Key Features
Tech Stack
Biggest low-hanging fruit UI improvement would be mobile responsiveness. It was a bit challenging testing on my phone.
It would be like jumping, and finding yourself ~250-400 meters away from where you lept by the time you landed.
That said, neat project, and way fun learning experience. Good job.
JK, nicely done! lots of fun to watch.
Completely unrelated bug: pitch control can go down only to -5 degrees.
How accurate are the simulations? I'm able to get orbit by turning 45 degrees as soon as I launch and then doing some minor burns at the height of the initial trajectory.
I don't feel like this strategy would work in real life.
We just don't do it on Earth because we need to get out of the atmosphere first for efficiency and structural reasons. But on the moon or another vacuum body, "diagonal kick followed by minor circularization burns at apogee" is pretty close to the optimal strategy. Even on Earth, it's similar to the trajectories proposed by SpinLaunch and other "space cannon" concepts.
Feedback on the UI side: It'd be cool if the stars used more random positions rather than sine/cosine looking patterns. There are lots of different approaches one could try (from simple random positions to very complex types of clustered randomness and brightness variation to realistic star maps). My suggestion would be take whichever approach sounds the most fun!
The main thing is the author did normal research and then gave it to AI to make it happen and verified it seemed to match what they asked for. That helps build a demo, it doesn't really help grow the knowledge you're coding from and it's a very hands off way to learn code (akin to reading a post of someone else doing something, reviewing their code, and putting it in your project). It loses the majority of the hands on time of really getting to think about how these new ideas will be applied at the detailed layer yourself, which is where learning really happens.
OTOH, asking AI to help validate your research and understanding before going in to write the code helps you learn things like "the rocket shouldn't be flying right at takeoff just because the planet is spinning" and having AI help debug code issues you can't figure out in the code you wrote yourself (but not having it fix them for you directly) helps you really test 100% of your own understanding instead of testing if you can say someone else's implementation seems right.
If the only goal is to get to generate output instead of learn then the calculus changes of course. E.g. yesterday I wanted to run an optical flow method for heart rate posted here in my browser. I knew I didn't really have the time to set aside to properly learn it more than a high level description of optical flow, but I just wanted to run the demo in my browser rather than python. So I took the description of the problem in the article, pasted it into AI, and asked it to convert it to a single page HTML app because all I wanted to do was play with it, not learn it.
If I were to be someone who just tells AI "implement drag" and lets it do it then sure, im not learning, but if I do my research outside and just use AI to translate what I give it into the language of computers I feel like im not only building something cool but im understanding whats happening because AI is just translating.
So basically while yes I do believe that AI can be harmful if you approach it inproperly, it helps novice programers implement cool things by just using english.
You could learn coding same way as you learned the physics and dynamics. Programming and physics aren't mutually exclusive. Actually every physicist is (was?) required to know (multiple) programming (languages).
>If I were to be someone who just tells AI "implement drag"
That'll mean at least you understand drag. Could do even worse, regards to learning at least rather result, prompting something like "make a cool-looking physically-realistic 2d rocket launch simulator", which we're at point that will most certainly return a functional app.
>but im understanding whats happening
Do you though? You depend on AI correctly translating your natural language input to code. Though arguably this is something LLMs excel at, since math (logic) also plays role you've to be able to at least read and review the resulting code for correctness. (Assuming you actually care about the physical accuracy that is.)
If it's your thing you could try implementing it and getting a single continuous burn to final orbit.
Awesome work BTW!
[1] https://ntrs.nasa.gov/citations/19740004402
[2] https://github.com/Noiredd/PEGAS
Edit: I now see it's on your roadmap, so I guess it is your thing.
https://github.com/lamont-granquist/KSP-KOS-PEG/blob/main/li...
Looking forward to seeing the next iteration. Nice work.
I've been toying with the idea of building something similiar but with a bunch of different space stuff, like a calculator for different rotating space station geometries, mars/lunar cycler orbits, or solar shade sizes/distances.
It's been many years since I've done this kind of stuff in school and it's great to be able to refresh yourself on this stuff. The kind of UI you're using makes it really friendly and approachable, like a game.
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