Enola: engineering intelligence layer for AI coding agents
Enola is an open-source engineering intelligence layer that helps AI coding agents understand existing codebases. It answers questions about change impact, dependency reachability, safe module deletion, refactoring priorities, and architecture drift. The tool uses LLMs to analyze code context and provide insights that reduce mistakes from both humans and AI agents.
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A framework that makes Clean Architecture's dependency rule structurally unbreakable, similar to OS kernel isolation. It ensures that code layers cannot violate inward-only dependencies at compile time, which is particularly useful for LLM-generated code where conventional review may be unreliable. Targets developers using LLMs to generate or modify codebases.
CleanSlate IDE with built-in agent manager for multi-agent coding workflows
CleanSlate is an IDE that integrates an agent manager directly, allowing developers to create, manage, and switch between multiple coding agents without leaving the editor. It solves the friction of toggling between separate agent management tools and the IDE, enabling seamless context preservation and multi-agent orchestration for developers working with LLM-powered coding agents.
Git-aware AI debugger that checks out old commits to fix production bugs
A tool that makes AI coding assistants (like Cursor or Claude Code) automatically checkout the git commit corresponding to a production error before debugging, preventing the agent from analyzing current code that has shifted. It solves the problem of AI agents hallucinating fixes because they look at the present state of files while the bug existed in a past commit.
Collection of 48 working AI agent examples in Python and TypeScript
A curated repository of 48 functional AI agent implementations covering common patterns like research, code review, SQL, data analysis, and web scraping. Each example is designed to be cloned and run immediately, solving the problem of broken or incomplete agent tutorials for developers building AI systems.
Developer completes Python-for-AI course covering agents and LLM evals
A developer completed a comprehensive Python-for-AI course that covered core Python, data structures, tooling, and agent-specific material including LLM evals, the Analyze-Measure-Improve cycle, and building a basic AI coding agent from scratch. The course also covered first-principles agent architecture with intelligence layer, memory, tools, validation, and control.