carschno

Stating the obvious, but you can of course see it as: there are more than enough workers that need the extra money so urgently that they are happy to screw up their health and social life. Whether they really "need" the money is another discussion.

Apologies for being nit-picky, but there is no etymological sense. The output of your LLM has the same etymological root, but a different meaning. In terms of translation, it is therefore plain wrong.

Honestly, I was triggered to correct this comment mostly because it illustrates how we tend to explain away mistakes made by an LLM. It's not about subtle 'connotation', but the meaning is just incorrect. No offense meant to the poster, this is a trap the world has been falling into at scale for the past few years.

Don't _ads in search_ account for 100% of their search revenue? Does Google Search offer any other paid services?

I don't know, and I think there is no easy answer. The point is: the investors don't know how to measure traction either, so they just measure GitHub activity instead, even at the very moment in which it becomes obvious that it does not capture actual traction. The absurdity lies in the statement that the developers still need to gain actual traction while putting additional effort into gaming that metric to satisfy their investors.

It's especially sensitive for a VC-backed startup that is measured thoroughly by GitHub activity, but we have to pull the trigger:

This sentence also illustrates the absurdity of this investment model. It imposes a trade-off between building good software, and complying with the investor's metrics. They probably call such metrics evidence-based, but this example shows that they arbitrarily capture some numbers to obscure the lack of meaningful measurements.

In literally must have missed that. When did Microsoft ever encourage energy saving? Is this related to power saving for extending laptop battery runtime? But then I don't get the link to renewable energy.

Anyway, I agree with the notion of the extreme energy-inefficiency of LLMs. The scale of it makes it hard to imagine any less efficient product will ever be invented.

You could abstract speech or other audio as a series of sounds, where time is indeed a factor. Speech, however, has patterns that are more similar to written language than to seasonal patterns that are typically assumed in time series. While trained on different data, the architecture of TimesFM is actually similar to LLMs. But not identical, as pointed out at https://research.google/blog/a-decoder-only-foundation-model...:

Firstly, we need a multilayer perceptron block with residual connections to convert a patch of time-series into a token that can be input to the transformer layers along with positional encodings (PE).

[...]

Secondly, at the other end, an output token from the stacked transformer can be used to predict a longer length of subsequent time-points than the input patch length, i.e., the output patch length can be larger than the input patch length.

On top of that, I exported my location timeline from Google Maps, my Uber trips, my bank transactions, and Shazam history. I would ask Claude Code to start with the photos and then gradually give it access to the different data exports.

Is anyone else feeling uncomfortable with that? It is a great project and I don't want to bash it with general concerns, but sharing all my financial and location details with any service seems like opening the floodgates to my house.

My concern is not even strictly related to AI, but about sharing all my most private data with any service. There is always a significant chance all of it is leaked sooner or later.

There are various technical corrections, with arguable pros and cons. However, they do not match the underlying problem stated above:

the rise of business types in tech company leadership

The page seems to be a copy from the original Mozilla press release from February 2nd: https://blog.mozilla.org/en/firefox/ai-controls/

It was discussed here: https://news.ycombinator.com/item?id=46858492

Here is the paper on which the article is based: https://link.springer.com/article/10.1007/s11269-025-04484-0

On the one hand, this exercise probably reflects a realistic task. Daily engineering work comprises a lot of reverse engineering and debugging of messy code. On the other hand, this does not seem very suitable as an isolated assignment. The lack of code base-specific context has a lot of potential for frustration. I wonder what they really tested on the candidates, and whether this was what they wanted to filter for.

Essentially, the LNM turns out to be unsuitable for various diagnostics it has been applied for:

Our findings reveal a foundational limitation: at its core, LNM involves a repetitive sampling of one and the same FC matrix. As a result, it systematically maps sets of local brain changes—whether they are patient lesions, magnetic resonance imaging-derived alterations, synthetic or random—onto the same nonspecific properties of the used FC data, producing highly similar networks across conditions.

The last addition was made in 2020.

I suppose you are right about the history of firearms. However, the novel was written in 1844, more than 200 years after the time in which it is set. Which makes me wonder if the author (Alexandre Dumas) knew and cared about the historic facts.

Good point, also to illustrate that open-source is not a panacea. It merely holds a higher potential for certain issues to be fixed/improved than.

It is concerning that GitHub hosts the majority of open-source software, while actively locking its users into a platform that is based on closed source for eerything except Git itself. This issue with Actions shows how maintaining proprietary software inevitably ends up rather low on the priority list. Adding new features is much more marketable, just like for any other software product. Enshittification ensues.

For those who can still escape the lock-in, this is probably a good occasion to point to Forgejo, an open-source alternative that also has CI actions: https://forgejo.org/2023-02-27-forgejo-actions/ It is used by Codeberg: https://codeberg.org/

Technically not OCR, but HTR (hand-written text/transcript recognition) is still difficult. LLMs have increased accuracy, but their mistakes are very hard to identify because they just 'hallucinate' text they cannot digitize.

I think the actual question was: why would a mail provider develop their own an email client?

I am having a really hard time communicating this problem to executives

When you hit such a wall, you might not be failing to communicate, nor them failing to understand. In reality, said executives have probably chosen to ignore the issue, but also don't want to take accountability for the eventual leaks. So "not understanding" is the easiest way to blame the engineers later.

Nice explanations! A (more advanced) aspect which I find missing would be the difference between encoder-decoder transformer models (BERT) and "decoder-only", generative models, with respect to the embeddings.

I mean, you probably didn't mean that

Correct, I think you've read too much into it. Grassroots marketing is not a pejorative term, either. Its strategy is to trigger positive reviews about your product, ideally by independent, credible community members, indeed.

That implies that those community members have motivations other than being paid. Ideologies and shared beliefs can be some of them. Being happy about the product is a prerequisite, whatever that means for the individual user.

It's called grassroots marketing. It works particularly well in the context of GenAI because it is fed with esoteric and ideological fragments that overlap with common beliefs and political trends. https://en.wikipedia.org/wiki/TESCREAL

Therefore, classical marketing is less dominant, although more present at down-stream sellers.

Permanent link: https://doi.org/10.1111/josl.12681

There are remarkable parallels to the Relotius scandal that took place at the German magazine Der Spiegel a few years ago (although in a bigger and more systematic way): https://en.m.wikipedia.org/wiki/Claas_Relotius#Fabrication_o...

Can you recommend some in Amsterdam?

Mailbox looks very solid, although I don't have long-term experience: https://mailbox.org

It provides email, online storage, video conferencing, calendar etc., all of it privacy-preserving by default. You explicitly don't have to provide any personal details.

You are looking at it from a product perspective. From a scientific perspective, it just means the respective benchmark is meaningless, so we don't know how well such a model generalizes.

I am not a biologist, but the first sentence seems bold to me:

Millions of years of evolution have led mammalian brains to develop the crucial ability to store large amounts of world knowledge and continuously integrate new experiences without losing previous ones.

My impression has always been that humans have been good at selective forgetting, hence keeping relevant memories and dropping others.

Edit: it looks like none of the authors has a biological background either. How serious do they mean the "neurobiologically inspired" claim?

I would start here: https://www.transkribus.org/

Experts in the field might know more specialized tools, or how to train an actually better Transkribus model without deep technical knowledge required.