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Sidekic AI, Giving You Infinite Memory

Hacker News

Sidekic AI, Giving You Infinite Memory

Hello Hacker News! I’m Obinna, and I’m building www.sidekic.ai with my cofounder Harsha. We’re building infinite memory for the world’s smartest people. A bit of marketing jargon, but here’s what we do. 1. We help you save any resource you find on the web, like websites, articles, videos and tweets. We’ll be releasing a browser extension soon to make this simpler! 2. Our AI automatically sorts, tags and organize your resources based on the content and the context. We put them into neat shareable collections called “StarterPacs” 3. Lastly we help you search through your resources that you’ve saved, but also from your friends, teams and communities. Our end goal is to use this pipeline to build a curated index to offer a more helpful and relevant search experience based on curated knowledge from experts. We chose to tackle this problem because we experienced first hand how stifling and time consuming it can be to search for information. We worked at LinkedIn and Apple previously, and search infrastructure at those companies was **. More importantly, we saw that many of our friends also had this problem, and the best way they found to solve it was asking a coworker or subject matter expert. Here is an example StarterPac: https://www.sidekic.ai/starterpac/8500825504404890 We just launched on product hunt today if you want to check us out! We’d appreciate any feedback, no matter how critical :) feel free to send an email as well! hello@sidekic.ai https://www.producthunt.com/posts/sidekic-ai Now for the fun stuff, how did we build this? It’s a pretty simple stack right now. Frontend is NextJs, tailwinds, daisyUI and Spline. Our backend is AWS Amplify for the application layer (for now, moving to AppWrite most likely) and a Python flask server for the AI functions. For our “AI” we use a combination of our own NLP models, and ChatGPT. GPT 3.5 has been more than enough for our purposes, so the running costs have been practically nothing. As you can tell, the application is a bit slow. We wanted to launch something that we weren’t proud of to get quick feedback. Thank you very much for reading! :)

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, new · Missing: mac, agents, macos
95%95% 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 · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, pipe · Missing: https docs, excited, just released
53%53% 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: soon · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency
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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