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This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . Code snippets are recreated and anonymized for illustrative purposes. The Symptom: A Revolving Door Instead of a Login Page It started innocently enough: I was clicking through our app and hit "Log in." Auth0's Universal Login page appeared, I entered my credentials, got redirected back to the app... and landed on the Auth0 login page again. And again. And again. No error message. No failed login attempt. Auth0 was happily authenticating me every single time — and our app was just as happily bouncing me right back, like a bouncer who checks your ID, nods, and then immediately forgets he checked it. The login loop. Every developer's favorite horror movie, now starring me. Red Herring #1: "It's the Frontend's Fault" My first suspect was the obvious one: the frontend callback handler. A Node.js/Express app sits in front of our Django API, handling the Auth0 redirect dance. A login loop screams "broken callback" or "state/nonce mismatch," so I spent a solid hour there: ✅ State parameter matched ✅ Nonce validated ✅ Callback URL whitelisted in the Auth0 dashboard ✅ ID token and access token both present in the response Everything the frontend touched was perfect. The tokens were real, signed by Auth0, freshly issued seconds ago. And yet the moment the frontend sent the access token to our Django API, the API answered with a flat 401 Unauthorized . Fine. New suspect. Red Herring #2: "Auth0 Must Be Misconfigured" Next stop: the Auth0 dashboard. Maybe the token lifetime was set to something absurd, like 5 seconds? Maybe the audience claim was wrong? Token lifetime: 3600 seconds. Normal. aud claim: matched our API identifier exactly. Signature: verified against the JWKS. Valid. So Auth0 was issuing perfectly good tokens, the frontend was delivering them intact, and Django was spitting them out. The bug had to be in the validation logic itself. Time to actually read the code we trusted blindly e

Clear the Lineup Submission The Bug AI applications can fail silently — producing wrong outputs, degraded performance, or unexpected behaviors without explicit errors. In my case, the issue was SQL drift: queries executed successfully but returned incomplete or unstable results due to unsafe wildcard usage (SELECT *). This silent failure propagated downstream, degrading model accuracy without obvious alerts. The Fix I introduced an agentic validation and inspection layer into the pipeline using LangGraph, StatesGraph, MCP, and A2A. Inspection Layer: Deterministic checks (SQL linters, schema validators). Validation Layer: Agentic reasoning about query safety. MCP Integration: Standardized access to profilers and monitoring APIs. A2A Collaboration: Agents exchanged context to enforce compliance. This combination allowed the system to detect unsafe queries and route them for human review before deployment. PR Link Here’s the merged PR where the fix was implemented: Continental-Thaligai Repository – Merged PRs https://github.com/NikhilRaman12/Continental-Thaligai/pulse#opened-pull-requests Code Snippet python from langgraph import Graph from statesgraph import State from mcp import MCPClient class SQLInspection(State): def run(self, query): if "SELECT" in query and "*" in query: return {"risk": 0.7, "message": "Wildcard SELECT may cause drift"} return {"risk": 0.1, "message": "Query safe"} graph = Graph() graph.add_state("sql_inspection", SQLInspection()) graph.connect("sql_inspection", "human_review", condition=lambda r: r["risk"] > 0.5) result = graph.run("SELECT * FROM transactions") print(result) Diff Example: diff SELECT * FROM transactions SELECT transaction_id, amount, date FROM transactions This change eliminated silent drift in query results and improved reliability in downstream AI pipelines. Outcome Silent SQL drift eliminated. Improved accuracy in downstream AI models. Added regression tests to prevent recurrence. Strengthened CI/CD pipeline with agentic saf

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. Demo...

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry. ...

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 Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built Warm Hearth — a landing page for a comfort food restaurant built around one idea: everything on the menu comes from the same wood-fired hearth in the back. Instead of treating "comfort food restaurant" as a generic brief, I anchored the whole page to that single hearth: An interactive hearth centerpiece. Right after the hero, there's a hand-drawn CSS/SVG fire pit you can click to "stoke." The flame flares, embers burst upward, and a small honest counter tracks how many times you've stoked it this visit — no fake global numbers, just a real, session-based response to your click. Four dishes, each with real cultural identity. Ramen, warm pies, a cheesy pasta bake, and gulab jamun — each with its own hand-drawn SVG illustration and a border motif pulled from its own cuisine (a jade-and-gold double line for the ramen, a scalloped pastry edge for the pies, an Italian tricolor accent for the pasta, gold paisley tones for the gulab jamun) rather than one generic card style stretched across all four. Living detail, not static photos. Steam rises off the ramen, pies, and pasta bake using the same wisp animation as the hero's hearth, so the whole page reads as one consistent "warmth" language. The gulab jamun gets a syrup shimmer and drip instead, since steam isn't the right detail for a syrup-soaked sweet. Price tags that hang like real kitchen tickets — pinned by a string, swaying gently, and giving a small "flicked" swing on hover instead of sitting flat on the card. Mira, an illustrated host in the corner who offers a rotating table tip when you click her — a small personal touch instead of a static "contact us" widget. Built for actual use, not just to look good in a screenshot: keyboard-focusable tab filters, a skip-to-content link, aria-live regions on the interactive parts, and full prefers-reduced-motion support that disables every animation without breaking the page. Dem

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . The symptom: good numbers, no users I publish small automation tools on a marketplace. By August I had 23 of them live. Store search looked fine — measured repeatedly, from a real browser, against the real production endpoint: Search term My rank (store UI, Aug 2) sitemap checker #3 google play audit #1 Real numbers after 89 days: 1 active user across all 23 tools. $0 revenue. A #1 ranking and one user is not a rounding error. It is a contradiction, and I spent a week and a half resolving it in the wrong direction. Eleven days of correct answers to the wrong question If ranking is fine and users are zero, the fault must be downstream — that was the reasoning. So I went looking for it, carefully: Demand analysis. Pulled 3,655 listings, then went deeper to 12,834 to check for sampling bias in the first pass. (There was one. I found it and corrected it.) Naming analysis. Split the corpus by whether the title contained a well-known platform name. Median users: 5 vs 2. Age-cohort analysis. Measured the base rate for new listings: only 11% (n=9) get their first user within 0–3 days of publishing, against 74% at 14–30 days. Mine were young. The zeros were, statistically, unremarkable. Acted on all of it. Renamed 5 tools. Added output schemas across the board — the platform's own quality score went from 74 to 78–79. Every one of those produced a defensible number. Not one of them changed anything. That pattern is the actual signal, and I missed it for too long: when every hypothesis confirms and nothing moves, stop testing hypotheses and start testing the instrument. "It reproduced" is not "it's correct" I had re-measured the ranking several times over those days. Same answer each time. I read that as confirmation. It isn't. Re-running a measurement under identical conditions reproduces the same bias just as faithfully as it reproduces the same truth . Repetition rules out transient noise and

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.