Alternatives
Products that do what Zingle – an AI code reviewer for data teams (SQL/dbt/Airflow/Spark) does
Hi HN, we’re Anant and Atishay, the co-founders of Zingle, an AI code reviewer for data teams. It automatically checks SQL, dbt, Airflow, and Spark code changes in github PRs for cost regressions, logic issues, data-quality gaps, and downstream breakages before they merge into the production. Here's a demo - https://youtu.be/dS0NnBjG2p4 You can try it on top 100 PRs for free at: https://getzingle.com We built this after managing 60+ dbt PRs per week for an enterprise client. Senior data engineers had very limited time to review PRs, and with AI-assisted coding the…
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Hey HN! I’m Julien, the founder of Code Inspector, a platform that helps developers and managers produce better code and reduce technical debt. We would love to get some feedback from the Hacker News community. Our platform inspects code, looks for defects (security, vulnerability, design, performance, lack of documentation), automates code reviews and reports on team activity. You can customize violation alerts to reduce false positives. We currently support GitHub, Bitbucket and Gitlab. I’d love to hear your thoughts on what you would expect from such a platform (what you like, dislike)…
2021
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Today you can easily adopt AI coding tools because you have git for branching and rolling back if AI writes bad code. We haven't seen this same capability for data and decided to build it ourselves. Nile is a new kind of data lake, purpose built for using with AI. It can act as your data engineer or data analyst creating new tables and rolling back bad changes in seconds. We support real versions for data, schema, and ETL. We'd love your feedback on any part of what we are building - https://getnile.ai/ What do you think?
Jan 2026
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Turn AI Coding into a structured, reviewable workflow
Jul 2026 · savyre.com
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Hi Hacker News! We're launching Zalor, an agent testing platform. Agents often break when you tweak system prompts, swap models, or add tools. Zalor automatically generates test scenarios and evaluates your agent so you know it's reliable before deploying to production. We currently support the OpenAI Agents SDK and are onboarding other frameworks. A GitHub integration is coming so you can get feedback on every update. Looking forward to hearing feedback from people building agents.
Mar 2026 · agents.zalor.ai
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2021 · codeapprove.com
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Hey HN! Today we’re launching Fabi 2.0 For the past year we’ve been working with a number of product, eng, marketing and data teams, and the common issue we’ve been seeing with all these teams, especially small and growing teams, is that getting basic answers from their data requires jumping between a bunch of different tools/copy-pasting data and spreadsheets, or building out a full BI stack. With our latest release, users can not only connect Fabi to databases and data warehouses, but also to applications like HubSpot, Stripe, Shopify etc. We offer hundreds of connectors, and the AI…
Jan 2026
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A couple of years ago, we had an interesting idea. When a development team within an organization consists of around 10 members or fewer, controlling the risks associated with database changes might be achieved through trust and real-time communication. However, as the team grows, the responsibilities within the team become more specialized. Imagine a scenario where different branches of the business exist, each with its own set of developers, team leads, testers, testing leads, DBAs, and more. Relying solely on traditional communication methods becomes increasingly challenging when it comes…
2023 · github.com
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Excited to share Nexa's public beta and get feedback from the HN community! -- The Problem Companies today spend way more time querying and analyzing data than acting on it. That needs to be flipped. At startups without data teams (who we’re built for), engineers waste precious time writing SQL queries for others (e.g. Growth, Product, Sales), and then those teams spend hours manually analyzing and formatting the data to make decisions. All of this is time we can give back with AI. -- Introducing Nexa With Nexa, connect your MySQL / PostgreSQL database or upload CSVs and extract…
2024 · nexahq.com
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Hey HN, A few friends and I have spent the past 6 years or so developing a way to write data transformation code in a way that can easily adapt to changes in logic or data elements both upstream and downstream without the need major refactoring, regression testing, or re-orchestration. We decided to open source the project about two months ago and published a CLI tool after we realized how big of a task it was to take on incumbents like stored procedures, dbt, and psyspark. It is early days for our community and we are looking to grow and engage with others to poke holes and contribute…
2024 · github.com
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Hey HN, We’re excited to announce Zant v0.1, an open-source TinyML SDK built in Zig, designed to optimize and deploy neural networks on resource-constrained devices. Unlike existing solutions, Zant focuses on performance, portability, and ease of integration, making it a strong alternative for anyone working on Edge AI and embedded ML. Why Zant? Most TinyML frameworks are either too high-level (requiring bloated runtimes) or too low-level (requiring extensive manual optimization). Zant bridges the gap by offering: - A lightweight but powerful code generation system to translate ML models…
2025 · github.com
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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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Hi y'all. Been working on something that should've been made a long time ago imo. It compiles codebases into O(1) hashmaps that the agent queries to discover the structure of your code/answer questions/write code. It also does complete static analysis checks on any writes the agent makes. Don't take my word for it though. Here are the benchmarks: https://benzi.fly.dev/benchmark. on 2/20 tests, Claude Code (mostly Sonnet on one task) regressed or timed out. Benzi didn't because of course, it has a map it can query and not get lost in the sauce. On the other 18 it…
28d ago · benzi.fly.dev
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AI agents are great at writing code but blind at runtime. They guess, print, and waste tokens. I built dbg to give them a real debugger experience. Since it is backend based with the few I implemented (still at basic level) it can support 15+ languages with one simple CLI (still some work needed but it is functional as it is): LLDB, Delve, PDB, JDB, node inspect, rdbg, phpdbg, GHCi, etc. Profilers too (perf, pprof, cProfile, Valgrind…) I also added GPU profiling via `gdbg` (CUDA, PyTorch, Triton kernels). It auto-dispatches and shares the same unified interface. (Planning to bring those…
Apr 2026 · redknightlois.github.io
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I’ve found that I don’t have any context about the data in my pipelines only know if the pipeline is successful or not. So I built panda-patrol which allows you to monitor each node in your DAG, use AI to generate data tests for your data, store data profiles, and more. All with this comes with dashboards and alerts. You can easily drop it into your Python-based data pipeline (i.e. Airflow, Dagster, Prefect, etc.) and just run your pipelines are you normally would — but with monitoring and more context. Hope its valuable to some people
2023 · panda-patrol.vercel.app
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