Building With AI – Learn how to build AI products that wow

Hacker News

Hey HN! Today we're launching Building with AI, a one-stop-shop to learn how to build AI products that wow We’ve spent thousands of hours with AI product leaders and experts from companies like OpenAI, Fireflies, and Gamma to deconstruct the tactics & best practices included in these resources. The backstory In 2017, my brother and I launched A.I. For Anyone—a nonprofit that taught AI fundamentals to over 70,000 people. Today, we’re co-founders of Autoblocks, a solution for testing and improving GenAI products. We realized two important things during this journey: 1/ The best product teams follow a set of best practices when building with AI, and 2/ Educational resources for AI product builders aren’t very good. What’s up ahead Season 2 of BWAI Podcast: Our flagship podcast is back for another season, letting you hear directly from top AI product builders and experts. BWAI Newsletter: Fresh insights delivered straight to your inbox! In each issue, we dig into a key idea at the intersection of AI and product building. BWAI Academy: Tactical guides that dig deeper into best practices from top AI product builders. Did we mention all of this is free? Excited to hear what you all think!

Visit product View on Hacker NewsJan 17, 2024

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, inbox, openai · Missing: mac, agents, macos

85%85% 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

59%59% 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, ide, 000 · Missing: https docs, just released, exist

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: education · Missing: mobile apps, ios, personal

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: builder · Missing: plus, platform, intuitive

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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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