标签：#weekendchallenge

This is a submission for Weekend Challenge: Dog Days Edition Dog Whisperer is an app that looks at a photo of your dog, figures out what it's probably feeling, and then actually says it out loud in a voice that matches the mood. Grumpy dog gets a grumpy voice. Dramatically offended dog gets... a dramatically offended voice. You get the idea. It also doubles as a pet log — meals, weight, and walks tracked over time in Snowflake, with trend charts so you can actually see if your dog's been eating more than usual or losing weight. Add your pets and start logging. Use your unique username to keep track of your pets! Here is App in action: https://dogwhisperer-whi6rye8zklcdtnedyxmww.streamlit.app/ Demo Code Kaku-g / dog_whisperer How I Built It I used Google's Gemini (model: gemini-3.5-flash-lite ) to infer the mood of the dog (or cat, lizard, ferret — whoever's in the photo) from a single image, then passed that straight into Gemini's native TTS (model: gemini-3.1-flash-tts-preview ) to give it a voice that actually matches the mood — a sleepy dog sounds sleepy, a dramatic one sounds dramatic. For logging and trends, I used Snowflake — compute, databases, and tables — to store meals, weight, and walks for every pet and power the trend charts in the app. So it's really two things working hand in hand: a generative AI pipeline paired with a data warehouse. The AI part is what makes the app fun — inferring your pet's mood and giving it a voice. The Snowflake part is what gives it a real use case , since it's something you could keep using for long. Prize Categories I used Google AI and Snowflake, so I'm submitting under both: 🏆 Best Use of Google AI 🏆 Best Use of Snowflake

This is a submission for Weekend Challenge: Dog Days Edition What I Built PawID & Care is an enterprise-grade, multi-role emergency response and biometric identification platform built for community animal welfare and stray management. Operating as a "112-for-dogs" system, the platform bridges cutting-edge artificial intelligence, distributed ledgers, enterprise analytics, and autonomous voice agents into a single unified workflow. The core goal is to solve the fragmentation in animal rescue: pet owners lose dogs, street animal injuries go unreported, and cities lack real-time community health tracking. PawID & Care provides instant biometric triage, role-based portals for Owners, Rescuers, and Vets, and real-time emergency voice dispatching. Demo 📹 Watch the Demo Video: https://youtu.be/_B-2dL2fDX4 Code GitHub Repository: [ https://github.com/GamersStop/paw-id-care ] bash # Clone the repository git clone [https://github.com/GamersStop/paw-id-care](https://github.com/GamersStop/paw-id-care) # Install dependencies npm install # Start the server npm start

This is a submission for Weekend Challenge: Dog Days Edition I built Caninography, a small digital archive for exploring dog breeds from around the world. I wanted it to feel more like a digital museum than a normal dog website. You can explore breeds, their origins, history, countries, characteristics and connections between them. The whole design is dark, clean and visual. I also kept it silent, so there is no distracting audio or player UI. Live Demo: canonigraphy.vercel.app GitHub: https://github.com/maisamabbas0323/canonigraphy.git How I Built It I built it with React, Vite and TypeScript. For the visual side, I focused a lot on typography, photography, smooth transitions and responsive layouts. I also added an interactive world atlas and a constellation-style view to explore breed relationships. For the content, I used Google Gemini to help create short and interesting breed information. I didn't wanted to make another chatbot. Instead, Gemini stays behind the experience and helps make the archive content more rich while people simply explore it. Prize Categories Best Use of Google AI Caninography is submitted for Best Use of Google AI. I used Google Gemini to generate concise, breed-specific information based on the archive data. The idea was to use AI in the background, not put a chatbot in front of the user. Built With React Vite TypeScript Google Gemini CSS SVG Canvas A Little About The Idea I always felt dog breed information is mostly shown as simple lists. So I thought: What if a dog archive felt like a museum? That small idea became Caninography. A place to explore their stories, origins and history — one breed at a time.

This is a submission for Weekend Challenge: Dog Days Edition What I Built PawSafe is an AI-powered web application that helps dog owners answer a simple but important question: "Can my dog eat this?" Users can enter the name of a food, upload a photo, or provide both. PawSafe then analyzes the information using Google's Gemini API and provides a simple safety assessment. The result is categorized into four levels: 🟢 Generally Safe 🟡 Use Caution 🔴 Not Safe ⚪ Unable to Determine Along with the result, PawSafe provides explanations, potential warnings, and safer alternatives when appropriate. My goal was to build something that was useful, simple to understand, and approachable for dog owners rather than making users search through multiple sources every time they encounter an unfamiliar food. Demo Live Demo Code GitHub Repository How I Built It PawSafe is a full-stack application built with: Frontend React Vite Tailwind CSS Lucide React Backend Node.js Express Multer CORS Google Gemini API Deployment Render GitHub The basic flow looks like this: User ↓ Food name / Image / Both ↓ React Frontend ↓ Express API ↓ Google Gemini ↓ Structured Analysis ↓ PawSafe Result Card One of the main technical decisions I made was to keep the Gemini API integration on the backend rather than exposing the API key in the frontend. The frontend sends the user's food information to the Express API. The backend then communicates with Gemini and returns the structured analysis to the frontend. I also wanted the application to support both text and images independently, while still allowing users to provide both when additional context is useful. Prize Categories Best Use of Google AI PawSafe is submitted for the Best Use of Google AI prize category. Google's Gemini API is the core intelligence behind the application. It is used to analyze both text-based and image-based food information and generate a structured safety assessment. The AI response is then presented through PawSafe's interface

This is a submission for the DEV Weekend Challenge: Dog Days Edition . What I Built Same sidewalk. Two bodies. Two completely different heat loads. Banx is my French Bulldog. Born October 5, 2022. He weighs 35 pounds — seven above the 28-pound ceiling in the French Bull Dog Club of America conformation standard. I call him my XL. He is purebred and he has never had airway surgery. The face that makes him Banx is also the conformation that puts French Bulldogs at higher risk of obstructed breathing and heat-related illness. Dogs cool themselves mostly by panting. Flat-faced dogs can do it less efficiently, and how much varies a lot between individual dogs. So the same afternoon — same sun, same pavement, same humidity — is a walk for one dog and something else entirely for him. Nothing on the outside tells you that. Enter a location. It pulls temperature and humidity, computes a heat index, and shows the load on a flat-faced dog beside a longer-muzzle dog across the day. Then it helps me think through the question I actually have when he's standing at the door: how stressful do the conditions look right now, how does that change with activity, and when does the environment get more favorable? It does not medically answer that for him, and the section below says exactly why it can't. Demo Live: https://banx-walk-safe.vercel.app Geolocation or city search. Works if you deny location. No API key. Code Vanilla HTML / CSS / JS. No framework. Repo is the project folder on the machine that built it; the production artifact is the Vercel deploy above. Weather: Open-Meteo . Heat index: NOAA/NWS Rothfusz / Steadman family. Why I Built It When I first got him I didn't know how any of this worked. We started at Ledge Street Park in Nashua and took the trails toward Main Street. First ten minutes he's got everything — all over the place, into everything, full Banx. Then he changes. He stops being all over it and starts just observing. Walking straight forward, taking it in, calm.

This is a submission for Weekend Challenge: Dog Days Edition What I Built Dogs have...

This is a submission for the DEV Weekend Challenge: Dog Days Edition . What I Built Every family with a dog eventually asks the same question: did anyone feed the dog? PawTwin turns that daily uncertainty into a shared game. It is a real-life Tamagotchi whose state is controlled by care performed for the family's physical dog—not a task manager with a mascot pasted on top. The family begins with a photo of the real dog. PawTwin creates a reusable pixel identity and places it inside an animated Phaser home. The environment communicates real needs: an empty bowl means food is due, a dry bowl means water is overdue, pacing at the door means a walk is waiting, and dirt particles mean hygiene has slipped. Completing and approving real care makes the twin playful again. A caregiver can claim feeding, water, walking, oral-care, ear-care or grooming work. Walks record time, approximate distance and a privacy-reduced route; other tasks can include private photo evidence. A guardian—not computer vision—reviews the proof and decides whether it counts. Approval can release a real Devnet SOL reward through a guardian-signed Solflare transaction. The V2 experience also gives the dog a life beyond reminders: Pet Memory learns explainable patterns such as who normally walks the dog and at what time, then creates personalized in-app reminders and a monthly family report. Find the Ball uses a future finalized Solana Devnet blockhash to choose one of four rooms, exposing the slot, blockhash, digest, salted commitment and nonce so anyone can reproduce the zero-stakes result. Life Album recreates the thick paper, metal rings, photo corners and protective cellophane of a family photo album, with private photos, stories, places and short recordings of the real pet's original sounds. Device Lab demonstrates a guarded Raspberry Pi ball-launcher contract while remaining clearly in simulator mode unless safe physical hardware is configured. The core loop is: Real need → visible game behavior

This is a submission for Weekend Challenge: Dog Days Edition Target categories: Best use of Snowflake + Best use of Solana. Ten days from now International Dog Day is August 26th. The date is not arbitrary and it is not a marketing pick. Colleen Paige founded it in 2004 and chose the 26th because that is the day her family brought her first dog home from a shelter , when she was ten years old. So the holiday is not really about dogs. It is about the gap between a dog sitting in a shelter and a dog sitting in a house, and about how many animals never cross it. That gap is the whole reason this project exists, and it is why the last tab of this dashboard is the quietest one. I did not start at the shelter. I started at a limp nobody saw. But every path I traced ran the same direction, and it always ended in the same room. This is a build post, so most of what follows is SQL. But I want to be honest about which end of the problem I was standing at when I wrote it. The problem, stated plainly Because a hackathon post should be able to say this in four rows before it earns the right to show you any SQL: The question In one line The issue A dog in pain is built to hide it, so the first human-readable sign of a chronic problem arrives months late - often at the point the relationship, not just the joint, has broken down. Why software has not fixed it Every consumer tracker compresses a day of movement into a scalar - steps, active minutes, a sleep score - and then thresholds it. The clinical signal is not in the magnitude. It is in the ordering , and averaging is precisely the operation that deletes ordering. What TELLTAIL tries Stop thresholding. Make the detector a regular expression over rows - MATCH_RECOGNIZE - so a differential diagnosis stays a sequence all the way down to the individual second that satisfied it. How you know it is not a demo Every finding is explainable to the second, the accuracy is printed in 44px type on the dashboard including the parts that are

This is a submission for the DEV Weekend Challenge: Dog Days Edition . Upload a photo of your dog. An AI accuses them of a crime. You're their defense attorney. What I Built Paw & Order is a browser game where your own dog is the defendant. You upload one photo and a few seconds later your dog has been arrested: The People vs. Biscuit Docket #PAW-042 DEFENDANT: Biscuit CHARGE: Grand Theft Sausage COUNSEL: You STATUS: Extremely suspicious Then the trial starts. The prosecutor puts a question to you, you pick a response, and the case branches from there. Three exhibits go into evidence: generated images of your dog, at the scene, with the frosting still on their muzzle. Two witnesses give statements, and at least one of them is usually lying. A trial runs a few minutes. At the end you get one of four verdicts: NOT GUILTY NOT GUILTY, BUT SUSPICIOUS GUILTY, BUT REASONABLE DOUBT GUILTY Plus a scoreline that isn't the same thing as winning: VERDICT NOT GUILTY Biscuit is free to commit additional crimes. Defense Performance: 94/100 You can lose the case and still score 96. You can win it badly. Every case has a hidden truth, generated before the trial begins. Sometimes the dog really did it, sometimes they're innocent, sometimes the evidence just lies. The client never sees any of it, so you're not hunting for a correct answer. You're building the strongest defense the facts allow. Choices decide the outcome. Replay the same case, answer differently, and the verdict and the score change with you. Demo Live: https://paw-order.pages.dev Bring a dog photo, or don't. The home page has a public docket of cases other players entered into the public record, and you can play any of them without uploading anything. Code ArjenPostma / Paw-Order dev.to weekend challenge submission Paw & Order Justice for every good boy. Upload a photo of your dog. AI generates a fictional criminal case around that dog. You defend them in court. Live: https://paw-order.pages.dev DEV Weekend Challenge:

This is a submission for Weekend Challenge: Dog Days Edition What I Built A game where...

This is a submission for Weekend Challenge: Dog Days Edition What I Built WikiPaw is an interactive Wikipedia-hopping game designed around dog breeds! Players are given a target dog breed to reach but start on a Wikipedia page located 2–3 outgoing link hops away from their target. To help navigate the maze of Wiki links, WikiPaw uses Gemini AI as an intelligent guide to evaluate your current page against the target breed and hint at how close you are to reaching your destination Demo Live project coming on Wikipaw wikipaw-demo.mov - Google Drive drive.google.com Code The code is hosted on my github and repo is called wikipaw How I Built It The following points describe how the project works: Core Game Loop: We construct a graph of Wikipedia links starting from a selected dog breed, traversing backwards 2–3 hops to select a fun starting article. Gemini AI Integration: On each page visit, the current Wikipedia article content and target breed details are sent to Gemini AI. The model analyzes semantic similarity, topical relevance, and contextual overlap to calculate a "proximity score" and generate dynamic hints for the player. Frontend/Backend: Built with a clean UI to render stripped Wikipedia content with active internal links while tracking the player's path and hop count. Prize Categories Best Use of Google AI : Leveraged Gemini AI to dynamically calculate semantic proximity between Wikipedia articles and generate context-aware hints for players.

This is a submission for Weekend Challenge: Dog Days Edition What I Built Tail Talk reads a dog's body language from a photo. You upload a picture. Gemini walks the whole dog; ears, eyes, mouth, tail, posture, weight distribution, hackles ; and reports what each part is saying, checks every rung of the canine ladder of aggression, and tells you what to do and what to avoid right now. The thing that pushed me to build it: dogs warn us for a very long time before they growl. A lip lick. A yawn that isn't about being tired. A head turned away. A crescent of white in the eye. These are the quiet rungs of a ladder, and it ends in a bite only because everyone in the room missed the first eight steps. Most bite incidents aren't a dog "snapping without warning"; they're a dog that asked politely, repeatedly, in a language nobody around it spoke. Tail Talk is a phrasebook for that language. My goal was deliberately narrow: not a breed identifier, not a cuteness scorer. Those exist. I wanted an evidence-first behavioural report; one that shows its working, and admits what it cannot see. Demo 🔗 Live app: https://tail-talk-rosy.vercel.app Five sample photos are built in, so you can get a full report in one click without uploading anything. Here's the read it gave on the Jack Russell sample — note that it caught the breed confounder on its own: ▎ Happy, energetic, and engaged · Safe to approach ▎ Arousal 60 · Comfort 85 ▎ ▎ Tail: tail held high and upright above the backline — confident, high interest or excitement, typical for this breed ▎ ▎ Caveat: "Jack Russell and terrier breeds naturally carry their tails high and erect, which represents normal breed posture rather ▎ than high tension." Code How I Built It benjaminsqlserver / tail-talk Read your dog's body language from a photo. Built with Next.js and Google Gemini for the DEV Weekend Challenge: Dog Days Edition. 🐕 Tail Talk Read the half of the conversation your dog is already having with you. Upload a photo of a dog and G

This is a submission for Weekend Challenge: Dog Days Edition What I Built Pawford...

This is a submission for Weekend Challenge: Dog Days Edition What I Built Dogs don't stay long enough. That is the entire idea behind STAY. STAY is an interactive experience about time, attention, and memory. It compresses the life of a dog into a few minutes, lets you experience the small choices that make up that life, and then gives you a way to preserve the moments that mattered. The experience begins with Milo as a puppy. You spend his life making ordinary decisions: take the walk finish some work play for a while take the photo go somewhere new stay home give him the shoe he already destroyed anyway There is no perfect route. There is no "good owner" score. Life keeps moving either way. Milo grows from puppy to young dog, adult, mature dog, and eventually senior. His behaviour changes with him. He reacts quickly when he is young. He becomes calmer as he grows. Familiar toys and places return years later. His movements slow down. And somewhere in the interface, the number of days he has left keeps falling. The thesis behind STAY is simple: You cannot give a dog more years. You can give their years more life. Eventually, Milo's life ends. There is no dramatic death animation. For the whole experience, you have learned to expect Milo to occupy part of the screen. Then that space is empty. What remains are the moments you chose to keep. Those memories can be preserved as a verifiable proof on Solana devnet. But STAY does not end with simulated Milo. After his Memory Box is complete, the experience asks one final thing: Milo's story was simulated. Yours isn't. From there, users can photograph or upload a real moment with their own dog, add a name, date, and short note, and create a cryptographic attestation for that memory. The photograph itself does not need to live on-chain. Instead, STAY hashes the image locally, creates a deterministic memory manifest, and commits the proof through a real Solana transaction. The day will still disappear. The proof that it happe

This is a submission for Weekend Challenge: Dog Days Edition What I Built Remember Neko the cat and those Tamagotchi desktop pets from the 90s? I rebuilt that magic for 2026 — except your pet is a golden retriever puppy powered by an LLM. PawBot is a Chrome extension + browser demo where a cute pixel puppy : 🐕 Roams your screen like a classic desktop pet 🏃 Chases your cursor when you get close 🍖 Eats anything you drop — PDFs, code files, emoji, pasted text 💬 Barks back with short, mood-aware responses via Google Gemini 😴 Falls asleep if you ignore it too long (with Zzz particles!) ❤️ Reacts to pets — click to pet, double-click to wave Feed it a .pdf and it might say "Mmm, crunchy homework!" Ignore it for 40 seconds and it curls up with sad puppy eyes. Demo Full walkthrough on YouTube: Quick try locally: git clone https://github.com/Varshithvhegde/pawbot.git cd pawbot/web python3 -m http.server 8080 # → http://localhost:8080 Or load the Chrome extension: clone the repo → ./scripts/sync-extension.sh → Load unpacked → select the extension/ folder. Code Varshithvhegde / pawbot AI desktop puppy Chrome extension — pixel pet that eats your files and barks with Google Gemini PawBot 🐾 An AI desktop puppy that lives in your browser. A Tamagotchi/Neko-style pixel pet that roams your screen, chases your cursor, eats files & emoji you drop, and barks mood-aware responses powered by Google Gemini . Built for the DEV Weekend Challenge: Dog Days Edition . Demo 🎬 Watch on YouTube · 📝 DEV submission · 🔗 GitHub Features Feature Description 🐕 Pixel puppy Full sprite animations — idle, walk, run, eat, sleep, bark, wave 🍖 Feed anything Drop files, emoji, or paste text — pup chases food then eats it 🏃 Chase mechanics Food falls from above → pup runs to it → 4-frame eat animation 💬 AI barks Google Gemini generates short mood-based responses 😴 Sleep mode Ignore pup 40s → falls asleep with Zzz particles ❤️ Pet & wave Click to pet, double-click to wave paw ⌨️ Hotkeys F throw treat · B bark ·

This is a submission for Weekend Challenge: Dog Days Edition What I Built I love dogs. There, I said it. And yet, I am also scared of them. It is a strange combination. I can happily watch dog videos for hours, admire every dog I see on the street, and still instinctively tense up when one suddenly runs towards me. A lot of that comes from a traumatic experience I had with a dog as a child. You can grow up knowing that one experience doesn't define every dog you will ever meet, but sometimes your instincts don't get the memo. While thinking about this challenge, I started wondering if the problem was partly not understanding what I was seeing . If a dog is wagging its tail, what does that actually mean? If it is staring at me, should I move away? If it is barking, is it excited, nervous, protective, or something else? And if a dog is approaching me on a footpath, what should I actually do? That question became Pawsitive . Pawsitive is an interactive learning app for people who feel nervous around dogs. Instead of telling people not to be afraid, it tries to make encounters feel less unpredictable by teaching them how to recognise common body-language signals, understand situations, and make calmer decisions. But then I realised there was another side to the interaction. A dog owner might see their dog happily walking towards someone and think: "Don't worry, he's friendly!" The person approaching might be thinking: "Please don't let that dog come any closer." Both people can be looking at the same dog while experiencing completely different situations. So Pawsitive has two learning paths: people who are nervous around dogs and dog owners . The first helps people understand dogs and build confidence. The second helps owners recognise when someone might be uncomfortable, why giving people space matters, and why "my dog is friendly" doesn't necessarily make an approaching dog less intimidating. That became the idea behind the whole app: Two perspectives. One better inte

What I Built Overview & Purpose Canine AI Whisperer is an intelligent multimodal veterinary ethology and behavioral intelligence platform designed to bridge the communication gap between dogs and their humans. Our core goal is to transform modern canine care by translating subtle physical micro-signals, acoustic vocalizations, and behavioral telemetry into actionable, real-time guidance—preventing behavioral escalation and strengthening the bond between pet parents and their dogs. Key Capabilities & Architecture Multimodal Visual Posture Decoder (Gemini Vision AI) Analyzes real-time camera streams or uploaded photos to detect subtle body language cues (ear carriage, commissure tension, tail angles, pupil dilation, and weight distribution). Generates instantaneous ethological diagnoses, arousal scores (0–100), and step-by-step de-escalation action plans. Acoustic Bark Spectrogram & Translation Captures live canine vocalizations to extract fundamental frequency harmonics (Hz), sound pressure intensity (dB), and temporal cadence. Accurately classifies barks, whines, growls, and howls into emotional motivations (e.g., territorial alert, separation distress, predatory excitement) with human-language translations. Canine Voice Synthesis (ElevenLabs Neural Audio) Gives dogs their own distinctive "inner voice" based on tailored ethological personas (e.g., The Hyperactive Herder, The Philosophical Frenchie, The Regal Retriever). Generates spoken translations and calming vocal cues using custom neural text-to-speech. Ultrasonic Whistle & Restorative Sound Studio Features a Web Audio tone generator capable of transmitting silent ultrasonic frequencies (up to 22,000+ Hz) for immediate recall and attention redirection without human disruption. Includes restorative harmonic frequencies (432Hz delta calm, 396Hz distress release, and 60 BPM maternal heartbeat loops) for crate conditioning and thunderstorm anxiety. Snowflake Data Cloud & Cortex ML Analytics Simulates an enterprise-g

This is a submission for Weekend Challenge: Dog Days Edition What I Built Three months after adopting my rescue, I noticed he was sleeping more and eating slower. I thought he was "settling in." Six months later, the vet told me he had Stage 3 arthritis. Completely treatable if caught early. I'm not alone. 60% of serious health issues in dogs are discovered after symptoms become severe. Owners spend $653 on average at emergency vets for things that were either totally normal or should have been caught weeks earlier. And 59% of dogs in the US are overweight without their owners realizing. PawWise is an AI vet friend that gives dog owners what they actually need: instant clarity. Upload a photo of your dog and get: Health Check : Body condition score, coat health, posture analysis, breed-specific risks Behavior Decoder : "Why is my dog doing this?" with breed context and training steps Emergency Triage : Is this an emergency? Green/Yellow/Orange/Red urgency with first aid Dog Court (fun mode): Your healthy dog committed a crime? AI generates a voice-acted courtroom drama The serious modes solve real problems. The fun mode gives you something to share when everything is fine. Demo simplynadaf.github.io Try it live: simplynadaf.github.io/dog-court Upload any photo of your dog. The AI will analyze it and give you a full health report with actionable next steps, spoken aloud in a calm voice. In Dog Court mode, upload evidence of your dog's "crime" (chewed shoes, stolen food, destroyed pillows) and listen to a full multi-voice courtroom drama where your dog gets legal representation. Code simplynadaf / dog-court 🏛️ Your dog committed a crime. AI gives them a fair trial. Built with Google Gemini + ElevenLabs for DEV Weekend Challenge: Dog Days Edition 🐾 PawWise AI That Actually Understands Your Dog 59% of dogs are obese and their owners don't know. 60% of serious health issues are caught too late. I built an AI that catches it in a photo. Live Site → • Watch Demo → • Read A

We're back with another DEV Weekend Challenge, a short bite-sized challenge planned to fit into your...

We are excited to announce the winners of our DEV Weekend Challenge: Passion Edition! The prompt was...