Vlm Run, Extract JSON from images, videos and documents in a simple API

Hacker News

Hey HN, We’ve been building out an API for ‘Visual ETL’ that we call vlm.run. We’ve been working with foundation models (GPT4o, Gemini) for a few months and kept running into failure modes like: - Hallucinations: even the best foundation models continue to hallucinate outputs for complex visual inputs, even when adhering to a schema. - Rate limits: frontier models like GPT4o are still too expensive or rate limited for high volume visual data. Our API is designed for production workloads which means speed, stability, monitoring and, if needed, private deployments. - Off the shelf schemas: Defining a schema takes trial and error to get right. We’ve put together a taxonomy for common visual tasks that are ready to go from day 1. Some examples we’ve put together: - Presentations: https://docs.vlm.run/guides/guide-pdf-presentations - TV News: https://docs.vlm.run/guides/guide-tv-news Sign up for an API key and try us out on a 2 week free trial. Check out our docs at https://docs.vlm.run/what-is-vlm-1 and reach out if you have questions!

Visit product View on Hacker NewsAug 13, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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