naren87
What we learned building a multi-agent PDF table extractor
We Split PDF Table Extraction Across 6 Agents and a Codegen Step
Show HN: We built Cursor, but for data transformations [Open Source]
Show HN: We built Cursor, but for data transformations (open source)
Show HN: We built Cursor, but for data transformations (open source)
Show HN: Visitran – Cursor for Data Transformations (open source)
Why LLMs Are Not (Yet) the Silver Bullet for Unstructured Data Processing
Unstract: Open-source platform to ship document extraction APIs in minutes
PDF Hell: Why is extracting data still a nightmare?
Vibe Coding vs. Vibe Engineering
Why LLMs Are Not (Yet) the Silver Bullet for Unstructured Data Processing
Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes
Unstract: Open-source platform to ship document extraction APIs in minutes
Specification Grounding: The Missing Link in Vibe Coding
Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes
Re:Which Table Format Do LLMs Understand Best? (Results for 11 Formats)
on Sunday October 05, 2025 @07:35PM (#45486214)
Great benchmark! It highlights an important but often downstream problem. In real-world pipelines, the bigger issue comes before this: extracting tables from PDFs or scans without breaking their layout. Once the structure is lost (merged headers, nested cells, footnotes, etc.), no data format can fully recover it.
Check out LLMWhisperer from Unstract —> it preserves table and layout fidelity when converting documents for LLM use. You can try it on complex PDFs or forms here: https://pg.llmwhisperer.unstract.com (no signup needed)
Layout preservation upstream often improves downstream accuracy more than choosing between CSV, JSON, or Markdown. Find more details here: https://unstract.com/llmwhisperer/