Results for “autonomous-development”

Mental models and decision frameworks for building autonomous agents that continuously learn, explore, and accumulate skills in open-ended environments without human supervision

自主Claude代码循环的模式与架构——从简单的顺序管道到基于RFC的多智能体有向无环图系统。

Autonomous evolutionary code improvement engine with tournament selection

当需要将个人学习、成长与价值创造整合为一个持续发展的系统时

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

Autonomous error recovery — detect failures, diagnose root cause, apply fixes, and resume without stopping

Analyzes game AI systems in a codebase, covering behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering, perception, difficulty adaptation, NPC dialogue, and AI debugging tools for Unity, Unreal, and Godot projects.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

当设定个人或工作目标，希望目标本身能带来持续的内在动力和满足感时

当意识到自己处于被动、重复、缺乏意义的生活状态，想要主动设计人生时

Automates the entire software development lifecycle from PRD to production with zero human intervention, using multi-agent orchestration and memory systems.

Turns a brief product idea into working, verified code by orchestrating requirements analysis, planning, parallel implementation, QA, and multi-perspective validation.

AutoAugment: Learning Augmentation Strategies from Data

Self-evolution and contribution system for makepad-skills. Use when capturing new patterns, correcting skill errors, contributing learnings, or setting up hooks for auto-triggered skill updates.

当需要主动设计和构建个人职业生涯，而非被动接受传统就业时

阶段② 详细设计。根据 design.md 第一章补全第二章各模块设计 + 第四章 ADR。

Orchestrate autonomous AI development pipelines through your Kanban board, managing multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.