Alternatives
Products that do what Layrda does
Make Any API Return Clean, Structured Data
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Extract web data into structured JSON, no scraper required.
Jun 2026 · tabstack.ai
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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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2021 · skruv.io
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Hey HN, I'm excited to share Orbital, a new approach for unifying APIs and Data sources! Rather than relying on glue code to bridge endpoints, Orbital leverages annotations in schemas & API specs to build the integration dynamically. The traditional method of crafting glue code often becomes repetitive and burdensome to maintain in the long run. With Orbital, developers embed tags to their existing API specs (OAP, Protobuf, etc), indicating where data can be sourced, and publish these specs to Orbital (which runs self-hosted). Consumers query these tags with our TaxiQL language, and Orbital…
2023 · github.com
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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
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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,…
2024 · anythingtojson.com
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Just launched DataFuel.dev on Product Hunt last Sunday, and I landed in the top 3! I built this API after working on an AI chatbot builder. Scraping can be a pain, but we need clean markdown data for fine-tuning or doing RAG with new LLM models. DataFuel API helps you transform websites into LLM-ready data. I've already got my first paying users. Would love your feedback to improve my product and my marketing!
2024 · datafuel.dev
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2015 · restful.io
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Turn any website into a clean structured data with one api.
27d ago · schemafetch.launchreadyal.com
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Hi All! We've spent a few months on getting an MVP together, and would love to get some feedback on whether this tool meets you needs. Here is a link to a demo video: https://www.youtube.com/watch?v=FBLi3vdKB-4&feature=emb_rel_pause Here's a link to our website: https://www.structure.rest And here's a blog article, I published today in the space: https://www.structure.rest/blog/using-a-data-analytics-stack-to-gain-business-insights
2020
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Hi HN! This Christmas I made `tysm`, a rust crates which simplifies using ChatGPT with Structured Outputs. Essentially you just tell it the rust type you want the output to conform to, and it handles creating the JSON Schema, passing it to ChatGPT's chat-completions API, and deserializing the response. Structured Outputs are very useful because they guarantee that the response will conform to whatever type you expect. I'm already finding my crate very useful since I love using Structured Outputs but I hate writing JSON Schemas myself. Technical details for rust heads: To generate the JSON…
2024 · github.com
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2023 · github.com
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Hey HN, A few friends and I have spent the past 6 years or so developing a way to write data transformation code in a way that can easily adapt to changes in logic or data elements both upstream and downstream without the need major refactoring, regression testing, or re-orchestration. We decided to open source the project about two months ago and published a CLI tool after we realized how big of a task it was to take on incumbents like stored procedures, dbt, and psyspark. It is early days for our community and we are looking to grow and engage with others to poke holes and contribute…
2024 · github.com
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