Working on open-source perplexity AI

Hacker News

Hey guys, I am think of building an open-source version of Perplexity to let devs play around with it. But with all the existing tools available what features would you want? Anything specific? What is missing? Currently working on - 1. Streaming text 2. Citations sources 3. Image and file upload 4. Chat history and storage 5. Temperature and custom instructions If you are in marketing or growth can anyone help me with what to focus on while building such an app? Also here is a very first version. Probably will break and most of the buttons also don’t work, built it in 3 days using Bing and OpenAI Will complete the rest of the app and share code in a month max.

Visit product View on Hacker NewsMar 19, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: perplexity, openai, using · Missing: mac, agents, macos

89%89% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

25%25% 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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