Misata
synthetic data engine using LLM and Vectorized NumPy
Details
- External ID
- 46289055
- Source
- HN
- Company
- —
- Product
- Misata
- Website domain
- github.com
- Launched
- Dec. 16, 2025
- Cohort
- —
- Upvotes
- 24
- Upvotes percentile
- 0.6870229007633588
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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?
Enrichment
- Theme
- database infrastructure and developer tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- synthetic data generation engine
- Manually corrected
- False
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 — 98 launches clear the similarity bar, closest 8 shown.
Attention rank: #35 of 99 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 46 days after the earliest competitor.
- Llm.sql · hn · 2026-04-24 · 8 upvotes · similarity 0.48
- Dbctx · hn · 2026-08-11 · 5 upvotes · similarity 0.47
- DDL to Data · hn · 2026-01-06 · 55 upvotes · similarity 0.47
- DeepTable · hn · 2026-03-31 · 8 upvotes · similarity 0.45
- A new benchmark for testing LLMs for deterministic outputs · hn · 2026-04-29 · 60 upvotes · similarity 0.44
- Smelt · hn · 2026-03-07 · 6 upvotes · similarity 0.44
- Eatmydata.ai · hn · 2026-06-10 · 8 upvotes · similarity 0.40
- Xpandas · hn · 2026-03-02 · 6 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
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