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🚀 New in Heptabase: PDF Parser - Extract text, tables, equations & images (even from scanned PDFs) → save as clean Markdown - Ask AI with precise context (page ranges & paragraphs) → responses include link refs back to the source - MAX mode ensures AI reads every word... show more

Heptabase

18,510 subscribers

36,690 görüntüleme • 1 yıl önce •via X (Twitter)

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RR profil fotoğrafı

RR1 yıl önce

yeah, finally a tool that understands PDFs are just as messy as my college notes

Dustin profil fotoğrafı

Dustin1 yıl önce

This new PDF parser feature sounds incredibly powerful for researchers and students alike. The ability to extract and organize content with precision will surely enhance information management and study workflows.

Robinounet profil fotoğrafı

Robinounet1 yıl önce

Literally me in front of the video: WOWOWOWOW, this is fucking insane.

rey profil fotoğrafı

rey1 yıl önce

can somebody tell me how it works technically, i'm really curious how they do this.

Ivan Selivanov profil fotoğrafı

Ivan Selivanov10 ay önce

It doesnt work for Russian texts 😭😭😭 For example, Цель Руководства BABOK reads as LeJb PyKOBOnCTBa BABOK

Saïd Aitmbarek profil fotoğrafı

Saïd Aitmbarek1 yıl önce

based extraction/content structuration features & great demo! let's launch you guys on whenever it helps

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Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦀/acc) for all the work here. Come check it out: Repo:

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MICROSOFT QUIETLY SHIPPED THE RAG PIPELINE EVERYONE KEEPS TRYING TO BUILD FROM SCRATCH it is called Kernel Memory, and it turns any pile of files into an AI memory that answers with citations. drop your data in and Microsoft's pipeline does the rest: > ingest PDFs, Word, PPT, Excel, web pages, even images > extract the text, chunk it, embed it into vectors > store the vectors with metadata and keep the originals in blob storage > ask a question and it pulls the right chunks with semantic plus hybrid search then it hands the answer back grounded, with links to the exact source and full token usage. and it plugs into whatever you already run: > Azure OpenAI, OpenAI, Ollama, or Anthropic for the models > Qdrant, pgvector, Redis, or Azure AI Search for the vectors > every step is a swappable handler, so you can replace any part with your own one heads up: Microsoft archived the repo in June 2026 and ships it as a reference blueprint, not production. but as a map for how memory and RAG are actually built, nothing is cleaner. run it in one command: docker run kernelmemory/service bookmark this. Microsoft already drew the blueprint, you just build on it.

NO1ennn

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