@saminsiddiqui

Heyyy everyone!

I wanted to make a quick update on Skywritten. The past few days were quite huge for the frontend architecture. We went from a single APOD viewer to a fully modular multi-source app with two distinct presentation shells!

Moreover, the Storybook Template is live. Instead of a continuous scroll, it renders a tactile 3D book using hardware-accelerated CSS rotateY transforms. We can flip pages using on screen controls, left/right keyboard arrows, or touch swipes on mobile.

I also implemented Dynamic Captioning and Narrative Syncing as a quality-of-life improvement.

Working with NASA’s API limits is indeed a hassle. I basically solved NASA API rate limits, eliminated video rendering bugs, and built a fast local lookup table containing every authentic APOD space photo published since June 1995.

Other than that, I had to compress 31 years of astronomical metadata into a dense, minified static asset.

This project will take more time than I thought. But I am excited to implement all of the features I have in mind!

First of all, I built a template router to “lazy load” template styles and scripts on demand. Assets are injected directly into the document head only when triggered, preventing initial DOM bloat.

After that, I integrated zero dependency JavaScript math directly to compute real-time cosmic displacement (kilometers traveled through space since birth) and exact lunar phase illumination.

Finally, I built cosmic-scroll as a template using hardware-accelerated IntersectionObserver reveals (will-change: transform, opacity) for smooth 60fps mobile scrolling, complete with an inline-editable narrative interface.

So basically, I finalized the core architecture and UI foundation for skywritten. After that, I bypassed generic frontend frameworks entirely to build a custom and highly optimized HTML/CSS shell utilizing absolute mathematical resets.

Also completed the raw math for the generative ambient starfield and implementing a deterministic PRNG (cyrb128 + mulberry32) so the visual backdrop remains perfectly mathematically consistent across all devices.

Migrated AeroGlass XAI to a Dedicated Server (Hack Club Nest)

The Nest server uses an aggressive reverse proxy that is currently rate-limiting some of the dynamic JavaScript chunks required to render the heavy Plotly/SHAP visualizations.

I spent this session getting AeroGlass XAI completely ready. I successfully deployed the dashboard to Streamlit Community Cloud and handled some caching optimizations for the heavy SHAP tensors using mmap_mode.

The project is officially live!

The core ML architecture is done, so my final focus was transforming AeroGlass XAI into a production-ready enterprise tool.

The ‘GO / NO-GO’ Pre-Flight Briefing: I built a new operational dashboard that allows dispatchers to select the specific port and starboard engines mounted to an airframe. The app runs a real-time check against the Attention BiLSTM. If the RUL dips below the 30-cycle threshold, the UI triggers a dynamic HTML/CSS ‘NO-GO’ lockdown, pulling the top 3 thermodynamic stressors from the SHAP array to justify the grounding to the mechanics.

Financial Interactivity (The ROI Sandbox): Predictive maintenance is only useful if it saves money. I upgraded the Operational ROI Calculator with a dynamic slider that simulates the ‘AI False Negative Rate.’ This stress-tests the financial model in real-time. If you push the AI’s error rate too high, the dynamic metrics instantly flip from Cyan (‘Millions Saved’) to Red (‘Millions Lost’)

The finish line is near the corner. It will be ready to ship soon!!!

The core engine of AeroGlass XAI is now fully operational. The goal of this project wasn’t just to predict jet engine failure but to force the neural network to explain its reasoning to human engineers.

Over the last couple of hours, I successfully wired the backend prediction arrays into a dynamic, cycle by cycle Explainable AI (XAI) interface using Streamlit and Matplotlib.

The Breakthrough: I caught the BiLSTM doing something fascinating while I was building the visualizer. Near the end of an engine’s life, the model was aggressively penalizing the Remaining Useful Life (RUL) based on Core Speed (S9) but simultaneously extending it based on HPC Static Pressure (S11). Because speed and pressure are physically correlated (Multicollinearity), the AI was mathematically ‘balancing’ its weights.

By wrapping the SHAP values in a color-coded UI (Red for Core Thermodynamic, Blue for Mechanical), the dashboard immediately exposed this AI logic flaw to the human eye. This is exactly why safety critical aerospace requires ‘Glass Box’ interpretability.

I’m currently building AeroGlass XAI, a web-based command center for jet engine maintenance. While I have a robust backend foundation using the NASA CMAPSS FD004 dataset, my focus for this hackathon is the “Glass Box” transition. I am taking pre-computed BiLSTM predictions and SHAP arrays and wrapping them into a production-ready Streamlit architecture.

I have just finalized a scalable, multi-file directory structure to ensure this isn’t just a script, but an enterprise-grade application. I’ve also implemented the native dark-mode aerospace aesthetic in the configuration.

My aim is to turn static math into a “Live Telemetry” simulator and an interactive XAI audit tool where users can drill down into thermodynamic sensor impacts in real-time.