41–50 of 284 posts
I think this for me is the most worrying: "You can see that for AI Allowed tasks, developers spent less time researching and writing code". My analogy to this is seeing people spend time trying to figure out how to change colors, draw shapes in powerpoint, rather than focus on the content and presentation. So here, we have developers now focusing their efforts on correcting the AI output, rather than doing the resear…
Earlier quoted context omitted.
I've been hacking on some somewhat systemsy rust code, and I've used LLMs from a while back (early co-pilot about a year ago) on a bunch of C++ systems code. In both of these cases, I found that just the smart auto-complete is a massive time-saver. In fact, it's more valuable to me than the interactive or agentic features. Here's a snippet of some code that's in one of my recent buffers: // The instruction should be…
What exact tool are you using for your smart auto-complete?
Here is the the methodology of the study: > To directly measure the real-world impact of AI tools on software development, we recruited 16 experienced developers from large open-source repositories (averaging 22k+ stars and 1M+ lines of code) that they’ve contributed to for multiple years. Developers provide lists of real issues (246 total) that would be valuable to the repository—bug fixes, features, and refactors t…
The sample size isn't 16 developers, it's 246 issues.
LLMs make me 10-20x more productive in frontend work which I barely do. But when it comes to low-level stuff (C/C++) I personally don't find it too useful. it just replaces my need to search stackoverflow. edit: should have mentioned the low-level stuff I work on is mature code and a lot of times novel.
This feels like a parallel to the Gell-Mann amnesia effect. Recently, my company has been investigating AI tools for coding. I know this sounds very late to the game, but we're a DoD consultancy and one not traditional associated with software development. So, for most of the people in the company, they are very impressed with the AI's output. I, on the other hand, am a fairly recent addition to the company. I was sp…
When it comes to software the entire reason maintainability is a goal is because writing and improving software is incredibly time consuming and requires a lot of skill. It requires so much skill and time that during my decades in industry I rarely found code I would consider quality. Furthermore the output from AI tools currently may have various drawbacks, but this technology is going to keep improving year over year for the foreseeable future.
I think this for me is the most worrying: "You can see that for AI Allowed tasks, developers spent less time researching and writing code". My analogy to this is seeing people spend time trying to figure out how to change colors, draw shapes in powerpoint, rather than focus on the content and presentation. So here, we have developers now focusing their efforts on correcting the AI output, rather than doing the resear…
This has been my observation too. It's a tool for the lazy.
LLMs make me 10-20x more productive in frontend work which I barely do. But when it comes to low-level stuff (C/C++) I personally don't find it too useful. it just replaces my need to search stackoverflow. edit: should have mentioned the low-level stuff I work on is mature code and a lot of times novel.
Interesting, I find the exact opposite. Although to a much lesser extent (maybe 50% boost). I ended shoehorned into backend dev in Ruby/Py/Java and don't find it improves my day to day a lot. Specifically in C, it can bang out complicated but mostly common data-structures without fault where I would surely do one-off errors. I guess since I do C for hobby I tend to solve more interesting and complicated problems like…
This entire concept hinges on AI not getting better. If you believe AI is going continue to get better at the current ~5-10% a month range, then hand waiving over developer productivity today is about the same thing as writing an article about the internet being a fad in 1999.
On the flip side, why would I use AI today if it presents no immediate benefit. Why not wait 5 years and see if it becomes actually helpful.
I think this for me is the most worrying: "You can see that for AI Allowed tasks, developers spent less time researching and writing code". My analogy to this is seeing people spend time trying to figure out how to change colors, draw shapes in powerpoint, rather than focus on the content and presentation. So here, we have developers now focusing their efforts on correcting the AI output, rather than doing the resear…
Now do a study that specifically gauges how useful an LLM (including smart tab completion) is for a frontend dev working in react/next/tailwind on everyday Jira tickets. These were maintainers of large open source projects. It's all relative. It's clearly providing massive gains for some and not as much for others. It should follow that it's benefit to you depends on who you are and what you are working on. It isn't…
As a backend dev who owns a few internal crappy frontends, LLMs have been the best thing ever. Code quality isn't the top priority, I just need to plumb some data to an internal page at BigCorp.