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I designed and built an AI Job Application Agent using Python, Streamlit, OpenRouter-compatible APIs, pypdf, python-docx, and optional web-search context. The workflow parses PDF, DOCX, or TXT resumes, compares resume evidence with a job description, and creates a structured application strategy containing a rubric-guided fit estimate, skill gaps, requirement mapping, resume suggestions, a cover-letter draft, interview questions, and a seven-day action plan. I handled the document-processing flow, prompting, interface design, company-research option, report presentation, and Markdown download experience. The result is a preparation aid, not an official ATS or hiring score.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Veranda, a luxury residential concept landing page
Veranda is a concept build I created for a fictional luxury residential brand. It is not client work or a real property. The images and hero motion were generated with AI as part of the creative process.
I wanted to explore something that matters when building high-fidelity landing pages: after several rounds of changes, how do you make sure the final page still looks like the design that was approved?
The site is built in Next.js with responsive layouts, self-hosted fonts, GSAP motion and an interactive walkthrough. I also built in a proper QA process around it, not just checking that the page loads and the buttons work.
It compares 12 sections across five screen sizes against approved visual baselines. It flags visual drift, missing baselines and changes that have not been reviewed. I also tested interactions, accessibility, contrast, performance, browser behaviour and reduced-motion states.
The main lesson for me was simple: a page can pass functional tests and still no longer be the page that was agreed.
Building a QA process that flags visual drift against approved baselines is such an underrated skill, most people just eyeball it after changes go in. What first made you start checking for that instead of trusting the build looked right?
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One sentence. One AI workforce. One production-ready company. Describe your problem, and autonomous AI agents transform it into a fully designed, built, tested, deployed, and continuously optimized software business.https://pislabs.lovable.app
