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*bad Devs
Always look on the official repository. Not just to see if it exists, but also to make sure it isn’t a fake/malicious one
*bad Devs
Or devs who don’t give a shit. Most places have a lot of people who don’t give a shit because the company does not give a shit about them either.
What’s the diff between a bad dev and a dev that doesn’t care? Either way, whether ist lack of skill or care, a bad dev is a bad dev at the end of the day.
I can be good at a trade, but if I’m working for a shit company with shit pay and shit treatment, they’re not going to get my best work.
You get out what you put in, that’s something employers don’t realise.
Nah they realize but all the laws are set to fuck us over not them. They just don’t care.
Garbage in, garbage out, after all
The difference is whether the fault for the leak of your personal data rests with the worker who was incompetent, or the employer who didn’t pay for proper secure software.
I say fault lies not with only one, but both.
Depends on the case TBH. If devs barely have time and are constantly crunching due to mismanagement, or are extremely disengaged due to mismanagement, I wouldn’t fault them.
Usually it’s the lacking processes, though. There are ways to make sure this doesn’t happen, and it doesn’t depend on the individual, but always the organization.
A good dev would unionize their workplace and push back. A dev who doesn’t care and just clocks on bad work because their boss sucks is not a good dev. Fight back.
Yeah sure, because everyone has the skills, time, energy and safety required to unionize a shitty workplace they only go to to be able to pay their rent.
Dev jobs are not hard to come by and they pay very well. It’s not like being a day laborer or something where we are scraping the bottom of the barrel. Have a little courage.
Looks like your mind is set. I wish you a good day and I hope you pick up a little more empathy along your way, and I hope some day you’ll get that a lot of people feel trapped where they are.
You’d be surprised how well someone who wants to can camouflage their package to look legit.
True. You can’t always be 100% sure. But a quick check for download counts/version count can help. And while searching for it in the repo, you can see other similarly named packages and prevent getting hit by a typo squatter.
Despite, it’s not just for security. What if the package you’re installing has a big banner in the readme that says “Deprecated and full of security issues”? It’s not a bad package per say, but still something you need to know
Oh, TIL
Edit: *YourWeb
Yeah, I’m confused on what the intent of the comment was. Apart from a code review, I don’t understand how someone would be able to tell that a package is fake. Unless they are grabbing it from a. Place with reviews/comments to warn them off.
The official repositories often have no useful oversight either. At least once a year, you’ll hear about a malicious package in npm or PyPI getting widespread enough to cause real havoc. Typosquatting runs rampant, and formerly reputable packages end up in the hands of scammers when their original devs try to find someone to hand them over to.
Can we fucking stop anthropomorphising software?
“Hallucinate” is the standard term used to explain the GenAI models coming up with untrue statements
They don’t come up with any statements, they generate data extrapolating other data.
So just like human brains?
in terms of communication utility, it’s also a very accurate term.
when WE hallucinate, it’s because our internal predictive models are flying off the rails filling in the blanks based on assumptions rather than referencing concrete sensory information and generating results that conflict with reality.
when AIs hallucinate, it’s due to its predictive model generating results that do not align with reality because it instead flew off the rails presuming what was calculated to be likely to exist rather than referencing positively certain information.
it’s the same song, but played on a different instrument.
when WE hallucinate, it’s because our internal predictive models are flying off the rails filling in the blanks based on assumptions rather than referencing concrete sensory information and generating results that conflict with reality.
Is it really? You make it sound like this is a proven fact.
Is it really? You make it sound like this is a proven fact.
I believe that’s where the scientific community is moving towards, based on watching this Kyle Hill video.
Here is an alternative Piped link(s):
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
I know I’m responding to a bot, but… how does a PipedLinkBot get “Kyle Hill” wrong to “Kyke Hill”? More AI hallucinations?
Op has a pencil in the top right, looks like it was edited
What standard is that? I’d like a reference.
It’s as much as “Hallucination” as Tesla’s Autopilot is an Autopilot
https://en.m.wikipedia.org/wiki/Tesla_Autopilot
I don’t propagate techbro “AI” bullshit peddled by companies trying to make a quick buck
Also, in the world of science and technology a “Standard” means something. Something that’s not a link to a wikipedia page.
It’s still anthropomorphising software and it’s fucking cringe.
I just want an LLM with a reasonable context window so we can actually write real working packages with it.
The demos look great, but it’s always just around 100 lines of code, which is beginner level. The only use case right now is fake packages.
Just use the AI Horde. iirc our standard is like 4K context and some people host up to 8K. Here’s a frontend
I have tried the copilot integration in edge out of curiosity, and if you feed the ai the context of the page the response can be useful. There is a catch, tho:
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when opening a document the accepted formats are html, txt, pdf. The documentation of a software package can be summarized but thr source will be the context of the page and not a web search, which is good in this casr
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when generating new information, the model can be far too sintethic, cutting out potentially useful informations.
I still think you need to read the documentation yourself, maybe using the AI integration only when you need a general idea of the document.
What I do is first reading the summary of the documebt by bullet point, than reading the pdf file as a whole. By the time I do so, the LLM has given enough of a structure to facilitate my readings…
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Claude is 200k
those are tokens not lines of code…
Yeah sorry, I thought that was clear. It’s how context is measured.
I use it for writing functions a lot, tell it the inputs and desired outputs it’ll normally make what i want. Recently gpt has got good at continuing where it left off too.
I’m not particularly interested. Some on my team are playing with it, but I honestly don’t see much point since they spend more time fixing the generated code than they would writing it.
And I don’t think it’ll ever really work well (in the near-ish future) for the most common type of dev work: fixing bugs and making small changes to existing code.
It would be awesome if there was some kind of super linter instead. I spend far more time reading code than writing it, so if it can catch bugs, that would be interesting.
In my experience with Codeium, it sometimes works ok for three or four lines of code at once. I’ve actually had a few surprises where it nailed what I was going for where I didn’t expect it. But most of the time, it’s just duplicating code from elsewhere in the same file, which usually doesn’t make sense.
It’s also pretty good for stuff where I’d usually build some exotic regex to search/replace (or do it manually, because it’d take longer to come up with the expression), like transforming an enum into a switch construct for its members, or mapping said enum to a string of the member’s name.
This is very far from taking over my job, though. I’d love to be more of a conductor than the guy playing all instruments in the orchestra at once.
To each their own of course. It just seems like the productivity gains are perceptual, not actual.
For an enum to a switch, I just copy the enum values and run a regex on those copied lines. Both would take me <30s, so it’s a wash. That specific one would be trivial with most IDEs as well, just type “switch (variable) {” and it could autocomplete an exhaustive switch, all without LLMs.
Then again, I’m pretty old school. I still use vim as my editor (with language server plugins), and I’m really comfortable with those kinds of common tasks. I’m only going to bother learning to use the LLM if it’s really going to help (e.g. automate writing good unit tests).
Sometimes those things are way more complex, for example when it’s about matching over a string rather than an enum to convert it into an enum. Typing out a regex would take me maybe 10mins or more, and with the LLM I can just describe roughly what I want (since it knows the language, I don’t have to explain it in detail, just something like “make this into a switch statement” is sufficient usually).
10mins at a time really adds up over a full work day.
This is the best summary I could come up with:
In-depth Several big businesses have published source code that incorporates a software package previously hallucinated by generative AI.
Not only that but someone, having spotted this reoccurring hallucination, had turned that made-up dependency into a real one, which was subsequently downloaded and installed thousands of times by developers as a result of the AI’s bad advice, we’ve learned.
He created huggingface-cli in December after seeing it repeatedly hallucinated by generative AI; by February this year, Alibaba was referring to it in GraphTranslator’s README instructions rather than the real Hugging Face CLI tool.
Last year, through security firm Vulcan Cyber, Lanyado published research detailing how one might pose a coding question to an AI model like ChatGPT and receive an answer that recommends the use of a software library, package, or framework that doesn’t exist.
The willingness of AI models to confidently cite non-existent court cases is now well known and has caused no small amount of embarrassment among attorneys unaware of this tendency.
As Lanyado noted previously, a miscreant might use an AI-invented name for a malicious package uploaded to some repository in the hope others might download the malware.
The original article contains 1,143 words, the summary contains 190 words. Saved 83%. I’m a bot and I’m open source!
Yeah, had that on my very first attempt at using it.
It used a component that didn’t exist. I called it out and it went “you are correct, that was removed in <older version>. Try this instead.” and created an entirely new set of bogus components and functions. This cycle continued until I gave up. It knows what code looks like, and what the excuses look like and that’s about it. There’s zero understanding.
It’s probably great if you’re doing some common homework (Javascript Fibonacci sequence or something) or menial task, but for anything that might reach the edges of its “knowledge”, it has no idea where those edges may lie so just bullshits.
Lie… no hallucinate…they lie and make shit up… just like a real hooman!! :))
I’m honestly starting to get tired about “people confuses advanced chatbot with Jarvis and bad things happen”.
Specially when it’s shitty/lazy devs that don’t code review.
