley
A Bash loop technique ('Ralph') that feeds prompts to AI coding agents repeatedly, enabling autonomous project development with iterative refinement.
by andrew-qu
Stripping a text-to-SQL agent down to a single bash tool produced a 3.5x speedup, 100% success rate, and 37% fewer tokens—proving that simpler agent architectures outperform elaborate tooling.
by anthropic
AI coding assistants boost productivity dramatically but create a supervision paradox: effectively overseeing AI requires the deep technical skills that may erode from overreliance.
by ryan-x-charles
Markdown has become a general-purpose programming language—AI agents like Claude Code compile structured specifications into working applications.
by den-delimarsky
Spec Kit provides a structured four-phase workflow for AI coding agents, replacing vague prompting with specification-driven development.
by geoffrey-huntley
Coding agents require only 300 lines of code in a loop with LLM tokens - understanding these fundamentals transforms you from AI consumer to producer.