raunakchowdhuri

Joined 4 years ago

Karma
99
Submissions
34
Member since
Nov 22
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Recent submissions

Comment · 7 months ago
The big one is that LLMs get lazy on repetitive tasks. They'll skip rows or consolidate entries instead of grinding through every last one. So you need verify-and-re-extract loops rather than single-pass processing. Breaking work into sub-agent chunks with explicit correctness criteria defined upfront (e.g., "line items must sum to the stated total") lets the system self-verify autonomously. At scale (28M+ fields), this approach actually outperformed expert human labelers!
Comment · 7 months ago
We've made a lot of changes in the past few months that make our standard extract much, much better, as well as Deep Extract for documents even longer than that. We'd love for you to give it a try!
Comment · 11 months ago
Have a slack channel with them, these are the versions they mentioned: posthog-node 4.18.1 posthog-js 1.297.3 posthog-react-native 4.11.1 posthog-docusaurus 2.0.6
Comment · 1 year ago
We're fixing it! This for some reason happens on only _some_ phones in our office so was hard to repro. I think has to do with Safari rendering. Will tone down our WebGPU usage
Comment · 1 year ago
dang no way! we were both in boston too
Comment · 1 year ago
this is exactly where we're going with this! glad you see the vision :)
Comment · 1 year ago
yep!
Comment · 1 year ago
comparisons to more outputs coming soon!
Comment · 1 year ago
We ran some benchmarks comparing against Gemini Flash 2.0. You can find the full writeup here: https://reducto.ai/blog/lvm-ocr-accuracy-mistral-gemini

A high level summary is that while this is an impressive model, it underperforms even current SOTA VLMs on document parsing and has a tendency to hallucinate with OCR, table structure, and drop content.

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