Alternatives
Products that do what An open-source tool that semantically profiles your data using LLMs does
The problem we solve is profiling tables: this is the initial step where you need to understand the table and identify any anomalies. During the process, many small decisions require semantic understanding. For example, missing values are normal for 'deathdate' (still alive) but abnormal for 'name.' For outliers, 100 for ages is fine, but some are -1, which is impossible! We use LLMs to semantically understand your tables and detect anomalies. You can try it by uploading a CSV, and we will email back the profile: https://cocoon-data-transformation.github.io/page/ Let me…
- 1LC
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com
- 2WO
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…
2024 · github.com
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- 4SG
Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.
2024 · github.com
- 5KG
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re
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- 7OS
Hey HN! We’ve built Pretzel, an open-source data exploration and visualization tool that runs fully in the browser and can handle large files (200 MB CSV on my 8gb MacBook air is snappy). It’s also reactive - so if, for example, you change a filter, all the data transform blocks after it re-evaluate automatically. You can try it here: https://pretzelai.github.io/ (static hosted webpage) or see a demo video here: https://www.youtube.com/watch?v=73wNEun_L7w You can play with the demo CSV that’s pre-loaded (GitHub data of text-editor adjacent projects) or upload…
2024 · github.com
- 8AN
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai
- 9RL
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…
Mar 2026 · github.com
- 10MS
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
- 11WW
2021 · superintendent.app
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- 13UL
Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…
2023 · github.com
- 14KO
We've open-sourced Klarity - a tool for analyzing uncertainty and decision-making in LLM token generation. It provides structured insights into how models choose tokens and where they show uncertainty. What Klarity does: - Real-time analysis of model uncertainty during generation - Dual analysis combining log probabilities and semantic understanding - Structured JSON output with actionable insights - Fully self-hostable with customizable analysis models The tool works by analyzing each step of text generation and returns a structured JSON: - uncertainty_points: array of {step, entropy,…
2025 · github.com
- 15D2
Hi! We are excited to announce the second release of Desbordante — an open-source, high-performance data profiler that is capable of discovering and validating many different patterns in data using various algorithms. Unlike existing data profilers, Desbordante focuses on discovering complex patterns in data, which are notoriously hard to extract. Since its inception in 2019, it has become the fastest open-source tool for these tasks. It also offers an array of patterns which have no alternative implementations. With this release, Desbordante now supports 17 types of patterns, such as:…
2024 · github.com
- 16DD
Gleb, Alex, Erez and Simon here – we are building an open-source tool for comparing data within and across databases at any scale. The repo is at https://github.com/datafold/data-diff, and our home page is https://datafold.com/. As a company, Datafold builds tools for data engineers to automate the most tedious and error-prone tasks falling through the cracks of the modern data stack, such as data testing and lineage. We launched two years ago with a tool for regression-testing changes to ETL code…
2022
- 17IU
Hello! I used an LLM to extract structured data from each comment on the “Ask HN: Who is Hiring? (January 2024)” post, available at: https://news.ycombinator.com/item?id=38842977. This created a dataset of 400+ jobs with data such as title, company name, location, is job remote, salary data, and the URL/email to contact. For the UI, a NextJS static site was created to filter and view the structured data.
2024 · hnjobsai.vercel.app
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- 19LS
2024 · github.com
- 20IT
2023 · github.com
- 21DB
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com
- 22HO
For the last year I’ve been developing Hyperparam — a collection of small, fast, dependency-free open-source libraries designed for data scientists and ML engineers to actually look at their data. - Hyparquet: Read any Parquet file in browser/node.js - Icebird: Explore Iceberg tables without needing Spark/Presto - HighTable: Virtual scrolling of millions of rows - Hyparquet-Writer: Export Parquet easily from JS - Hyllama: Read llama.cpp .gguf LLM metadata efficiently CLI for viewing local files: npx hyperparam dataset.parquet Example dataset on Hugging Face Space:…
2025 · hyperparam.app
- 23OS
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com
- 24LB
Hey HN, Spark event logs run into 100s of MBs and offer a wealth of insight into your workloads but making sense of them has always been quite a bit prohibitive. We’ve recently built a lightweight tool that automatically parses Spark event logs and surfaces targeted insights to help you optimize your data jobs. Whether you’re chasing down a bottleneck or balancing performance vs. cost, the profiler got you covered with real-time configuration recommendations, data skew analysis, and more. Curious how it works in action? Check out this quick Loom video for a walk-through:…
2025 · datasre.ai
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