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Unify Browser – WebKit Browser Built with SwiftUI and MLX

Hacker News

Unify Browser – WebKit Browser Built with SwiftUI and MLX

Hi everyone! Unify is a WebKit based browser with an AI research assistant built right in. You heard that right. No chromium here. The project started out as a simple RAG engine to answer queries, but it evolved to become much more in a short period of time. The goal is simple: find the best context, without prompting input, to best answer your questions. One of the biggest limitations of apps like ChatGPT is their lack of personalization. Beyond the chat window, your conversations with the model don’t contribute much to its understanding of your personal context. Every session starts from scratch, as if the model has a blank slate. This isn’t how humans work, and Unify is designed to bridge that gap. Unify indexes any file format, allowing you to simply drag and drop files from your computer to make them part of your conversational context. The next time you have a question, your conversations from the past, with your files, now have the possibility to aid in answering questions that are personal to you. You can query across thousands of documents without worrying about token limits, lost context, or prompt engineering. LLMs shine when they have enough context to produce insightful, non-generic answers. Unify makes that possible. So why is it a browser? In some ways using the term “Browser” makes Unify a trojan horse product. But the most important source of them all is the internet. It is the gateway to all information. Just like with files on your computer, Unify will also index any website with just a click. No more copy-pasting text into a chat window, hoping you’ve captured the right context. By integrating both web content and personal files into a single stream, Unify helps eliminate workflow fragmentation and streamlines the search process. Unify also offers local inference using the MLX framework. Powered by Llama 3.2-4b, it allows you to take your conversations private when needed. While larger online models are still better for generating the best answers, Unify gives you the flexibility to switch to secure, offline, and private inference without sacrificing functionality. Full disclaimer, I am a solo developer working on this. I would love any and all feedback I can get on this. Please let me know how I’m doing! You can get Unify on the Mac App store. Check it out here: https://apps.apple.com/us/app/unify-ai-browser/id6478436147?...

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apple · Missing: agents, macos, agent
99%99% 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: started · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, answers · Missing: mobile apps, ios, entrepreneurs
52%52% 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
10%10% 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.

Incorrect prediction on native model

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