Akshat  Saladi

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Maker History

Forums

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17d ago

Zoomie - Dog Activity Tacker - Close your dog's rings.

Your dog has rings to close, too. Zoomie is the Apple-native activity tracker that turns walks, fetch, scent work, training, and cuddle time into a rewarding daily habit. - Three rings: Paws (movement), Playtime (sessions), Wags (interactive photo game) - Earn treats, badges, and hit goals for any energy level or weather - Live Activities, Dynamic Island, Siri, widgets, and multi-dog profiles - Built in SwiftUI — fast, private by design.
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18d ago

Fog: Smart Notes & Folders - Smart suggestions. You decide.

Welcome to Fog 2.0! I've reimagined Fog around one idea: Fog helps, you decide. - Fresh coat of paint: redesigned to feel at home on iOS/iPadOS. - Smart Title Suggestions: AI proposes titles, never overwrites your work. - Flexible Folders: group notes with instant summaries. - Ask Your Notes: natural-language search across your notes. - Universal Experience: core features on every device, enhanced local AI on Apple Intelligence. - Lock screen controls: start a new note straight from sleep.
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3mo ago

Built a notes app that groups your notes automatically using Apple's on-device Foundation Models

I've been building Fog, a notes app that tries to solve a problem I kept running into: notes apps either make you organize everything manually (folders, tags) or don't organize at all.

Fog uses Apple's on-device Foundation Models framework (the same one powering Apple Intelligence) to read your notes and group related ones into "Clouds" automatically no manual tagging, and nothing ever leaves your device since it's all on-device inference.

The interesting technical challenge was the clustering logic. I ended up using a union-find (disjoint set) structure combined with NaturalLanguage embeddings to figure out which notes are actually related, then merge them into clusters that update as you add more notes. Getting the similarity threshold right so Clouds don't over-merge unrelated notes or fragment obviously-related ones took a lot of tuning.
Demo video (56 sec, shows the auto grouping)
Curious if anyone else here has worked with Apple's on-device Foundation Models yet how are you handling similarity/clustering tasks, and did you run into similar tuning headaches with thresholds?

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