Lemmafit: Make agents prove that their code is correct
Details
- External ID
- 47298874
- Source
- HN
- Company
- —
- Product
- Lemmafit: Make agents prove that their code is correct
- Website domain
- github.com
- Launched
- March 8, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.4108241082410824
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Enrichment
- Theme
- coding agent interfaces and environments
- Vertical
- Horizontal
- Function
- Agent / copilot
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- verify agent-generated code correctness
- Manually corrected
- False
Could you build this?
No Automating formal software verification where AI coding agents produce and prove mathematical lemmas regarding program correctness requires deep research expertise in formal methods and automated theorem provers.
What it would actually take: The system must translate generated code into formal specifications consumable by interactive theorem provers (like Lean, Coq, Isabelle) or SMT solvers (Z3). An agentic feedback loop must iteratively formulate invariant claims, generate proof steps, and interpret solver errors to achieve machine-checked proof certificates. This requires specialized knowledge in formal verification, type theory, and proof assistant tooling.
Discussion
5 comments analyzed.
Competitors mentioned: Dafny (direct alternative for formal verification)
Concerns raised: Proofs can take over 20 minutes to complete, LLM doesn't get proofs right in one shot, requires iteration, Limited analysis on LLM proof correctness without special prompting, Unclear how often proofs succeed without intervention
Feature requests: Worked examples showing simple project derivation with TypeScript and Dafny, Case studies demonstrating real-world usage, Better documentation for users unfamiliar with Dafny
Competitors
Other products that read as similar to this one — 1515 launches clear the similarity bar, closest 8 shown.
Attention rank: #798 of 1516 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 130 days after the earliest competitor.
- Agent-to-code JIT compiler for Z3-theorem-proving agents · hn · 2025-11-13 · 6 upvotes · similarity 0.66
- Vet · hn · 2026-03-05 · 17 upvotes · similarity 0.65
- Hubo · hn · 2026-07-25 · 11 upvotes · similarity 0.65
- writ · github · 2026-09-21 · 9 upvotes · similarity 0.63
- Rubric AI · yc · 2026-03-11 · 14 upvotes · similarity 0.62
- Prism: Lightning fast inference for coding agents · yc · 2026-09-24 · 4 upvotes · similarity 0.62
- claude-mimic · github · 2026-09-13 · 9 upvotes · similarity 0.62
- self-compact-pi-agent · github · 2026-09-20 · 54 upvotes · similarity 0.60
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a agent / copilot tool for Agriculture yet.