Some of it might be market-signaling to the broader energy industry: "hey would you PLEASE build more power plants and power lines? Look at all this money we have, we will pay for it!"
Butterick's is a wonderful resource despite the site itself being a little bit of a UI/UX pain (the invisible left/right page turn zones always get me), and as long as you ignore a few of his strange quirks (like the mentioned penchant for SMALLCAPS LINKS).
Applying his basic rules on line length, font choice, point size, and line spacing can massively improve any document. And it's one of the only serious resources for people whose "real job" isn't typesetting: most stuff online is for design pros who use InDesign or similar, not Microsoft Word (or html/css) like the rest of us.
Butterick's advice on tables is great: delete ALL the borders, then slowly add back only the ones you need.
Disagree on one point re: TFA - Butterick's version of the scientific paper is much improved, largely because of the narrower margins. It just looks bad on the page because the image is small. Print both on 8.5x11" paper and the Butterick version would be much better.
I also share something of an "efficient market hypothesis" with regards to Claude Code. Given that Anthropic is basically a hothouse of geniuses recursively dogfooding their own product, the market pressure to make the vanilla setup be the one that performs best at writing code is incredibly high. I just treat CLAUDE.md like my first draft memo to a very smart remote colleague, let Claude do all its various quirks, and it works really well.
My sense is that a powerful enough AI would have the sense to think something like "ah, this sounds like a video game! Let me code up an interactive GUI, test it for myself, then use it to solve these puzzles..." and essentially self-harness (the way you would if you were reading a geometry problem, by drawing it out on paper).
Evidence: trust me bro. Really, where is the actual evidence that models are "collapsing" from too much AI-generated training material? Evals are up, subjective perception of model usefulness is up (for me, certainly), and if anything the slop levels are down, or at least stable. I find it hard to believe that seven-figure software engineers at top labs aren't being careful about how much post-ChatGPT-era internet content is going into their training data.
In this very post you can see why: the dplyr code is just so much more readable. Like a lot of python, dplyr reads almost like pseudocode: take this dataset, select the columns that start with "bill", then filter so that bill_length is less than 30. So simple and so little fluff!
It doesn't matter if they are unprofitable at full usage, as long as there are enough users (like me!) who barely ever max out but still pay the $100/month. The people who love Claude Code enough to max out the 20x plan every day, that's probably the best influencer marketing campaign you could ever buy anyways.
Also, commercial insurers are essentially cross-subsidizing Medicare: the higher revenue from commercial insurers is partly why Medicare can be paid less. Similar dynamics exist with drug prices: the high US cost is a cross-subsidy to other countries. Maybe this is good (someone's got to fund R&D), maybe this is bad (it's a net wealth transfer to the elderly), but it's an important part of the dynamic either way.
To be fair to SSI, they were very explicit about their plan: "we are going to take money and not release anything until we one-shot superintelligence."
If you invested in that you knew what you were getting yourself into!
Also, decision trees (but not their boosted or bagged variants) are easy (well, easy-ish) to port manually to an edge device that needs to run inference. Small vanilla NNs are as well, but many other popular "classical" ML algorithms are not.
One of the ML textbooks (ESL maybe?) I read described decision trees as (paraphrasing) "really great - they are interpretable, fast to fit, work on lots of different types of data and outcomes, insensitive to scaling and distributional issues, don't have too many tuning parameters...except they just don't work very well." That latter problem can be solved with bagging or boosting, though you are bargaining away many of the other advantages.
You, using normal Claude under the consumer ToS, cannot use it to make weapons, kill people, spy on adversaries, etc. The Pentagon, using War Claude, under their currently-existing contract, can use it to make weapons and spy on (foreign) adversaries, but not to (autonomously) kill people. I don't love this but I am even less excited about the CCP having WarKimi while we have no military AI.
Right - for the same reasons a Waymo is safer than a human-driven car, an autonomous fighter drone will ultimately be deadlier than a human-flown fighter jet. I would like to forestall that day as long as possible but saying "no autonomous weapons ever" isn't very realistic right now.
I personally would love it if AI would say "Sorry Dave (or Pete), I'm afraid I can't spy on Americans for you," and I'd happily pay higher taxes to force the Pentagon to use that AI.
Anthropic put out another blog post about modernizing/migrating away from COBOL several months ago IIRC, it is surprising that this was not priced in already
All the comments are about having babies but as the article points out the actual proximate reason the US population is declining right now is the immigration enforcement approach taken by the Trump administration. Fewer people are coming the the US (including legally), and more people (including legal residents) are being deported or are leaving, and as a result there is net outflow of people from the country. The situation will only worsen with the incipient war on H1B and other visas.
Yes, I especially love it when the auto-playing video automatically goes to the lower-right hand corner of the screen when you scroll down so you don't miss anything. So convenient! Can't wait for this exciting new feature.
The only thing that worries me is this snippet in the blog post:
>This constitution is written for our mainline, general-access Claude models. We have some models built for specialized uses that don’t fully fit this constitution; as we continue to develop products for specialized use cases, we will continue to evaluate how to best ensure our models meet the core objectives outlined in this constitution.
Which, when I read, I can't shake a little voice in my head saying "this sentence means that various government agencies are using unshackled versions of the model without all those pesky moral constraints." I hope I'm wrong.
I'm reminded of the viral tweet along the lines of "Claude just one-shotted a 10k LOC web app from scratch, 10+ independent modules and full test coverage. None of it works, but it was beautiful nonetheless."
This is generally happening in the context of RCTs where valid causal inferences are (almost always) guaranteed via the study design, regardless of whether your analysis is frequentist or Bayesian.
Indeed, even more broadly online "Bayesian" seems to have taken on the form of "I know Bayes' Rule and think about base rates" as opposed to "Do you prefer jags or stan for MCMC?"