FluidX3D VS Searx

Could even use this as a revers GLOBAL ORBITING SYSTEM [GoS] - Whereby a single satellite|probe dispels a lander to a planet with the Quantum Magnetic Cannister, and that QMC signals its global location to the satellite launcher, and the satellit can extrap its location based on the absolute location of the ground guys... (might need more than one ground magnet-moles?)

How can this be measured? Can fluidx3d do martian magnetics? [0]

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[0] https://github.com/ProjectPhysX/FluidX3D

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EDIT: "Mars does not have a magnetosphere"

ELI5: "how do electronics work when there is zero magnetic field around them? A complete antimagnetic environ?"

I've never heard any mention about making any electrical device work on a planet (such as mars) in a complete magnetically dark location?

How is there gravity on mars if there is no magnetic field for a planet, and how can mass, the size of a planet not produce magnetism/gravity if its not made of iron-sh (the RED of the planet)

ELI5, please.

See my post in this thread about dimples/barnacles...

But have you seen this guys package: https://github.com/ProjectPhysX/FluidX3D

Yes, I've made that super easy. You can change the VRAM capacity of your hardware as one number in the setup script and it will automatically scale the simulation up or down. See the documentation for details.

Also see this GitHub Issue on the problem: https://github.com/ProjectPhysX/FluidX3D/issues/59

If you want to play with the software yourself, FluidX3D is on GitHub: https://github.com/ProjectPhysX/FluidX3D

This is my opinion about what is happening. Production CFD solvers are really complicated and take a lot of time and energy to write. Engineers learning to use a CFD solver to get a job done is really time consuming and there are all sorts of issue that come up like actually trusting the new solver. Both of these things have really held back GPUs in this area. There are really only two ways out of this, either you write a solver from scratch and get people to adopt it (double hard) or you take an existing solver and modify it to run on GPUs (still pretty hard). The first option is very hard but ultimately the way to go in my opinion. The second option results in very poorly optimized GPU code and honestly just gives a bad name to GPU computing in my opinion. Take OpenLB for example, https://www.openlb.net/show-cases/highly-resolved-nozzle-simulation-performed-using-multi-gpu-support/. Terrible terrible performance compared to what you could get if you wrote the solver from scratch on the GPU, for example, https://github.com/ProjectPhysX/FluidX3D.

I think searx was largely built by a single person.

https://github.com/searx/searx

I use Recoll to index all of it. Recoll WebUI exposes an API, which I plugged into Searx.

The quality of Google search results has been awful for many years now, but if you still want to use it, the only usable way is via a frontend like Searx[1]. Using any of Google's frontends for any of their services is an exercise in frustration from dodging ads and fighting their hostile UI.

[1]: https://github.com/searx/searx

RSS-Bridge has a Google search adapter. You can also fake it with Searx (which offers RSS feeds of search results).

I've recently wondered how effective local models were at chaining together thoughts as proposed in https://ofir.io/self-ask.pdf. Turns out they are indeed capable of doing so while also creating reasonable chains of thoughts that are easily as good as OpenAI's models. To make it completely free to run I used SearX running inside a Docker container with a second model curating the search results for the main model to get answers from the web.