Eduardo Bruggmann

project finished and deployed.

The core logic of PeerDrop is now implemented. The room system is fully operational, enabling users to create and join isolated sessions, and peer-to-peer communication between connected clients is working as expected. The signaling flow is stable and connection negotiation is functioning correctly.

Additionally, the initial MVP version of the UI is complete, providing a simple interface to support room management and peer interaction.

Reimplemented the core messaging system to ensure reliable real-time communication between clients and the server. This included refining message handling, improving internal flow, and stabilizing edge cases discovered during testing.

Finalized the CLI UI, focusing on clearer user interaction, command flow, and overall usability within the terminal.

In addition, deployed the server-side application to Render and published the client-side package to npm, completing the full delivery pipeline from development to distribution.

After evaluating the initial implementation, I decided to restart the project with a new architecture. While the core functionality remains the same, the previous structure was limiting maintainability and future extensions, especially as the CLI grew in complexity.

I performed a full refactor from scratch, reorganizing the codebase and introducing additional libraries to better support the project’s goals. In particular, I integrated @inquirer/prompts to improve user interaction, command flow, and overall CLI UX in a more structured and extensible way.

This refactor focused on cleaner separation of concerns between the client, server, and command handling logic, resulting in a more robust foundation for further development, bug fixing, and feature expansion.

Set up the initial project structure and tooling.
Implemented the basic WebSocket server and client to enable real-time communication from the CLI.

With the core connection working, the next steps will focus on bug fixing, refinement, and improving the overall CLI UI/UX.

Implemented a pie chart visualization to represent simulation state distribution in a clearer and more intuitive way. This involved mapping live simulation data into aggregated values suitable for categorical visualization across different scenarios.

In parallel, refactored the statistics system to better support multiple data shapes, separating time-series metrics from aggregate data used by charts. This improved the flexibility of the stats layer and ensured the data passed to each visualization matches its intended semantic meaning.

These changes make the visualization system more robust and allow new chart types to be added without modifying core simulation logic.

Added a statistics system to track and visualize simulation data in real time, including a simplified initial chart view.

Alongside this, performed a major refactor across the simulation architecture to unify agent behaviors, rules, and termination conditions between scenarios. This significantly reduced duplication, improved consistency, and made it easier to extend existing and future simulations.

The refactor focused on clarifying responsibilities between the engine, scenarios, and UI layers, resulting in a more maintainable and scalable codebase.

Implemented two new simulation scenarios: Diffusion-Limited Aggregation (DLA) and Forest Fire.
Both were added with a focus on modularity and ease of extension within the existing agent-based architecture.

Also added an initial README to document the project’s purpose, core concepts, and current features.

Add new scenario: flocking!

Deployed the full portfolio project (frontend and backend), configured environment variables, and fixed a few minor bugs found during production testing. Everything is now live and working as expected.

URL: https://stackfolio-puce.vercel.app/

Refactored the core simulation logic and overall architecture to better separate engine, scenarios and UI.

Implemented the first functional scenario (predator-prey) using agent-based modeling and global rules.

Added a minimal visual interface to control and observe the simulation during testing.

Built the core architecture of the simulation engine using object-oriented JavaScript.

Implemented the main simulation loop, world management, entities and agents with independent behavior.

Added a basic HTML5 Canvas renderer to visualize entities in real time and validate the engine loop.

Next steps are adding simulation controls (start, pause, reset) and implementing the first real scenario (predator-prey).

Finished implementing the chat UI and finalized the overall interface. The project is now fully responsive and complete, with all core features working as intended.

Worked on implementing the chat logic and flow using n8n, connecting it to the frontend application. I also built and finalized most of the UI, focusing on usability and layout.

Backend API and n8n flow working!

Project completed 🎉
After a long development cycle, the project is now fully functional. All core features were implemented, critical bugs were fixed, and the frontend and backend are properly integrated. Authentication, user management, and media handling are working as intended. From here on, the focus will be on improvements, polish, and future iterations.

I’m currently implementing the final parts of an existing project.
This includes finishing remaining features, refining the system logic, and making final adjustments to ensure stability, consistency, and readiness for real use.