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.
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 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 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 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 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 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 microsoft
A systematic approach to providing AI agents with targeted project information through custom instructions, planning agents, and structured workflows to improve code generation quality.
Techniques for providing AI agents and LLMs with optimized context