Mammal – (Yet another) LLM client built on Tauri

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Mammal is an LLM client built on Tauri. It supports a bunch of providers (esp. the openai API compatible ones). It's a side project borne out of my desire to have a local store of my LLM interactions, and I took it as a chance to experiment with building on Tauri. Lots of my experimentation with prompting is with productionizing prompts in mind, so I have lots of thoughts about moving in that direction. But the basic client is useful right now (if not entirely bug-free). Feedback welcome :)

Visit product View on Hacker NewsNov 22, 2024

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

73%73% 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 · Strong signals: supports, compatible · Missing: reddit linkedin, podcasting, created

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

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

44%44% 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

43%43% 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 · Missing: mobile apps, ios, personal

31%31% 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

15%15% 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.

Correct prediction on native model

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