Key contributions included
- Successfully flashed the ESP32-S3 devices and resolved all dependency errors blocking setup.
- Improved web dashboard legibility so users can clearly understand what each metric is measuring.
- Added clear separation between Demo Mode and Live Mode to improve usability and presentation clarity. As part of this I implemented automatic population of 12 hours of simulated data points to better showcase data collection behavior.
- Established successful connections across all API ports, verified through the API health endpoint.
- Completed the full final presentation slideshow and began drafting the final written report.
Is your progress on schedule or behind?
Progress is lagging behind schedule. This week’s work closed key technical blockers (firmware flashing and API connectivity), improved dashboard quality, and completed major final-deliverable prep (presentation + report start), which barely allows for room to finish our firmware/hardware wiring.
What deliverables do you hope to complete in the next week?
Complete end-to-end validation of live data flow and dashboard behavior.
Perform final QA pass on dashboard readability and Demo/Live mode transitions and rehearse the final presentation.
What new tools did you find necessary to learn about?
To design, implement, and debug this project I used several tools and workflows across both embedded systems and web development. For hardware, I learned more about the ESP32-S3 flashing process, how to diagnose and resolve dependency/environment issues that can prevent firmware deployment. For software, I had to look into dashboard design for readability, data presentation techniques (including simulated/faux data generation for demo scenarios), and API service verification through health endpoints and port connectivity checks.
What learning strategies did you use?
I relied heavily on iterative troubleshooting (testing, identifying specific errors, and refining fixes), official documentation for ESP32/toolchain setup and API behavior, and forum threads.
A major part of my process was using AI. I used it to talk through problems step-by-step, learn unfamiliar concepts quickly, brainstorm and evaluate feature ideas, and iterate on improvements from a stakeholder/user perspective. This made it easier to move from technical fixes to user-centered decisions, especially when refining dashboard clarity and deciding which features would best communicate value in demo and live use cases.