Vips – Emacs Interface for OpenAI's GPT API and DeepL's Translation API

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I am excited to share vips.el, an Emacs interface for OpenAI's GPT API and DeepL's translation API. With vips.el, you can work directly with OpenAI's GPT-4 and GPT-3.5-turbo models, customize parameters for fine-tuning the output, and translate text seamlessly using the DeepL API, all within Emacs. The tool is designed to be easy to install and use, with a range of keyboard shortcuts for quick access to its features. It's distributed as free software under the terms of the GNU General Public License. You can find more details, including installation steps and usage instructions, in the project's README file. I would love to hear your feedback and suggestions!

Visit product View on Hacker NewsSep 13, 2023

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

89%89% 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: para, including · Missing: supports, reddit linkedin, podcasting

68%68% 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: excited, io, including · Missing: https docs, just released, exist

49%49% 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: interface · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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