Publishing Your AI Application Online

Hacker News

Greetings, If you are developing an AI application, or are in the process of doing so, this announcement may be of significant interest to you. I am currently developing a tool designed to facilitate the online publishing of AI applications. This tool supports both synchronous and asynchronous apps and includes features such as response streaming, all within a user-friendly chat interface. Moreover, it is fully implemented in Python. For those interested in early access, I invite you to join our waitlist. By joining, you will also receive access to several open-source applications I have developed, which could serve as valuable references for your projects. Please find the waitlist link below: https://cycls.typeform.com/waitlist1?typeform-source=docs.cy... Thank you for your attention, and I look forward to helping you bring your AI applications to a wider audience.

Visit product View on Hacker NewsApr 27, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, open · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

25%25% 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 · Missing: arr, mrr, revenue

10%10% 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 · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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