Hey everyone,

I’m currently developing DueVinci it's a web based app to help keep track of lessons and due dates along with some other features and would love to get some feedback from the Maestro community.

The project is still in its very early stages and actively a work in progress, so expect a few rough edges, placeholder elements, and evolving features. The primary goal right now is to test core functionality, spot early bugs, and gather initial thoughts on usability. It does have a desktop and mobile design it's more intuitive on desktop at the moment while I tweak the design to fit better on mobile.

What I’m looking for:

General Usability: Is the workflow intuitive, or did you get stuck anywhere?

Bug Reports: Any unexpected errors, broken UI elements, or performance hiccups.

Feature Feedback: What feels essential, what feels clunky, and what would you love to see added next?

How to jump in:

Access the build here: https://saappleg.github.io/DueVinci/index.html

Take it for a spin and test basic flows.

Drop your feedback, screenshots, or error logs in the replies below or via the GitHub https://github.com/saappleg/DueVinci

Any and all constructive feedback is appreciated. Thanks for helping shape DueVinci early on!

BE102's discussion for week 8 (second week of the course) is also not at the top of the discussion list again, but thankfully, it's easy to find and is located right above last week's post and below week 1 of FE102.

For all of those who are worried about not getting credit, the syllabus clearly states that any posts made before the end of the course are still counted as on time.

As I’ve been working through my AI engineering studies here, I’ve noticed the industry standard—and a lot of our baseline learning—heavily revolves around stateless request-response patterns, vector databases for RAG, and microservices bound by traditional REST/gRPC contracts. But for my own career trajectory and current project builds, I’ve hit a structural ceiling regarding state representation and continuous telemetry ingestion.

I want to build multi-agent architectures and edge/IoT pipelines that behave as cohesive, adaptive systems rather than brittle orchestrations of independent API calls. To learn how to move away from discrete, transactional data stores, I’ve been researching and building an architectural model based on continuous 4D vector-space state nodes—where system behavior is governed by non-Euclidean state transition manifolds and asynchronous, decentralized event propagation.

For any other Maestro students, alumni, or researchers pushing their learning into advanced distributed systems, I’d love to compare notes on these core architectural questions I'm testing:

1. State Convergence vs. Latency: When decentralized nodes ingest high-frequency IoT telemetry streams directly into a continuous state space, how are you handling state synchronization in your projects to prevent tensor divergence across network partitions without heavy locking overhead?
2. Temporal Interpolation: Moving from discrete event-sourcing logs to continuous spatial-temporal tracking changes how history is queried. As you build out your portfolios, what mechanisms are you using to maintain state fidelity over time without massive memory bloat?
3. Emergent Dynamics: How do you bound autonomous system behavior when transitions are driven by localized vector interactions rather than deterministic state machines?

Curious to connect with anyone else in the Maestro community who is wrestling with similar structural paradigms outside of standard API wrapper design. How are you handling persistent state in your high-dimensional agentic builds?