IdeaWell

Project ideas from Hacker News discussions.

AI will make formal verification go mainstream

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Execution and Automated Testing Essential for AI Agents

Users stress that AI coding agents require environments to run code, execute tests, and self-debug to produce reliable results.
"without the ability to run tests the AI will really go off the rails super quick" (QuercusMax).
"having a template for creating a project that includes at least one passing test... helps a lot" (simonw).
"LLMs are very good at looking at a change set and finding untested paths" via reviewer agents (planckscnst).

2. Human Developer Tools Boost AI Productivity

Formatters, linters, debuggers, and tests aid humans and AI alike, with agents adapting via training data or prompts.
"Isn‘t it funny how that’s exactly the kind of stuff that helps a human developer be successful and productive, too?" (ManuelKiessling).
"anything that's helpful for human developers... will also help LLMs be more productive. For largely identical reasons" (formerly_proven).
"agents generate code that conforms to Black quite effectively" from project patterns (simonw).

3. Formal Verification Promising but Challenging for AI

AI could automate proofs, leveraging type systems (e.g., Haskell, Lean), but specs, ownership issues, and changing requirements limit mainstream use.
"formal verification tools... They're potentially a fantastic unlock for agents" (simonw).
"LLM agents tend to struggle with semi-complex ownership... reach for unnecessary/dangerous escape hatches" in Rust (formerly_proven).
"the biggest reason formal verification isn't used much... requirements are changing constantly" (Analemma_).

🚀 Project Ideas

TraceFlow AI

Summary

  • A "runtime behavior verification" service that connects coding agents to live execution environments (Docker/cloud) to prevent "hallucination loops."
  • It allows agents to perform "spray and pray" debugging safely, using tools like tmux, Playwright, and gdb to inspect the code they just wrote.
  • Solves the problem of agents saying "Aha! I know what the problem is!" repeatedly without actually validating the fix.

Details

Key Value
Target Audience Developers using Claude Code, Codex, or custom coding agents.
Core Feature Instrumented sandbox with real-time feedback loop (logs, screenshots, debugger output).
Tech Stack Docker, Python (FastAPI), Playwright, tmux, gdb.
Difficulty Medium
Monetization Revenue-ready: Monthly subscription based on compute hours/sandbox instances.

Notes

  • "Without the ability to run tests the AI will really go off the rails super quick, and it's kinda hilarious to watch it say 'Aha! I know what the problem is!' over and over."
  • This addresses the specific need for agents to "exercise and validate the code" through interactive tools like capture-pane in tmux.

SpecForge

Summary

  • A "Natural Language to Formal Spec" translation layer that generates TLA+ or Lean 4 specifications from PR descriptions or design docs.
  • It acts as an "Oracle" that forces the coding agent to adhere to mathematical invariants (e.g., "user data never touches analytics without anonymization").
  • Lowers the "PhD-level" barrier to entry for formal methods by letting the LLM handle the proof script generation while checking it against a formal kernel.

Details

Key Value
Target Audience High-reliability engineering teams (FinTech, Infrastructure, Security).
Core Feature Automated generation and mechanical checking of TLA+/Lean proofs for specific modules.
Tech Stack Lean 4, TLA+ (TLC/Apalache), Python/Rust backend.
Difficulty High
Monetization Revenue-ready: Seat-based enterprise license or per-verification-run fee.

Notes

  • "Proof-checking solves... reward specification and grounding. You can run your solver for a long time, and if it finds an answer, you can trust that without worrying about reward hacking or hallucination."
  • This targets the "vibe coding" shift toward functional, strongly typed, and formally verifiable languages.

Agentic Style-Shield

Summary

  • A "semantic linter" and proxy that ensures LLM-generated code doesn't "poison the context" with formatting diffs or minor syntax stylistic drifts.
  • It automatically intercepts agent output, runs it through black/prettier/ruff, and returns the strictly formatted version to the agent's context before the agent continues.
  • Prevents the "context-poisoning effect" where an LLM generates multiple variations of a file due to minor formatting inconsistencies.

Details

Key Value
Target Audience Individual developers and teams using "vanilla" LLM interfaces for bulk coding.
Core Feature Post-processing proxy that forces zero-diff auto-formatting on agent output.
Tech Stack Go / Node.js, integration with Prettier, Black, and Ruff.
Difficulty Low
Monetization Hobby (Open Source) or Revenue-ready: SaaS API.

Notes

  • "I've directly faced this problem with automatic code formatters... This caused both extra turns/invocations with the LLM and would cause context issues... filling the context with multiple variants of the file."
  • Users noted that even frontier models still generate "empty indented lines" or "trailing whitespace" despite instructions; this tool automates the "pre-commit" fix within the agent loop.

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