MildlyTyped
Just Strict Enough
What it does
MildlyTyped is a pragmatic data prep & schema tool for developers. Clean and normalize messy CSVs, run validation rules, and inspect inferred column types with manual overrides. Generate TypeScript and Python models for your application layer — predictable, developer-friendly, and just strict enough for real-world data.
Does a similar job
all alternatives →- ASA SQL database implemented purely in TypeScript type annotations2020 · github.com · ▲426
- WOWhatTheDuck – open-source, in-browser SQL on CSV files2024 · github.com · ▲119
WhatTheDuck is an in-browser sql tool to analyze csv files that uses duckdb-wasm under the hood. Please provide feedback/issues/pull requests.
- KAKysely, a type-safe SQL query builder for TypeScript2023 · kysely.dev · ▲90
- RLRobust LLM extractor for websites in TypeScriptMar 2026 · github.com · ▲72
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
- NANasty, a cross-warehouse, type-checked, unit-testable analytics library2024 · getnasty.dev · ▲47
Hey HN - our team wants to open source a project called NASTY (NASTY Abstract Syntax Tree thingY) that we built for ourselves. NASTY was built to maintain testable/composable data pipelines. Our team was ripping our hair out trying to maintain dbt/SQL scripts across different data warehouses (Redshift, BigQuery, Postgres, Snowflake) on top of ever shifting data foundations maintained by our customer's internal data teams. NASTY is the result of our learnings from field experience. We wanted to write abstractions so that we could reuse code. We wanted to bundle those abstractions…
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