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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?).

Useful context for this is that token usage keeps rising at an exponential pace. I mean, we don't have numbers for the big labs, but Openrouter's numbers are quite telling (can't post link because corporate decided to block all "non-validated AI tools"), and I think they're probably representative of the global trend. +500% year to date, +50% over the month of May alone. It's unsurprising that providers are struggling to find and pay for the compute.

> In Bitcoin you don't generate cash, you earn block rewards for acting as a consensus broker which otherwise would require a central banking settlement layer. This activity, tied directly to the transaction layer, acts to maintain the equilibrium between increases in goods and services and expansion of the money supply.

Block rewards have no connection to transaction volume or economic activity, the protocol is designed such that bitcoin supply increases at a predictable (and diminishing) rate. Bitcoin is deflationary by design, which is one of the major issues that stopped it from becoming anything other than a speculative store of value.

> at it's core (pun intented) Dark matter is something to make equations fit without any other thought behind it or whether there might be several things behind it or god forbid that we juddge the equations themselves

Another way to interpret dark matter is that we can observe something using several different ways, but all those ways use gravity. When trying to observe this something using electromagnetism, we see nothing. It doesn't seem so crazy then to hypothesize that this something only interacts with gravity, and not electromagnetism.

OpenAI wrote a couple months ago that they do not consider SWE Bench Verified a meaningful benchmark anymore (and they were the ones who published it in the first place): https://openai.com/index/why-we-no-longer-evaluate-swe-bench...

Not GP, but BMAD has several interview techniques in its brainstorming skill. You can invoke it with /bmad-brainstorming, briefly explain the topic you want to explore, then when it asks you to if you want to select a technique, pick something like "question storming". I've had positive experience with this (with Opus 4.7).

As a data architect I dislike the term NoSQL and often recommend that my coworkers not use it in technical discussions, as it is too vague. Document, key-value and graph DBs are usually considered NoSQL, but they have fairly different use cases (and I'd argue that search DBs like Elastic / OpenSearch are in their own category as well).

To me write scaling is the main current advantage of KV and document DBs. They can generally do schema evolution fairly easily, but nowadays so can many SQL DBs, with semi-structured column types. Also, you need to keep in mind that KV and document DBs are (mostly) non-relational. The more relational your data, the less likely you are to actually benefit from using those DBs over a relational, SQL DB.

> Certainly can’t compete with using VS Code or TeXstudio locally, collaborating through GitHub, and getting AI assistance from Claude Code or Codex.

I have a phd in economics. Most researchers in that field have never even heard of any of those tools. Maybe LaTeX, but few actually use it. I was one of very few people in my department using Zotero to manage my bibliography, most did that manually.

> - It's backed by nothing.

Money is never backed by nothing, or it's worthless. It may not be backed by anything physical, but it's always backed by some form of trust. National currencies are backed by trust in the corresponding government and institutions.