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Superclass – GPT-Powered Document Classification Service

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Superclass – GPT-Powered Document Classification Service

Recently, one of our customers told us about how they have been using some of the common data classification tools and how they are using classical machine learning. It fairly painful to operate, especially on-prem. I pitched an idea to create a quick proof of concept for a classification service that uses GPT. It is incredibly easy to host and maintain. You can swap the model and the provider underneath based on your usage as well as environment. I thought it might be a good idea to open-source it and put it out there. Goal is that, you could just run: docker run -p 8083:8083 -e OPENAI_API_KEY=your_openai_key ghcr.io/adaptive-scale/superclass:latest And the classification service is available right away. Implementation supports text files, pdf, docx, pptx, images like jpeg, png etc. Also, containerized service means, all dependencies like tessaract, documentation processing libraries are just baked into the containers. Just run the classification as a service. I just verified some usecases, worked like charm for me. May be it is pretty useful for others. Feel free to give me some feedback.

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, dock · Missing: agents, macos, agent
76%76% 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
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · 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 · Strong signals: host · 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
14%14% 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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