Onelook – Create applications by describing them in human language

Hacker News

Hey HN! We are the team at Onelook. Demo: https://demo.onelook.ai/ Blog post: https://medium.com/@onelook/introducing-onelook-a-radical-ne... Onelook allows users to create fully-functional applications by describing them in human language. And by fully-functional we really mean backend included. Think of it as Retool for non-technical people or Copilot to build apps. Existing tools are for technical teams and even Retool, which is a great product, requires setting up connection to databases, write queries, and call REST APIs. We understand it is impossible to create a fully-functional app just by describing it in one go. Our approach is to model the conversation between a client and a product manager. In this case, the AI is the PM. The user chats back and forth with the AI to refine the app. Thank you!

Visit product View on Hacker NewsMar 26, 2024

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

94%94% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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

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