wenc

There are distortions in that "market" because credit cards aren't just for payment acceptance and fraud handling (which are narrow functions).

In the U.S., many credit cards bundle short term credit, rewards, travel benefits, insurance etc. This bundling is why merchants can pay up to 2-3% in fees.

The payment clearance and settlement parts are much cheaper. That's why many countries are able to build domestic payment rails (e.g. Pix in Brazil) that process transactions at low cost.

I just asked GPT6-Astra to transcribe a page from Newton's Principia to LaTeX. It did an amazing job outputing a XeLaTeX doc with a mixture of text and TikZ. Looks really good.

> The population of the west who fit into zoomer near millennials wouldn't even be close to 500m.

There are many people of all ages not in the west from very populous nations who use Insta, FB and WhatsApp.

I’ve learned that US usage patterns are not representative of rest of world.

More saliently, HN users’ conception of others’ usage patterns are absolutely not representative.

I lived in Montreal as a poor student and would eat ground horse because it was affordable (Quebec has a horse meat industry - viande de cheval). It tasted like a leaner ground beef and cost the same.

I also ate canned snails which were also affordable (CAD1.99 per can if I remember correctly).

I do this in one word “eli5” (explain like I’m 5). It’s a Redditism that it understands.

I also have a writing steering file that makes Opus’ writing less insufferable. Otherwise it’s really bad.

I also have an interlocutor skill that makes it less epistemically arrogant (ie Less Wrong asshole tendencies). With this skill I can have a real discussion with it instead of it trying to one up me.

I'm one of those Canadians with a Ph.D. who left for the U.S.

I didn't actually want to leave, but I was staring at the prospect of not unemployment, but underemployment.

Canadian industry doesn't really that many companies that can absorb Ph.Ds. It's not a money issue -- I was willing to accept Canadian pay. This was ca 2010s, and there were very few homegrown Canadian companies that had enough scale to need high level R&D (this was before Shopify and the rest). It was either you did your own startup and found your own funding or you worked entry level engineering jobs doing things like testing or audit work.

All the design and original engineering work happened at HQ in the US or Europe -- the Canadian office is usually just a branch office.

In the US, it's analogous to Chicago offices of big tech firms. They're just marketing and sales offices -- the real engineering work happened on the coasts (SF/Seattle or NYC).

So I reluctantly left Canada for a US company that although not famous, had a global footprint. Whatever I worked on immediatley had global impact because engineering was done in the US office and replicated worldwide. I learned a lot about how to operate at scale and over international markets.

Canada is a small country that has an education system that punches above its weight -- Canadian schools are excellent. But it has an industry that unfortunately doesn't. Somehow Canada has tried and failed a few times, with Nortel, with Blackberry, etc. to develop champions. Something is preventing Canadian companies from scaling. Even far smaller countries like Sweden have companies that are global (IKEA, Saab, Volvo etc), so I'm not sure why we can't do it too.

Maybe the old saying that Canadians are "hewers of wood and drawers of water" has some truth -- it's easier to extract resources than to compete on differentiated product.

That is absolutely hilarious. Dario Amodei, a romcom lead.

Echoing the other folks, I have a different experience.

I profile sql performance and LLMs find more opportunities than I could. All it takes is real data, a sql repl and an agent. Just ask the agent to use the repl to EXPLAIN and profile the sql. It works amazingly most of the time.

I just fed this to GPT 5.6 Sol:

  Counterexample to Jacobian conjecture:

  ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

GPT wrote some SymPy code to check it. The response?

"As written, this is an explicit counterexample to the Jacobian conjecture. I checked it using exact symbolic algebra.

I do not see an algebraic catch in what you typed. Unless a term or exponent differs from the intended expression, it appears to disprove the conjecture. This deserves serious independent checking rather than casual dismissal."

Waiting for someone to write the Lean proof.

History shows that DRAM is a brutally cyclical business with boom and bust cycles. It's also heavily capital intensive. You build an expensive fab now, and then demand plummets and you're stuck selling at near cost while carrying enormous fixed assets.

IBM is already not the best at making strategic calls. I can't imagine them being saddled with DRAM business with its repeated busts.

There is graveyard of DRAM companies that never made it.