UiForm – Never write a parser again, make any file LLM-ready
Hey HN! UiForm is a document processing SDK that (1) makes any file LLM-ready, eliminating the need to write custom parsers for each format, and (2) improves structured data extraction through built-in Chain-of-Thought prompting (repo: https://github.com/UiForm/uiform, site: uiform.com). We’ve been analyzing shipping documents with LLMs for over a year with Cube. While building, we faced two major challenges in document analysis: First, each client had different document formats (PDFs, Excel sheets, emails) requiring custom parsers. Second, getting consistent, structured…
UiForm – Never write a parser again, make any file LLM-ready launched on January 15, 2025 with 6 votes and more than 75% of that year's launches.
What it does
In the maker’s words, at launch
Hey HN! UiForm is a document processing SDK that (1) makes any file LLM-ready, eliminating the need to write custom parsers for each format, and (2) improves structured data extraction through built-in Chain-of-Thought prompting (repo: https://github.com/UiForm/uiform, site: uiform.com). We’ve…
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Beyond text splitting – improved file parsing for LLMs2024 · github.com · ▲206Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
Extend UI – open-source UI kit for modern document appsJun 2026 · extend.ai · ▲252We're open-sourcing 14 components & examples today for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more. It's MIT licensed and fully customizable. Demo video here: https://share.extend.ai/kRmSGKRF When we started, we tried every file viewer and document component library we could find. Unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for https://extend.ai/. It was only ever meant to be internal, but enough customers kept asking for it that we decided to…
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…
PDF to MD by LLMs – Extract Text/Tables/Image Descriptives by GPT4o2024 · github.com · ▲191I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`. In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown. The project is open-source and available on GitHub. Feedback is welcome.
LLMWhisperer – Prep complex documents ready for use in LLMs2024 · llmwhisperer.unstract.com · ▲12Hey HN! Over the last few months, we’ve seen many tools here trying to tackle the problem of making complex, unstructured documents ready for LLMs. The complexity primarily includes parsing highly complex documents in terms of format, layout, design, complex tables, checkboxes, etc, with high accuracy and reliability. LLMWhisperer is our take on the problem. LLMwhisperer solves most of the document complexity with reliable accuracy. With our user-friendly playground (https://pg.llmwhisperer.unstract.com/), you can effortlessly test your document use case. No sign-up is…
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