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TL;DR: I created a Python package to make running agentic automated theorem provers (e.g., Aristotle, Numina-Lean-Agent, Claude Code, etc...) as simple as open-atp prove Lemma.lean result/ claude I took a class on formal verification back in 2022 when I was an undergraduate. There was something incredibly satisfying…
Developer tool for formal-methods engineers and researchers who waste hours wiring LLM-based ATPs. Wraps multiple agentic provers behind a single CLI and Python package for reproducible, CI-friendly proof attempts.
Primary: individual and small teams of developers/researchers using Lean, Coq, or similar theorem provers who run proofs weekly. Secondary: dev teams in safety-critical domains (finance, aerospace, embedded) that need repeated proofs in CI. Enterprise: verification teams at mid-large companies and consulting firms that embed formal proofs into client work.
5 competitors are named in the Pro brief, with positioning and the gaps they leave open. Hobby ($19) opens the solution, market signals, and go-to-market plan.
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Analysis, scores, and revenue estimates are for educational purposes only and are based on AI models. Actual results may vary depending on execution and market conditions.
Developers running multiple AI coding agents struggle with state, branching, and reproducibility. An IDE that orchestrates parallel agents in isolated git worktrees solves coordination, visibility, and safe experimentation.
Postgres incidents cost hours of debugging across logs and metrics. An AI-first observability autopilot synthesizes internals into a single health score, prioritized fixes, and actionable runbooks so teams resolve incidents faster.
AI agents fail or behave unpredictably because skills are brittle and unmeasured. SkillOpt automates skill authoring, testing, and continuous optimization with instrumentation and learning-based tuning.
Automations and tests fail when apps send six digit OTPs by email. This service reads transactional inboxes, extracts time bound codes reliably, and returns them via a secure API to enable end-to-end automation.
Docs drift from reality and teams rebuild on incorrect assumptions. Use git-native document provenance, CI checks, and AI semantic diffing to keep docs correct, versioned, and testable alongside code. Reduce bugs and onboarding time.
React components lack automated WCAG 2.1 AA verification, causing manual audits and lost dev time. Build an integrated audit suite - eslint rules, CI runner, runtime checks and AI remediation suggestions to cut testing time by 70%.
