Makes total sense, AI images are higher resolution than classical pictures (which are limited by the dexterity of the painter), so you're basically getting 11.67 pictures in each.

Just so happens this friend had an oxygen concentrator in storage. He'd acquired it during Covid back when they feared hospitals might be overloaded or highly risky.

This brings back memories. At the start of Covid I bought two used oxygen concentrators, a few "toy" concentrators off Amazon, and lots of tubing, hoping to build my own high-flow nasal cannula setup. I even went on Alibaba and bought a pallet of CPAP machines, but they couldn't deliver on time and the supplier said they were needed in developing countries so I canceled the order. I highly recommend this kind of thing as an exercise in agency, particularly the part where I had to understand the reasoning behind various medical standards and get out from behind chesterton's fence.

Anyway, glad your child is ok!

Blue Origin just landed their New Glenn rocket on a barge! This was an orbital mission to Mars, which makes them the second company to land an orbital booster. For context, New Glenn has over twice the payload (45 tons) of Falcon 9 (~18 tons reuseable), and half the next version of Starship (100 tons). Not many were expecting this.

SpaceX did this in December 2015, nearly 10 years ago. But going forwards, their lead will likely be smaller. The next milestone is to land a booster five times (June 2020), and between Blue, Rocket Lab's Neutron, and numerous Chinese companies I would be surprised if it takes until 2030.

My guess based on reading anecdotes like these and Berger's books is that the algorithm is a vast improvement over anyone else's engineering practices, but it alone doesn't tell you what else you need to run a company. Maybe systems engineering is the missing piece, maybe some other management philosophy.

If you look at the major SpaceX programs, they are: Falcon development, operations, Starlink, and Starship. The first three were wildly successful, and Starship is late but technically and operationally superior to other companies (e.g. Raptor engines are double the chamber pressure of BE-4 and there have been 10x the test flights), with successes directly traceable to each step of the algorithm, and wasted energy due to not doing something else when appropriate. Raptor 3 engines are only possible to make as cheaply as Elon wants because they had a vast number of parts deleted; yet they also "accelerate"d to build hundreds of Raptor 2s which are now obsolete.

Hard to answer this question because there's a tradeoff between noise, airflow, and surface area with air purifiers. Eg if you cover your ceiling with air filters, the noise will be minimal.

I'd say if you have enough output power and are only limited by uv exposure, it's vastly more effective. I had to buy a couple of expensive Clean Air Kits purifiers to get ~25 air changes per hour in a smallish room, but 100+ equivalent ACH is possible with far-UVC, either by using light filtered to 222nm or keeping it in some kind of ceiling louver that traps the light. Not sure how the cost compares though, as they seem to be limited by output power / $ rather than safety limits.

This paper happened to be the only one with a perfect score at NeurIPS 2025. Congrats to the authors!

My rent, also in a small room in a Bay Area group house, is around $1050. This is an interesting group house phenomenon where if rent is $1800 on average, the good rooms go for $2600 and the bad ones have to be $1000 to balance out total rent. The best rooms in a group house are a limited supply good and bc people (or even couples) often are indifferent between group house with good social scene and a $4000 luxury 1bed, prices are roughly similar. There is lots of road noise, but I realized I could pay $1000 for extra-thick blackout curtains, smart lightbulbs, etc. to mitigate this, which has saved me thousands over the past couple of years.

As for everything else, my sense is it's not for most people. To have expenses as low as OP's you basically need to have only zero-cost or cost-saving hobbies like cooking and thrifting, and enjoy all aspects of them. I got into cooking at one point but didn't like shopping and wanted to use moderately nice ingredients, so when cooking for my housemates the ingredients (from an expensive grocery store through Instacart) came out to $18/serving. A basic car is also super useful, bay area or not.

I am probably one of the people OP mentions, with a bunch of financial anxiety despite being able to save close to $100k/year, but this is largely due to a psychological block keeping me from investing most of my money.

I wouldn't take one or two datapoints on a single benchmark too seriously, especially with a methodology as fiddly as time horizon and concerns like Ryan's. Nevertheless seems like a good thought that you replicated using time estimates from commit data, as the original difficulty estimates seemed likely to be noisy. I'll be interested to see if the trend continues and if the same is currently true with OSWorld (Looks like they had a big update so maybe it's possible to get individual task data now.)