Yes, a function of supply and demand of everything. [1]

> your argument had nothing to do with price of housing, you were only talking about density and its effect on livability

My argument is that there is no reason to assume that increasing supply by blanket deregulation is a simple and effective solution to the housing crisis that has no downsides.

> If people "generally prefer" midrise cities, they will move there.

Many people don't want to move there because there are not enough economic opportunities, not because they dislike good traffic or green spaces.

> The great thing about allowing density is that people will stop moving in when they don't like it anymore.

If you solve all possible tragedies of the commons of that approach, sure.

[1] https://en.wikipedia.org/wiki/Sonnenschein–Mantel–Debreu_the...

[1] https://www.lemonde.fr/en/france/article/2026/04/20/french-h...

[2] https://www.politico.eu/article/copenhagen-denmark-mette-fre...

[3] https://www.vanguardngr.com/2026/01/top-10-most-expensive-ci...

[4] https://www.theguardian.com/commentisfree/2025/jul/10/housin...

[5] https://www.japantimes.co.jp/news/2025/09/29/japan/society/r...

[6] https://www.tandfonline.com/doi/full/10.1080/02697459.2026.2...

I don't understand how transforming every urban area into Kowloon Walled City will solve that unless your plan is to make it so dense that people will finally find it undesirable to live there?

EDIT: the writeup from context.ai themselves seems quite informative: https://context.ai/security-update, it seems like it was a personal choice of one of the Vercel employees to grant full access to their Google workspace.

I remember Apple introducing sandboxing for Mac apps, extending deadlines because no one was implementing it. AFAIK, many apps still don’t release apps there simply because of how limiting it is.

Ironically, the author suggests to install his software by curl’ing it and piping it straight into sh.

It’s like what they do in support or sales. They have conversational data and they use it to improve processes. Now it’s possible with code without any sort of proactive inquiry from chatbots.

Ie, the derivation of “knowledge units” will be passive. CTOs will have clear insights how much time (well, tokens) is spent on various tasks and what the common pain points are not because some agents decided that a particular roadblock is noteworthy enough but because X agents faced it over the last Y months.

I conjecture that the core value proposition of DSPy is its optimizer? Yet the article doesn't really touch it in any important way. How does it work? How would I integrate it into my production? Is it even worth it for usual use-cases? Adding a retry is not a problem, creating and maintaining an AI control plane is. LangChain provides services for observability, online and offline evaluation, prompt engineering, deployment, you name it.