This is an incredibly dumb and dangerous attitude to hold and you need to rethink things if you've become so overconfident in your driving because you've raced or done "iron butts", whatever those are. Driving on real roads isn't like racing and you need to separate your attitudes and driving styles in each situation before your arrogance causes an accident. Remember that it's not just your life on the line but the other innocent people around you too.

In any case, it's irrelevant - as far as I know, wearing headphones or earphones while driving a motorcycle is illegal whether or not you happen to be playing music in them, because how would anyone else know? If you get in an accident and get charged with distracted driving, that's on you. If you want earplugs, just wear them. They're much cheaper and more effective than sound cancelling headphones if you genuinely just want to block noise.

I'd suspect it's because in the vast majority of training data, these are both things that get added later, if at all.

I think that the owner needing to repaint between tenants is considered normal wear and tear here.

I agree that a dehumidifier helps but you basically need one in every room. Where I live you can easily take out 10 liters a day from every room during the humid season (which is the maximum capacity of the machine I rented).

The worst, and again very common, is when the paint is so cheap it can't be cleaned easily - when you use anything that can actually clean the mold (soapy water + a bit of vinegar is my preference, but baking soda, very weak bleach solution, or commercial mold cleaners) it also destroys the paint.

Why?

Oligarchs (trillionares and billionaires as they get called in the US) are fundamentally incompatible with democracy.

From your link.

Have you ever tried doing evals on moderately complex but bounded tasks?

I spent some time doing it when testing these "token reducing" tools like Headroom, RTK etc. as well as customizing my Pi tools. What I found interesting was that despite LLMs being deterministic, for a given toolset and prompt, the results were highly consistent for a given eval, across multiple models (I tested at the time using GPT 5.4 mini, 5.5, 5.3 codex, Gemini 3 flash, initially running sets of 5 evals on each task but once I realized how consistent the results were, dropping to sets of 3.

Aside: in my tests, RTK and Headroom made the overall context use higher for roughly equivalent results. The context use for those specific toolcalls went down but the number of model turns and overall context use went up.