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tailgate – Build generative-AI features without a backend

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tailgate – Build generative-AI features without a backend

Hey HN! A couple of weeks ago I realized that many static websites could benefit from generative-AI features, but site owners don't want the hassle of setting up a backend or attempting to build a custom application. I went ahead and built tailgate, a dead-simple way to add generative-AI features to a website without worrying about backend infra. Simply import the library, add decorators to some elements, and instantiate it. Content will hydrate on page-load. I think there are a lot of site owners who can use tailgate to help inform, engage, and retain site visitors in new ways. I'd love to hear about your use-case! (Caveat: for now, the backend is self-hosted, but I'm happy to set up a managed version given enough interest.)

Visit productView on Hacker NewsOct 12, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
84%84% 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
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% 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
18%18% 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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