Faster progress was absolutely worth it. Spending years agonizing over theory to save a bit of electric would have been a massive disservice to the world.
I've been using copilot for several months. If I could figure out a way to measure its impact on my productivity, I'd probably see a single digit percentage boost in "productivity". This is not life-changing for me. And for some tasks, it's actually worse than nothing. As in, I spend time feeding it a task, and it just completely fails to do anything useful.
I've been using it for over a year I think. I don't often feed it tasks with comments so much as go about things the same as usual and let it autocomplete. The time and cognitive load saved adds up massively. I've had to go without it for a bit while my workplace gets its license in order for the corporate version and the personal version has an issue with the proxy, and it's been agonizing going without it again. I almost forgot how much it sucks having to jump to google every other minute, and it was easy to start to take for granted how much context copilot was letting me not have to hold onto in my head. It really lets me work on the problem as opposed to being mired in immaterial details. It feels like I'm at least 2x slower overall without it.
> I almost forgot how much it sucks having to jump to google every other minute
Even allowing for some hyperbole, your programming experience is extremely different from mine. Looking anything up outside the IDE, let alone via Google, is by far the exception for me rather than the rule.
I've long suspected that this kind of difference explains a lot of the difference in how Copilot is perceived.
Claiming LLMs are a massive boost for coding productivity is becoming a red flag that the claimant has a tenuous grasp on the skills necessary. Yeah if you have to look up everything all the time and you can't tell the AI slop isn't very good, you can put out code quite fast.
I don't know about you but LLMs spit out garbage nonsense frequent enough that I can't trust their output in any context I cannot personally verify the validity of.
Same here. The Cody autocomplete is so off base all the time that it’s deactivated.
I serve Cody a direct question about 1-3 times a week. Of that, it gets maybe 50% correct on the first try. I don’t bother with a second try because by then I’ve already spent the equivalent amount of time looking at the relevant library source code and/or docs would have taken.
If you're already a competent developer, I think that's a reasonable expectation of impact on productivity. I think the 'life-changing' part comes in helping someone get to the point of building things with code where before they couldn't (or believed they couldn't). It does a lot better job of turning the enthusiasts and code-curious into amateurs vs. empowering professionals.
> turning the enthusiasts and code-curious into amateurs vs. empowering professionals.
I'm firmly in #2. My other comment goes over how.
I'm intrigued to see how devs in #1 grow. One might be wary those devs would grow into bad habits and not thinking for themselves, but it might be a case of the ancient Greek rant against written books hindering memorization. Could be that they'll actually grow to be even better devs unburdened by time wasted on trivial details.
If you are in maintenance mode your visits to Copilot will be rare. If you are building greenfield, use goes through the roof. All those test cases, nevermind all the POC and framework scaffolding and other boilerplate that is now completely unacceptable as a use of developer time.
I'm building "greenfield". I still use it at least daily, but the benefit just struggles to outweigh the cost of invoking it. Maybe I don't understand how to use it.
It really depends on what you are doing and what tech you are using. I use it to teach me or build out ideas quickly or solve for complex issues. Mostly these days I use it as a memory aid or to bounce ideas off it. In my job I have to move quickly and stay focused as I'm driving improvements to a tech stack that reaches across four verticals, each having their own quirks and tech stacks. It's great for jogging my memory and helping to flush out ideas and approaches that I then bounce off the dev teams. Super helpful.
"Even just in programming" the jury is still out. None of my coworkers using these are noticeably more productive than the ones who don't. Outside of programming no one gives a shit except scammers and hype chasers.
The people writing articles for journals that aggregate and approximate other sources are in mortal terror of LLMs. Likewise graphic designers and anyone working in (human language) translation.
I don't fear that LLMs are going to take my job as a developer. I'm pretty sure they mark a further decrease in the quality and coherence of software, along with a rapid increase in the quantity of code out there, and that seems likely to provide me with reliable employment forever. I'm basically employed in fixing bugs that didn't need to exist in the first place and that seems to cover a lot of software dev.
They're not scared of LLMs because of anything about LLMs. It's just that everyone with power is publicly horny to delete the remaining middle class jobs and are happy to use LLMs as a justification whether it can functionally replace those workers or not. So it's not that everyone has evaluated chatgpt and cannily realized it can do their job, they're just reading the room.
Are you sure it’s a life changing productivity booster? Sometimes I look at my projects and wonder how would I explain it to an LLM what this code should have done if it didn’t exist yet. Must be a shitton of boilerplate programming for copilot to be a life-changing experience.
Overviews aren’t code though. In code, for me, they don’t pass 80/20 tests well enough, sometimes even on simple cases. (You get 50-80% of an existing function/block with some important context prepended and a comment, let it write the rest and check if it succeeds). It doesn’t mean that LLMs are useless. Or that I am antillamist or a denier - I’m actually an enthusiast. But this specific claim I hear often and don’t find true. Maybe true for repetitive code in boring environments where typing and remembering formats/params over and over is the main issue. Not in actual code.
If I paste the actual non-trivial code, it starts deviating fast. And it isn’t too complex, it’s just less like “parallel sort two arrays” and more like “wait for an image on a screenshot by execing scrot (with no sound) repeatedly and passing the result to this detect-cv2.py script and use all matching options described in this ts type, get stdout json as in this ts type, and if there’s a match, wait for the specified anim timeout and test again to get the settled match coords after an animation finishes; throw after a total timeout”. Not a rocket science, pretty dumb shit, but right there they fall flat and start imagining things, heavily.
I guess it shines if you ask it to make an html form, but I couldn’t call that life-changing unless I had to make these damn forms all day.
If 20 mins of informations can legitimately be condensed into 20 seconds, it sounds like the original wasn't worth reading in the first place. Could have skipped the llm entirely.
I upvoted you, because I think you have a valid point. The tone is unnecessarily aggressive though.
Effective and information-dense communication is really hard. That doesn't mean we should just accept the useless fluff surrounding the actual information and/or analysis. People could learn a lot from the Ignoble Prize ceremony's 24/7 presentation model.
Sadly, it seems we are heading towards a future where you may need an LLM to distill the relevant information out of a sea of noise.
> it sounds like the original wasn't worth reading in the first place
But if that's the only place that contained the information you needed, then you have no choice.
There's a lot of material out there that is badly written, badly organized, badly presented. LLM's can be a godsend for extracting the information you actually need without wasting 20 minutes wading through the muck.
My experience with overviews is that they are often subtly or not so subtly inaccurate. LLMs not understanding meaning or intent carries risk of misrepresentation.
And here you're assuming that making software engineers more productive would be a service to the world. I think the jury is out on that one as well. At least for the majority of software engineering since 2010.
Yes, a large amount for - in the grand scheme of things - a short period of time (i.e., a quantity of energy usage in an intense spike that will be dwarfed by energy usage over time) can accurately be described as “a bit”.
Of course, the impact is that AI will continue to become cheaper to use, and induced demand will continue the feedback loop driving the market as a result.