Yeah, for better or worse, writing style is practically uncorrelated with agentic performance, which is all the rage right now and the thing that most popular benchmarks currently prioritize.
And, in this case, the dollar-amount increase in GDP serves as a virtual quantitative proxy for the increase in mowed lawns (and the value thereof). In other words, the participants in this economy are collectively ~$200 richer with their mowed lawns than they were without them.
If everyone goes around mowing lawns for each other, the economy is richer in lawn mowing at the expense of all the other things that would have been funded had everyone mowed their own lawns and purchased different services instead.
I am confused with this, if "everyone mowed their own lawns" then the net result will be exactly the same, everyone will be busy the same and not poorer, just without money movement.
This is not the same. If everyone wants mowed lawns, and everyone is busy working on that, there is no opportunity cost, everyone is working on their top priorities. The broken window fallacy is a fallacy because the headline gdp figure doesn't account for the destruction of the window which cancels out the benefit. In the grass mowing analogy nothing has been destroyed, useful and priority work has been done all around.
If the pricing is fair and at arms' length. What's happening in reality is as if they are mowing each others' lawns at wink wink nudge nudge $1000. Not a good proxy for actual value created.
In the real world, you have to pay taxes. So people are incentivized to claim less value for the lawns mowed, or even just do it themselves, instead of benefiting from the division of labour.
But also importantly the government of the residents' country is about 39% ($78) richer, if say the participants are honest in reporting this and the country is the UK and the participants are people like you and me in the tech industry who frequent HN and would think to do something like this.
Why would anyone assume this uses an LLM? It classifies bird sounds, not human language. I don't mean this as an attack, I'm genuinely curious! This seemed obvious to me, and I want to know what line of thinking might lead one to believe that an LLM is the better (or more likely) tool for this job over a purpose-built classifier.
Newer models[1] are being trained in ways that prioritize coding and agentic performance over raw knowledge[2] such that they increasingly rely on external tools for accessing hard data and information.
> A dashboard like this one is designed to answer a bounded set of analytical questions ~ requests per day, requests per day for one agency, all-time totals by borough. Each question can be answered by GROUP BY queries, so we can precompute them all ahead of time and save each result as its own small table, called a grouping set. Stack all of the grouping sets in one Parquet file, one section per set, and you have a data cube. A grouping set is only useful if it either enables a question to be answered, or reduces the latency of pulling the data.
It's a good question. In a sense, the cube is caching, just materialized ahead of time instead of memoized on demand. A result cache still needs a live database behind it for misses; the cube has no misses, since every question the dashboard is designed to answer has data in the cube already. And for this experiment, the goal was to forgo a database to serve the data in the first place.
I provide caveats for when this would work vs. when it doesn't in the post. For a lot of customer-facing dashboards, I think it's probably pretty good.
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