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bennofs

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I for one enjoy the convenience of automatically installing python versions. Yes I know how to do it manually. Yes it is possible to install multiple versions. But that does not mean I want to do it every time, just to test how things behave in different python versions. For that, it's also okay if it does not install the most performant version of the interpreter.

The limit could depend on a lot of other things. In theory, it's possible that you are paying 100% of revenue on debt service yet still the best option is to take on more debt. This is if you believe that investing the money raised via additional debt provides returns that outweigh the increase in debt service costs. So in that situation, additional debt can even lower the debt service / income ratio.

So this is meant to illustrate that the limit on debt is more a function of the concrete circumstances. In this line of argumentation, debt is not limited by a mathematical formula that just takes GDP and similar statistics as input.

Yes, that is a limitation. But this limitation is not too bad.

In many cases, a bug in the translation simply makes the proof impossible. Somebody then investigates why the proof does not go through and finds the bug in the translation.

We only have a problem if the bug in the translation specificially cancels a bug in the original code. If there is no systematic risk, it is quite unlikely that two bugs cancel each other in this way.

I am no economist but couldn't it be that raising pay in few sectors does not cause inflation? I would think that depends on what the people receiving more money spend it on. If they only spend it on things where we have no bottleneck and there is still excess capacity, how would that create inflation? Inflation should only happen if the increased paycheck is used to compete for scarce resources/products. So I think predicting inflation requires a more detailed analysis, especially you need to have a cycle of increased prices and pay.

Construct a TM which enumerates all possible variable assignments and checks if the SAT problem is satisfied then halts if so. You can construct this TM in polynomial time, and it halts exactly if the SAT problem is satisfiable. So this is a polynomial reduction from SAT to the halting problem.

But it breaks down if you have cross-language cycles, because no garbage collector of either language will see the full cycle.

Well, turning a static binary into a docker image is basically just adding some metadata on how to run it. Whereas turning an image into a static binary is much harder, so it makes sense the world standarized on the more flexible format.

User, process, and filesystem namespacing. System call filtering. Ensuring even if there is a remote exec exploit there is no bash or anything else in the container for an attacker to use.

All of these are supported by systemd out of the box. You can use `SystemCallFilter=...`, and various options to restrict the file system (like `ProtectHome` and `ProtectSystem`) plus tons of other linux kernel features (capabilities, no new privileges).

For some cryptocurrencies, a 51% attack would only be able to halt the network (prevent new transactions) but wouldn't be able to cause a double spend or other safety failures.

Are there many package manager that do not have either pre-, post- or build scripts or plugins allowing arbitrary code execution during build?

pkg managers that do have that: cargo (build.rs), pip (setup.py), npm (install scripts), apt/rpm/pacman (postinstall hooks)

Maybe the only exceptions are Go and Java package managers?

One problem with this is that formatting now becomes a multi-file thing. You have now have to figure out what names actually refer to, which depends on building a project model to correctly resolve imports, etc....

Compare this with traditional formatters, which you can run on each single file individually, this is much more complex and also harder to parallelise.