Alternatives
Products that do what ProveDone does
Prove your agent actually did the work
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Trace, evaluate, and improve AI agents in production
30d ago · telerik.com
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Proves vulnerabilities before reporting them.
8d ago · dashboard-seven-self-13.vercel.app
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Agent runs often fail after expensive model calls and executing tools that have real-world side effects. This problem is made even worse by how common it is to deploy agents to serverless environments. When your agent dies, it needs to be restarted, but doing so safely isn't easy and everyone building agents has to solve this same problem of durability. The stack you're running probably already has half of what you need for durable execution already though, ie, a queue or job runner that can invoke work at least once. kassette gives you the other half by journaling completed steps to object…
Jul 2026 · github.com
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Hi HN, Over the past two years I’ve built and debugged a fair number of production pipelines—mainly retrieval‑augmented generation stacks, agent frameworks, and multi‑step reasoning services. A pattern emerged: most incidents weren’t outright crashes, but silent structural faults that slowly compromised relevance, accuracy, or stability. I began logging every recurring fault in a shared notebook. Colleagues started using the list for post‑mortems, so I turned it into a small public reference: 16 distinct failure modes (semantic drift after chunking, embedding/meaning mismatches,…
2025 · github.com
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Finding the balance between humans and machines has been the thread through my career. I always had a thing for AI and couldn't wait for it to actually deliver on what was promised. Now it does, and it's more important than ever that humans and machines keep working well together. AI makes creation trivial. Text, code, design, strategy, in seconds. But one thing AI does not create: independently verifiable proof of when something existed. Reproducing is now cheaper and easier than creating. Chronology becomes contestable at the moment it matters most. The universal problem was known. I…
Mar 2026 · umarise.com
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Provenance scan for files sent to you — free, in seconds
26d ago · seenpool.com
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Hi HN, I've been building MCPSpec, an open-source CLI for MCP server reliability. Record sessions, generate mock servers, catch Tool Poisoning, and fail your CI build when something's wrong — without writing test code. There are ways to validate MCP servers today — the MCP Inspector, ad-hoc SDK scripts, unit tests for server internals — but nothing that handles regression detection, security auditing, mock generation, and CI pass/fail checks in one tool. MCPSpec does that: 1. Record a session against your real server, replay it after changes to catch regressions 2. Generate a standalone…
Feb 2026 · light-handle.github.io
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Hello HN, I work on vision systems for structural inspection. A common pain point is usually that while we have a lot of "healthy" images, we often lack a reliable "Golden Set" of rare failures (like shattered porcelain) to validate our models before deployment. You can't trust your model's recall if your test set only has 5 examples of the failure mode for example. So to fix this, I built a pipeline to generate datasets. In this example, I took 7 real-world defect samples, extracted their topology/texture, and procedurally generated 200 hard-to-detect variations across different…
Feb 2026
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2021 · github.com
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We're two engineers that got fed up with context switching. Why do we need to do a git push, open a browser tab, wait for some task to actually start, bite nails (or read HackerNews) for 10 minutes or more while dependencies are being installed for the 100th time and finally end up with an invalid YAML error. And for some reason this usually happens in the final stage of the pipeline leading up to the inevitable git commits "Fixed", "Fixed again", "Test", "Really fixed this time". We can do better. We set out to build Zippy. A CI/CD system that works from your terminal. No context…
Mar 2026 · zippy.sh
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hi all. i’ve been shipping a small open project that tries to answer that question with evidence, not vibes. in 70 days it reached \~800 stars. the core claim is simple: many AI failures are not noise. they repeat because the geometry and ordering underneath are stable. if so, we should be able to name each failure mode, set acceptance targets, and stop shipping the same bug twice. ### what it is * a compact Problem Map of 16 reproducible failure modes in RAG and agents. * each item has a minimal fix and measurable gates. examples: * Semantic ≠ Embedding: metric and normalization mismatch.…
2025 · github.com
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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
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