2026-02-17: Computational mechanics, evolution strategies, interactive proofs, net censorship, China

2026-02-17 — 2026-02-17

This past week Dan’s cranked out four new posts and gone back over seven older ones, so yeah, he’s been busy. He’s trying to sort out “entropy” versus “info” — basically, mess and surprise versus what you actually know — without tying himself in knots. He also had a go at “evolution strategies” for neural nets, which is just training by chucking lots of random tweaks at a model and keeping the ones that do better, like breeding the best sheep and culling the duds. Then there’s “interactive proof”: a back-and-forth question game where you can check a claim without being handed the whole secret recipe, plus a blunt look at net censorship and who gets to decide what you can see. The updates circle around China and Chinese, how science gets out into the world, a bit of Aussie money talk, and some cleanup on “classification” — that’s just sorting things into buckets, but doing it without lying to yourself about how neat the buckets are.

digest
Figure 1

1 Newly published

1.1 Computational mechanics

Here’s a funny one: Dan’s talking about how you turn a messy random stream of data into a proper set of “states” you can use to guess what comes next. The trick in computational mechanics is: two different pasts count as the same state if they lead to the same odds for every possible future, and from that you get an “ε-machine” — basically a tidy little state diagram where the next state is nailed down once you see the next symbol. He also gets stuck into the bookkeeping of randomness versus structure: the entropy rate is the churn you can’t predict, and Cμ is how much past the process has to hang onto to stay sharp. Then he lines it up against HMMs and those predictive-state thingos (PSRs) so you can see who’s doing “hidden causes” and who’s just tracking forecasts. Worth a look if you’re sick of models that either memorise too much junk or throw away the one bit of the past that actually matters.

1.2 Evolution strategies for neural nets

Reckon this one’s like training a dog without telling it what it did wrong — you just try a bunch of little random nudges to the net’s weights, see which nudge scores best, then lean that way. Dan spells out how “evolution strategies” do that with Gaussian noise, and why you end up chasing a slightly blurred-out score so rough, jagged targets don’t break your run. He gets stuck into the bits that stop you getting flogged by noise: use paired + and − nudges so the luck cancels, and make every try look at the same mini-batch so you’re comparing like with like. Anyone should care if their setup has discrete choices, if-statements, or a simulator in the loop where backprop’s a pain, and they’ve got plenty of gear to run heaps of forward passes while only sending back one number each time.

1.3 Depate and interactive proof

Picture a courtroom where one side has to prove a claim, and the other side’s job is to catch any sneaky bits — that’s an interactive proof, and it’s the same rough idea behind AI “debate”. Dan’s been mapping these back-and-forth games out: how many rounds they take, how much grunt you need, and how it scales from the easy stuff up to the truly nasty classes like NP and NEXP. The bit that matters is his worry that a huge neural net might be bloody hard to explain to a smaller judge — like asking a kid to justify a tax return. He’s also poking at “neural interactive proofs” and the whole problem of slick, obfuscated arguments, then trying to model it as a leader–follower game so you can see what each side will actually do. If we ever want to trust big AI systems, we need ways to check their claims without needing an even bigger AI watching over them.

1.4 Net censorship

Funny thing is, “net censorship” isn’t just someone deleting a post — it’s all the sneaky ways a govt or a platform can make stuff hard to find, or look like it never existed. Dan’s gone through Turkey’s heavy-handed tricks, then he’s poked around the Lumen database, which is basically a public filing cabinet of takedown notices and legal threats. He’s also widening the idea past straight-up bans into softer stuff like shadow-bans, where you can still talk but nobody bloody hears you. Worth a look if you’ve ever wondered whether the internet’s “down” or you’re just being quietly steered.

2 Updated

2.1 China

Here’s the bit that might save you a swear in an airport lounge: China’s firewall isn’t just “VPN on, she’ll be right” — it’s a cat‑and‑mouse game, and the plain big-name VPN apps get picked off pretty quick. Dan’s boiled it down to the real trick, which is using tools like Shadowrocket with Shadowsocks and other newer tunnel tricks (Hysteria2, VLESS) that try to look more like normal web traffic. Not a magic cloak, just harder to spot than the usual one-size-fits-all VPN. He’s also chucked in a simple pointer for Mandarin stuff, so you’re not stuck guessing what half the menus and warnings mean.

2.2 Disseminating science

Dan’s poking at the ugly truth of science publishing: it’s meant to share knowledge, but it’s run like a scoreboard—impact factors and that h-index—so people chase points, not good science. Then everyone acts shocked when researchers lean on Sci-Hub and the like, because the paywalls don’t match how people actually read.

2.3 Money, Australian-style

Here’s the rude shock: the ATO might pre-fill your return, but once you’ve bought and sold shares it can turn into a bloody jigsaw with mystery 8‑digit codes and numbers that don’t match your broker. Dan’s basically saying “don’t trust the magic” — use a proper tracker (like Sharesight) or you’ll be doing late‑night detective work on your own money. He’s also clocked that Aussie crypto is a bit like buying gear in a tiny town: the AUSTRAC mobs are mostly local, spreads are wide, fees can bite, and some apps let you ‘buy’ coins but won’t let you move them anywhere. And if you’re freelancing, bad luck mate — you’re a sole trader, BAS and all, so you want invoicing/books gear that’s made for one person, not a whole office full of busybodies.

2.4 London

London’s not just a big city — it’s a city that hogs the lot, so big it breaks the usual ‘cities come in neat sizes’ rule. Dan’s calling it a “primate city”, which is just nerd-speak for “one bully city that sits off the top of the chart”, like London (and Paris) being the weird outlier. The fun bit is how that bigness leaks into daily life: people talk in postcodes like they’re GPS coords, and a postcode tells you where you sit in the pecking order. He also leans harder into class as something you can spot in plain stuff like where people shop, not just accents and fancy titles. Makes you look at London less like a place and more like a machine that’s got its own rules.

2.6 Chinese language

Dan’s been collecting Chinese slang that’s basically a running commentary on “the system’s cooked”. You’ve got 差不多 (chabuduo) — the ‘near enough, she’ll be right’ habit — then “lying flat” and these “rat people” terms for folks who opt out and hide from the grind, and 内卷 (involution) for everyone competing harder just to go nowhere. The clever bit is how the words line up: corner-cutting on one end, burnout and withdrawal on the other, and a whole lot of pointless treadmill in the middle. It’s like learning a language through what people complain about when they’re sick of it all.

2.7 Classification

Here’s a funny sting in the tail: you can’t sort things into neat buckets without already deciding what ‘counts’ as similar. That Ugly Duckling thing says if you treat all features as equal, anything can look just as alike as anything else — so a classifier only works because you’ve baked in a bias about what matters.

3 Minor tweaks