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LlamaChat - interact with your favourite LLaMA models on macOS
Hacker NewsLlamaChat - interact with your favourite LLaMA models on macOS
Excited to launch a side-project that I've been working on over the past few weeks, LlamaChat: an open-source SwiftUI app which allows you to chat with your local LLaMA models right on your Mac. Powered by the excellent llama.cpp (and Swift wrapper, llama.swift), LlamaChat allows you to import your local LLaMA models (and Alpaca, GPT4All, and soon, Vicuna and Koala) and interact with them locally.
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9points
Made the leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor93%93% 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: excited, llama, ide · Missing: https docs, just released, exist66%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, podcasting63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive33%33% 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, revenue16%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, cryptocurrency9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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