StickerSlack

102h 15m

by nihaal (neon443)

Use any Slack emoji or Giphy GIF in iMessage Since my last ship, I reimplemented core views in UIKit for better performance, and fixed a lot of concurrency related issues and improved performance significantly. Scrolling through large emoji packs and having many downloaded emojis does not have a hit anymore as previously the frame rates would drop if there were many downloaded emojis. Also, shared packs now have a link preview and are routed through my domain so the URL doesn’t look scary 😭. It’s now easier to add emojis to packs from browse and search with a menu. Searching now doesn’t reinstantiate the UITableView, and has animations demo vid: https://copy.neon443.xyz/Untitled%20Project.mov

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Voyago -- AI-assisted travel agent proof-of-concept

26h 29m

by Harry

PSA: If you're looking for demos to see all parts of project, please reference the demo hub here! I was asked to include only the main app as the demo link. https://docs.google.com/document/d/1ATQCNs4iA9GmtCfzy3Zy8waQqAyNNWDVDrxhREHo9P4/edit?usp=sharing What is this? For now: A multi-agent system that learns your restaurant preferences through doomscrolling, and recommends you more restaurants! (+ the data extraction pipeline needed to collect the dataset it needs) In the future: A more agent-oriented agenetic system that will learn not only on restaurants, but about your opinion on certain attractions, attendable events, etc via doomscrolling. It will then help you build a trip itinerary by recommending you places / events you may enjoy. What it does (currently): 1. You tell Voyago where you’re staying at / your location (limited to Vancouver for now) 2. Voyago gives you a list of restaurants, presented in a reels-like format. It tells you the following things for every restaurant: - Genre (cuisine type) - Price range for full meal - Address - Operating hours - Top tags associated with the restaurant - The rough route + estimated time for you to either walk or drive there 3. For every restaurant, you can read the description and rate it out of 10 based on how much you like it 4. As you rate more and more, Voyago will use the numerical input you give to weigh which tags you like and which ones you do not. 5. Once enough tags are accumulated, the LLM will occasionally generate a summary of your preferences 6. Voyago will gradually reduce the likelihood of restaurants containing tags that already have very strong pro / anti signals, and vice versa for those with tags that are not frequently rated. 7. As you scroll, you can click "AI recommendation" button on the left side panel to let the agent pick out restaurants that you may enjoy, according to what Voyago has learned about your preferences Background The problem with planning a trip these days is that when you search up “things to do / eat in Vancouver”, it is always the same things that rise to the surface of your search engine. In reality, as a Vancouver resident myself, I can confirm that I’ve had far richer experiences at places which are hardly reachable by search engine unless if you dig vigorously. This is a problem because it demonstrates that only the top few percentile of places or establishments get the vast majority of attention, and becomes the "must-go meta" for most travellers. Voyago aims to fix this by giving equal exposure to more niche places and asking what *you* like... and what better way to consume large amounts of content than via doomscrolling?! Ultimately, the question comes down to what “motivates one’s happiness”. I want to build a personal agentic AI tour guide who will help you plan your itinerary. The way it does it is to 1. Learn about what is there to do in the city and what about them (places, events, restaurants, etc) 2. Learn and potentially reason about what specific factors “motivate your happiness”, 3. Thus, recommend places for you to go. This is quite an ambitious project that I will continue, and it has a lot of features I have to add to accurately model the constraints someone may have when planning their travels. Currently, the fully processed dataset is limited to restaurants within Vancouver. The demo version contains 47 locations total. This is mainly a proof of concept, but it does work as intended and it demonstrates how, if we're able to process more data of say, attractions, we're able to easily integrate it into this system. But I guess for now it's of some use. I have very strong deciphobia about where to eat and will often spend such a long time walking around, looking at maps, and carefully analysing the menus at the food court, only to resort to McDonalds (the default safe option) in the end

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Agath

14h 52m

by Ethan

Agath is a mobile FRC scouting application built with React Native and Expo. It allows scouts to record robot data during autonomous, teleop, and endgame phases, then sync that data to a backend server or transfer it offline via QR codes. The app is built for FRC Team 2638 but is open-source and any team may fork and adjust the code accordingly.

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faster-slack-client

41h 55m

by alice

an almost native slack desktop app built with wails

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Kryptopedia 2026

108h 18m

by dominic

FRC 2539's scouting app for Rebuilt! a Flutter mobile app and a Ruby on Rails app for data sync.

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faster-slack-client

41h 55m

by alice

an almost native slack desktop app built with wails

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