Alternatives
Products that do what Autolabel, a Python library to label and enrich text data with LLMs does
Hi HN! I'm excited to share Autolabel, an open-source Python library to label and enrich text datasets with any Large Language Model (LLM) of your choice. We built Autolabel because access to clean, labeled data is a huge bottleneck for most ML/data science teams. The most capable LLMs are able to label data with high accuracy, and at a fraction of the cost and time compared to manual labeling. With Autolabel, you can leverage LLMs to label any text dataset with <5 lines of code. We’re eager for your feedback!
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2022 · github.com
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aiq lets you use a single CLI command to auto-label text data with a language model API & train an efficient classifier on the labels. Training and inference are super fast (both run on CPU).
2024 · github.com
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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
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After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com
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2025 · simonwillison.net
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2020 · github.com
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Hey HN, I’m Jordan cofounder of Humanloop (YC S20) and I’m excited to show you Programmatic — an annotation tool for building large labeled datasets for NLP without manual annotation. Programmatic is like a REPL for data annotation. You: 1. Write simple rules/functions that can approximately label the data 2. Get near-instant feedback across your entire corpus 3. Iterate and improve your rules Finally, it uses a Bayesian label model [1] to convert these noisy annotations into a single, large, clean dataset, which you can then use for training machine learning models. You can…
2022 · programmatic.humanloop.com
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2023 · github.com
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Hi HN! I am Maria, solo founder of DataQA (https://dataqa.ai/), a tool to search and label documents for various NLP tasks (e.g. entity extraction, entity linking, etc). I have worked as a data scientist and ML engineer for the better part of a decade, and over that time have specialised mainly in applications involving natural language processing (NLP). One of the key questions I have always had at the back of my mind is whether my time was well spent. Whenever I spent more time on feature engineering or trying different models, I always wondered whether I would get better…
2021
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2023 · github.com
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Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai
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Hey HN! We’re Kevin and Steve. We’re building PromptTools (https://github.com/hegelai/prompttools): open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. Evaluating prompts, LLMs, and vector databases is a painful, time-consuming but necessary part of the product engineering process. Our tools allow engineers to do this in a lot less time. By “evaluating” we mean checking the quality of a model's response for a given use case, which is a combination of testing and benchmarking. As examples: - For generated…
2023 · github.com
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Hi HN - Marcello and Vaibhav here. We built smolmodels to experiment with using LLMs for ML development. It's a fully open-source library that generates complete model training and inference code from natural language descriptions. It combines graph search with LLM code generation to find a model that gives as good predictions as possible. The core idea is that LLMs are overkill for a lot of predictive tasks. Smolmodels automates the trial-and-error process of finding the right model architecture and training approach, letting you build small, specialised models. You can either provide your…
2025 · github.com
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Hey HN! We're excited to share our new open-source project, Marvin. Marvin is a high-level library for building AI-powered software. We developed it to address the challenges of integrating LLMs into more traditional applications. One of the biggest issues is the fact that LLMs only deal with strings (and conversational strings at that), so using them to process structured data is especially difficult. Marvin introduces a new concept called AI Functions. These look and feel just like regular Python functions: you provide typed inputs, outputs, and docstrings. However, instead of relying on…
2023 · github.com
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OrchestraML▲82From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app
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We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
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Hi HN, I’ve been working on an OCR pipeline specifically optimized for machine learning dataset preparation. It’s designed to process complex academic materials — including math formulas, tables, figures, and multilingual text — and output clean, structured formats like JSON and Markdown. Some features: • Multi-stage OCR combining DocLayout-YOLO, Google Vision, MathPix, and Gemini Pro Vision • Extracts and understands diagrams, tables, LaTeX-style math, and multilingual text (Japanese/Korean/English) • Highly tuned for ML training pipelines, including dataset generation and…
2025 · github.com
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