perlgeek
For example, I have linux-image-generic installed, description "Generic Linux kernel image This package will always depend on the latest generic kernel image available." and then "Depends: linux-image-6.8.0-147-generic, linux-modules-extra-6.8.0-147-generic, linux-firmware, intel-microcode, amd64-microcode"
Why isn't there linux-image or linux-modules-* listed as an affected package?
First I thought it was the source package / binary package distinction, but e.g. linux-image-7.0.0-38-generic says "Source: linux-signed-hwe-7.0", and linux-signed-hwe-7.0 isn't listed as an affected package either.
So what's going on here?
Seriously, they have professionalized attacks on crypto infra to a degree that whenever you hear about a massive heist, you can be pretty sure it's North Korea.
Is this similar?
I wonder why gitlab even has its own regex compiler. Or is this a vulnerability in an underlying library or programming language?
* I use LLMs to resolve the more annoying merge conflicts. It's not super hard work, but I don't like it, and I don't think I learn much from doing it.
* I implement a feature by hand, write some basic tests, and tell the AI to review it and write tests for cases that I missed (or was too lazy to do, most of the time)
* For 80% of the bugs that I fix, I have a pretty good idea going in where the bug might be. In the other cases, I ask the LLM to generate a list of hypotheses what's wrong. Often (but not always) it turns out one of the hypotheses was right.
* Sometimes it's not so easy to figure out how to trigger a bug in a test case. Haven't done it yet, but I plan to ask LLMs to experiment to try to do that for me.
* I use LLMs to generate tools that I wouldn't otherwise write (not high enough priority, too lazy, ...). For example, we had a rabbitmq connector that occasionally kept getting stuck without reconnecting in rare patterns of network interruptions. It wasn't really high priority for us (rare, easy to work around by restarting the service), so I didn't want to spend days of engineering on it. I asked Claude to write me a test harness that reproduced the issue, and it did. Having a bunch of automatically runnable scenarios made the actual fix not too hard.
* Sometimes, I have some software design problems where I feel I have a pretty good solution, but something about it feels off. Talking this through with an LLM often helps to refine or change the approach.
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This approach to AI usage doesn't make me 10x more productive, but I do think it's a net benefit both for code quality and my enjoyment of the job. I don't know if, in a few months or years from now, I'll still be competitive like this, but right now, I'm doing fine.
The more types of potential errors your compiler catches, the fewer unit tests you need. Haskell needs fewer (and potentially different) tests than Perl or Python.
The other thing where unit tests really shine is testing a complex algorithm. If you think of a problem with a very well-defined output (like sorting, searching, some types of computation, graph algorithms, geometry problems etc.), then it's pretty easy to come up with a stable API and thus stable tests. I'm pretty convinced that unit tests give you more bang for the buck than plain-English test plans that you need to spend tokens on to evaluate.
But now it is integrated into the charting / medical records software that here clinic uses, and now it saves her lots of work, and even flags discrepancies between things her patients said and the other medical records.
This illustrates that for the average user, AI works if it's properly integrated into software. For most people, saying "look, here's AI, it can do everything that you ask it for" isn't really practical. It's not even practical for me, a power-user and programmer, because I want my AI usage be subject to constraints (sovereignty, well-defined scopes what it's able to do). Doing that all by myself sounds tedious and challenging.
So for most people, AI is a building block, that hopefully will be used to build tools they actually enjoy using. Don't expect everybody to build their own tools, even though they possibly could, using AI.
Opinions don't necessarily equate with a desire to remove them, there could also be (dis)-incentives like taxes, removal of subsidies etc.
If it's fed to farm animals for meat, that reduces its efficiency by an order of magnitude. There's still plenty of discussion to be had if that is water well spent. It probably depends on a lot of factors.
Why would it be any different for inference? If we believe OP, it'll just become part of regular compute infra, and thus part of the renting-out-compute business model.
I think it's an open question if the current generation of inference investment will pan out, but in the long term, there'll be a balance between investment cost and margin, just as in every other industry.
I'd argue that most human classification is pre-conscious / System One. You see a table, you recognize it as a table without asking yourself "is this a table?"
I guess their marketing implies that it moves classification into system one response time.
Why is that a problem? Just don't read her stuff if you're not interested.
There are lots of people talking all the time about stuff I'm not interested in.
I've noticed that my own mind always searches for corner cases, error conditions and failure modes. This can be super helpful when planning new features (or explaining to stakeholders that certain conditions must be met first, when they want some new features), but when it extends to other things besides software, it can be psychologically unhealthy.
When c is approximately 10^7 times lower, so is the wavelength (if we keep the frequency the same), so now-visible light would have wavelengths far shorter than an atom or a molecule, and probably couldn't be easily detected by biological means.
You could say: sure, maybe we'll just see light at lower frequency. Maybe that's true, but the energy of a single photon is E = hf, where h is the Planck constant. So if we assume much lower-frequency photons, they have much lower energy, and thus become harder to detect.
Lots of other things could be different, like chemistry, heat conduction etc.
At a height of roughly 1.8, at c = 5km/h, light would take 0.1 seconds to travel from head to toe. Nerve signals are much slower than c, so human bodies likely wouldn't exist as we know them.
The list of potential implications to consider is fascinating, endless and maybe even a bit overwhelming :-)
Heck, paying with card is the default, if you do pay cash, the cashier sometimes has to cancel the card payment before accepting the cash.
It also talks about uses cases where such things are allowed to be recorded, and forbids all others.
> IMO it is much more practical to regulate and penalize the activity of using such a network than to preempt its creation.
Hard disagree. If such a network is created, foreign adversaries (or domestic ones that don't abide by the laws) can hack them. Has happened quite a number of times.
And that's the problem, because if there isn't an expectation of privacy, and we record everything that's "in public", we land in a dystopic hellscape.
The problem is really that "privacy" vs. "public" isn't a binary choice. Yes, I expect people to see me in public if they happen to walk by. That's quite different from every movement being recorded and made searchable. Most privacy laws don't really account for that being possible.
If a security bug is exploited in the wild, it's an n-day if it's been first exploited n days after the publication of the bug, and a zero-day if it's been exploited before or on the day of the publication.
When a bug is not yet exploited in the wild, it's just a discovery of a bug, not a zero-day.
> mRNA vaccines are also quite different. Do they modify the DNA? Of course not. So that's already very different.
And yet it took more than 30 years after the first mRNA experiments to develop a successful vaccine. Why it should be so much faster for CRISPR & Co?
The CRISPR-Cas9 gene-editing tool was developed in 2012, so I don't find it surprising that merely 14 years later, there's only one approved treatment. From discovery to approval, drug development often takes 10-15 years, and often much longer for novel techniques. So I'd say it too early to call it overhyped for treatments.
Finally, I think we'll see a lot of treatments that don't use CRISPR-Cas9, but related gene editing techniques, but it'll take another 10 to 20 years.
Take a look at https://en.wikipedia.org/wiki/MRNA_vaccine#History for how long another novel technique has been in development before it became really widespread with the mrna-based covid-19 vaccines.
If that's not what you want, you'd need something like a virus to spread it. But then you have to ask yourself: what if that virus mutates? The specialization to certain gene markers is an evolutionary disadvantage, so evolution will tend to make it lose that restriction. Ooops.
> Much like other CRISPR therapies, delivery is a critical challenge, i.e., getting the large genome-cutting enzyme to all the targeted cells efficiently.
makes me think this is in vitro so far. So, years to decades away from being available for actual treatment in humans. Still good news.