LLMO – An LLM pair programmer in your terminal

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Hello HN! LLMO (Elmo) is an AI pair programming tool I created that's become an indispensable part of my workflow. https://github.com/knowsuchagency/llmo LLMO is designed to meet you where you are – your terminal. It provides real-time, interactive programming assistance. With its "staging area" feature, you can keep files in the context window and update the AI about your ongoing coding tasks without the hassle of copying and pasting every time you make changes to your code. Key features include: - Interactive Chat: Get real-time programming assistance directly in your terminal. - Staging Area: No need to copy and paste updates. Simply add your files to the AI's context. - Model Customization: Choose the OpenAI model that fits your needs. - Personality: By default, Elmo loves to make bodybuilding references. You can turn this off through a CLI flag or environment variable The recommended way to install LLMO is via `pipx install llmo` https://pypa.github.io/pipx/ As a sidenote, LLMO uses Textual which runs the terminal in application mode, meaning that you can't simply copy content as you would normally. In iterm2, you can hold down the `option` key to select text. You'll need to refer to the documentation for your own terminal for more information. I hope you find LLMO as useful as I have!

Visit product View on Hacker NewsJun 5, 2023

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

92%92% 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: created · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% 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 · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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