Tabby – AI Coding Assistant Runs on Apple M1/M2 GPU

Hacker News

Hi HN, after five months of hard work since Tabby's previous Show HN ( https://news.ycombinator.com/item?id=35470915 ), I'm thrilled to share you the release of Tabby v0.1.1. Previously, Tabby ran exclusively on CUDA devices, posing a significant barrier for developers looking to effectively utilize LLMs in their day-to-day coding. The Tabby team made a significant contribution by enhancing support for the StarCoder series models (1B/3B/7B) in llama.cpp. This enhancement allows these models to run on Metal, providing comparable performance to that of an NVIDIA GPU. I eagerly look forward to receiving feedback from the community and witnessing the enhanced on-edge deployment experience! References: [1] Apple Installation Instructions: https://tabby.tabbyml.com/docs/installation/apple/ [2] Launch blog: https://tabby.tabbyml.com/blog/2023/09/18/release-0-1-1-meta...

Visit product View on Hacker NewsSep 18, 2023

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

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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