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Humanish AI – open-source Version of OpenAI

Hacker News

Humanish AI – open-source Version of OpenAI

Chase here - our mission is to build an open source AI company focused on developing products for enterprises. The repo is at https://github.com/humanishai (please take a moment to star!) and our home page is https://beta.humanish.ai After a decade of working at several labs and companies including NASA and Target, I originally quit my job to start a generative media company at YC. We didn’t manage to solve that problem, but we learned how hungry people are to use the biggest AI models. However, when we signed up for access we were put on a waitlist, it bothered us how we needed to send our users data to third parties and get approval before launching our product. Waiting weeks or even months for access felt exclusive if we could get access at all to the big models, and it felt wrong from a privacy perspective. AI should be accessible to anyone. We designed Humanish AI to solve these problems. We made Humanish AI with a set of features that can be extended easily. All of the code will be open source. And all of the models will be too. There is a Discord server where we will train models together with the Open Source community. We'll post on Twitter and Instagram. Anyone can help out training, adding features, or making suggestions on Github rather than being excluded. I am personally stoked to read every single one of your suggestions and reply myself! You can have source code access to every part of the process from data collection and cleaning, to training and infrastructure, to backend and frontend, to challenger/champion deployment systems, and we are building a cloud version you can sign up for too. That means our plan is to have the best parts of the 3rd party deep learning model providers but are more privacy and developer friendly. We’re thinking of adding features around many more modalities, so please let us know what you would find useful for an enterprise you know. We are platform and language agnostic, with a very simple setup. If you want Python, we give you a library. For anything else, there’s a REST API you can signup for to get early access https://beta.humanish.ai The repos have instructions for AWS, Terraform, or Docker. We’ve launched this repo under MIT license so any developer can use the tools. The goal is to not charge individual developers. We make money by charging a license fee for things like multiple users, user permissions, training or fine tuning custom models, providing a hosted version, support, and any new features or models an enterprise needs. Give us suggestions for your enterprise: github.com/humanishai Leave a star! Is there something you want? Make a suggestion in Github discussions or make a PR and I'll personally merge it in. For any new paid feature, to help us out, or for investment reach out to sales at contact@humanish.ai

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
97%97% 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: model, user, dock · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
60%60% 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: personal, month, users · Missing: mobile apps, ios, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, exclusive · Missing: plus, intuitive, reviews
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money, paid · 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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