AppWizzy Professional Vibe-coding Webinar Recap

We recently hosted our first AppWizzy webinar to show how the vibe-coding platform simplifies rapid software development. It was hands-on: we turned ideas into working applications, thanked everyone who joined, and invited you to future sessions.

More than four hundred registrations came in. After filtering bots, around eighty-five real participants remained. Many were already active in our Slack, so the session felt like a workshop instead of a one-way presentation.

From Flatlogic Generator to AppWizzy

For several years, the main product in this space was Flatlogic Generator. As the platform evolved, it split into two directions. Flatlogic now focuses on services and custom delivery, while AppWizzy spun out as a separate brand: a professional vibe-coding platform.

The idea is to keep the speed and conversational feel of low-code tools but avoid their limits. Instead of pushing everything into a shared, opaque environment, every project in AppWizzy runs on its own virtual machine. You can log, debug, download the code, move it elsewhere, and scale it as needed. It feels closer to classic software development, just with an AI engineer sitting next to you.

That AI engineer is powered by open-source coding agents and large language models. Rather than building our own from scratch, we orchestrate battle-tested AI technologies. At the time of the webinar, the platform supported models such as Gemini and had plans to add more code-focused engines.

How building with AppWizzy feels

All three apps were built the same simple way. We picked a template and tech stack (PHP for all three), named the project, and described what it should do. You can type this description or use voice. From that, the AI suggested basic roles, asked if the app should be public or behind a login, and drafted short user stories. For speed, we kept one role and a simple auth choice.

AppWizzy then created a VM and generated the first version of each app. Once the VM was ready, a live link appeared and the app already worked. In the editor, we saved checkpoints like “Basic Kanban v1” or “AI analysis v1” and could roll back if a change broke something. We even took screenshots of the live app and sent them to the AI so it could see the real interface instead of guessing.

Demo 1: Kanban-style task manager on PHP and MySQL

We started with a classic Kanban board where you create tasks, assign them, and drag them between columns. We chose the PHP template because a large share of production web apps still run on LAMP-style stacks. After generation, a live link appeared with MySQL-backed persistence. We created a test task, refreshed, saw it persisted, and saved “Basic Kanban Board v1” as a rollback point.

The audience pushed us to add drag-and-drop. The first version lost state on refresh; after a screenshot and another iteration, the board updated status correctly and cards stayed put. We colored columns, added registration and login so tasks belong to accounts, and showed how credits are spent and refunded, how to pause a VM to save hosting, and how to download the full PHP source as a ZIP.

Demo 2: Burnout analysis with chat, survey, and AI

Next, we built a chat-style burnout assistant. It asked around ten questions on energy, mood, and attitude to work or study, then sent answers to AI and returned a summary with charts. The first version only handled conversation; it thanked you but did not process data.

We wired the answers into the built-in AI layer. Each template has an AI folder with a local API file that calls external models using a project secret, so no manual setup is needed. A new “Analyze my results” button appeared. Running the survey again produced a “My Burnout Analysis” section with numeric scores (for example, exhaustion and cynicism), explanations, and simple charts. Based on feedback, we moved results out of the chat, guided the AI with a screenshot, and even added follow-up questions about the analysis. The app ended with a clean chat, a separate results view, working AI analysis, and a clear path for future extensions.

Demo 3: CSV analytics and AI-driven chart generation

The third app was the most complex: an AI-powered CSV analytics tool with charts. Upload a CSV, let AI read the header and sample rows, get a plain-language description plus chart suggestions, then type what chart you want (for example, “pie chart of respondents by country”). We used a real survey dataset about starting web apps. Uploading produced a clear summary and ideas for visualizations. Data stayed on the VM; only the snippet sent for AI analysis left the machine, and you can always download and self-host for stricter setups.

Natural-language charting was the tricky part. We wanted a free-text field, not just a dropdown. Early attempts failed: a dropdown appeared, and once the text field showed up, the chart still broke. The backend prepared the right data, but the browser showed raw HTML. We copied that HTML into the AI chat, added logging, and watched it fix and break things (including a few 500s) — reminders that these are real code bugs and rollback is always an option. With time running out, we switched to a prepared version built earlier, which already worked end to end: upload the survey CSV, let AI describe it, type “pie chart of which of the following best describes your current role,” and see a proper pie chart of roles.

Q&A Highlights

How widely is this technology used in practice?

Many apps built with this platform and related tools run in production across the United States, Europe, and Australia: CRM and SaaS systems, medical and insurance software, weather forecasting tools, and fitness or habit-building apps with gamified elements.

What are typical project timelines?

Timelines depend on complexity. A simple dashboard or internal workflow can often be built in days. Larger, deeply integrated systems evolve over months or years. AppWizzy does not remove complexity itself, but it removes boilerplate and speeds work so teams can build more on their own.

Do you use AppWizzy internally?

Yes. The registration system for this webinar was built with AppWizzy. Internally, it powers project management, accounting helpers, and other small applications that would otherwise require customizing off-the-shelf SaaS products. When a small but important process appears, it is often faster to build a focused app in AppWizzy than to adapt an external service.

Where can the platform be accessed?

AppWizzy can be accessed both through the AppWizzy site and the older Flatlogic entry point. Both connect to the same infrastructure. Over time, AppWizzy will become the main product home, while Flatlogic focuses on services, but for now either entry point works.

Thank you for joining!

Thank you for making the webinar a success. The interactive format fostered teamwork and a supportive community atmosphere. We have shared additional resources and invite you to join our community channels for ongoing support, discussions, and updates.

This webinar highlighted AppWizzy's strengths in simplifying and accelerating web application development. We will keep improving the platform based on your feedback and emerging trends. Stay tuned for future webinars that dive deeper into advanced features and use cases. Your continued engagement will help us grow together.

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