Passion Roast App Uses Gemini AI to Score and Roast Your Hobbies from Photos

·2 views

A developer has built Passion Roast, a web app submitted to the DEV Community Weekend Challenge, that uses Google's Gemini AI to analyze a photo of a user's hobby setup or collection. Users upload an image and provide the name of their passion, after which the AI scores their devotion out of 100, delivers a personalized roast, and issues a mock diploma. Unlike generic AI responses, the app is designed to reference specific visual details Gemini identifies in the uploaded photo. The backend is built with Node.js and Express, using the Gemini multimodal API to process both the image and text in a single call, while the frontend uses plain HTML, CSS, and JavaScript with no frameworks. The live app is publicly accessible and the source code has been shared on GitHub.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Direct AI Provider APIs vs Aggregators: Real Cost Data Shows 97.5% Savings

A CTO shared a detailed cost comparison after his startup received a $48,000 invoice from a direct AI provider, which also suffered an outage during a major product launch and slow support response times. He found that using an aggregator with DeepSeek V4 Flash instead of GPT-4o direct reduced token costs by 97.5% across every growth stage, from MVP to 100,000 users. At scale, processing 5 billion tokens monthly cost $1,250 through the aggregator versus $50,000 going direct. Beyond pricing, he cited vendor lock-in, ecosystem dependencies, and geopolitical access barriers as additional reasons to avoid single-provider commitments. His solution was building a model router that assigns different AI models to different tasks based on complexity and cost requirements.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Tifo Forge Lets Fans Design Animated Stadium Tifos Using AI

A developer has built Tifo Forge, an interactive web app that transforms user-selected inputs into animated stadium tifo displays. Users choose a national team, a supporter emotion, and a visual symbol, after which Google's Gemini AI generates a structured design plan including a slogan, layout, and animation style. The browser then renders the result as a procedural SVG stadium display with sequenced card-flip animations. The tool requires no account or file uploads, and is designed to deliver the full tifo reveal in under a minute. The final output can be replayed, reset, or exported as an SVG poster via the live site at tifo-forge.vercel.app.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Five Emotional Triggers Behind High-CTR Titles and How AI Can Engineer Them

A content strategy guide published on DEV Community argues that click-through rate (CTR), not content quality, is the primary factor determining how widely platforms distribute a piece of content. The piece explains that major platforms like YouTube, X, and Substack use small-sample traffic pools to test content, amplifying only those that exceed a CTR threshold — typically above 7–10% on YouTube, where the platform average sits between 2–5%. The author frames CTR and content retention as two separate probabilities that multiply together to determine overall reach, warning that optimising only for quality while neglecting CTR yields minimal distribution. The guide identifies five hardwired emotional triggers that drive reflexive clicks and presents AI-assisted prompting techniques to systematically generate title variants based on each trigger. The core argument is that most creators lose the distribution game not due to poor content, but because their titles describe rather than emotionally activate.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Frontend Developer Builds Windows Bridge Card Game App Using Electron and C++

Dave, a front-end web developer and novice bridge player, built a Windows desktop app for contract bridge as part of a weekend coding challenge. The app uses the double dummy simulation method, allowing players to view all 52 cards across four positions to analyze optimal trick-taking strategies. Built with Electron, the app connects an HTML/CSS/JavaScript frontend to a C++ backend that interfaces with the open-source DDS bridge solver created by Bo Haglund, Soren Hein, and Martin Nygren under the Apache 2.0 license. The main technical hurdle was linking the frontend to the C++ engine, which Dave resolved by directly using precompiled .dll and .lib files rather than building from source. He plans to expand the app with interactive hand play and a scoring mechanism in future iterations.

0 comments Read more at DEV Community