>> My workflows involve opening and closing up to hundreds of terminal or tmux tabs a day.

What?!?

Without an explanation of what they author is calling out as flaws, it is hard to take this article seriously.

I know engineers I respect a ton who have gotten a bunch of productivity upgrades using "AI". My own learning curve has been to see Claude say "okay, these integration tests aren't working. Let me write unit tests instead" and go on when it wasn't able to fix a jest issue.

__need__ is doing a lot of work here. There is no forcing function to get OEMs to do this ASAP: 1) the market doesn't really care that much 2) there are no regulations around this (and even if they were, can you immediately recall a tech exec going to jail for breaking the law ... )

good is doing a lot of heavy lifting here. The point of TLA+/Pluscal is to have a proof of the soundness of the design.

This highlights one of my personal bugbears. People have a mental barrier when it comes to recurring, low-cost payments; even though the net sum is small in comparison to other things that they wouldn't think twice to pay for.

A $5 latte every workday comes to (260 * 5) $1300 annually. Obsidian sync is $96. Why would you not pay this amount for a tool you use everyday?

If anyone else was searching for the dataset, it is at https://huggingface.co/datasets/AvaLovelace/StableText2Lego

It contains " contains 47,000+ different LEGO structures, covering 28,000+ unique 3D objects from 21 common object categories of the ShapeNetCore dataset".

Local inference instructions are over at their github page - https://github.com/AvaLovelace1/LegoGPT/?tab=readme-ov-file

Yes. In this case, it is the artist's sole right to reproduce said images, based on their creative output.

>> decreases scarcity, it doesn't increase it

What does scarcity have to do with stealing? You can steal bread and reduce food scarcity, but that is still theft.

[1] - https://www.inc.com/bruce-crumley/doge-cost-cuts-zero-in-on-...

I have theorized some root causes:

- They cannot differentiate between well-meaning friends and high quality information i.e. there is a fallacy of "this person is honest, hence this forward they just sent me is true".

- Starting from at least my generation (born in late 80s), there is an understanding of "echo chamber effects", personalizing newsfeeds for engagement etc. There is some inoculation against content meant to trigger/resonate with specific sub-groups. I have found this to be completely lacking in discussions with my parents/their generation.

All these make it hard to move them out of the dis-information locus they fall into.

>> But in the limit of tokens generated, the chance that they generate the correct answer still decays to zero.

I don't understand this assertion though.

Lecun's thesis was errors just accumulate.

Reasoning models accumulate errors, track back and are able to reduce it back down.

Hence the hypothesis of errors accumulating (at least asymptotically) is false.

What is the difference between "Probability of correct answer decaying to zero" and "Errors keep accumulating" ?

In the previous paragraph, the author makes the case for why Lecun was wrong with the example of reasoning models. Yet, in the next paragraph, this assertion is made which is just a paraphrasing of Yecun's original assertion. Which the author himself says is wrong.

>> Instead of waiting for FAA (fully-autonomous agents) we should understand that this is a continuum, and we’re consistently increasing the amount of useful work AIs

Yes! But this work is already well underway. There is no magic threshold for AGI - instead the characterization is based on what percentile of the human population the AI can beat. One way to characterize AGI in this manner is "99.99% percentile at every (digital?) activity".

But, you can find this attitude pervading the whole linux desktop ecosystem. This post may as well be titled "Why it will never be the year of the Linux Desktop".

KANs have no advantage in terms of computability. Why are they a promising pathway?

Also, the splines in KANs are no more "explainable" than the matrix weights. Sure, we can assign importance to a node, but so what? It has no more meaning than anything else.

There are only two objective measurements needed:

-is it making progress towards its goal?

-is it able to acquire capabilities it didn't have previously?

I am not sure if even the first one is objective enough.

Dismissing the argument without stating why you aren't convinced just comes across as a form of AI ludditism.