Show HN: Geomatic – A command-driven geometry studio enabled with autodiff
55 points - today at 8:25 AM
All commands have the format `output = \func inputs` or just `\function inputs`. Points and scalars are built on the fly. Eg `\line a b` to an empty canvas creates points `a` and `b`, and joins them with a line.
One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to write and load their own visualization, which can be broadcasted and differentiated through.
herpdyderp
today at 3:54 PM
- When I have an example open, I can't type any commands.
- When I open an example, I expected to actually... see an example. I'm not gonna read the wall of text. I don't even understand what this is yet, that's why I tried to see an example.
hbwang2076
today at 9:56 AM
Clean implementation. One thing I always look for: how does this degrade when things go wrong? Good error handling is what separates weekend projects from tools people actually use.
james_marks
today at 3:57 PM
As an adult, I now write clean error handling first thing.
The person it benefits the most is the author, when they are building it and the errors-per-use are as high as they’ll (hopefully) ever be.
Pretty cool. Curious, why a one time payment? Why not, say, a smaller monthly payment?
Not OP, but for me personally I’m tired of subscriptions. I’m grabbing the one time payment before OP changes his mind.
Not a fan of subscription hell myself, I plan to use one-time payment for all my products. Implementation wise one time payment is much simpler than setting up smaller payments that cap at a fixed amount.
Cool idea but not very mobile friendly it seems.
What is autodiff?
Automatic differentiation. For any DAG with a scalar output, it allows calculating its partial derivative wrt the input parameters.
I don't understand what the use automatic differentiation for in this context.
dsecurity49
today at 9:47 AM
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embirdating
today at 10:02 AM
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Armonsrer
today at 10:17 AM
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