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?
I wanted to use a new codebase, but the documentation was weak and the examples focused on the fringe features instead of the style of simple use case I wanted. It’s a fairly popular project, but one most would set up once and forget about.
So I used an LLM to generate the code and it worked perfectly. I still needed to tweak it a little to fine tune some settings, but those were documented well so it wasn’t an issue. The tool saved me a couple hours of searching and fiddling.
Other times it’s next to useless, and it takes experience to know which tasks it’ll do well at and which it won’t. My coworker and I paired on a project, and while they fiddled with the LLM, I searched and I quickly realized we were going down a rabbit hole with no exit.
LLMs are a great tool, but they aren’t a panacea. Sometimes I need an LLM, sometimes ViM macros, sed or a language server. Get familiar with a lot of tools and pick the right one for the task.
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.
Same here. I never tried it to write code before but I recently needed to mass convert some image files. I didn’t want to use some sketchy free app or pay for one for a single job. So I asked chatgpt to write me some python code to convert from X to Y, convert in place, and do all subdirectories. It worked right out of the box. I was pretty impressed.
But when it works, it can save a lot of time.
I wanted to use a new codebase, but the documentation was weak and the examples focused on the fringe features instead of the style of simple use case I wanted. It’s a fairly popular project, but one most would set up once and forget about.
So I used an LLM to generate the code and it worked perfectly. I still needed to tweak it a little to fine tune some settings, but those were documented well so it wasn’t an issue. The tool saved me a couple hours of searching and fiddling.
Other times it’s next to useless, and it takes experience to know which tasks it’ll do well at and which it won’t. My coworker and I paired on a project, and while they fiddled with the LLM, I searched and I quickly realized we were going down a rabbit hole with no exit.
LLMs are a great tool, but they aren’t a panacea. Sometimes I need an LLM, sometimes ViM macros, sed or a language server. Get familiar with a lot of tools and pick the right one for the task.
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.
Same here. I never tried it to write code before but I recently needed to mass convert some image files. I didn’t want to use some sketchy free app or pay for one for a single job. So I asked chatgpt to write me some python code to convert from X to Y, convert in place, and do all subdirectories. It worked right out of the box. I was pretty impressed.
May I introduce you to the wonderful world of open source instead?