tomtom1337

Thanks for sharing those options!

Completely agree. It quite clearly didn't "completely" forget, it just didn't display that knowledge in the 800 runs

Hehe, yes, that's also one

This feels a lot like in genetics where certain genes go dormant, but can be turned back on again.

Oh I see! Thanks for explaining that - I did notice that sometimes a subset that I didn't expect was chosen.

Did the LLM explain its reasoning for how it is showing the higher dimensions? Or perhaps this is a standard way of attempting to display higher dimensional space?

Really cool idea! Beat level 6! I'm still not sure how to explain how it is showing more than 3 dimensions on a 2D screen though! Would be nice to be able to choose which dimensions are represented at the various N-dimensions, but the UI might be tricky to get nice.

One criticism is that you have to install the same package, torch, but from different Python indexes in order to install the cpu version or gpu version, on Linux. On windows, `pip install torch` gets you the cpu version. On linux, that gets you a ton of Nvidia extras that take a lot of space.

GPU support should really be a optional extra eg `torch[gpu]` or `torch[nvidia]`.

Well done! Based on your last sentence, it's probably a good idea to do something very different for a little while

I was wondering what projection this "actually" was, but "The Ewual Earth map projection" is its own thing: https://en.wikipedia.org/wiki/Equal_Earth_projection

I love this little fzf bash function that I wrote a few years ago: https://gist.github.com/thomasaarholt/141df779f3f16b641701b4...

It opens a fuzzy finder window showing the branches you have in the repo, sorted by recency. Also shows the latest committer and the date. Hit enter on the one you want to swap to it.

What are good alternatives when you need a common "single source of truth" schema shared between multiple languages? We use protobuf between c# and Python.

Could you explain more about what "straightening the neck" entails in terms of exercise? I'm interested.

Wow! This looks really cool! Looking forward to playing more with this?

Great response! I'm just a layman here (former material scientist) but it's fun to think about this stuff!

You mean the argon gas used as medium specifically? I assume the purge gas is something else, cheaper?

Sorry, I guess. There were no comments here after a couple of hours and I literally felt bad, so I commented without being very exact in my writing. Regardless, great job, and thank you.

This is extremely cool reading! I'm impressed that they actually fined Elkjøp (as they should!) but very surprised that they didn't keep you informed!

Thank you for sharing!

This is literally what talaas has done with chatjimmy.ai.

Try it, it's llama 3.1 8B at 16000 tokens per second.

chatjimmy.ai https://taalas.com/the-path-to-ubiquitous-ai/

It is SO NICE to see people working on making fast, nice-to-use tools. It's a lovely experience to use diffshub. Thank you for creating it, and than you for the great write-up! (I made it "that far" )

Have you split your 100k loc codebases into smaller crates? If you take a look at eg gitoxide's repo, they've split it in many smaller crates. I think that might help with keeping the scope for the ai small and maybe help with keeping contracts tight and well-defined.

You could probably use just an X-scanner, and instead of a CCD line sensor, use a regular 2D image sensor if you used a "1 pixel wide" slit aperture to crop the image perpendicularly to the direction that the prism disperses the light. So instead of a single pixel being dispersed, you disperse a line.

You would reduce the time required by the root of the number of pixels you want (assuming a square image).

(This is what we do in momentum-resolved electron energy loss spectroscopy. In that situation we have electromagnetic lenses that focus the electrons that have been dispersed, so we don't have as bad a chromatic aberration problem as the other response mentions).

I would love to see e.g. a butterfly image with a slider that I could drag to choose the wavelength shown!!

This is called chromatic aberration, for those who are intrigued.

Given that regular phone cameras have sensors that detect RGB, I wonder if one could notice improved image sharpness if one had three camera lenses (and used single-color sensors) next to one another laterally, with a color filter for R, G and B for each one respectively. So that the camera could focus perfectly for each wavelength.

This is very interesting, but like sibling comments, I'm very curious as to how you run this in practice. Do you just tell Claude/Copilot to do what you describe?

And do you have any prompts to share?

I used it to map out storage locations and refill stations at our online grocery picking stations, then export it to read in using geopandas in order to calculate the shortest distances between all locations!

I get it but I think you're essentially saying "I learnt it this way and it's hard to change once I learnt it that way". My argument is that "when teaching others, it's better to teach them to use switch and restore rather than checkout".

I'd recommend using git switch instead of checkout, since the checkout command is so overloaded. And restore instead of checkout for restoring changes.

Definitely do not parse each row into eg pydantic models. You lose the entire performance benefit of pandas / polars by doing this.

If you need it, use a dataframe validation library to ensure that values are within certain ranges.

There are not yet good, fast implementations of proper types in Python dataframes (or databases for that matter) that I am aware of.

Any suggestions for «orchestrating» this type of experiment?

And how does one compare the results in a way that is easy to parse? 7 models producing 1 PR each is one way, but it doesn’t feel very easy to compare such.

You need something "more" on the website before you ask people to create an account. "Team workspace that stays fast" isn’t clear enough for me, at least. What is a workspace? What does the interface look like? Is it in the browser? Is it an app?

People will go "what is this?", "huh, I’m not gonna make a user for this, can’t tell what it is". Those are my 2 cents.

Haha, I started reading this, got interrupted, came back and got confused by the graph. Then came to the comments, saw your comment, reloaded the post and voila!

Thank you for a lovely post!