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Practical LLM Tips from the "AI That Works" Workshop
Hacker NewsPractical LLM Tips from the "AI That Works" Workshop
I recently attended an AI training/camp, one of the organizers was the author of 12 factor agents, and they shared their experiences-what’s worked, what hasn’t, and what it really takes to make AI useful in practice. I’ve organized the notes in a format that’s easy to skim and visually clear. If you’re interested in easy to understand details, you will find it interesting.
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, visual · Missing: mac, macos, cursor70%70% 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, podcasting59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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