Alternatives
Products that do what BugiaData does
A test data API with schema that delivers multi-locale data
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pg_jsonschema is a solution we're exploring to allow enforcing more structure on json and jsonb typed postgres columns. We initially wrote the extension as an excuse to play with pgx, the rust framework for writing postgres extensions. That let us lean on existing rust libs for validation (jsonschema), so the extension's implementation is only 10 lines of code :) https://github.com/supabase/pg_jsonschema/blob/fb7ab09bf6050... happy to answer any questions!
2022 · github.com
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Most feedback tools are built like people actually want to report bugs. They don’t. Unless you make it dead-simple, or better yet - a little fun. After shipping a few SaaS products, I noticed a pattern: Bugs? Yes. Bug reports? No. Not because users didn’t care but because reporting bugs is usually a terrible experience. Most tools want users to: * Fill out a long form * Enter their email * Describe a bug they barely understand * Maybe sign in or create an account * Then maybe submit it Let’s be real: no one’s doing that. Especially not someone just trying to use your product. So I built…
2025
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2023 · jsongenerator.io
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Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai
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I had already posted the project a couple of years ago, and it gained some interest, but a lot of stuff has been done since then, especially regarding performance, a completely new JSON store, a REST API, various internals refactored, an improved JSONiq based query engine allowing updates, implementing set-oriented join optimizations, a now already dated web UI, a new Kotlin based CLI, a Python and TypeScript client to ease the use of Sirix... First prototypes from a precursor stem already from 2005. So, what is it all about? The system uses ideas from ZFS (a keyed index trie, storing…
2023 · github.com
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Hi HN! I'm excited to share a project I've been working on for the past year: Docland. It is an API documentation browser that generates documentation on demand (through compilation, not LLMs) for Java packages. Instead of relying on Javadoc, the built-in doc generator, I created the engine from scratch to give the documentations a modern look, build fast search indexes, and enable link resolution to other packages. I built Docland because I constantly found it frustrating to locate and view API definitions when programming. You'd have to Google the function/class name, skip all the SEO…
2024 · docland.io
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Hey HN, I’ve built Fahmatrix, a minimal, fast Java library for working with tabular data — inspired by Python’s pandas, but designed for performance and simplicity on the JVM. After working extensively with Python’s data stack, I often ran into limitations related to speed, especially in larger or long-running data workflows. So I built Fahmatrix from scratch to offer similar APIs for manipulating CSVs, performing summary statistics, slicing rows/columns, and more — but all in Java. Features: Lightweight and dependency-free CSV/TSV import with auto-headers Series/DataFrame…
2025 · github.com
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2024 · typeschema.org
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Bugpilot▲94Turn errors, DOM, + screenshots into an AI-ready Markdown
Jun 2026 · chromewebstore.google.com
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2020 · kretes.dev
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Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).…
Dec 2025 · github.com
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2021 · github.com
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