Everybody chooses a favorite depending on their domain.

A function executes, and some error happens:

- Return error value: try to handle the error ASAP. The closer to the error the more detailed the information. Higher probability of recovery. Explicit error code handling throughout the code. Example: maybe you try again in one millisecond because the error is a very low probability but possible event.

- Exception: managing errors requires a high-level overview of the program state. Example: no space left on device, inform the user. You gather the detailed information where the error happened. The information is passed as-is or augmented with more information as it bubbles up the stack until someone decides to take action. Pros: separate error handling and happy path code, cleaner code. Cons: separate error handling and happy path code, unhandled errors.

Worst case scenario: you program in C. You don't have exceptions. You are forbidden to use setjmp because rules. A lot of errors are exposed directly to the programmer because this is a low-level language. You return error codes. Rules force you to handle every possible return code. Your code gets incorporated as an appendix to the Necronomicon.

One reason in Julia for having an organization with multiple repositories is how unnecessary is in Julia to have big packages. It is better to have smaller more focused packages and combine them as necessary. Julia needs to improve some things but I don't think I have found a more modular language.

In some fields throwing away and rewriting is the standard, and it works, more or less. I'm thinking about scientific/engineering software: prototype in Python or Matlab and convert to C or C++ for performance/deployment constraints. It happens frequently with compilers too. I think migrating languages is actually more successful than writing second versions.

People adapt to the circumstances. A lot of Python uses are no longer about fast iteration on the REPL. Instead of that we are shipping Python to execute in clusters on very long running jobs or inside servers. It's not only about having to start all over after hours, it's simply that concurrent and distributed execution environments are hostile to interactive programming. Now you can't afford to wait for an exception and launch the debugger in postmortem. Or even if you do it's not very useful.

And now my personal opinion: If we are going the static typing way I would prefer simply to use Scala or similar instead of Python with types. Unfortunately in the same way that high performance languages like C attracts premature optimizers static types attract premature "abstracters" (C++ both). I also think that dynamic languages have the largest libraries for technical merit reasons. Being more "fluid" make them easier to mix. In the long term the ecosystem converges organically on certain interfaces between libraries.

And so here we are with the half baked approach of gradual typing and #type: ignore everywhere.

From TFA:

"There is no legal responsibility for a landlord to enforce the payment of taxes by their tenants, nor any suggestion that Aziz should be paying the bill."

This usually makes some sense. But in this case it is obviously being abused. I guess that a police investigation would need to track the flow of money back to the landlord. Or put in place better legislation. Banks for example cannot claim ignorance about their clients and are required to deny access to their services if something does not look good.