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Show HN: Geomatic – A command-driven geometry studio enabled with autodiff

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.

- 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.

3 hours agoherpdyderp

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.

9 hours agohbwang2076

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.

3 hours agojames_marks

Pretty cool. Curious, why a one time payment? Why not, say, a smaller monthly payment?

8 hours agodmos62

Not OP, but for me personally I’m tired of subscriptions. I’m grabbing the one time payment before OP changes his mind.

6 hours agosowow

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.

4 hours agonivter

Cool idea but not very mobile friendly it seems.

6 hours agoroger_

What is autodiff?

10 hours agoddxv

Automatic differentiation. For any DAG with a scalar output, it allows calculating its partial derivative wrt the input parameters.

10 hours agonivter

I don't understand what the use automatic differentiation for in this context.

3 hours agoRexxar

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9 hours agodsecurity49

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9 hours agoembirdating

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