Alternatives
Products that do what Formally Verified Leaderless Log Protocol for Kafka does
We open-sourced the TLA+ and Fizzbee verified spec behind Ursa's storage engine. Verification across ~200K states caught a design bug that years of production missed. We then handed the spec to Claude Code — it produced a working Rust implementation (concurrent producers, compaction, fencing) without back-and-forth. We think verified specs are the best harness for coding agents: open-source the spec, let anyone implement it.
- 1FV
To my knowledge, this is the first formally verified implementation of a 3D constructive solid geometry (CSG) operation: mesh intersection, implemented in Lean 4 and verified against a concise specification that pins down the surface of the resulting mesh exactly and guarantees practical well-formedness conditions on the triangulation. This project is also an experiment in avoiding having to trust AI-generated code. A human reviewer only needs to read 93 lines of formal specification and run the Lean checker to certify the correctness of the kernel, skipping the intricate 1000+ lines of…
Jul 2026 · github.com
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Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
- 3TO
At Cajal (YC W26) we’re excited to share Talos (https://github.com/cajal-technologies/talos), an open source framework for formal verification of WebAssembly modules in Lean. AI is now writing tons of the code that gets pushed to production. As code generation gets cheaper, verification becomes the bottleneck. We believe in a future where every piece of software comes with a mathematical proof that it does what its author intended - in doing so, eliminating many classes of exploits. Talos is part of the foundation for that. Talos provides a Wasm interpreter optimized for…
Jun 2026 · github.com
- 4RE
I've been building an open source formal methods system (fizzbee.io) for the past few years. Today I'm launching a new app built on the same technology. It performs requirements engineering using formal verification to uncover gaps and produce precise instructions for your coding agents to follow. When given a prompt, it - asks high signal follow-up questions - converts to formal spec and identifies complex requirements gaps - generates validation scenario At the end, it produces a specification document that can be shared with coding agents. In my trials on various projects, it produces…
Jul 2026 · fizzbee.ai
- 5IB
I've always been fascinated by cryptographic principles, so I decided to dive deep into zero-knowledge proofs and implement a Groth16 proof verifier entirely in Rust. This project validates cryptographic proofs and demonstrates how succinct, verifiable computations can be achieved. Using Rust's powerful libraries like arkworks, I implemented pairing checks, public input commitments, and proof validation logic while ensuring the code remains modular and efficient. I handled the complexities of affine group operations, scalar multiplications, and optimized public input verification to make…
2024 · github.com
- 6TP
Mar 2026 · github.com
- 7TS
2017 · timber.io
- 8AF
2022 · github.com
- 9IS
Aug 2026 · github.com
- 10FA
We've been working on an open-source coding agent that generates code alongside machine-checkable proofs. We'd love feedback from the HN community, especially from people interested in formal verification, Lean, Dafny, or AI coding agents. Currently, only 3 langauges can be verified.
Jul 2026 · github.com
- 11RA
2022 · github.com
- 12MC
Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.com
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- 14TT
The Thiele Machine is a formally verified universal computational model the surpasses Turing machines in key ways. It's fully proven in Coq (including kernel theorems and universality containment), features a python implementation for simulation and includes hardware designs in Verilog for potential FPGA/ASIC builds. The core idea: a paradigm shift using μ-bits for stricter computation under real-world constraints, tying into physics (e.g., Noether’s theorem) and emergence in chaotic systems. The repo includes a 13-chapter thesis (PDF and sources), proofs, and tools for exploration.…
Jan 2026 · github.com
- 15LA
Hey HN! We built *lapisla*, a theorem proving platform designed to make formal proofs more open and collaborative. We developed a kernel, editor, proof registry, and platform ── all accessible directly in the browser. Why we built this: Existing theorem provers are powerful, but they often feel isolated and difficult to extend. Unlike modern programming languages, which thrive on package managers and shared libraries, theorem proving tools lack a seamless way to integrate others' work. lapisla aims to bridge this gap by: * Offering a verified public proof registry for sharing and discovering…
2025 · github.com
- 16ID
Frustrated by the complexity and resource drain of multi service monitoring stacks, I built Simon. I wanted a single, lightweight dashboard to replace the heavy stack and the constant need for an SSH client for routine tasks. The result is a resource efficient dashboard in a single Rust binary, just a couple of megabytes in size. Its support for various architectures on Linux also makes it ideal for embedded systems and lightweight SBCs. It integrates: Comprehensive Monitoring: Realtime and historical metrics for the host system and Docker containers (CPU, memory, disk usage, and network…
Nov 2025 · github.com
- 17QF
Hi HN, I’m sharing the alpha release of S2C, a state machine replication system built atop S3. The goal is to enable a distributed application to maintain consistent state without needing a quorum of nodes for availability or consistency. The idea came from a side project that was using S3 and where I needed strongly consistent distributed state but wanted to avoid adding a separate consensus dependency. I initially tried to use S3 directly for coordination, but it became messy. Eventually, I realized I need a replicated state machine with a deterministic log, and then it ended up as a…
Jan 2026 · github.com
- 18AL
- 19CB
I built a small benchmark to test CLI coding agents on blind bug detection. A challenger agent injects bugs and writes ground truth (`bugs.json`). A different reviewer agent audits the repo without seeing ground truth, and an LLM matcher scores bug-to-finding assignments. Current run: 50 repos, 150 challenges, 450 reviews, 2,603 injected bugs. Weighted detection: Claude 58.05%, Codex 37.84%, Gemini 27.81%. LLM-judge benchmarks are easy to get wrong, so I’d really appreciate critical feedback on benchmark fairness, scoring/matching methodology, and obvious failure modes I’m missing. Full…
Feb 2026 · github.com
- 20RL
I've just released rust-loguru, a logging library for Rust that aims to combine intuitive API design (inspired by Go/Python's Loguru) with Rust's performance characteristics. Some highlights: - Consistently outperforms the standard log crate by 50-80% in benchmarks. - Multiple log levels and an extensible handler system -File rotation with better performance than log4rs and other alternatives - Error handling utilities and context helpers I built this because I wanted a logger that was both ergonomic to use and high-performance. The benchmarks comparing it to slog, log, and tracing are…
2025 · crates.io
- 21HP
2020 · crates.io
- 22LR
2024 · docs.rs
- 23LA
2019 · github.com
- 24SA
I built SpecMind, an open source developer tool for spec driven vibe coding. It keeps architecture and implementation aligned from the first commit instead of letting them drift apart. With AI assistants writing more of our code, projects move faster but architectural consistency is often lost. Each developer or AI can introduce new patterns, and after a few sprints, the structure becomes fragmented. SpecMind helps prevent that by generating and maintaining living architecture specs directly from your code. It works in three steps: 1. analyze – scans your codebase and generates…
Nov 2025 · github.com
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