jupyter-cadquery VS solvespace

supports WebGL over SSH/MoSH

https://www.brow.sh/docs/introduction/ :

> The terminal client updates and renders in realtime so that, for instance, you can watch videos. It uses the UTF-8 half-block trick () to get 2 colours from every character cell, thus simulating basic graphics.

https://github.com/fathyb/carbonyl :

> Carbonyl originally started as html2svg and is now the runtime behind it.

Always wondered how brew.sh added the brew sprite there; that's real nice.

TIL that e.g. Kitty term can basically framebuffer modified Chrome?

https://github.com/chase/awrit :

> Yep, actual Chromium being rendered in your favorite terminal that supports the Kitty terminal graphics protocol.

FWIW Cloudflare has clientless Remote Browser Isolation that also splits the browser at the rendering engine.

A TUI Manim renderer would be neat. Re: Teaching math with Manim and interactive 3d: https://github.com/bernhard-42/jupyter-cadquery/issues/99

What would you add to make it easier to teach with this entirely CPU + software rendering codebase?

What prompts for learning would you suggest?

- Pixar in a Box, Wikipedia history of CG industry: https://westurner.github.io/hnlog/#comment-36265807

- "Rotate a wireframe cube or the camera perspective with just 2d pixels to paint to; And then rotate the cube about a point other than the origin, and then move the camera while the cube is rotating"

- OTOH, ManimML, Yellowbrick, and the ThreeJS Wave/Particle simulator might be neat with a slow terminal framebuffer too

What a great idea.

TIL about jupyterlab-blockly https://jupyterlab-blockly.readthedocs.io/en/latest/

https://jupyterlab-blockly.readthedocs.io/en/latest/other_ex... :

> The JupyterLab-Blockly extension is ready to be used as a base for other projects: you can register new Blocks, Toolboxes and Generators. It is a great tool for fast prototyping."

jupyter-cadquery: https://github.com/bernhard-42/jupyter-cadquery

"Generate code from GUI interactions"

CADQuery

> Couldn't you equivalently use any STL/STEP/AMF viewer?

I'm not sure. A quick feedback loop is important. With OpenSCAD and CadQuery, you write code that defines the geometry. You then want to see what the geometry looks like, and possibly debug it. For this, you generally want to be able to give certain parts a different color, or opacity, wireframe, etc.

STL is out; it has to tessellate geometry turning it into triangles. AFAIK, it only supports one object. This means a sensible wireframe is out, and so are multiple parts. AMF has similar drawbacks. STEP files might work.

Generally, my understanding is many people write OpenSCAD code in their editor of choice, and then simply save the file. When you open an existing file in OpenSCAD GUI, it monitors it for changes, and refreshes. So this is great.

That said, I misspoke a bit. CQ-Editor is definitely somewhat close to OpenSCAD. It still has a - in my view - unnecessary code editor. But the last standalone release is over a year ago, and I found it to be extremely buggy on macOS. It crashes quite often. Meanwhile, Jupyer-CadQuery [0] works great.

> Seems a good choice to me that the GUI is a separate/subordinate project. I suppose it is somewhat necessary to have it at all, easier to gain popularity if you can show screenshots and have a single app 'quickstart'.

Generally, I think this is true. My personal opinion is I can be productive with something that has a minimal set of features but is rock-solid; over something that has gobs of features but is buggy. That was my main issue with FreeCAD. Ease of installation is another big one. For all it's issues, OpenSCAD gets both of these things right.

[0] https://github.com/bernhard-42/jupyter-cadquery/

I had some success with using the pre-built image. It’s good enough if you want to play around with the example notebooks out of the box if you run the container without any volume mapping.

Yep. I currently use https://github.com/bernhard-42/jupyter-cadquery; it is a jupyter-based notebook integrating cadquery. If you can do it with OpenSCAD you can do it with cadquery, the difference being working with real CAD primitives, a richer language and more features. If you do not need some of the more advanced operators which are missing in the internal CAD engine it is a very solid choice in my opinion for parametric modelling at its peak.

Neat, you might be interested in this cadquery integration with Jupyter notebooks, https://github.com/bernhard-42/jupyter-cadquery

yes thats the one. the python version is easier to work with imho, you can set it up with jupyter-cadquery + anaconda (https://github.com/bernhard-42/jupyter-cadquery) and generate results in the browser / vscode . depending on your understanding of python it shouldnt be too difficult, you probably cant take it across directly but a lot of the default values etc should transfer. and the rest just requires looking at existing code + cadquery documentation.

One day I'll finish my keyboard using joshreve's framework, It definitely is a way better experience when doing lots of changes, especially with (https://github.com/bernhard-42/jupyter-cadquery)

customising my own version similar to dactyl tracer and I'm using joshreve's port to python ( https://github.com/joshreve/dactyl-keyboard ) with jupyter cadquery (https://github.com/bernhard-42/jupyter-cadquery), which lets you customise/generate your keyboard in the browser and view the output more easily, after which you can export straight to stl (https://imgur.com/a/HX0DLxw)

Can second this!

However, I would recommend https://solvespace.com! It hits a sweet spot between features vs complexity/learning effort.

We use rebase on solvespace, along with sensible squashing so most commits along master are pretty self contained. You can see the clean history here:

https://github.com/solvespace/solvespace/commits/master/

I changed a behavior to the "more standard" one because it felt obviously right. This was a 3 line change. But the was enough backlash right there in the pull request. So I spent a couple hours remembering how to add a configuration option to keep the old way for those guys:

https://github.com/solvespace/solvespace/pull/1425

> If you like Linkage, you might also like Solvespace.

No, I mean Brent Curry's Linkage[1] bicycle design software, not David Rector's Linkage Mechanism Designer and Simulator[2].

You should read Wikipedia article.[0]

N.B. About SolveSpace, as I'm its experienced user[youtube,patreon], I may say next: yes, it could be used for bike mockup, as any other CAD, but it still has a lot of limitations and even does not export correct STEP files yet[3], and in FreeCAD such STEP could fixed only partially.[video]

So, for serious 3D CAD work I highly recommend use FreeCAD (and LibreCAD for 2D CAD work) instead of SolveSpace, and use SolveSpace only as a helper tool like a calc or as a notepad for noting ideas.

About Linkage Mechanism Designer and Simulator, it is only useful for planar (2D) kinematics analyze, and if You are looking an alternative for it take a look on Pyslvs[4], that is in part based on SolveSpace's solver.

[0] https://en.wikipedia.org/wiki/rattleCAD#History

[1] https://bikechecker.com/

[2] https://blog.rectorsquid.com/linkage-mechanism-designer-and-...

[3] https://github.com/solvespace/solvespace/issues/206

[4] https://github.com/KmolYuan/Pyslvs-UI

[video] https://www.youtube.com/watch?v=F3LJMeqUDrU

[youtube] https://www.youtube.com/@appsoft

[patreon] https://patreon.com/app4soft

C++ this file covers all the math for working with NURBS curves and surfaces:

https://github.com/solvespace/solvespace/blob/master/src/srf...

There is a lot more in other files - triangulation, booleans, creation - but the core math functions are there in very readable form.

>> Maybe somebody has statistical survey of how much of the existing deployed CPU core count is typically used?

My guess is very few cores are used on average. I did some testing with Solvespace to see which build options contributed most to performance:

https://github.com/solvespace/solvespace/issues/972

Obviously using OpenMP for multi-core was the big win. But what's not shown is that in typical usage (not the test I ran) if you're dragging some geometry around it will use all cores (in my case 4 cores / 8 threads) at about 50 percent utilization. That percentage probably drops as more cores are thrown at it due to Amdahl's Law. In other words, throwing double the cores at it will give a good boost to a lot of code that is already taking less than half the time (wall clock time, not CPU time).

We added OpenMP to a number of functions for significant performance gains. And in fact, any remining single-thread operation that gets the parallel treatment is likely to have a significant impact on overall performance since that is where most of the time is spent now. At this point we're more focused on features and bugs.

Algorithmic improvements are possible and I'd like to do those in the future, but they are much harder to do than sprinkling some #pragmas around critical loops. That will improve the scalability though, where multithreading really did not.