21–30 of 284 posts
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.
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.
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…
Like what if by focusing on LLMs for productivity we just reinforce old-bad habits, and get into a local maxima... And even worse, what if being stuck with current so-so patterns, languages, etc means we don't innovate in language design, tooling, or other areas that might actually be productivity wins?
I’ve been around tech for a long time. At this point, I’ve lost count of how many hype cycles I’ve seen hit the “hold on, everything sucks” stage. Generative AI is seemingly at the hold on, everything sucks stage and it’s getting repetitive.
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 black and white.
They averaged producing 47% more code on the AI tasks, but took only 20% more time. The report here biases over these considerations, but I’m left wondering: was the extra code superfluous or did this produce better structure / managed debt better? If that extra 47% of code translates to lower debt and more consistent throughput over the long term, I might take it, given how crushed projects get from debt. Anyway, it…
AI isn't very good at being concise, in my experience. To the point of producing worse code. Which is a strange change from humans who might just have a habit of being too concise, but not by the same degree.
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…
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 research and improving their ability to deliver code in the future.
Hmm...
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.
Things like "apply this known algorithm to that project-specific data structure" work really well and save plenty of time. Things that require a gut feeling for how things are organized in memory don't work unless you are willing to babysit the model.