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.
Me too. Claude Opus 5's English is insufferable. Opus 4.6-4.8 was more reasonable.
I've moved to Codex 5.6-Sol. Much saner English, much better at execution, and gets stuff done in a matter-of-factly kind of way (Claude Code is a mess these days -- it gets things wrong and goes around in circles).
But I'm harness agnostic and am not locked in. I just keep my issues in Kata Tracker (https://www.katatracker.com/) and switch harnesses/model when I need to.
Being loyal to a particular model/harness seem unwise to me.
> Me too. Claude Opus 5's English is insufferable. Opus 4.6-4.8 was more reasonable.
I suspect this degradation is happening because the AI labs are using the LLM's output to feedback into the input, to create a thinking loop, and they're optimizing that.
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.
Same. I once asked for ELI5 and it rewrote the report using dinosaurs and construction vehicles...
I regularly find it helpful to say things like "Write with ELI10 clear sentences and established technical terminology (e.g. API)" or sometimes just "Write it like you are explaining to a colleague" works well.
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.
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.
The average check price in NYC Chinatown can easily be under $10 if you get the right combination of appetizers/snacks.
Furthermore there are places where you can get food by the pound. Technically you can get a prepared meal at Whole Foods ($12.99/lb salad bar) if you get it just under 0.76 lbs. If you're light eater, then every Whole Foods in the country qualifies as cheap eats. Then the map will be saturated.
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."
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