Orange intelligence, an open source alternative to Apple Intelligence

Hacker News

Hi HN! I’m excited to share Orange Intelligence, an open-source alternative to Apple Intelligence for macOS. Orange Intelligence allows you to interact with any text on your macOS system in a more powerful and customizable way. It brings a floating text processor that integrates seamlessly with your workflow. Whether you’re a developer, writer, or productivity enthusiast, this tool can boost your efficiency. Key Features: Floating Text Processor: Trigger a floating window by double-tapping the Option key to process selected text. Run Any Python Function: From basic text manipulations to running large language models (LLM) like OpenAI or local LLaMA, you can execute any Python function on the fly. Full Customization: Want to add your own functions or logic? Just write them in Python, and they’ll appear in the floating window. How does it work? Capture: Uses AppleScript to simulate a global Cmd+C and capture selected text from any active macOS app. Process: A floating window pops up, letting you choose what to do with the text (run a function, format it, or apply an LLM). Replace: After processing, the app returns focus to the original application and pastes the processed text back with a global Cmd+V. Why open source? I built this to overcome the limitations of Apple’s proprietary tools, and I wanted to make it fully customizable and extendable. Orange Intelligence is built with Python and PyQt6, so it’s easy to adapt, extend, and contribute to. It’s not just a text processor—it’s a platform for building custom workflows, whether you want to automate simple tasks or integrate with complex AI systems. If you’re on macOS and you’re interested in boosting your productivity with Python and AI, I’d love for you to try it out and give feedback: https://github.com/sharingan-no-kakashi/orange-intelligence . I’m looking forward to your thoughts, ideas, and contributions. Thanks!

Visit product View on Hacker NewsJan 26, 2025

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

95%95% 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 · Missing: supports, reddit linkedin, podcasting

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

68%68% 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: way · Missing: mobile apps, ios, personal

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

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

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

Correct prediction on native model

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