necovek

I believe this is the usual tension that's present even in private companies: are professional managers better than field experts at managing work?

The pure ideal is to get a field expert who is good at management — but sometimes hard to attain!

The question then really becomes if — for a manager — a senior expert is better than an experienced manager who can detect exactly when to pull in the experts? A great manager would be great, but these are similarly rare (rarer than good experts in a field).

And this is probably always on a case-by-case basis: it completely depends on the circumstances, individual qualities, and supporting people around them to decide which is better for a given situation.

The same holds here: the best you can get is an expert with a wide grasp of the entire field they will be active in, supported by good, smart common-sense people to manage and guide their thinking and solutioning in the right way.

I believe this is also what colours our views of the general academic infighting — academics are rarely rotated out except in the lower ranks, so it's a different type of skillset of building long-term alliances that drives their progression through the academic field and available careers in a particular institution (not even talking about "academic points" through publishing in recognized journals, which is a different discussion altogether — even with that criteria satisfied, to move to the next rank of professorship, you need to be elected by your colleagues).

Basically, academic tenure is opposite from democracy.

I laughed out loud at this — my dad is a retired Uni professor, so I know exactly what you are talking about — but I'd point out that the "student list" has around 140 seats in the parliament, so 80 academics hopefully still has another 60 common sense people who are not all about being right all the time, and who bring other perspectives too.

That's if it was stolen from you directly.

If it's stolen from a bank, it's their obligation to recuperate you.

FWIW, most countries have a type of insurance if a legally operating bank goes under, at least some of your savings are protected (eg. in Serbia that's up to 50kEUR).

If it was filed as a security issue, most well-set-up projects with public issue trackers would keep it closed off only to a reporter and internal security audit team.

Perhaps it was never really declassified in the tracker.

Indeed, mixing all the language features and anti-features with non-native Python libraries is a big reason too — both because it mitigates many of the performance issues a JIT would solve, but mostly because it complicates or makes the implementation unfeasible altogether.

Perhaps it's worse that some even succeed in building very complex systems that can handle a lot of load too (see eg. Instagram)? ;-)

Python has a known set of trade-offs, mostly related to loop performance. But if you understand the trade-offs and available mitigations, it should not stop you from building good software. You know, just like any other ecosystem.

Obviously, I am not saying making a JIT for Python is impossible: both PyPy and GraalPy prove otherwise. We are talking about full ecosystem support, not just the base language and stdlib.

Can you just replace CPython with PyPy or GraalPy in every project? And why not? :)

The type of dynamic language gymnastics done with Python is huge: any sufficiently well known library basically employs some sort of meta-programming of the language:

  * Typing is a non-obligatory add-on to the syntax
  * SQLAlchemy and other ORMs override basic operators like "=" and employ metaclass programming heavily to create Python-like SQL query builders
  * Pytest rewrites all the assertions as it side-steps bytecode generation (so your .pyc files are different than what they would be without pytest)
  * Many libraries depend on monkey-patching live library calls, including ones to external dependencies
  * The way async was added in is another bolt-on where the same code behaves differently in different contexts
  * Decorators can introduce any side-effect you imagine, and nobody (including core libraries) follow the golden rule of "don't do, only decorate"
  * Many debugging and profiling mechanisms (including REPL) depend on these properties to function — including observability tooling like Sentry, Datadog etc.
  ...

JavaScript never had that level of flexibility (for better and for worse), and by extension, it's easier to build an alternative implementation and keep it compatible with the rest of the world that claims to be JavaScript.

Production of surplus value is what generally qualifies as a "job" — I certainly agree there is potential for tools to replace/augment any job we think of, but we'll simultaneously continue inventing new jobs.

Just like economies are mostly a service-industry today, a concept that barely made any sense 100 years ago.

Still, buying tooling companies' shares makes sense when that company has something which is hard for anyone else to replicate: but we are seeing that it really is not with a number of proprietary and open weights competitors who generally go head-to-head with minor differences in capability.

This only to say that believing valuations are unreasonable does not mean you can't even be an "AI maximallist" — but markets do not work in a simple cost/value-provided equation for a long time now.

A general statement I made is mostly to refute that people invest exclusively when the data is clearly supporting that investment with guaranteed returns: basically, the fact that people will buy does not mean that they have guaranteed returns.

Some might be looking to capitalize early and exit quickly; some are creating demand because they have other drivers in their portfolio (eg. Nvidia or Micron); some might really believe in the company; and some might be gambling with their disposable investment money for their high-risk/high-gain part of portfolio.

I find that HP/Palm Pre with WebOS was best even if we ignore the physical keyboard (sliding left/right on the touch area below the screen or on the screen with a button pressed really made it easy to select text easily and precisely). Nokia N9 with Maemo/Meego was second best but it excelled in the cards interface for multi-tasking.

Some of those were taken into other OSes but nowhere were they implemented so smoothly.

You might be surprised to learn that yes, regularly pointing a tool at the codebase and seeing it found no issues is indeed enough to claim compliance with cybersecurity regulations.

While we may use deterministic tools like static code analysis, dependency scanners and similar today, people are already replacing/augmenting them with LLMs.

While every breach will trigger a root cause analysis process in a compliant environment, the solution is never going to be "all code in a deployment was manually reviewed by someone at the company" because this would extend to tooling (like compilers, scanners but also packaging and deployment tools) and all dependencies.

So I am pretty sure your example is already used in a number of places especially with "LLMs are better at security than humans" marketing being pushed around — this really makes it easy to sell that excuse to the gullible management who wants to believe this.

People participate in betting and chance games even if the odds are against them.

Sometimes, there is no logic behind what people do, ultimately hoping to strike gold once while wasting money otherwise.

Or nothing since there is no market or spending with no jobs for people?