MyOllama – Open-Source Mobile Client for Ollama LLMs

Hacker News

MyOllama is an open-source mobile client that enables interaction with Ollama-based LLMs from your iOS/Android device. Technical highlights: - Built with Flutter for cross-platform support - Supports remote LLM access via IP configuration - Implements custom prompt engineering capabilities - Compatible with multiple LLM architectures (Llama, Gemma, Qwen, Mistral) - Image recognition support for compatible models - Persistent conversation management - Localization for English, Korean, and Japanese Key differentiators: - No cloud dependencies - runs entirely on your local machine - Privacy-focused architecture - Zero subscription fees - Custom instruction templating - Streamlined API integration with Ollama backend The app is available on the App Store and the source code is released under GNU license on GitHub. GitHub: [ https://github.com/bipark/my_ollama_app ] App Store: https://apps.apple.com/us/app/my-ollama/id6738298481 Looking for contributors and feedback from the community.

Visit product View on Hacker NewsNov 25, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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: mac, model, apple · Missing: agents, macos, agent

84%84% predicted probability of success on Product Hunt, 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

58%58% 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: ios, apps · Missing: mobile apps, personal, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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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Open-Source RAG CLI for Ollama

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