What about the problems negative externalities from unchecked crowding and bus culture? Like overcrowding, sky high prices for housing, very high prices for everything, broken infrastructure that is to expensive to fix and over capacity?
>so that the people who live in them aren't unfairly pushing costs onto other people in society
>The money generated from these endeavours could be used to fund the development of mass public transportation infrastructure in more, dense, sustainable areas
So you still want to tax some people to subsidize other people. Why?
In Germany they penalize using cars by taxing cars and fuels. They reward using public transport by subsidizing it. Car owners pay so public transit users get cheaper rides.
So people are using public transit in Germany not because they love to but because they are forced to.
And the system is far from perfect, Germans are complaining because public transportation is having a lot of delays. You can't reach every point you need, you have to wait and some times public transportation is dirty and filled with dirty, rude, violent people, drug addicts and drunks. Not a lovely way to travel.
Most web projects use JSON files for configuration. There are also some XML files for project configuration. If anything, you can run into too much configuration files.
From my experience it's awesome to write C# with AI. But both Opus and GLM usually one shot the modification to the file so I didn't experienced cases lately where AI had to fix compile errors. True, I gave the AI agents the lsp for C#, so maybe that helps.
Hmm, I am working on C# microservices based apps since 6 years ago. And I always deployed on cloud using Linux, usually in Kubernetes. I used Google Cloud and AWS beside Azure.
The whole stack is open source, Kubernetes, Docker, Hashicorp tools, Postgres, Redis, MongoDB, RabbitMQ, NATS, Kafka, Prometheus, Elastic Search, Kibana, Grafana and so on and so forth.
>Just use Go. LLMs have seen a ton of it, they write it well, it compiles practically instantly, and it has all the advantages of a typed compiled language.
Or any of the faster typed languages you are most comfortable with, as you might need to look at the code some times. LLMs are great at writing and understanding C# and Java.
Also there are still considerations like domain, team expertise, org ecosystem etc. to consider. I love to use Rust for most things, but now I'm working with an org that primarily has expertise in Java, and I'm not going to rock the boat for barely any reason. Python is also still useful for most ML stuff, and Django is quite a pleasure to work with (although it wouldn't be my first choice).
The great thing about LLM-assisted coding is that an experienced software engineer can acquire decent familiarity with a language quite quickly. And then has a useful sparring partner for understanding and using the quirks and features of a new language.
Same here, working with a team that knows Java, so I'm letting Claude write Java.
If I compare the results to another team that uses Python with Claude I see slightly better results on the Java side. Not because Claude knows that better, but because the tools are more rigid by default which creates more of a self correcting loop for Claude. The Python side has Pydantic, but it's a bit of an afterthought, while in Java you can't skip the type checking.
In the end you can do the same things on both sides, it's 95% a team/engineering culture difference. So pick the language that the team knows best.
