Ergonomically call LLM in bulk from CLI

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Hi! I've found myself repeatedly writing little scripts to do bulk calls to LLMs for various tasks. For example, run some analysis on a large list of records. There are a few "gotchas" to doing this. For example, some service providers have rate limits, and some models will not reliably return JSON (if you're asking for it). So, I've written a command for this. What I've tried to do here is let the user break up prompts and configuration as they see fit. For example, you can have a prompt file which includes the API key, rate limit, settings, etc. all together, or break these up into multiple files, or keep some parts local, or override parameters. This solves the problem of sharing settings between activities, and keeping prompts in simple, committable files of narrow scope. I hope this can be of use to someone. Thanks for reading.

Visit product View on Hacker NewsJan 20, 2025

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

86%86% 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

82%82% predicted probability of success on Indie Hackers, 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.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

Incorrect prediction on native model

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