How are you using new AI technology? Maybe you're only deploying things like ChatGPT to summarize long texts or draft up mindless emails. But what are you losing by taking these shortcuts? And is this tech taking away our ability to think?
But we only need it because Google Search has been rotted out by the decision to shift from accuracy of results to time spent on the site, back in 2018. That, combined with an endlessly intrusive ad-model that tilts so far towards recency bias that you functionally can’t use it for historical lookups anymore.
LLMs are a great tool
They’re not. LLMs are a band-aid for a software ecosystem that does a poor job of laying out established solutions to historical problems. People are forced to constantly reinvent the wheel from one application to another, they’re forced to chase new languages from one decade to another, and they’re forced to adopt new technologies without an established best-practice for integration being laid out first.
The Move Fast And Break Things ideology has created a minefield of hazards in the modern development landscape. Software development is unnecessarily difficult and overly complex. Proprietary everything makes new technologies too expensive for lay users to adopt and too niche for big companies to ever find experienced talent to support.
LLMs are the breadcrumb trail that maybe, hopefully, might get you through the dark forest of 60 years of accumulated legacy code and novel technologies. They’re a patch on a patch on a patch, not a solution to the fundamental need for universally accessible open-sourced code and well-established best coding practices.
People are forced to constantly reinvent the wheel from one application to another, they’re forced to chase new languages from one decade to another, and they’re forced to adopt new technologies without an established best-practice for integration being laid out first.
The problem with the open source best coding practices ivory tower is that it’s small, and short, and virtually lost in the sea of schlocky trees surrounding it.
we only need it because Google Search has been rotted out
Not entirely. AI can do a great job pulling data from multiple sources and condensing into an answer. So even if search was still good, instead of hitting several sites and putting together a solution, I can hit one.
reinvent the wheel
That depends on how you use it. I use it to find relevant, existing libraries and provide me w/ examples on how to use it. If anything, it gets me to reinvent the wheel less.
It can certainly be used naively to get exactly what you’re talking about, and that’s what’s going to happen w/ inexperienced users, such as college students. My point is that, like power tools, it can be a great tool in an experience hand, and it can completely ruin the user if they’re inexperienced.
The tendency of code-generating large language models (LLMs) to produce completely fictitious package names in response to certain prompts is significantly more widespread than commonly recognized, a new study has shown.
The format of the answer came in the blurb under the link
Sure, and that works really well if I just need a quick fact check. I use DDG and use that feature a ton.
But that doesn’t work when more context is needed, like in a comparison. I find myself clicking through and skimming a dozen pages, and with an LLM I end up only needing 3-4 pages after reading its summary to confirm what it said.
AI Code Tools Widely Hallucinate Packages
Sure, which is why I always verify things like that. I ask it to compare popular libraries that accomplish a task, then look for evidence that my preferred option does what I want (issues on the project page) and is actively maintained (recent commits, multiple active contributors, etc). The LLM is just there to narrow the search space and give me things to look for.
To do that with regular search would take a bit longer since I’d need to compare each library to each other to find relevant blogs and whatnot. So even if search worked better, it would still take longer.
Sometimes it breaks down and I go back to my old method, but it’s usually worth a shot.
I use LLMs a lot less than my coworkers, but I do use them periodically when I think it’ll be useful. I’ve been a dev for a long time (10+ years), so I find I usually know where to look already. I discourage our junior devs from relying on it too much and encourage our senior devs to give it a shot.
But we only need it because Google Search has been rotted out by the decision to shift from accuracy of results to time spent on the site, back in 2018. That, combined with an endlessly intrusive ad-model that tilts so far towards recency bias that you functionally can’t use it for historical lookups anymore.
They’re not. LLMs are a band-aid for a software ecosystem that does a poor job of laying out established solutions to historical problems. People are forced to constantly reinvent the wheel from one application to another, they’re forced to chase new languages from one decade to another, and they’re forced to adopt new technologies without an established best-practice for integration being laid out first.
The Move Fast And Break Things ideology has created a minefield of hazards in the modern development landscape. Software development is unnecessarily difficult and overly complex. Proprietary everything makes new technologies too expensive for lay users to adopt and too niche for big companies to ever find experienced talent to support.
LLMs are the breadcrumb trail that maybe, hopefully, might get you through the dark forest of 60 years of accumulated legacy code and novel technologies. They’re a patch on a patch on a patch, not a solution to the fundamental need for universally accessible open-sourced code and well-established best coding practices.
I feel this.
The problem with the open source best coding practices ivory tower is that it’s small, and short, and virtually lost in the sea of schlocky trees surrounding it.
Not entirely. AI can do a great job pulling data from multiple sources and condensing into an answer. So even if search was still good, instead of hitting several sites and putting together a solution, I can hit one.
That depends on how you use it. I use it to find relevant, existing libraries and provide me w/ examples on how to use it. If anything, it gets me to reinvent the wheel less.
It can certainly be used naively to get exactly what you’re talking about, and that’s what’s going to happen w/ inexperienced users, such as college students. My point is that, like power tools, it can be a great tool in an experience hand, and it can completely ruin the user if they’re inexperienced.
Google could already do that. The format of the answer came in the blurb under the link, pertinent to the search.
AI Code Tools Widely Hallucinate Packages
Sure, and that works really well if I just need a quick fact check. I use DDG and use that feature a ton.
But that doesn’t work when more context is needed, like in a comparison. I find myself clicking through and skimming a dozen pages, and with an LLM I end up only needing 3-4 pages after reading its summary to confirm what it said.
Sure, which is why I always verify things like that. I ask it to compare popular libraries that accomplish a task, then look for evidence that my preferred option does what I want (issues on the project page) and is actively maintained (recent commits, multiple active contributors, etc). The LLM is just there to narrow the search space and give me things to look for.
To do that with regular search would take a bit longer since I’d need to compare each library to each other to find relevant blogs and whatnot. So even if search worked better, it would still take longer.
Sometimes it breaks down and I go back to my old method, but it’s usually worth a shot.
I use LLMs a lot less than my coworkers, but I do use them periodically when I think it’ll be useful. I’ve been a dev for a long time (10+ years), so I find I usually know where to look already. I discourage our junior devs from relying on it too much and encourage our senior devs to give it a shot.