AI

AI – Nest for the Rest

Hacker News

AI – Nest for the Rest

I used Gaussian Processes, a machine learning algorithm recently mentioned in a Wired article (https://www.wired.com/2017/02/ai-learn-like-humans-little-uncertainty/), to create a simple Nest-like AI. Check out what I did here: http://slides.com/innumero/smarthomeai-01#/ . How would you use it? Is it worthwhile to develop a AI SaaS?

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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac · Missing: agents, macos, agent
62%62% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% 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 · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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