That would likely mean same amount of people get the vulnerability, just 7 days later.

The compromised packages were removed from the registry within hours.

Because everyone got updates immediately. If the default was 7 days, almost no one would get updates immediately but after 7 days, and now someone only finds about after 7 days. Unless there is a poor soul checking packages as they are published that can alert the registry before 7 days pass, though I imagine very few do that and hence a dedicated attacker could influence them to not look too hard.

If I remember correctly, in all the recent cases it was picked up by automated scanning tools in a few hours, not because someone updated the dependency, checked the code and found the issue.

So it looks like even if no one actually updates, the vast majority of the cases will be caught by automated tools. You just need to give them a bit of time.

While reading articles like this, I feel like we're just in the "denial" stage. We're just trying to look for negatives instead of embracing that this is a definite paradigm shift in our craft.

I don't think the argument is correct. Reasoning LLM will check itself and search multiple sources. It's essentially doing the same mental process as human would. Also consulting multiple LLMs completely breaks this argument.

IME, even when an LLM is right, a few follow-up questions always lead to some baffling cracks in its reasoning that expose it has absolutely no idea what it's talking about. Not just about the subject but basic common sense. I definitely wouldn't call it the "same mental process" a human does. It is an alien intelligence, and exposing a human mind to it won't necessarily lead to the same (or better) outcome as learning from other humans would.

Author’s central point is that an LLM answer “is optimized for arrival, not for becoming” (to paraphrase from the Google “Lucky” part).

So a reasoning LLM that does the comparisons and checks “like a human” still fails the author’s test.

That said, this still feels like a skill issue. If you want to learn, see opposing views gather evidence to form your own opinions about, LLMs can still help massively. You just have to treat them research assistants instead of answer providers.

but the point is that the metal process should be done by yourself. it is the difference between finding the answer myself or asking my classmate to just share his answer with me. in the latter case i am not learning what my classmate learned.

>Count them. 17 companies. 16 in the United States. 1 in Canada. Zero in the EU.

We regulated innovation out of the market. Why are you surprises that the only companies finding your data valuable are in the US?

Maybe it's just me but I don't count tracking people as innovation. Tell me what's innovative about it.

Tracking people is dystopian. But only collection of data allowed us to train the AI. I don't think EU has issues with tracking people unless a private party does it.

First CDs would take hour and a half to write with a laser. Once engineers take over the tech, it will might get faster.

If they get the read speed up to a couple of GBit/s (~100x current max write speed), 4.8TB might be a good fit for 32k movies.

Of course there are people out there watching 32k movies.

Was 4k not enough?

Am I the only one who's still content with 720p?

The display has some bearing on this. Generally, 1080p is good enough but some cinematography benefits from better resolution and as a result, requires a better display.

Do you have a license to ask these questions?

Still too much work. Can we get an AI that writes intents for me?

AI Bubble

whats that?

Human action is inevitable. Ask Mises.

Well, Fortran is still used somewhere too

You mean everywhere. It's just hidden behind abstraction layers or Fortran libraries like BLAS/LAPACK, which are used by NumPy, R, Julia, MATLAB, Excel, TensorFlow, PyTorch (for some backends), and basically anything that involves linear algebra.