We recently hosted our first AppWizzy webinar, where we demonstrated how our innovative vibe-coding platform simplifies rapid software development. This engaging session showcased AppWizzy’s key features, providing a hands-on look at quickly turning ideas into functional applications. We want to say a huge thank you to everyone who participated and encourage you to stay tuned for future webinars and more exciting real-time app-building sessions.

More than four hundred registrations came in for the event. After filtering out bots, around eighty‑five real participants remained. Many were already part of our community and appeared regularly in our Slack, which gave the session a workshop feeling rather than 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 naturally split into two directions. Flatlogic became focused on services and custom software delivery, while AppWizzy spun out as a separate brand and product: a professional vibe‑coding platform.

The philosophy behind AppWizzy is to keep the speed and conversational experience of low‑code tools but avoid their usual hard limits. Instead of pushing everything into a shared, opaque environment, every project in AppWizzy runs on its own virtual machine.

When a new app is created, a fresh VM is provisioned in the cloud. That VM runs a real stack and can be treated like any other server. At the moment, the platform ships with templates for PHP, Python, and Node.js, with more stacks planned. You can log, debug, download the code, move it elsewhere, and scale it as needed. It is closer to “classic software development” than to a drag‑and‑drop toy, just with an AI engineer sitting next to you.

That AI engineer is powered by open‑source coding agents and large language models. We used to experiment with our own internal agent, but decided it was much more sensible to orchestrate existing, battle‑tested AI technologies. At the time of the webinar, the platform supported models such as Gemini and had plans to add Code‑like engines as well.

With that context set, we moved into what most people really came for: watching three very different applications come to life:

  • Kanban Task/CRM App: Complete with auth and intuitive drag-and-drop.
  • AI-powered Burnout Survey Chat: Featuring automatic scoring and visual analytics.
  • AI Data Explorer: Upload CSVs, ask questions in plain English, receive instant visual insights.

How building with AppWizzy actually feels

All three apps were built in the same simple way. We picked a template and tech stack (in this case, PHP for all three), gave the project a name, and described what we wanted it to do. You can type this description or use voice, which feels like just talking to a teammate. From that description, the AI suggested basic roles (like admin or team member), asked if the app should be public or behind a login, and drafted short “as a user I can…” stories. For the webinar, we kept it very simple: one role and a yes/no choice on authentication.

Then AppWizzy created a new virtual machine and generated the first version of the app. As soon as the VM was ready, we got a live link and the app already worked: pages opened, forms submitted, and data was saved. In the editor, we could save checkpoints like “Basic Kanban v1” or “AI analysis v1” and roll back if an AI change broke something. We could also take a screenshot of the live app and send it to the AI so it could “see” the real interface instead of guessing. With that flow in place, we moved on to the first real example.

Demo 1: A Kanban‑style task manager on PHP and MySQL

The first app we built was a simple, familiar one: a task board that sits somewhere between a to‑do list and a lightweight CRM. We gave it a webinar‑style name and, using voice input, asked AppWizzy for a classic Kanban board where you can create tasks, assign them, and drag them between columns. We chose the PHP template on purpose, since a huge share of real‑world web apps still run on PHP and LAMP‑style stacks.

After processing the description, the AI suggested several roles, but to keep things fast, we left only one: someone who can log in, create, assign, and move tasks. Once the VM was ready, a live link appeared with a working Kanban board backed by MySQL. We created a test task, refreshed the page, and saw it persisted, then saved this first state as “Basic Kanban Board v1” to have a safe rollback point.

From there, the audience pushed us to add drag‑and‑drop. The AI first made tasks draggable, but the state didn’t survive a page refresh. We described the problem and sent a screenshot; after another round of changes, the board finally updated the task status correctly, and cards stayed in their new columns. We then improved the look by giving each column its own color and, as a final step, added registration and login so tasks belong to specific accounts. While those heavier changes were running, we showed how credits are spent and refunded, how a VM can be put to sleep to save hosting costs, and how the full PHP source code can be downloaded as a ZIP and run anywhere. At that point, the first app was effectively complete: a Kanban board with drag‑and‑drop, colors, auth, per‑account tasks, and portable code.

Demo 2: Burnout analysis with chat, survey & AI

The second app focused on a very different area: burnout and emotional state. We used the PHP template again and kept just one public role. The idea was simple: a chat‑style assistant asks around ten questions about energy, mood, and attitude to work or study, then sends the answers to AI and returns a burnout summary with charts. The first version only handled the conversation; it thanked you and claimed to be “analyzing,” but nothing was actually saved or processed.

Next, we asked AppWizzy to wire the answers into the built‑in AI layer. Each template has an AI folder with a local API file that calls external models (like OpenAI) using a project secret, so no manual setup is needed. After this step, a new “Analyze my results” button appeared. Running the survey again and clicking it produced a proper “My Burnout Analysis” section with numeric scores (for example, exhaustion and cynicism), text explaining what they mean, and simple charts. Based on feedback, we also moved the results out of the chat into a calmer panel, guided the AI with a screenshot, and it even added an option to ask follow‑up questions about the analysis. At that point, the burnout app had a clean chat for questions, a separate results view, working AI analysis, and a clear path for future extensions like auth, history, or advisor roles.

Demo 3: CSV analytics and AI‑driven chart generation

The third app was the most complex one: an AI‑powered CSV analytics tool with charts. The idea was simple on paper: upload a CSV file, let AI read the header and a few rows, get a plain‑language description of the dataset plus chart suggestions, and then type what chart you want (for example, “pie chart of respondents by country”) so the app can generate it automatically. We used a real survey dataset about starting web apps, created another PHP app, and wired the upload to the built‑in AI layer. After that, uploading the CSV produced a clear summary of the data and ideas for useful visualizations. At the same time, we explained how data is handled: each app runs on its own HTTPS‑secured VM, files stay on that machine, and only the data sent for AI analysis goes to the AI provider; for stricter setups, the code can be downloaded and self‑hosted.

The harder part was the natural‑language chart generation. We wanted a free‑text field, not just a dropdown, so someone could describe both chart type and dimension in one sentence. The first attempts failed: a dropdown appeared instead, and even when we got the text field, the chart didn’t render properly. The backend was preparing the right data (country names and counts), but the browser showed raw HTML instead of a page. We copied that HTML into the AI chat, asked for more logging, and watched it fix and break the app a couple of times, including some HTTP 500 errors-good reminders that these are real code bugs, not platform issues, and that rollback is always an option. Because time was running out, we switched to a prepared version built earlier in the day, which already had the full flow working: upload the same 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 based on about 120 responses. That final demo showed what the feature is capable of, even if the live build didn’t quite reach the finish line within the hour.

Q&A Session

  • How widely is this technology used in practice?
    There are already many applications built with this platform and related tools running in production across the United States, Europe, and Australia. These range from CRM and SaaS systems to medical and insurance software, as well as weather forecasting tools and fitness and habit-building applications with gamified elements.
  • What are the typical project timelines?
    Timelines depend heavily on complexity. A simple dashboard or a small internal workflow can often be built in a matter of days. Larger, deeply integrated systems with complex business logic naturally evolve over months or even years. AppWizzy does not remove complexity itself, but it does remove a lot of repetitive boilerplate and speeds up the work, allowing teams to build more on their own instead of waiting for long custom development cycles.
  • Do you use AppWizzy internally?
    Yes. The registration system for this webinar was built with AppWizzy. Internally, it is used for project management, accounting tools that help prepare and transform documents, and for other small applications that would otherwise require customizing off-the-shelf SaaS products to fit real workflows. When a small but important process appears, it is often faster to build a focused app in AppWizzy than to search for and customize an external service.
  • Where can the platform be accessed?
    Right now, AppWizzy can be accessed both through the AppWizzy site and through the older Flatlogic entry point. Both connect to the same database and infrastructure. Over time, AppWizzy will become the main product home, while Flatlogic will focus more on services, but at the moment either entry point works.

Thank You for Joining!

Thank you for joining our webinar and making it such a success! We were thrilled by the active engagement and the valuable questions and insights you shared. The interactive format fostered teamwork and built a supportive community atmosphere. We’ve shared additional resources and encourage you to join our community channels for continuous support, discussions, and updates.

This initial webinar highlighted AppWizzy’s strengths in simplifying and accelerating web application development. We are committed to enhancing our platform based on your feedback and emerging industry trends. Stay tuned for upcoming webinars, where we will dive deeper into advanced features, various use cases, and current software development trends. 

Your continued engagement and feedback will be instrumental in helping us grow together!