Clevis – Build and sell AI powered apps without code

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After seeing a lot of simple AI powered web apps being launched lately, the idea of building a tool to create them appeared in my mind. Using Clevis, you can quickly create apps by connecting a series of steps. The first step to an app is usually some sort of user input like a text field or a file upload. Then, that input can be processed by AI tools like ChatGPT and the output can be displayed to the end user as plain text, HTML or a file. Examples of other apps you could build using Clevis: 1. SEO Assistant: An app that analyses any website URL for SEO improvements 2. Cover Letter Writer: Users can input basic information, provide a link to a job advertisement, and receive a meticulously tailored cover letter as output. 3. Travel Planner: App that creates a beautifully designed travel itinerary wherever you are planning to go. You may ask: What is the difference between using a Clevis app and just using ChatGPT? The answer is the UI. By collecting input from the user in a structured way using forms and image uploads, you can make sure that the model receives the best data to do its job. The output can also be styled with HTML instead of just plain text. For creators, it is a way to quickly create AI powered apps and start selling them instantly. This project is very early stage and any feedback or ideas are highly appreciated!

Visit product View on Hacker NewsJun 29, 2023

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

91%91% 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: model, apps, user · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, 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

31%31% 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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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