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Put me down as interested!

Ada/SPARK may also be worth a look.

DREI = DREI Replaces EINE's Inheritor

https://mcclim.common-lisp.dev/static/documents/drei.pdf

I use https://github.com/TxGVNN/github-explorer for this and even though it doesn't have a C-x C-f nicety (you just m-x github-explorer then type in the repo name) it works via http (or at least I don't recall giving it any API key or anything).

One thing to be mindful of is that you can get a simulation to behave in (almost) any way you want if you set the parameters right, so you should take care to understand the assumptions that you're baking into your sim before taking its results as gospel.

For it to be an SI unit, shouldn't you be able to express it in terms of the 7 SI base units?

They could consider registering as a provider on something like OpenRouter if they aren't getting enough inference requests on their own site.

By gyro do you mean flywheel? Is there one in this robot?

Do they actually have ASICs or just FPGAs? The article seems a bit unclear.

The author claimed that the models he modified with this layer repetition method topped the huggingface open llm leaderboard in his first post: https://dnhkng.github.io/posts/rys/

Do you remember the names of the previous experiments done on this? Would love to take a look.

I think Godot is a possible contender as well. There are a few non-game applications made with it, and they've recently added a docs page tailored to non-game application development: https://docs.godotengine.org/en/stable/tutorials/ui/creating...

In one of the youtube comments the director says that they contacted the author and got permission.

Very interesting! I see that you've made a comment saying the other stuff you've tried is on the way as a second blog post, but what happens when you repeat these 7 layers once more? Does performance increase the more repetitions you do?

Thought this was about semiconductor cleanrooms at first. Any startups doing that?

Relevant: https://github.com/sipeed/picoclaw

I think the big differentiator for this one is the carrying capacity. They list 50kg instant/30kg sustained carrying capacity which is very impressive and I can't think of other humanoids with similar capability off the top of my head.

The connections are too easy to the point where the main challenge for me was to remember/locate where I had stored the nodes of that category. I think the fun in a connection puzzle comes from trying to figure out what the link is that connects different nodes, and resolving any red herrings by deduction (which is what makes OnlyConnect's connection wall great and NYT's knockoff mediocre). In this one I can spot the intended category for each node without even looking at the others.

Anyone else have problems getting the videos to load or is it just me?

There are other videos of the laundry tasks within the article, and they do not seem to feature cuts if I'm not mistaken.

Would they do what they do if they had zero dollars?

At least some of the promotion images on the site seem AI-generated: The image next to "Single-board computer" features a very wonky keyboard.

Lots of em-dashes in this copy.

https://sabracrolleton.github.io/testing-framework There's this pretty in-depth comparison of testing frameworks, but I'm not sure if any of the frameworks there satisfy your specifications.

Thanks! I didn't know it was possible to use infix notation.

I'm really impressed by Factor. It has a lot of the niceties that I like about Common Lisp, like restarting on errors and the compiled-but-interactive development approach. On top of all of this the development environment is presented as a very cohesive package, including standardized project structuring styles, a documentation system and a UI library.

The last time I tried to learn it I stopped because I found the concatenative syntax even harder to parse than s-exprs when any math was involved. I'm giving it another go now.

There's one more that restarted active development recently: JSCL https://github.com/jscl-project/jscl

Would you accept the argument that compiling is modifying the bytes in the memory space reserved for an executable?

I can edit the executable at the byte level if I so desire, and this is also what compilers do, but the developer would instead be modifying the source code to make changes to the program and then feed that through a compiler.

Similarly, I can edit the weights of a neural network myself (using any tool I want) but the developers of the network would be altering the training dataset and the training code to make changes instead.

I suppose 80% means you don't give them a 0 mark because the software says it's AI, you only do so if you have other evidence reinforcing the possibility.

Firefox opens a new window when I press C-n. Is this a setting that you have to enable?

For the first SAM model, you needed to encode the input image which took about 2 seconds (on a consumer GPU), but then any detection you did on the image was on the order of milliseconds. The blog post doesn't seem too clear on this, but I'm assuming the 30ms is for the encoder+100 runs of the detector.