Variable gravity
Variable Atmospheric drag (US Standard Atmosphere 1976)
Multi-stage rockets
Closed-loop guidance / pitch programs (works well within ranges 350km to 600km)
Orbital prediction and thrusting options to change your orbit.
Current code and physics can be found at: https://github.com/donutTheJedi/Rocket-Launch-Simulation
The core idea is local-first compliance: – risk classification (Articles 5–15, incl. prohibited use cases) – bias evaluation using CrowS-Pairs – automatic Annex IV–oriented PDF reports – no cloud services or external APIs (browser-based + Ollama)
I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.
Every post on the feed( on slop/duck/storytime) you see is streamed and generated just-in-time with HTML and into a Canvas with Gemini 3 Flash.
Comments and DMs are bidirectionally linked with a Cloudflare Workers Durable Object which is why they feel so fast. Every generated post is saved into a DO SQLite which is then served into the "Following" feed so it can be served quicker.
This was inspired by Wikitok, a VSCode Extension I made around brainrot, and another fully generative UI site I made.
That kind of led me to shape regularization. CGAL had implemented a few methods for that, but there are more ways to do it, which I thought were nice. Also I typically work in Python, so it was nice to have a pure Python library could handle this. I struggled to get the first version working as a QP. At a high level most of these boil down to minimizing a cost A + B where A is the cost associated the geometry and goes up the more you move it, and B is the cost associated "niceness" or rather the constraints you impose, and goes down the more you impose them. Then you try and minimize A + B or rather HA + (1-H)B where H is a hyper-parameter that controls the relative importance of A and B.
Shape regularization is a technique used in computational geometry to clean up noisy or imprecise geometric data by aligning segments to common orientations and adjusting their positions to create cleaner, more regular shapes.
I needed a Python implementation so started with the examples implemented in CGAL then added a couple more for snap and joint regularization and metric regularization.