Autoblocks Annotate – data annotation platform for AI products

Hacker News

Hey HN! I'm Adam, one of the co-founders at Autoblocks AI. We're excited to share Autoblocks Annotate, a free annotation platform purpose-built for AI products. We're convinced that the best way to build great AI products is to have humans actually review and annotate the outputs from your AI systems. Today, the teams that do this are using spreadsheets or other homegrown systems that get complex and messy quickly leading to a poor experience for the reviewers and the folks consuming the results. Our solution allows you to systematize the entire process and enables easy consumption of the results via API. You can use our TS or Python SDKs to easily upload data for review, assign jobs to others on your team, and download the results to use for fine-tuning or analysis. Our UI supports displaying data as raw text, markdown, html, or can even iframe external links (for example Google Drive links). Looking forward to hearing everyone's thoughts and questions on how this would fit into your workflow!

Visit product View on Hacker NewsSep 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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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