Kimchi: Turn raw ideas into build-ready docs for AI execution
Kimchi is a tool that takes a raw product idea and generates a structured set of build-ready documents: EPICs with user stories, locked decisions (tech stack, architecture, API contracts), and an execute.md that an AI coding agent (like Claude Code or Codex) can follow without re-prompting. It solves the problem of vague specifications and endless back-and-forth in AI-assisted development, helping developers and founders get consistent builds from their ideas.
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JackHamr: AI agent that builds and deploys software from a single request
JackHamr is a platform where autonomous AI agents handle the full software development pipeline: from spec and mockups to code, tests, PR, and deployment. It uses LLMs to generate specifications, mockups, and code, with approval gates at key stages. The product aims to streamline development for teams or individuals by automating repetitive tasks.
Keel: open-source AI CTO for non-technical product builders
Keel is an open-source AI agent that acts as a virtual CTO for product builders and 'vibe coders' with limited technical experience. It uses LLMs to guide users through the product development process, helping them avoid common pitfalls like over-optimistic coding agents and lack of technical oversight. The tool aims to bridge the gap between non-technical founders and software development.
JackHamr: AI agent platform that builds and ships software from plain-English specs
JackHamr is a platform where AI agents autonomously build, test, and ship software end-to-end. Given a plain-English request, it generates specs and mockups, waits for approval, then codes, tests, and deploys the feature. It solves the problem of manual software development by automating the entire pipeline for developers.
Kote: Capture and reuse engineering context from AI chats and Git
Kote automatically captures engineering context from AI assistant chats and Git activity, storing it for later retrieval during pull requests or quick notes. It solves the problem of losing valuable debugging or architectural context by eliminating the need for manual documentation. Targeted at developers who use AI coding assistants.
Developer shares practical AI coding workflow: paper first, then Fable/Opus for planning, Sonnet for implementation
A developer on Reddit outlines a structured AI-assisted coding workflow for full projects, emphasizing upfront planning on paper before involving any model. The pipeline uses Fable for planning, Fable/Opus to break work into markdown files, and Sonnet for implementation under a safety hook, contrasting with the common 'vibe coding in agent mode' approach.