Alternatives
Products that do what BuildButler does
Take back control of your CI and Flaky Test data using AI
- 1OS
We are building Quary (https://quary.dev), an engineer-first BI/analytics product. You can find our repo at https://github.com/quarylabs/quary and our website at https://www.quary.dev/. There’s a demo video here: https://www.youtube.com/watch?v=o3hO65_lkGU As engineers who have worked on data at startups and Amazon, we were frustrated by self-serve BI tools. They seemed dumbed down and they always required us to abandon our local dev tools we know and love (e.g. copilot, git). For us and for everyone we speak to, they end up…
2024 · github.com
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2021 · test-hub.io
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AI has made building fast and cheap, but finding the right problems still feels hard. I built World’s Backlog (https://worldsbacklog.com ) to collect real problems directly from people working inside different industries. Contributors post workflow pain, others validate it, and builders can study severity, frequency, and willingness to pay before building anything. Would love feedback from builders and people who feel real pain at work.
Dec 2025 · worldsbacklog.com
- 12IM
I built BuzzBench because I was frustrated with how complex performance testing tools have become. And I was ending up writing my own scripts to test endpoints and manually check out resource usage at the time of testing. Checkout demo: https://www.youtube.com/watch?v=yAnbZMoQvmQ
2025 · buzzbench.io
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Hi HN! We’re Clemens and Felix from Cito - thrilled to show you what we’ve built to help data engineers stay on top of data quality issues. Think Datadog meets Incident.io. Tests in dbt are great when checking whether specific expectations are true, but don’t work well for use cases where data patterns may change over time. When relying on testing alone, data teams regularly face situations where business stakeholders identify data issues in dashboards first, eroding trust. In such situations, understanding the implications of an issue and debugging can be a very manual and time-consuming…
2022 · citodata.com
- 15BY
Hey HN - We're building wispbit (https://wispbit.com/) - a tool that lets you build your own AI code reviewer. We built this because we worked in big and complex codebases where we kept hitting booby traps - often the same ones. People forgot things, or quit altogether, amplifying the problem. We looked for other ways to fix this, but the solution is usually a combination of: - Writing a linter rule - too difficult and time consuming. - Writing docs and having frequent meetings on alignment - basically a full time job. - Using plug and play code reviewers - too generic and…
2025
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I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
2025 · codii.dev
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For the last 6 months, I've been building ORUS Builder, an open-source AI code generator. My goal was to fix the biggest issue I have with tools like v0, Lovable, etc. – they generate broken, non-compiling code that needs hours of debugging. ORUS Builder is different. It uses a "Compiler-Integrity Generation" (CIG) protocol, a set of cognitive validation steps that run before the code is generated. The result is a 99.9% first-time compilation success rate in my tests. The workflow is simple: 1.Describe an app in a single prompt. 2.It generates a full-stack application…
Nov 2025
- 24MC
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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