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
Back to feedPost
Hello everyone. ☕️I’d also like to share that I train software testers and help them improve their professional skills through a program I partially present here. This will give you a clear understanding of the level of service we provide to our clients.🌷
- Performance Testing. Test Suite
Types of performance testing and how websites work.
Client-server architecture, HTTP requests, and responses.
Installing and configuring JMeter for performance testing.
Creating load scenarios, analyzing metrics, and reporting.
Installing Java and working with CMD/Terminal.
- Security Testing. API Testing. Collections
Cybersecurity basics and causes of web vulnerabilities.
QA strategies for building secure systems.
Checklist-based testing to prevent hacks and data leaks.
Working with Postman and Swagger: API requests and analysis.
MySQL databases and SQL for direct data validation.
- AI Application Testing
Differences between AI testing and traditional software testing.
Testing strategies for CV (vision), NLP (text), and generative systems.
Methods for testing LLMs without a ground truth.
Evaluating AI quality using metrics (e.g., F1 Score).
QA as an analyst and UX researcher in AI projects.
- Automated Testing: Selenium / Java / Git
Purpose and effectiveness of automated testing.
Working with Selenium WebDriver.
Java basics for writing reliable test scripts.
Creating, running, and analyzing automated tests.
Working with Git: commits and branch management.
- Agile
Understanding Agile: flexibility, iterations, and feedback.
Scrum methodology: User Stories, Backlog, and sprints.
Daily Scrum practice and task visualization on a Scrum board.
Sprint Retrospectives for improving processes and efficiency.
Thanks 🌱🫰
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
Related posts
Ticketify is a full stack web application for buying, selling, and managing digital tickets for events 🎟️
The main idea behind the project is to offer a simpler, safer, and more intuitive experience for both people who want to buy tickets and organizers who need to manage their events, sales, and ticket validation.
Unlike many ticket-selling platforms that often feel unclear or difficult to use, Ticketify aims to centralize the entire flow into a more organized experience: discovering events, buying tickets, receiving digital entries, and validating them through QR codes.
The application has two main types of users:
🛒 Buyer user
Buyers can explore published events, search by name, category, date, or price, view the details of each event, and purchase tickets through Mercado Pago integration.
Once the purchase is completed, they can access their digital tickets, view the status of each ticket, and check their purchase history with detailed information.
Implemented features for buyers:
• Sign up and log in
• Google login through OAuth 2.0
• Email verification
• Password recovery
• Event exploration and search
• Filters by category, date, price, and name
• Ticket purchase with Mercado Pago
• Digital tickets with QR codes
• Ticket status: valid or used
• Purchase history
• Purchase detail
• PDF receipt download
• User profile management
🎟️ Organizer / seller user
Organizers can create and manage events, define ticket types, manage sales, and validate tickets on the day of the event.
They also have a dashboard where they can view important metrics such as tickets sold, revenue generated, and the status of their events.
Implemented features for organizers:
• Event creation and editing
• Event image/banner upload and update
• Ticket type management
• Event publishing and closing
• Dashboard with key KPIs
• Sold tickets overview
• Total revenue overview
• Management of their own events
• Ticket validation through a QR scanner using the camera
• Manual validation by code
• User profile management
🔐 Security and authentication
Ticketify also includes features focused on account security and protecting the purchase flow:
• JWT authentication
• OAuth 2.0 with Google
• Email account verification
• Password recovery
• Alerts for logins from a different IP address
• Protected routes based on user role
• Differentiation between buyer and organizer
📧 Automated emails
The application sends automated emails at important moments in the user flow:
• Account verification
• Password recovery
• Purchase confirmation
• New login alerts
⚙️ Technologies used
The project was built with the MERN stack:
• MongoDB
• Express.js
• React
• Node.js
It also integrates:
• JWT for authentication
• OAuth 2.0 with Google
• Mercado Pago for payments
• Resend for email delivery
• Cloudinary for image management
• QR scanner for ticket validation
Ticketify was designed as a complete solution for events, where design, security, and user experience work together to make buying, selling, and validating tickets faster, more reliable, and more functional.
Neemb – OKR, Kanban & Time Tracking Productivity Platform
I developed Neemb, a modern productivity and task management platform designed to help individuals, freelancers, and teams organize work more efficiently. The platform combines Kanban-based project management, OKR goal tracking, time tracking, workspace management, and team collaboration features into one clean and easy-to-use experience.
The website introduces the product through a responsive landing page, multilingual interface, pricing section, product feature pages, and clear calls to action for signup and login. I focused on creating a smooth SaaS-style user experience that communicates the product value quickly while supporting both personal productivity and team-based project workflows.
The platform also includes support for project organization, priorities, subtasks, workspace limits, file attachment handling, Telegram access, and scalable pricing structure for free and enterprise users. My work covered the frontend structure, responsive UI, product presentation, authentication flow connection, and integration-ready architecture for the main web application.
Tech Stack: Vue.js, TypeScript, Node.js, Supabase, PostgreSQL, Supabase Auth, Supabase Storage, Telegram Bot API, Responsive UI Design, SaaS Architecture.
Looks amazing.
What Happens When You Audit a 100% AI-Built App
40+ issues. 12 critical. One app built entirely with Claude Code.
The Project
A solo founder came to me with a web and mobile app they had built almost entirely using Claude Code — an AI-powered coding tool. The development speed was impressive. The app was functional, the UI was clean at first glance, and they were close to launch.
Before going live, they wanted an independent QA audit. No ongoing engagement, no test management overhead — just an expert set of eyes on the product before real users touched it.
That's where QAura came in.
What I Found
Over the course of the audit, I logged 40+ issues across functional, UI/UX, and consistency categories. Of those, 12 were classified as critical — meaning they either broke core user flows, exposed incorrect data handling, or would directly impact user trust on launch day.
Here's where the issues clustered:
1. Edge Cases the AI Never Considered
The majority of the critical issues fell into one category: edge cases.
AI coding tools are excellent at building what you describe. If you tell Claude Code "create a form that submits user data," it will build exactly that — and it will likely build it well. But it won't ask: what happens if the user submits with an empty field? What if the network drops mid-submit? What if the input contains special characters?
These weren't exotic scenarios. They were the kind of inputs real users produce every day. And in most cases, the app either crashed silently, showed a generic unhandled error, or — more dangerously — appeared to succeed while doing nothing.
The dev had described features at a high level. The AI had implemented them at a high level. The gap between those two was where the bugs lived.
2. Regression After Fixes
Once the first round of issues was reported, the dev went back to Claude Code to fix them. This is where things got instructive.
Several fixes introduced new failures in adjacent features. A correction to the login flow broke a downstream session handling behavior. A UI fix on one screen misaligned elements on another. The AI fixed what it was told to fix — precisely and nothing more.
This isn't a criticism of the tool. It's a fundamental characteristic of how AI-assisted development works right now: it's reactive, not holistic. Without a human tracking the full scope of what changed and why, regression is almost guaranteed when fixes start stacking up.
3. No Consistency Across Error States
This one was subtle but significant. Across different features of the app, the same class of error — say, a failed network request — was handled in completely different ways. One feature showed a modal. Another showed an inline message. A third showed nothing at all.
Each individual implementation was defensible in isolation. But across the product, the result was an inconsistent, unpredictable experience that would erode user confidence fast.
This is something AI tools are structurally bad at catching. Each prompt is a new context. There's no entity holding the whole product in its head, ensuring that decisions made in Feature A are consistent with Feature B. That's a human job — specifically, a QA job.
The Bigger Picture
AI coding tools are genuinely useful. They help founders ship faster, reduce early development costs, and make technical execution accessible to people who couldn't build before. I'm not here to argue against them.
But shipping fast and shipping well are two different things. The issues I found weren't signs of a bad product — they were signs of a product that had never been tested by someone whose job is to find what's wrong.
The AI built what it was told to build. A QA audit asked the questions no one had asked yet.
What This Means for Founders Building with AI
If you're using AI tools to build your product — whether that's Claude Code, Cursor, Copilot, or anything else — a QA audit before launch isn't optional overhead. It's risk management.
The surface-level functionality will likely look fine. The edge cases, the regression paths, the consistency gaps — those require a different kind of attention. The kind that comes from someone who's spent years breaking software on purpose.
That's what QAura is built for.
Trending
Claude
Claude has entered the design space. How are you using Claude Design?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
portfolioreview
The best portfolios tell a story, not just show a grid. Share yours for feedback.
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?