Misata
synthetic data engine using LLM and Vectorized NumPy
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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).Performance: Generates ~250k rows/sec on my M1 Air.It’s early alpha. The "Graph Reverse Engineering" (describe a chart -> get data) is experimental but working for simple curves.pip install misataI’d love feedback on the simulator.py architecture—I’m currently keeping data in-memory (Pandas) which hits a ceiling at ~10M rows. Thinking of moving to DuckDB for out-of-core generation next. Thoughts?
HN · launched Dec 16, 2025 · 24 upvotes (top 31%) · 0 views
Niche: mock and synthetic data generators · 28 launches, 1 in the last 7 days · Explore this niche →
Category and tags
- Horizontal
- Data infrastructure
- Developer
- AI feature
- Hobby / open-source project
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More like this: all 28 launches in mock and synthetic data generators →
Could you build this?
Partial While translating natural language to schema rules using an LLM is standard, generating relational and temporally coherent synthetic data efficiently requires advanced constraint satisfaction solving and vectorized numerical operations.
What it would actually take: A production version requires a hybrid compiler architecture: an LLM parser that generates an Abstract Syntax Tree (AST) representing relational and temporal constraints, mapped into a constraint satisfaction problem (CSP) solver or mathematical graph (like Z3 or custom DAG solvers), which drives vectorized NumPy/Polars execution. Deep expertise in numerical computing, relational algebra, and constraint satisfaction is necessary to ensure performant generation at scale.
Discussion
2 comments analyzed.
Concerns raised: Unclear if synthetic data is derived from existing data or generated from scratch
Feature requests: Incremental schema updates with iterative refinement across multiple development cycles, Support for testing MVPs with dummy/synthetic data
Competitors
Other products that read as similar to this one : 207 launches clear the similarity bar, closest 8 shown.
Attention rank: #77 of 208 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 48 days after the earliest competitor.
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