Anything To JSON – a language model for structured extraction
Hey HN! Erik here from banana.dev We’ve trained a small(ish) language model on structured extraction, and today we’re launching a playground for it at https://anythingtojson.com. Give it a try! This model continues our work on structured generation, following last week’s launch of Fructose[1], a python client for strongly-typed LLM responses. There seem to be two distinct halves of the problem intended to be solved by Fructose and structured generation: 1. the reasoning ability of the model, such as performing chain of thought, creative acts, and natural language tasks. In a way,…
Anything To JSON – a language model for structured extraction launched on March 20, 2024 with 18 votes, #1,173 of 4,270 launches that month and more than 75% of that year's launches.
In plain words
Anything To JSON is a language model from banana.dev trained specifically for extracting structured data from text and converting it to JSON format. It uses constrained generation and fine-tuning to reliably pull information from unstructured content without generating reasoning or creative responses. The tool is designed for developers who need to parse text data into consistent JSON structures for downstream applications, with a playground available for testing.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! Erik here from banana.dev We’ve trained a small(ish) language model on structured extraction, and today we’re launching a playground for it at https://anythingtojson.com. Give it a try! This model continues our work on structured generation, following last week’s launch of Fructose[1], a python…
Does a similar job
all alternatives →
LLMs can generate valid JSON 100% of the time2023 · github.com · ▲854Outlines 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…
Fructose – LLM calls as strongly typed functions2024 · github.com · ▲218Hi HN! Erik here from Banana (formerly the serverless GPU platform), excited to show you what we’ve been working on next: Fructose Fructose is a python package to call LLMs as strongly typed functions. It uses function type signatures to guide the generation and guarantee a correctly typed output, in whatever basic/complex python datatype requested. By guaranteeing output structure, we believe this will enable more complex applications to be built, interweaving code with LLMs with code. For now, we’ve shipped Fructose as a client-only library simply calling gpt-4 (by default) with json…
Structured output from LLMs without reprompting2023 · automorphic.ai · ▲174Built 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
GPT-JSON – Structured and typehinted GPT responses in Python2023 · github.com · ▲174Hey HN, I've been using GPT a lot lately in some side projects around data generation and benchmarking. During the course of prompt tuning I ended up with a pretty complicated request: the value that I was looking for, an explanation, a criticism, etc. JSON was the most natural output format for this but results would often be broken, have wrong types, or contain missing fields. There's been some positive movement in this space, like with jsonformer (https://github.com/1rgs/jsonformer) the other day. But nothing that was plug and play with GPT. This library consolidates…
Tabstack Structured ExtractionJun 2026 · tabstack.ai · ▲199Extract web data into structured JSON, no scraper required.
A new benchmark for testing LLMs for deterministic outputsApr 2026 · interfaze.ai · ▲60When 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…
More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art

PixVerse R2▲381A real-time world model you can explore and change
AI · 28d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, March 2024
the whole month →

🌐 The Internet Computer! Free, Open-Source, and Self-Hostable. - HeyPuter/puter
Dev tools · 2024 · github.com
Microlaunch▲1,115Launch and get feedback on both the idea and product
Dev tools · 2024 · microlaunch.net


