Synth Studio

What it does

Generate privacy-preserving synthetic data with differential privacy guarantees. Upload datasets, train generators, and evaluate quality.

We learn patterns from your data, but never copy actual records. Share synthetic data freely it can't be traced back. Built-in differential privacy with configurable budget controls. Your data patterns are learned, not copied. Train ML models on your data, or use schema-based generation to create data instantly without training. Define your columns and types, get realistic data in seconds. No training, no waiting. Perfect for prototyping. Compare your synthetic data against the original. Get clear scores for statistical similarity and privacy. Automatic detection of names, emails, SSNs, and other sensitive fields before you generate. Export privacy reports and model cards as PDFs for HIPAA,…from synthdata.studio

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  Synth 2020 · ▲5

  Realistic, synthetic test data for your app

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  Tonic Fabricate Data AgentNov 2025 · tonic.ai · ▲94

  The AI agent for synthetic data generation

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  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…

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  Accelerate your vision AI model creation!

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  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…

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  Work · Feb 2026 · wallstreetraider.com