Microsoft laid off the Faster CPython lead Mark Shannon and ended support for the project, where does this leave the Verona project?

They belong to Microsoft Research not DevDiv, so while that doesn't protect them from layoffs, certainly gives them some protection being under different management.

Microsoft Research sites tend to be based in collaborations with university research labs.

> Microsoft Research sites tend to be based in collaborations with university research labs.

Oh that should help with the project's stability and funding.

Would be helpful to know why you think this. Even if there are common reasons that others could point to (and please don't; it won't be helpful), your comment doesn't make any sense without that context.

Why?

How does using their Python tools help Microsoft?

It's a large corporation. I'm certain someone asked that question and got an answer before starting producing Python tools. It's management's job to ask that question and get answers, you know.

Embrace, extend, destroy

Extinguish! Mind the alliteration! :)

The filename of the formal paper[1] reveals the internal codename: "Pyrona".

1: https://www.microsoft.com/en-us/research/wp-content/uploads/...

Is anyone familiar with Instagram's cinder?

https://github.com/facebookincubator/cinder

cinder includes changes for immutable module objects. I wonder if the implementation is similar? Or is cinder so old that it would be incompatible with the future noGil direction?

I've been programming with Python for over 10 years now, and I use type hints whenever I can because of how many bugs they help catch. At this point, I'm beginning to form a rather radical view. As LLMs get smarter and vibe coding (or even more abstract ways of producing software) becomes normalized, we'll be less and less concerned about compatibility with existing codebases because new code will be cheaper, faster to produce, and more disposable. If progress continues at this pace, generating tests with near 100% coverage and fully rewriting libraries against those tests could be feasible within the next decade. Given that, I don't think backward compatibility should be the priority when it comes to language design and improvements. I'm personally ready to embrace a "Python 4" with a strict ownership model like Rust's (hopefully more flexible), fully typed, with the old baggage dropped and all the new bells and whistles. Static typing should also help LLMs produce more correct code and make iteration and refactoring easier.

You think of code as an asset, but you're wrong: code is a cost.

Feature is what you want, and performance, and correctness, and robustness; not code

Older code is tested code, that is known to work, with known limitations and known performances

A corollary is that if at all possible try to solve problems without code or, failing that, with less code.

Given that you want to solve problems with a computer, what is the alternative to code?

Why do you want to use a computer? When I hang pictures I have never once thought “I want to use a hammer”

That is almost never truly a given. And even if it is, how you use the computer can be more important than the code.

And if you already have some code, simplifying it is also an option.

Restating the context as one where the problem doesn't exist.

Any fool can write code.

If there isn't one, then as little code as possible.

I agree, older code is evidence of survivorship bias. We don't see all of the code that was written with the older code that was removed or replaced (without a code repository).

> I'm personally ready to embrace a "Python 4" with a strict ownership model like Rust's (hopefully more flexible), fully typed, with the old baggage dropped and all the new bells and whistles. Static typing should also help LLMs produce more correct code and make iteration and refactoring easier.

So...a new language? I get it except for borrow checking, just make it GC'ed.

But this doesn't work in practice, if you break compatibility, you are also breaking compatibility with the training data of decades and decades of python code.

Interestingly, I think as we use more and more LLMs, types gets even more and more important as its basically a hint to the program as well.

I think people are still fooling themselves about the relevance of 3GL languages in an AI dominated future.

It is similar to how Assembly developers thought about their relevance until optimising compilers backends turned that into a niche activity.

It is a matter of time, maybe a decade who knows, until we can produce executables directly from AI systems.

Most likely we will still need some kind of formalisation tools to tame natural language uncertainties, however most certainly they won't be Python/Rust like.

We are moving into another abstraction layer, closer to the 4GL, CASE tooling dreams.

> I think people are still fooling themselves about the relevance of 3GL languages in an AI dominated future.

I think, as happens in the AI summer before each AI winter, people are fooling themselves about both the shape and proximity of the “AI dominated future”.

It will be approximately the same shape and proximity as “the Internet-dominated future” was in 2005.

"Since FORTRAN should virtually eliminate coding and debugging…" -- FORTRAN report, 1954 [1]

If, as you seem to imply and as others have stated, we should no longer even look at the "generated" code, then the LLM prompts are the programs / programming language.

I can't think of a worse programming language, and I am not the only one [2]

However, it does indicate that our current programming languages are way to low-level, too verbose. Maybe we should fix that?

[1] http://www.softwarepreservation.org/projects/FORTRAN/BackusE...

[2] https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...

[3] https://objective.st/

4GL and 5GL are already taken. So this is the 6GL.

https://en.wikipedia.org/wiki/Programming_language_generatio...

But speaking more seriously, how to get this deterministic?

Fair enough, should have taken a look, I stopped counting when computer magazines buzz about 4GLs faded away.

Probably some kind of formal methods inspired approach, declarative maybe, and less imperative coding.

We should take an Alan Kay and Bret Victor like point of view where AI based programming is going to be in a decade from now, not where it is today.

That future is far from inevitable, the first question we SHOULD ask is if it's a good idea to go down this path.

Assemblers and compilers are (practically) deterministic. LLMs are not.

That's the wrong distinction, and bringing it up causes pointless arguments like are in the replies.

The right distinction is that assemblers and compilers have semantics and an idea of correctness. If your input doesn't lead to a correct program, you can find the problem. You can examine the input and determine whether it is correct. If the input is wrong, it's theoretically possible to find the problem and fix it without ever running the assembler/compiler.

Can you examine a prompt for an LLM and determine whether it's right or wrong without running it through the model? The idea is ludicrous. Prompts cannot be source code. LLMs are fundamentally different from programs that convert source code into machine code.

This is something like "deterministic" in the colloquial sense, but not at all in the technical sense. And that's where these arguments come from. I think it's better to sidestep them and focus on the important part: compilers and assemblers are intended to be predictable in terms of semantics of code. And when they aren't, it's a compiler bug that needs to be fixed, not an input that you should try rephrasing. LLMs are not intended to be predictable at all.

So focus on predictability, not determinism. It might forestall some of these arguments that get lost in the weeds and miss the point entirely.

LLMs are deterministic. So far every vendor is giving them random noise in addition to your prompt though. They don't like have a free will or a soul or anything, you feed them exactly the same tokens exactly the same tokens will come out.

If you change one letter in the prompt, however insignificant you may think it is, it will change the results in unpredictable ways, even with temperature 0 etc. The same is not true of renaming a variable in a programming language, most refactorings etc.

That's not how they are being used though, is it?

Only if you set temperature to 0 or have some way to set the random seed.

Locally that’s possible but for multi tenant ones I think there’s other challenges related to batch processing (not in terms of the random seed necessarily but because of other non determinism sources).

Missed the part?

> Most likely we will still need some kind of formalisation tools to tame natural language uncertainties, however most certainly they won't be Python/Rust like

No, I didn't miss it. I think the fact that LLMs are non deterministic means we'll need a lot more than "some kind of formalization tools", we'll need real programming languages for some applications!

How deterministic are C compilers at -O3, while compiling exactly the same code across various kinds of vector instructions, and GPUs?

We are already on the baby steps down that path,

https://code.visualstudio.com/docs/copilot/copilot-customiza...

Take a look at the following: https://reproduce.debian.net/

Granted, lot's of different compilers and arguments depending on packages. But you need to match this reproducibility in a fancy pants 7GL

And still its behaviour isn't guaranteeded if the hardware isn't exactly the same as where the binaries were produced.

That is why on high integrity computing all layers are certified, and any tiny change requires a full stack re-certification.

You moved the goal posts and declared victory - that's not what deterministic means. It means same source, same flags, same output. Under that definition, the actual definition, they're 99.9% deterministic (we strive for 100% but bugs do happen).

You keep being you, but you also have to admit, not only do you move goal posts, but most of arguments are on dollies, performing elaborate choreographies that would make Merce Cunningham blush.

pjmlp did originally say "compiling exactly the same code across various kinds of vector instructions, and GPUs".

You have a point, but in making it I think you're undermining your argument.

Yes, it's true that computer systems are nondeterministic if you deconstruct them enough. Because writing code for a nondeterministic machine is fraught with peril, as an industry we've gone to great lengths to move this nondeterminism as far away from programmers as possible. So they can at least pretend their code code is executing in a deterministic manner.

Formal languages are a big part of this, because even though different machines may execute the program differently, at least you and I can agree on the meaning of the program in the context of the language semantics. Then we can at least agree there's a bug and try to fix it.

But LLMs bring nondeterminism right to the programmer's face. They make writing programs so difficult that people are inventing new formalisms, "prompt engineering", to deal with them. Which are kind of like a mix between a protocol and a contract that's not even enforced. People are writing full-on specs to shape the output of LLMs, taking something that's nondeterministic and turning into something more akin to a function, which is deterministic and therefore useful (actually as an aside, this also harkens to language design, where recently languages have been moving toward immutable variables and idempotent functions -- two features that combined help deal with nondeterministic output in programs, thereby making them easier to debug).

I think what's going to happen is the following:

- A lot of people will try to reduce nondeterminism in LLMs through natural language constrained by formalisms (prompt engineering)

- Those formalisms will prove insufficient and people will move to LLMs constrained with formal languages that work with LLMs. Something like SQL queries that can talk to a database.

- Those formal languages will work nicely enough to do simple things like collecting data and making view on them, but they will prove insufficient to build systems with. That's when programming languages and LLMs come back together, full circle.

Ultimately, my feeling is the idea we can program without programming languages is misunderstanding what programming languages are; programming languages are not for communicating with a computer, they are for communicating ideas in an unambiguous way, whether to a computer or a human or an LLM. This is important whether or not a machine exists to execute those programs. After all, programming languages are languages.

And so LLMs cannot and will not replace programming languages, because even if no computers are executing them, programs still need to be written in a programming language. How else are we to communicate what the program does? We can't use English and we know why. And we can't describe the program to the LLM in English for the same reason. The way to describe the program to the LLM is a programming language, so we're stuck building and using them.

Do you like have any idea what you're talking about? Or are you just making it up for internet points? The target is part of the input.

Lemme ELI5

https://github.com/llvm/llvm-project/tree/main/llvm/test/Cod...

You see how this folder has folders for each target? Then within each target folder there are tests (thousands of tests)? Each of those tests is verified deterministically on each commit.

Edit: there's an even more practical way to understand how you're wrong: if what you were saying were true, ccache wouldn't work.

Yes I agree this is likely the direction we're heading. I suppose the "Python 4" I mentioned would just be an intermediate step along the way.

I think the question is: What is the value of that intermediate step? It depends on how long the full path takes.

If we're one year away from realizing a brave new world where everyone is going straight from natural language to machine code or something similar, then any work to make a "python 4" - or any other new programming languages / versions / features - is rearranging deck chairs on the Titanic. But if that's 50 years away, then it's the opposite.

It's hard to know what to work on without being able to predict the future :)

> It is a matter of time, maybe a decade who knows, until we can produce executables directly from AI systems.

They already can.

If you're lucky.

If you're even luckier it will perform the desired function fast enough.

Why not spend the money on lottery tickets instead?

Wild thought: maybe coding is a thing of the past? Given that an llm can get fast&deterministic results if needed, maybe a backend for instance, can be a set of functions which are all textual specifications and by following them it can do actions (validations, calculations, etc), approach apis and connect to databases, then produce output? Then the llm can auto refine the specifications to avoid bugs and roll the changes in real time for the next calls? Like a brain which doesn't need predefined coding instructions to fulfill a task, but just understand its scope, how to approach it and learn from the past.

I really want to meet these people that are letting an LLM touch their db.

Fast forward to the near future, why wouldn't it with the correct restrictions? For instance, would you let it today run SELECT queries? as Hemingway once said "if it's about price we know who you are".

I'd think LLMs would be more dependent on compatibility than humans, since they need training data in bulk. Humans can adapt with a book and a list of language changes, and a lot of grumbling about newfangled things. But an LLM isn't going to produce Python++ code without having been trained on a corpus of such code.

It should work if you feed the data yourself, or at the very least the documentation. I do this with niche languages and it seems to work more or less, but you will have to pay attention to your context length, and of course if you start a new chat, you are back to square one.

It’s just as questionable to declare victory because we had a few early wins and that time will fix everything.

Lots of people had predicted that we wouldn’t have a single human-driven vehicle by now. But many issues happened to be a lot more difficult to solve than previously thought!

How would you debug a programming language made for LLMs? And why not make an LLM that can output gcc intermediate representation directly then?

You wouldn't, this would be a bet that humans won't be in the loop at all. If something needs debugging the LLM would do the debugging.

Yeah, I can imagine how that goes:

Oh, there's a bug in this test case, deletes test case.

Oh, now we're missing a test case, adds test case.

Lather, rinse, repeat.

One has to wonder, why would there be any bugs at all if the LLM could fix them? Given Kernighan's Law, does this mean the LLM can't debug the bugs it makes?

My feeling is unless you are using a formal language, then you're expressing an ambiguous program, and that makes it inherently buggy. How does the LLM infer your intended meaning otherwise? That means programmers will always be part of the loop, unless you're fine just letting the LLM guess.

  Kernighan's Law - Debugging is twice as hard as writing the code in the first place.

The same applies to humans, who are capable of fixing bugs and yet still produce bugs. It's easier to detect bugs with tests and fix them than to never have introduced bugs.

But the whole idea of Kernighan’s law is to not be so clever that no one is available to debug your code.

So what happens when an LLM writes code that is too clever for it to debug? If it weren’t too clever to debug it, it would have recognized the bug and fixed it itself.

Do we then turn to the cleverest human coder? What if they can’t debug it, because we have atrophied human debugging ability by removing them from the loop?

Lol.

> embrace a "Python 4" with a strict ownership model like Rust

Rust only does this because it targets low-level use cases without automatic memory management, and makes a conscious tradeoff against ease of programming.

100% coverage won't catch 100% of bugs of course

You described the thinking behind py2many.

Code in the spirit of rust with python syntax and great devx. Give up on C-API and backward compat with everything.

Re: lifetimes

Py2many has a mojo backend now. You can infer lifetimes for simple cases. See the bubble sort example.

Ownership models like Rust require a grester ability for holistic refactoring, otherwise a change in one place causes a lifetime issue elsewhere. This is actually exactly what LLM's are doing the worst at.

Beyond that, a Python with something like lifetimes implies doing away with garbage-collection - there really isn't any need for lifetimes otherwise.

What you are suggesting has nothing to do with Python and completely misses the point of why python became so widely used.

The more general point is that garbage collection is very appealing from a usability standpoint and it removes a whole class of errors. People who don't see that value should look again at the rise of Java vs c/c++. Businesses largely aren't paying for "beautiful", exacting memory management, but for programs which work and hopefully can handle more business concerns with the same development budget.

Rust lifetimes are generally fairly local and don’t impact refactoring too much unless you fundamentally change the ownership structure.

Also a reminder that Rc, Arc, and Box are garbage collection. Indeed rust is a garbage collected language unless you drop to unsafe. It’s best to clarify with tracing GC which is what I think you meant.

While I go into another direction in a sibling comment, lifetimes does not imply not needing garbage collection.

On the contrary, having both allows the productivity of automatic resource management, while providing the necessary tooling to squeeze the ultimate performance when needed.

No need to worry about data structures not friendly to affine/linear types, Pin and Phantom types and so forth.

It is no accident that while Rust has been successful bringing modern lifetime type systems into mainstream, almost everyone else is researching how to combine linear/affine/effects/dependent types with classical automatic resource management approaches.

At the point which you describe we could easily write Rust or even just C

> As vibe coding becomes normalized

Just want you to know this heart monitor we gave you was engineered with vibe coding, that's why your insurance was able to cover it. Nobody really knows how the software works (because...vibes), but the AI of course surpasses humans on all current (human-created) benchmarks like SAT and bar exam tests, so there's no reason to think its software isn't superior to human-coded (crusty old non "vibe coded" software) as well. You should be able to resume activity immediately! good luck

Welcome to the flight, this is your captain speaking. Just want to let you know our entire flight system was vibe coded to the strict standards you expect from our industry, iterated and refined in a virtual environment over twenty virtual-years, with no fallible human eyes reviewing it - even if it were possible to review the mountain of impenetrable machine-generated code. The pilot will be controlling the plane via a cutting-edge LLM interface, prompt-engineering our way to our overseas destination. Relax, get comfortable, and pray to the collective intelligence distilled from Reddit posts.

What percent of applications require that level of reliability?

Vibe coding will be normalized because the vast, vast majority of code is not life or death. That literally what “normal” means.

Exceptional cases like pacemakers and spaceflight will continue to be produced with rigor. Maybe even 1% of produced code will work that way!

Do we really want to go further down this path?

Things that used to work just fine are already breaking badly because of AI.

this is black and white thinking. if the practice of "let the AI write the code and assume it's fine because I'm only an incurious amateur anyway" becomes normalized, the tendency of AI to produce inaccurate slop will become more and more part of software we use every day and definitely will begin impacting functions that are more and more critical over time.

I mean, why not just write Rust at that point? Required static typing is fundamentally at odds with the design intent of the language.

A lot of people want a garbage collected Rust without all the complexity caused by borrow checking rules. I guess it's because Rust is genuinely a great language even if you ignore that part of it.

> a garbage collected Rust

By the way, wouldn't it be possible to have a garbage-collecting container in Rust? Where all the various objects are owned by the container, and available for as long as they are reachable from a borrowed object.

Isn't this what Rc is?

D and Go exist.

There are alternatives out there

Isn't garbage collected Rust without a borrow checker just OCaml?

Pretty much, I would say, in fact, I like OCaml better if we put the borrow checker aside.

Thankfully, like many other languages that rather combine models instead of going full speed into affine types, OCaml is getting both.

Besides the effects type system initially introduced to support multicore OCaml, Jane Street is sponsoring the work for explicit stack allocation, unboxed types, modal types.

See their YouTube channel.

Yeah, I have watched a couple of videos and read blog posts from Jane Street. They are helping OCaml a lot!

Or even better imo, Reason ML.

Not only that: Rust is considerably faster and more reliable. Since you're not writing the code yourself, Rust would be an objectively better choice.

Who are we trying to fool?

You pretty much described Mojo

I'd rather love to see confluent persistence in python, i.e. a git-like management of an object tree.

so when you create a new call stack ( generator, async sth, thread) you can create a twig/branch, and that is modified in-place, copy on write.

and you decide when and how to merge a data branch,there are support frameworks for this, even defaults but in general merging data is a deliberate operation. like with git.

locally, a python with this option looks and feels single threaded, no brain knots. sharing and merging intermediate results becomes a deliberate operation with synchronisation points that you can reason about.

This is achievable with deepdiff today: https://pypi.org/project/deepdiff/

Maybe not as performant as if you designed your data structures around it. But certainly achievable.

interesting project thanks for the pointer

however it's not what I have in mind

the point is not being able to dog and patch object graphs

the point is copy on write of data that isn't local to the current call stack, automatic immutability with automatic transients per call stack.

a delta will then guide the merge of branches. but the Delta emergency from the CoW, instead of being computed as in deepdiff.

Sounds like a fun job, I’d love to do something like this in my 9 to 5.

It’s also amazing how much work goes into making Python a decent platform because it’s popular. Work that will never be finished and could have been avoided with better design.

Get users first, lock them in, fix problems later seems to be the lesson here.

Python is about 35 years old at this point. It was the better language that had the better design and the fixed problems at some point in time.

Sure, maybe a committee way back in 1990 could have shaved off of some the warts and oopsies that Guido committed.

I’d imagine that said committee would have also shaved off some of the personality that made Python an enjoyable language to use in the first place.

People adopted Python because it was way nicer to use compared to the alternatives in say, 2000

I would say it was closer to 2005 that Python really took off. Coincidentally around when I started using it, but I remember a noticeable increase in "buzz".

Yes, writting CGI with python the configuration language was so much better than with perl the shell replacement!

> Get users first, lock them in, fix problems later seems to be the lesson here.

Or with a less cynical spin: deliver something that's useful and solves a problem for your potential users, and iterate over that without dying in the process (and Python suffered a lot already in the 2 to 3 transition)

2 to 3 was possibly precisely because of user lock-in and sunk cost. This kind of global update was unprecedented, and could have been totally avoided with better design.

> could have been totally avoided with better design

This is why taxi drivers should run the country!

Imo it is less about locking anyone in (in this case) and more about what Python actually enables: exceedingly fast prototyping and iteration. Turns out the ability to ship fast and iterate is actually more useful that performance, esp in a web context where the bottlenecks are frequently not program execution speed.

Python has compounding problems that make it extremely tricky though.

If it was just slow because it was interpreted they could easily have added a good JIT or transpiler by now, but it's also extremely dynamic so anything can change at any time, and the type mess doesn't help.

If it was just slow one could parallelise, but it has a GIL (although they're finally trying to fix it), so one needs multiple processes.

If it just had a GIL but was somewhat fast, multiple processes would be OK, but as it is also terribly slow, any single process can easily hit its performance limit if one request or task is slow. If you make the code async to fix that you either get threads or extremely complex cooperative multitasking code that keeps breaking when there's some bit of slow performance or blocking you missed

If the problem was just the GIL, but it was OK fast and had a good async model, you could run enough processes to cope, but it's slow so you need a ridiculous number, which has knock-on effects on needing a silly number of database/api connections

I've tried very hard to make this work, but when you can replace 100 servers struggling to serve the load on python with 3 servers running Java (and you only have 3 because of redundancy as a single one can deal with the load), you kinda give up on using python for a web context

If you want a dynamic web backend language that's fast to write, typescript is a much better option, if you can cope with the dependency mess

If it's a tiny thing that won't need to scale or is easy to rewrite if it does, I guess python is ok

> If it was just slow because it was interpreted they could easily have added a good JIT or transpiler by now, but it's also extremely dynamic so anything can change at any time, and the type mess doesn't help.

See Smalltalk, Common Lisp, Self.

Their dynamism, image based development, break-edit-compile-redo.

What to change everything in Smalltalk in a single call?

a becomes: b

Now every single instance of a in a Smalltalk image, has been replaced by b.

Just one example, there is hardly anything that one can do in Python that those languages don't do as well.

Smalltalk and Self are the genesis of JIT research that eventually gave birth to Hotspot and V8.

Or Elixir / Erlang instead of Java / Kotlin, and Go instead of Python, for this use case.

I agree that fast iteration and the „easy to get something working” factor is a huge asset in Python, which contributed to its growth. A whole lot of things were done right from that point of view.

An additional asset was the friendliness of the language to non-programmers, and features enabling libraries that are similarly friendly.

Python is also unnecessarily slow - 50x slower than Java, 20x slower than Common Lisp and 10x slower than JavaScript. It’s iterative development is worse than Common Lisp’s.

I’d say that the biggest factor is simply that American higher education adopted Python as the introductory learning language.

For American higher education, It was Pascal ages ago, and then it was Java for quite a while.

But Java is too bureaucratic to be an introductory language, especially for would-be-non-programmers. Python won on “intorudctoriness” merits - capable of getting everything done in every field (bio, chem, stat, humanities) while still being (relatively) friendly. I remember days it was frowned upon for being a “script language” (thus not a real language). But it won on merit.

Isn't this so common in computer science, haven't you heard of Worse is Better [1]?

[1]: https://en.m.wikipedia.org/wiki/Worse_is_better

The JS playbook.

I wish Python had moved to the BEAM or something similar as part of the 2 to 3 transition. This other stuff makes me cringe.

I'm a big BEAM person, but python 3.0.0 was released december 2008. At that time, I believe OTP R12 was current, and it only gained SMP support in R11. [1] In 2008, I don't know that it would have been clear that the BEAM would be a good target. And I don't know how switching to BEAM then would have addressed what I think is the core issue python 3 was working on, unicode strings; BEAM didn't start taking on unicode until R13 and IMHO, is kind of on the slow end of unicode adoption (which isn't always bad... being late means adopting industry consensus with less of the intermediate false steps)

[1] https://erlang.org/euc/08/euc_smp.pdf

Python’s core developers don’t even seem to care about other Python implementations (only about CPython).

There’s no way they would move to, say, PyPy as the official implementation - let alone to a VM designed for a completely different language.

At the time of the original Py3 release, PyPy was not ready for wide use. Otherwise maybe there could have been a chance of it replacing CPython. They were in too big a hurry to ship Py3 though. Tragedy.

Which is a pity, Python ends up being the only major dynamic language, where for all pratical purposes there is no JIT support, because while there are alternative implementations with great JIT achievements, the comunity behaves as if all that effort was for nothing other than helping PhD students doing their thesis.

This looks like a pivot on the Project Verona research, as there have not been much other papers out since the initial announcement, regarding the programming language itself.

This is the true Python concurrency effort! I know, I have followed many! (Life of Brian)

So they sounded out the Faster CPython team, which is now fired (was van Rossum fired, too?):

"Over the last two years, we have been engaging with the Faster CPython team at Microsoft as a sounding board for our ideas."

And revive the subinterpreter approach yet again.

this will work very well with free threaded python you don't need sub interpreters. I agree that it's the most promising approach I've seen yet.

Microsoft just fired 3% of its staff, more than it ever did before. I would stick with type-checked free-threaded Python with locks and queues. Someone should be able to enhance the type checker to also check for unsafe mutation of variables.

Only 3%. Hard to say this effort was affected

3% of the whole company - but a lot of Python specialists were in that 3%. Including, apparently, the entire "Faster CPython" team.

"fearless concurrency" reminds of the buzzword for another language

Yeah, programming used to be performed by programmers.

Being afraid of concurrency, or pointers or whatever; makes no sense.

Being aware, on the other hand, is a very good idea.