At least in Seattle, I believe the high minimum wage is due to CoL in the city, which is very high due to rent cost, which is high because of geography and not building a lot. If there was a building boom which led to a surplus of rental units, and rents went down, you wouldn't need such a high minimum wage.
I thought Seattle has started addressing the housing shortage? At some point though for a rapidly growing city you can’t really lower housing prices below the cost of building housing, and construction labor and cost of materials becomes a constraining factor (5 story housing projects can’t be built super cheaply).
WA state added a law to override local zoning regulations in order to encourage density.
I have seen new multi apartment construction in Ballard, Cap Hill, and some other places, but there is a construction backlog going on for years.
Also, the problem in Seattle, same as SF, is that there are too many SFHs vs multi apartment buildings/townhomes.
It will take several years until offer surpasses demand.
Seattle will always have high real estate prices due to its relatively high desirability in the world. What it doesn't have is a large supply of immigrants and/or illegal immigrants willing to work for low wages in restaurants, like NYC and California.
I'm sure it'd be better to find graphs about King County or the greater Seattle area, but I found these graphs from the City of Seattle [1]. What I get from the graph is that there is growth in housing units, but there's also growth in population, so there's probably a lot of years at the current trends, before the housing shortage is 'satisfied'.
Thanks for providing the data. I think that backs up my assertion that Seattle doesn't have a housing shortage as such.
The relative growth in housing units since 2010 has been higher than the relative growth in population and jobs, so housing price growth since 2010 can't be explained by a shortage of supply.
The issue is that the rapid pace of growth means there isn't much old stock housing on the market to provide cheaper options to homebuyers -- most of the housing on the market will be new stock for which the minimum price will be driven by the cost of construction.
I've been seeing that housing supply / demand and pricing is complicated by many other factors beyond population. It can less or more obvious depending on location.
I've watched one of my favorite cities housing go crazy because of many factors including investors buying up properties for airbnb, developers focusing on catering to the coming influx of higher paid amazon / oracle people, and so many betting on those future increases that everything else goes up.
Adding to that, becoming a popular place for people to buy a second (or third / fourth home) - whether it's for a temporary move, to shelter their kids going to college who have chosen here instead of Chicago, trying a lower tax place to move with remote work being easier post 2020, etc..
Good point about new construction costing more, and that in itself has many factors. and depending on exactly when things were purchased making big differences.
With the limited supply of builders, most are choosing to build more expensive places.
Sadly even if we made this place less attractive for people to move to, many of the properties wouldn't go on the market, many would just hold on to the property as a stable investment.
So population numbers are not the primary weight in the supply / demand equation in many places is something I have been learning.
> A thorough MOA study can take up to two years and cost around $2 million; however, using AI, his group did enterololin’s in just six months and for just $60,000.
Beautiful, finally something for ai/machine learning that is not a coding autocomplete or image generation usage.
It would be very interesting to keep track of this area for the next 10 years, between alpha fold for protein folding and this to predict how it will behave, how cost is reduced and trials get fast tracked
> We thus frame molecular docking as a generative modeling problem—given a ligand and target protein structure, we learn a distribution over ligand poses.
I just hope work on these very valid use cases doesn’t get negatively impacted when the AI bubble inevitably bursts.
I do check the standard library for things that sound like they should be there as their common enough. My experience tells me this approach is not as common as you would expect, same for C# in msft, I don’t know how many people using framework knew about array segment.
Testing is the case I've found AI actually useful. Write one good test, maybe happy path, and then tell your AI to test for scenario XYZ and how it should fail, etc.
The generated code is in general 90% there.
This allows me to write many more tests than before to try to catch all scenarios.
You could have a pipeline in some cloud provider to run the tests, and distribute the load across machines if tests are independent to reduce the time, if that's more important. If it's ok to just run the overnight, keep it on.
