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What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
An unofficial community-maintained Ruby SDK for E2B
The best-benchmarked open-source AI memory system. And it's free.
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, OpenClaw, Factory Droid, Trae, Google Antigravity). Turn any folder of code, docs, papers, images, o…
A mocking library for testing stripe ruby
Stripe webhook integration for Rails applications.
Companion code for the "Building AI-Powered Apps with RubyLLM" workshop
Self-hosted error tracking for Rails - zero recurring cost, full data ownership.
Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA
OpenTelemetry instrumentation for RubyLLM. 💬🔭
Reorderable drag-and-drop lists for modern browsers and touch devices. No jQuery or framework required.
The speed of fixtures with the syntactic sugar of factories.
Natural language SQL query builder for Rails, powered by AI
A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows
A Rails agent framework for RubyLLM — define AI agents with prompts, schemas, caching, logging, cost tracking, and a built-in dashboard for monitoring LLM usage in production.
mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local
Barman - Backup and Recovery Manager for PostgreSQL
Ruby client for Typesense: https://github.com/typesense/typesense
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
OpenTelemetry Instrumentation for AI Observability