Alternatives
Products that do what Synthetic Data Factory does
Create ML-ready datasets for niche problems in minutes
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- 2AT
A bunch of developers and myself have created RepliByte - an open-source tool to seed a development database from a production database. Features: - Support data backup and restore for PostgreSQL, MySQL and MongoDB - Replace sensitive data with fake data - Works on large database (> 10GB) (read Design) - Database Subsetting: Scale down a production database to a more reasonable size - Start a local database with the prod data in a single command - On-the-fly data (de)compression (Zlib) - On-the-fly data de/encryption (AES-256) - Fully stateless (no server, no daemon) and lightweight…
2022
- 3OS
Hey HN! A few months ago we shared our AI dataset generator as an open source repo, and the response was incredible (https://news.ycombinator.com/item?id=44388093). We got requests from folks who wanted to use it without the hosting overhead, so we created both options: a hosted version (https://www.metabase.com/ai-data-generator for instant use and the source code fully open (https://github.com/metabase/dataset-generator) for anyone who wants to self-host or contribute. Looking forward to seeing how you use it and what you build on top of…
Sep 2025 · metabase.com
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2025 · github.com
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- 6AG
Hey HN, We just shipped a new AI-powered feature... BUT the "AI" piece is largely in the background. Instead of relying on a chatbot, we've integrated AI (with strict input & output guardrails) into a workflow to handle two specific tasks that would be difficult for traditional programming: 1. Identifying the most relevant base URL from HAR files, since it would be tedious to cover every edge case or scenario to omit analytics, tracking, and other network noise. 2. Generating synthetic data for API requests by passing the API context and faker-js functions to GPT-4. The steps are broken down…
2024 · docs.multiple.dev
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- 8SO
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
- 9CA
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
- 10TS
2022 · github.com
- 11NO
Hey HN, we're Evis and Nick and we're excited to be launching Neosync (https://www.github.com/nucleuscloud/neosync). Neosync is an open source platform that helps developers anonymize production data, generate synthetic data and sync it across their environments for better testing, debugging and developer experience. Most developers and teams have some version of a database seed script that creates some mock data for their local and stage databases. The problem is that production data is messy and it’s very difficult to replicate that with mock data. This causes two big…
2024 · github.com
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I built DDL to Data after repeatedly pushing back on "just use production data and mask it" requests. Teams needed populated databases for testing, but pulling prod meant security reviews, PII scrubbing, and DevOps tickets. Hand-written seed scripts were the alternative slow, fragile, and out of sync the moment schemas changed. Paste your CREATE TABLE statements, get realistic test data back. It parses your schema, preserves foreign key relationships, and generates data that looks real, emails look like emails, timestamps are reasonable, uniqueness constraints are honored. No setup, no…
Jan 2026
- 16MS
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
- 17DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
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- 19ID
Hello, Hacker News! I'm Erik, cofounder of Release (YCW20). At Release we help “virtualize” your environment, so you can quickly reproduce it for various purposes: remote development, testing, staging, or even running production. But, today I would like to introduce our most popular feature that we are now offering for free: Instant Datasets! Instant Datasets allows you to easily create multiple pools of datasets and distribute them to your teams and it cleans up after itself. Imagine you have multiple production databases in RDS and you need that data when doing testing and development. I…
2023
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- 23PG
Hi HN , I got tired of writing the same boilerplate over and over — DB setup, auth, routes, security — every time I built a backend. So I built Pipo360 — an AI-powered tool that generates production-ready backends in under 60 seconds, from just a plain-text description. How it works: Type what you need “Create a task management API with user auth and MongoDB” Hit Generate Get real, exportable code Auth (JWT) Database schema CRUD routes Deployable to Vercel, AWS, etc. No templates. No lock-in. Just code that works. Why it’s different: Built with Gemini AI + human supervision (to ensure real…
2025 · pipo360.xyz
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Paste real JSON, get 1,000 coherent rows that match it
Jun 2026 · jsondevtools.org
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