Alternatives
Products that do what Automatic chaptering – From raw transcripts to structured documents does
Hey HN, I have been working in the last weeks on the task of automatically extracting a table of contents from a raw (audio or video) transcript, aka a 'chaptering' task. That turned out to be more difficult than I inially thought, especially because I needed to keep the timestamp data, and because I had to deal with long transcripts, and LLMs tend to 'forget' part of input data when it is too long. I was also surprised that I could not find any open-source solution for that, in standard libraries like Langchain or LLamaIndex, despite the wide range of possible use cases (text summarization,…
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Hi HN, I built a small tool to solve a recurring pain when editing podcasts: scrubbing back and forth just to find where a sentence or idea actually ends. How it works: - Upload an audio file (MP3/WAV/M4A) - AI transcribes the audio and suggests cut points at sentence or paragraph boundaries - Automatically split and export segments, or adjust them manually if needed Website: https://transcribeandsplit.com/ This came out of my own frustration with editing long recordings and manually hunting for the right cut points. I wanted something that actually understands the…
Nov 2025
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I built a CLI tool in Go that extracts structured data (JSON, CSV, Parquet) from messy PDFs and HTML pages. The core idea: LLMs are great at understanding structure but wasteful for bulk data extraction. So smelt uses a two-pass architecture: 1. A fast Go capture layer parses the document and detects table-like regions 2. Those regions (not the whole document) get sent to Claude for schema inference — column names, types, nesting 3. The Go layer then does deterministic extraction using the inferred schema This means the LLM is never in the hot path of actual data processing. It figures out…
Mar 2026 · github.com
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Hey! We’ve built a data extraction tool to flexibly automate data and document processing. You’ve probably seen a few of these, so have we! A few of us have been varyingly stuck trying to automate the extraction of borrower financials for the past 5 years. We think that there are a few missing features of most data extraction tools. * They are usually too complex to quickly get up and running * They are overly constrained in terms of what workflows and documents they support We’ve always felt like speed and flexibility were sticking points, so we went slightly orthogonal to the alternatives.…
2024 · go.sea.dev
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LLM-Powered document extraction & analysis tool
2024 · dataku.ai
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I made a simple, easy to use transcription app which is free! Thanks everyone for giving it a go!
2024 · transcripto.xyz
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We build LlamaExtract, a tool that allow you to automatically extract a data model from a collection of documents, and then reusing this datamodels (JSON Schema) to extract data from documents. Available as a Python library and as an API. Announcement blog: https://www.llamaindex.ai/blog/introducing-llamaextract-beta...
2024 · github.com
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Hi Show HN, we are Dave, Marcin, Alena, and Adrian, authors of data load tool (dlt), a Python library that automatically creates datasets from any kind of messy, unstructured data. We launched dlt on HN 7 months ago with a mission to make getting datasets fast and easy. Now dlt helps its users to code around a thousand new data sources each month and to maintain many thousands of live datasets in production. Today we are releasing *dlt-init-openapi,* a Python CLI tool that generates a dlt data pipeline from any OpenAPI spec. It brings the time to create a dataset down to a few minutes.…
2024 · colab.research.google.com
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I've been using LLMs for long discovery and research chats (papers, repos, best practices), then distilling that into phased markdown (build plan + tests), then handing those phases to Codex/Claude to implement and test phase by phase. The annoying part was always the distillation and keeping docs and architecture current, so I built Unpack: a lightweight GitHub template plus docs structure and a few commands that turns conversations into phases/specs and keeps project docs up to date as the agent builds. It can also generate Mintlify-friendly end-user docs. There are other…
Feb 2026 · github.com
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Hey HN! I built Transcrib.ee to help me generate transcripts for lectures on YouTube with no captions, especially multilingual lectures. I was constantly frustrated with videos that had no transcripts or inaccurate ones, so I built a tool and decided to share it with everyone. This tool uses Groq (an AI inference engine that's different from Grok and offers very fast processing) and OpenAI's Whisper model (really accurate in multilingual) to quickly transcribe any YouTube video, regardless of the language. It's been a game-changer for my studies! To make it even faster, I created a Chrome…
2024 · transcrib.ee
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hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
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There are two parts for this project: 1) The LLM-powered pipeline to extract citations (books + authors) from books and resolve them using both Wikipedia and Goodreads with offline copies I have. The result is data associating Books/Authors to other Books/Authors with accurate bibliographical information spanning centuries. 2) A WebGPU + D3.js powered visualization tool written by Claude Code so I'm able to deal with all this data on the browser on a more or less comfortable experience for the viewer. I spent some months on a off with this project, and definitely the most…
Feb 2026 · thiagolira.github.io
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Hello It's been quite frustrating to find an online tool that will easily extract tables from PDFs. So I built one. https://www.tablex.dev Hope you enjoy it.
2023
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Live captions, transcripts & AI notes — 100% on your Mac
27d ago · meetingscribe.insforge.site
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Live demo here: http://fonctionlabs.com:8000 Similarly to aka_sh (guess we were working parallelly on similar topics), I created with my brother a chainlit-based webapp, which summarizes Youtube videos in order to gain time. It works as an RAG-based LLM, and is very light in the sense that it does not use RAG libraries like langchain or llamaindex. You can use it with your own OpenAI API key. It also supports local models like Mistral, or Llamma. It is ofc open-source, and you can deploy with Docker if you choose. Some of the next steps are: - using whisper to be able to compute a…
2024 · github.com
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