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This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . Project Overview I run a set of local policy guards around an AI coding agent. They are ordinary PreToolUse hooks: before the agent is allowed to perform an action, the proposed tool call is handed to a small Python script as JSON on stdin . The contract is two exit codes. exit 0 → allow exit 2 → block, and send the reason back to the agent as feedback There are several. One refuses access to credential paths. One intercepts destructive shell commands. One enforces a directory boundary. And one — malformed-read-guard.py — blocks the agent from reading files that contain corrupted tool-call syntax, because reading that syntax makes the model start emitting it too, and the session locks up. They had been working for weeks. One of them had also, for some of that time, been doing nothing at all. Bug Fix or Performance Improvement The symptom Same file. Same bytes. Two locations. Placed at an ASCII path → guard fires, exit 2 , read blocked. Placed under a directory whose name contains Japanese characters → exit 0 , read allowed. No exception. No stack trace. No log line. Nothing anywhere said a decision had been skipped. The hook ran, the hook returned "allow", and the agent read a file it was supposed to be protected from. The mechanism Three steps, and the ugly part is that each one is individually defensible. 1. The payload is UTF-8. The reader is not. Hook input is always UTF-8. But on Windows, Python opens sys.stdin using the locale encoding — on this machine, cp932 . So this line data = json . load ( sys . stdin ) decodes UTF-8 bytes as cp932. 2. Mojibake does not raise. That is the whole problem. cp932 is permissive enough that UTF-8 bytes map onto some sequence of characters. You do not get a UnicodeDecodeError you can catch and log. You get a string that is merely wrong, and it flows onward as valid data: 'C:\\...\\self-catering\\_\udc85部\\再開メモ.md' ← what the guard actually rec

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 DEV's Summer Bug Smash: Smash Stories powered by Sentry. A quick note...

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 Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built I created Balan Coffee & Roastery , a polished landing page for a fictional Vietnamese comfort café in Saigon. The concept is inspired by the quiet comfort of slow phin coffee, butter toast, and small sweet treats. Rather than treating coffee as a quick purchase, I wanted the site to feel like a calm daily ritual: slow, warm, familiar, and personal. Visitors can explore the menu, learn the café story, find visiting information, and interact with a small pixel-art coffee brewing experience. Highlights: Responsive editorial-style coffee shop landing page Vietnamese coffee-inspired menu, story, ritual, and visit sections Clear navigation and accessible interactive controls Consistent number and price typography throughout the site A lightweight interactive mini-game: Pixel Phin Brew Dose beans into the phin Grind the beans Bloom the coffee Let the phin drip Serve the finished cup Built without heavy UI, game, or animation libraries Demo Live demo: Balan Coffee & Roastery Source code: GitHub repository Journey I wanted to create something that felt more like a coffee ritual than a typical restaurant landing page. The visual direction uses warm cream tones, deep coffee browns, generous spacing, subtle texture, and an editorial layout inspired by a slow morning at a Saigon café. I paid attention to small details such as consistent tabular numerals for prices and opening hours, responsive layouts, visible interaction states, and reduced-motion support. The feature I enjoyed building most was Pixel Phin Brew . I wanted the interaction to be understandable instead of just decorative, so each button clearly explains the next brewing action. Every correct step updates the pixel scene, progress indicator, and feedback message until the final cup is served. The project was built with React, TypeScript, Vinext/Vite, and custom CSS. I kept the implementation lightweight and avoided add

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 Frontend Challenge - Comfort Food Edition, CSS Art . Inspiration My other two entries were about a moment and a ritual. This one is about the opposite: the dish you fall back on when you have no skill, no energy and no plan. Boiled eggs are what you make when you cannot cook. Six minutes, one pan,and the comfort is precisely that it is not possible to get it wrong . Two choices made it worth drawing rather than just worth eating. A glass bowl, so you can see the boil. In a steel pan the interesting half of this is hidden. Glass also turned out to be the exact opposite problem to the terracotta in my chai piece — unglazed clay is matte and forgives a sloppy gradient, glass shows you every single one. An induction hob, for the light. I finished the fridge piece saying the one piece of advice I'd give is pick a scene with a light source in it . So I did it again on purpose. The element ring is the only warm thing in an otherwise cold grey kitchen, and it lights the water from underneath. Everything here is a div , a gradient or a shadow. No SVG, no images, no canvas. Demo Press Turn off the heat and give it a few seconds. The ring dies back, the bubbles thin out, and the eggs slowly stop moving — then put it back on and watch the pan come to the boil in stages. That build-up is the part I'd most like you to see, and it's the whole subject of this post. Journey Nothing in this picture is transparent The obvious way to draw a glass bowl is backdrop-filter . I'd advise against building a picture on it — support is uneven enough that the piece falls apart somewhere, and it's expensive. So the transparency is painted. The water is drawn first as its own element, and then a front wall of highlights sits over the top of it: two vertical speculars down the sides for the curve of the glass, a soft wash across the middle for the thickness of the pane, and a rolled lip at the top, which is the one place glass is genuinely opaque enough to draw as a solid.

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 Frontend Challenge - Comfort Food Edition, Perfect Landing What...

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built This is a blast from the past, but it's so lovely I couldn't resist submitting it for this challenge. Kitchen-Sune is a community-driven international recipe book built in collaboration with Front-End Foxes members when we pivoted our nonprofit's efforts from in-person workshops to an international online boot camp during the pandemic. It brings together comfort food recipes from around the globe into a sleek, accessible, and user-friendly Vuepress web application where food lovers and developers alike can discover new dishes. We were happy to host recipes from Ukraine, Kenya, Nigeria, and everywhere in between. Between Jalebi Babies, Moin-Moin, Puff Puff, and Strawberry Mush, we've got you covered for comfort food! Demo Live Demo: Kitchen-Sune App GitHub Repo: https://github.com/FrontEndFoxes/kitchen-sune Check out a preview of the recipe book (this recipe for maple syrup candy came with a video): Journey Revisiting and showcasing this project was a nostalgic process. Working on a community recipe project taught me the importance of building inclusive, easy-to-navigate web interfaces for diverse content as an educatioal tool. We used to use this repo as a way to train boot camp enrollees in how to use GitHub and make a PR to a repo. What I Learned: No matter where we are in our journey, food brings us together. What I'm Proud Of: How timeless and clean the aesthetic remains, making it easy for anyone to find a warm, cozy meal to cook or a snack to throw together (Pandemic Cookies, anyone?). What's Next: Let's keep going! Add your recipe via a PR to the GitHub repo. And I'd love to have more photos to show off your work.

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

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built For the Perfect Landing prompt, I built Masala Dosa House , a warm and modern landing page inspired by one of my favorite comfort foods — South Indian Masala Dosa . 🇮🇳 The idea was to create a fictional restaurant website that feels like stepping into a familiar neighborhood dosa spot. The landing page focuses on: 🍽️ Hero section featuring Masala Dosa 🥞 Signature dishes 🥥 Chutneys and sambar 🌿 Traditional South Indian food experience ❤️ A warm, welcoming visual design 📱 Responsive layout for desktop and mobile ✨ Smooth interactions and animations 🎨 Food-inspired colors, typography, and visual elements 📍 Restaurant-style call-to-action sections Rather than creating a generic restaurant landing page, I wanted the entire experience to communicate the feeling behind comfort food — warmth, familiarity, and home . Demo 🍽️ Live Project: Masala Dosa House — A Taste of Home View the Masala Dosa House project on CodePen Journey I started by thinking about what makes a food website feel different from a regular landing page. For me, comfort food isn't only about the food itself. It's about the experience around it — the aroma, the warmth, the familiar presentation, and the feeling of sitting down for a meal that you already know you'll enjoy. That became the design direction for Masala Dosa House . I used a warm visual palette inspired by dosa, banana leaves, spices, chutneys, and traditional South Indian dining. The layout was designed to keep the food as the main focus while making the page easy to navigate. Building the experience I structured the landing page around a simple restaurant journey: Discover → Explore → Choose → Visit The hero section introduces the restaurant and immediately establishes the comfort-food theme. The menu section highlights signature dishes, while supporting sections provide more context about the restaurant and its food. I also focused on making the

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . Inspiration For my CSS Art submission, I wanted to create something that represents comfort food from South India — Masala Dosa . 🇮🇳 A crispy, golden dosa served with potato masala, coconut chutney, tomato chutney, and a warm bowl of sambar is more than just a meal. It's one of those dishes that immediately feels familiar and comforting. I decided to recreate the entire plate using HTML and CSS , without using food images or external graphics. The goal was to turn a simple plate of masala dosa into a small CSS illustration while keeping the focus on CSS techniques such as: CSS gradients Radial and repeating gradients Border-radius based shapes Box shadows Pseudo-elements CSS animations Responsive layouts Layering and positioning The project is called "CSS Masala Dosa — A Plate of Comfort" . Demo 🍽️ Live CodePen Project: CSS Masala Dosa — A Plate of Comfort View the CSS Masala Dosa project on CodePen Journey I started with the idea of creating a single plate entirely from CSS . Instead of using an image for the dosa, I built the main shape using layered gradients and rounded shapes. The different colors and textures help create the crispy, golden appearance of the dosa. Then I added the individual elements of the meal: 🥞 Masala Dosa — built using multiple gradients, shadows, and layered shapes. 🥔 Potato Masala — represented using small CSS shapes for potato pieces, onions, and curry leaves. 🥥 Coconut Chutney — created using a circular CSS shape with subtle texture details. 🌶️ Tomato Chutney — another CSS-only circular element with layered gradients. 🥣 Sambar — built as a small bowl using nested circular elements and gradients. 🌿 Banana Leaf — created with gradients, shadows, and a CSS vein to give it a natural appearance. ♨️ Steam — animated using CSS @keyframes to give the dosa a freshly-served feeling. One of the things I particularly enjoyed was creating the food textures without images