ferris-booler

> an AI engineer in the US at 146,000 to 189,000, a median of 70,000 euros in Germany, 111,000 francs in Zurich.

Interested to hear from European HNers: does this sound accurate? I know EU pay bands are lower than US but a 50% cut even after adjusting for currency feels brutal. Also curious why the Swiss number is so much higher?

Yeah I hit this feeling with Claude one too many times and switched from Anthropic Pro to OpenAI Pro. GPT-5.6 so far has been a significantly better technical writer in my opinion, and it's much faster in conversations. My guess is that Claude's fantasy-jargon is a symptom of training failure, not a sneaky intelligence edge (I could be wrong).

Tangential but I use OpenCode with GPT-5.6 rather than Codex because I could not figure out how to require Codex to ask me before editing files. OpenCode UX is still imperfect though.

> 2027 — A morning at Conduit

> I put a band around my head, and open my laptop at Conduit. It’s 9 am. My device pairs with my laptop over Bluetooth. I open, without loss of generality, Codex.

At least we are reassured that short-range networking protocols will be invariant under the apocalypse.

I'm hitting this too! And I assumed it was a backwards-compatibility issue with my live conversation with Opus 4.7, but then I hit it in a fresh conversation with Opus 4.8. Vibe code release bug I guess?

What strikes me in this case (and I haven't seen in other comments) is that it's a _disproof_ of a conjecture put forth by Erdős and supported (at least according to OpenAI) by other professional mathematicians. Erdős, one of the greats, thought that the limit was O(n^{1 + o(1)}), which GPT disproved.

We can argue about recombination/interpolation of training data in LLMs, but even if this was an interpolation, the result was contrarian rather than a confirmation. Any system that can identify an error in Erdős's thinking seems very useful to me (though perhaps he did not spend much time thinking about or checking this particular conjecture).

IMO your question is the largest unknown in the ML research field (neural net interpretability is a related area), but the most basic explanation is "if we can always accurately guess the next 'correct' word, then we will always answer questions correctly".

An enormous amount of research+eng work (most of the work of frontier labs) is being poured into making that 'correct' modifier happen, rather than just predicting the next token from 'the internet' (naive original training corpus). This work takes the form of improved training data (e.g. expert annotations), human-feedback finetuning (e.g. RLHF), and most recently reinforcement learning (e.g. RLVR, meaning RL with verifiable rewards), where the model is trained to find the correct answer to a problem without 'token-level guidance'. RL for LLMs is a very hot research area and very tricky to solve correctly.

An LLM uses constant compute per output token (one forward pass through the model), so the only computational mechanism to increase 'thinking' quantity is to emit more tokens. Hence why reasoning models produce many intermediary tokens that are not shown to the user, as mentioned in other replies here. This is also why the accuracy of "reasoning traces" is hotly debated; the words themselves may not matter so much as simply providing a compute scratch space.

Alternative approaches like "reasoning in the latent space" are active research areas, but have not yet found major success.