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I agree with your take, particularly because of this line in the article:

| the skills that define a strong blindfold chess player are the same as those of a programmer who can thrive behind a Claude Code terminal whilst not reading nor writing any code.

If you're actually not reviewing the outputs, you're just getting a fuzzy description of the state of the chessboard.

But I (and everyone I work with) use Claude Code in a workflow where I -do- review the outputs, or at least I make an honest effort to try. Rather than blindfolded, I think bullet (1-minute) chess is a fairly good analogy for this: you have all the info you need to keep your mental model up to date with reality, but the pace of change is too fast to do a good job unless you have a lot of preexisting chess expertise.

The 'growth in completed actions runs' graph is interesting. I assume the periodic drops are weekends, so intuitively the floor of those drops corresponds more with hobby/personal projects than people at work. It looks like there's a sharp uptick specifically in that floor since July ish.

Thanks! The population/demographic data is all freely available from the US Census and stored in a PostGIS database (hence the site being US-only for now- if I wanted to make it global, there are global gridded population data sets but they aren't as detailed).

The geography data is all pulled in w/ some long-running background tasks into a rails app (that hosts both draw.city and another site I've shared here in the past - blockatlas.com). To find the pop of place that the user circled, it essentially queries for census blocks whose centroid is fully contained in the area you drew (there is a quicker census-tract-based fallback for huge areas).

Putting demographic data e.g. "% walk to work" onto an arbitrary polygon is a bit trickier, since that sort of data isn't published at the block level. We use census tracts (bigger, neighborhood-sized areas that do have this data), and for partially-contained tracts we compute the fraction of the tract's population estimated to live in the area, and allocate demographic info according to that fraction.

I've been building various geography tools & games, found a fun domain for this one. It's set up as a wordle-like daily challenge, I've backfilled some "past challenges" so there's more than one day to play, and there is also a free-draw mode that tells you population + some census ACS demographic info & points-of-interest data from OSM.

Happy to answer any questions about the data or the technical side! This only covers US areas at the moment.

Another (semi serious) possible explanation of this is that LLMs were trained on a huge volume of text from Reddit. I wonder if you'd see similar inhuman-looking behavior if you did an iterated prisoners dilemma with a group of Reddit power-users as the participants?

I think the fun/hobbyist/urbanist answer is that people just like to know facts about where they live, I've spent hours now looking up different places' data. The more commercial answer if I were pitching this as a product is that 'who lives within X minutes of this point' is a big question in CRE site selection etc if e.g. you're deciding where to expand a franchise. The walk-time version is more of a fun demo of the engine than anything else.

Thanks for the feedback! The site (and I suppose this post) doesn't really do much to answer "what's the point?"

I'd like to! It'd be pretty straightforward for the parts based on OpenStreetMap i.e. generating travel time areas / points of interest, I'd just need a bigger database. Ingesting global population & demographic data would be a bit of a bigger project since the site is built around US Census territory boundaries at the moment.

It'd definitely something I want to add, but since I'm quite limited in the time I spend on the project, I think it makes sense to keep it US-only for now. Thanks for trying it out!

Yep, street view is not working at all for me

It seems silly to expect someone to read the whole book before evaluating it when the creator didn't even read the whole book before publishing it

It seems that Slack being fully down and unusable only reaches the "incident" category on their scale - I'd hate to see what counts as "outage"

I think the core problem at hand for people trying to use AI in user-facing production systems is "how can we build a reliable system on top of an unreliable (but capable) model?". I don't think that's the same problem that AI researchers are facing, so I'm not sure it's sound to use "bitter lesson" reasoning to dismiss the need for software engineering outright and replace it with "wait for better models".

The article sits on an assumption that if we just wait long enough, the unreliability of deep learning approaches to AI will just fade away and we'll have a full-on "drop-in remote worker". Is that a sound assumption?

It seems like he did everything! I first heard of Von Neumann in international relations & economics classes as the person who established game theory, then later in CS classes as the creator of mergesort, cellular automata, Von Neumann architecture, etc.