Cai – The fastest CLI tool for prompting LLMs

Hacker News

While using LLMs I realized 2 points: - I often still prefer Google, because I feel like I can get an answer quicker - I'd rather ask a smaller LLM a few questions than gpt-4 just one - The latency of LLMs is often enough to lose your momentum or abort the generation So I asked myself how I could built the fastest LLM prompt for the CLI? My best guess is to use the fastest language (Rust ) and the fastest LLM (Mixtral powered by https://groq.com ) And it's a game changer for me! At this speed it can replace most Googling, reading man pages, looking stuff up, … I can't wait to extend it with more features! =) Do you any ideas how to get it even faster?

Visit product View on Hacker NewsMar 28, 2024

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

85%85% 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.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, 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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

10%10% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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