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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.

Easy way to get a fair result from an unfair coin toss: Flip the coin twice in a row, in this case starting with the same side facing up both times, so it's equally unfair for both tosses. If you get heads-heads or tails-tails, discard and start over until you get either heads-tails or tails-heads, which have equal probabilities (so you can say something like HT = "heads" and TH = "tails").

This works even if the coin lands heads 99% of the time, as long as it's consistent (but you'll probably have to flip a bunch of times in that case).

Reminds me of a cool proof I saw recently that there are two numbers a and b such that a and b are both irrational, but a^b is rational:

Take sqrt(2)^sqrt(2), which is either rational or not. If it's rational, we're done. If not, consider sqrt(2) ^ (sqrt(2) ^ sqrt(2)). Since (a^b)^c = a^bc, we get sqrt(2) ^ (sqrt(2))^2 = sqrt(2)^2 = 2, which is rational!

It feels like a bit of a sleight of hand, since we don't actually have to know whether sqrt(2)^sqrt(2) is rational for the proof to work.

When my primary care doc referred me to a dermatologist for a suspicious mole, I could not find an actual dermatologist who would see me in less than ~8 months. I ended up seeing a physician's assistant, which I'm still uneasy about since there's been a study that shows that PA's seem to have a lower success rate vs. doctors [1], and the educational requirements are very different for PAs.

As a layperson, it seems like we (patients / society) would benefit from having more doctors, i.e. opening up more residency slots and admitting more people to med school, but there's probably a lot I don't understand about the issue. Not sure if it's a lack of political willpower to do this, or if there are other reasons why the number of doctors we train is so restricted.

[1] https://pubmed.ncbi.nlm.nih.gov/29710082/ ("PAs performed more skin biopsies per case of skin cancer diagnosed and diagnosed fewer melanomas in situ, suggesting that the diagnostic accuracy of PAs may be lower than that of dermatologists")

This article really resonates with me - I've heard people (and vector database companies) describe transformer embeddings + vector databases as primarily a solution for "memory/context for your chatbot, to mitigate hallucinations", which seems like a really specific (and kinda dubious, in my experience) use case for a really general tool.

I've found all of the RAG applications I've tried to be pretty underwhelming, but semantic search itself (especially combined with full-text search) is very cool.

I think something with the "wow" factor of the Vision Pro but the form factor of a pair of glasses would be the holy grail of AR/VR. I wonder if there are fundamental tradeoffs which would make that impossible in the near term? I think it would remain very niche indefinitely in that case.

I bought the short-story collection this is a part of and liked it a lot: https://qntm.org/vhitaos

A lot of the stories are free to read online without buying it but I thought the few dollars for the ebook was worth it

I just made an update which should enable "population density" type queries to work a bit better - there is now an option to divide by 'LAND_AREA' for any variable (though this should probably be limited a bit), and this option will be automatically selected for queries including 'density' or a few related strings e.g. 'per sq. mile'

Thanks for trying it out - adding land area and supporting queries like "population density" will definitely be doable, and I'd like to make the legend and map color scheme a bit better (and ideally user-configurable) as well.

Really appreciate the feedback!

Hi - thank you for trying it out! These are both definitely real issues with the current approach. I've tried to reign in the "selecting an overly specific table" issue in the final "LLM-selects-from-search-results" stage but clearly have some work left to do there.

As far as the second issue - when people search for things way outside of the available data - I have not done much to address this, but really should. This happens for more plausible queries too, e.g. "Crime Rate" seems like it could be cataloged by the Census, but is not part of the tables indexed by the site (ACS Detailed Tables). It selects variables somewhat randomly here when it should really say something like "no relevant results found"

This lines up with how I make tech stack decisions for my own projects. But I think it's not always obvious going into something if it's going to end up being a money-making endeavor or just an educational project in the end, so I'll fall somewhere in between.

What makes the most sense to be is to be really selective about what new technologies to use, and try to really learn ~one thing per project. E.g. my current project is a small search engine, and I've spent a lot of time exploring / figuring out how to use LLM Embedding models and vector indices for search relevance (vs. falling back on using ElasticSearch the same way we use it at work), but I'm using tools that are familiar to me for the UI/db/infrastructure.

That upstream provider being Snowflake, according to this article: https://www.hudsonrock.com/blog/snowflake-massive-breach-acc...

(posted on HN here: https://news.ycombinator.com/item?id=40534868)