The implementation was so borked, SamA went back on Reddit and apologised: https://old.reddit.com/r/ChatGPT/comments/1o6jins/updates_fo...

Model re-routing happens for coding tasks too. For example, in OpenAI support pages used to (at least 1 month ago when I checked) mention that if they automatically use a cheaper -mini to accomplish the task behind the scenes, you’ll be charged -mini prices even if you selected a more expensive model. I just checked again and they’ve removed it, but there’s probably archives.

Finally, even if they’re the same weights, you don’t know what quantisation you’re running at. Adaptive quantisation based on load (given workday peaks), or similar techniques, have been happening since the ChatGPT 3.5 days; the techniques are probably more advanced now.

And API is much more profitable (relatively) than subscribers for them.

Businesses outside of the US, like the EU, might have significant competitive advantages.

You can finetune and mould it to whatever you want.

Whatever you feel about Anthropic, good or bad, this is not fair, and this is not good for the industry.

Because this wouldn't be considered a jailbreak with any other model; which would just do the request.

The terms you signed obligate you to pay your balance. Whether your credit card works or not doesn't negate your legal obligation.

When you see a thinking summary like "Now writing the function..."; the raw thinking is actually writing the function in its internal thinking. Occasionally, the summariser misses and you get to see the raw text from models like Opus.

You can also try an open weight LLM like Qwen3.6 and see something that probably resembles the shape of frontier model thinking in some loose way.

The further you go away from this line, e.g. a mortgage, the more likely a court of law would void the contract. As with many things in law, the specifics (if it makes to trial) is case-by-case and "it depends"; with settlement being generally based on a party's estimated chances of succeeding/costs should it go to trial.

I learned a lot of stuff about networking, how AWS works (VPCs, IAM, CloudWatch, etc) from trial and error, and hobby projects like personal websites (free tier), hosting a Minecraft server, etc.

Being too overprotective can have negative consequences on folks who are responsible. One of the things I love about the technology and internet communities, etc is that you're mostly judged based on how you act and behave; not your age or other visible characteristics.

Setting boundaries in your prompt / markdowns helps; for example if I tell it to not use any web browser automation, I have seen Fable respect both the rule and the spirit of it (no weird hacks etc).

It does seem to treat some simple debugging tasks as more complicated than it actually is. OP’s post is probably a good example.

If Google starts calling ads “Best Links” that doesn’t make it correct nor canonical; the correct term is still ads.

Traditionally, distillation is when you get the actual logits of a model response (not exposed via API for years) and then use that to train a model.