Native macOS app for chatting with Ollama models

Hacker News

Hello everyone, I've built a simple macOS app for chatting with models downloaded by Ollama - https://github.com/sheshbabu/Chital It's written in Swift, consumes less memory and loads fast. It has these features: * Support for multiple chat threads * Switch between different models * Markdown support * Automatic chat thread title summarization It's my first time working with Swift and Xcode, and it has been an interesting journey. The performance of the application is a big plus when building native apps, but I wonder if I'll be able to add features like document processing, RAG and other capabilities that are easy to implement using Python. Give it a try and let me know what you think :)

Visit product View on Hacker NewsSep 30, 2024

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Actual performance

5points

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Launch Intel predictions

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

91%91% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% 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

43%43% 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

21%21% 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.

Incorrect prediction on native model

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