yfontana

No we wouldn't, most of those viruses infect other animals as well, and would just start reinfecting humans.

The whole article is about the fact that, in the UK, this is an issue that isn't talked about enough. Also, the most likely reason why not all humans are dark-skinned is that it makes you much more susceptible to vitamin D deficiency outside of the topics.

Was it ever in doubt that psychoactive drugs have significant effects on the brain?

They claim the attack wasn't just "compromised laptop ==> drain wallets" but had more steps in-between, with the attackers using image registries as an entry point into their systems.

Yeah, I personally draw a distinction between data viz and BI. AI has definitely changed the former, it's pretty cool to be able to design visuals with claude design: you're not bound by what libs can do, html + svg lets you visualize data exactly how you want (and you can even get fancy with 3d stuff, with the pitfall of just showing eye candy with no real insights). I've built some fun stuff like that myself (also with duckdb-wasm).

But BI is more than just visualization: you need stuff like solid data models and semantic layers, tools that help enforce data governance (so that the same metric doesn't get re-defined in 12 different places, possibly with slight variations each time), proper access control (with RLS/CLS). Sure you can probably vibe-code your own BI tool, but it's gonna take quite a bit more work than just the viz part.

From the Github benchmark numbers, one GPU can do about 26k tokens/s when serving 256 users, which is about 2.25B tokens/day. A cluster would need to be close to 70k GPUs to serve 155T tokens/day (which seems doubtful).

but a Slack channel, maybe

GLM 5.3, an open model, is also quite good at reverse engineering.

A significant part of it is that with Opus 5.5, cache reads are priced at 5% of inputs, instead of 10% for previous models.

Jev is a classifier. The big thing about it is that it has high accuracy on domains it wasn't fine-tuned for, like an LLM, but with speed and cost comparable to traditional classifiers.

> in this case tracking sales pipelines

Salesforce does a lot more than that

There are still plenty of shoes that will last for a while. You just need to avoid the super-light ones. Those are designed to go fast, not to be durable.

That particular issue is known as RED-S: https://en.wikipedia.org/wiki/Relative_energy_deficiency_in_...

But over-training can happen even with sufficient nutrition (even if it is less likely, as nutrition is a major part of post-exercise recovery).

This article was written in 2015. Nowadays most elite ultra-runners do have coaches, and follow data-driven training regimes. This is a trend in all professional (and, to a lesser degree, amateur) sports, but it is definitely quite noticeable in a sport where athletes used to basically be dudes and girls who liked running fast in the mountains and did that a lot. A lot of that data-driven training is aimed at maximizing training load without going into overtraining.

As a result of this and other optimizations (most notably higher carbs consumption during races, but also better heat training, better recovery protocols, better shoes, etc.), races keep getting faster. The article mentions the course record for the WS100 dropping by 50' between 2004 and 2012. Since then, it has dropped by another full hour (13h46 this year).

If what China has been doing is considered generous, then so should the tens of billions in foreign aid that the West has sent to the South over the years.

I'm not saying that China's investment in the South hasn't had positive effects. But "generosity" is rarely a relevant lens when analyzing international relations.

China has been using that rhetoric of "helping" the global south ever since it emerged as a super power. In most cases, it has a lot less to do with generosity than with securing natural resources and international influence.

My personal mental model is that this is the AI version of the low-code web app tools that have existed for a while (like Outsystems, Power Apps...). Those tools are a bit of a nightmare for devs to work with and they have all sorts of limitations. Yet they still carved themselves a fairly significant niche in the market, as they allow building apps fairly quickly and cheaply... as long as you stick to simple apps. I don't know if Lovable will be able to capture that market, but that's how I try to make sense of this.

That sentence is written as an introduction to an article about embeddings compression. So yeah, it does take a bit of a shortcut from "search" to "vector storage", but that's irrelevant to the rest of the article.

Something interesting that the original study looked at, but isn't mentioned in the linked article: this effect was not observed for non-Alzheimer's dementia deaths. There seems to be something specific to spatial reasoning and Alzheimer's.

> "Become a cabbie" is NOT an intervention that is going to help whatsoever.

How about promoting spatial awareness and reasoning exercises (which is the actual suggestion of the article)?

For me (Firefox on Windows, not in reader mode) the graphics stop showing at the "585 builds" paragraph.

Besides, if something doesn't work in reader mode, it's also likely to not work with screen readers. People who use those don't have much of a choice in what they can enable or not.

It's cool until it breaks halfway through on Firefox, making the page unusable in that browser.

How come no other big model seems to be able to deliver the same type of extremely low cache cost though, if their techniques are public?

That act applies just as much to those American and Chinese models within the EU.

Yeah, medical computer vision is a (fascinating) field with a lot of ongoing research. SOTA models are highly specialized, and are only getting good enough to be used by actual doctors and patients. Using a general purpose LLM to do this is similar to giving a credit card to Openclaw and telling it to make you rich through the stock market & cryptos.

Doesn't track with mine. I've been stuck with Sonnet 4.6 with one of the clients I work for. It writes code fine, but it's not nearly as good as the more recent models for everything else. It's fairly common for it to suddenly go off the rails for no good reason, so I can't really trust it with agentic loops. It's also not very good at diagnosing non-trivial issues. It's not uncommon for it to suggest whole lists of irrelevant / nonsensical reasons for something not working. Then I copy/paste the code and some context into chatgpt and it hones in onto the correct issue right away, even with inferior tooling.

Having to lug 2 phones around has always seemed like more trouble than it's worth to me. I also don't like having multiple devices to do stuff that a single one could do, for environmental reasons, but that's not a very wide-spread opinion.

So I do have work stuff on my personal phone, but with no notifications whatsoever. Only works because I'm in a position where it's acceptable to require all communications to go through emails or messaging apps though.

I bet the first failure of a large stablecoin will be fun (for external onlookers at least).

Can't speak for 26, but a year ago I worked on a project that migrated from v5 to 11 because of improved image segmentation capabilities. My understanding is that the newer versions don't necessarily have better precision/recall, but they tend to be faster for equivalent results, and have increased capabilities.

Had a very similar experience. Opus went "look, t-sne shows your features are neatly clustered" (it didn't) and left it at that. Fable didn't fully explore the problem/data, but it did go much further, implementing models to check for correlations and adjust feature clusters. Opus was able to finish the job after Fable was cut, but required much prodding (doing exactly what you described: pointing it towards things that look off and asking it, are you sure that's all there is to this?).