Comment by latenightcoding

This is good if front end is something you just need to get through. It's terrible if your work is moving to involve a lot of frontend - you'll never pick up the skills yourself

As the fullstacker with a roughly 65/35 split BE/FE on the team who has to review this kinda stuff on the daily, there's nothing I dread more than a backender writing FE tickets and vice versa.

Just last week I had to review some monstrosity of a FE ticket written by one of our backenders, with the comment of "it's 90% there, should be good to takeover". I had to throw out pretty much everything and rewrite it from scratch. My solution was like 150 lines modified, whereas the monstrous output of the AI was non-functional, ugly, a performance nightmare and around 800 lines, with extremely unhelpful and generic commit messages to the tune of "Made things great!!1!1!!".

I can't even really blame them, the C-level craze and zeal for the AI shit is such that if you're not doing crap like this you get scrutinized and PIP'd.

At least frontenders usually have some humility and will tell you they have no clue if it's a good solution or not, while BEnders are always for some reason extremely dismissive of FE work (as can be seen in this very thread). It's truly baffling to me

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 generating a whole array of dynamic C-dispatchers from a UI-library spec in JSON that allows parsing and rendering a UI specified in YAML. Gemini pro even spat out a YAML-dialect parser after a few attempts/fixes.

Maybe it's a function of familiarity and problems you end using the AI for.

This is exactly my experience as well. I've had agents write a bit of backend code, always small parts. I'm lucky enough to be experienced enough with code I didn't write to be able to quickly debug it when it fails (and it always fails from the first run). Like using AI to write a report, it's good for outlines, but the details are always seemingly random as far as quality.

For frontend though? The stuff I really don't specialize in (despite some of my first html beginning on FrontPage 1997 back in 1997), it's a lifesaver. Just gotta be careful with prompts since so many front end frameworks are basically backend code at this point.

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 skipped if all of its named
    // outputs have been coalesced away.
    if ! self.should_keep_instr(instr) {
      return;
    }

    // Non-dropped should have a choice.
    let instr_choice =
      choices.maybe_instr_choice(instr_ref)
        .expect("No choice for instruction");
    self.pick_map.set_instr_choice(
      instr_ref,
      instr_choice.clone(),
    );

    // Incref all named def inputs to the PIR choice.
    instr_choice.visit_input_defs(|input_def| {
      self.def_incref(input_def);
    });

    // Decref all named def inputs to the SIR instr.
    instr.visit_inputs(
      |input_def| self.def_decref(input_def, sir_graph)
    );

The actual code _I_ wrote were the comments. The savings in not having to type out the syntax is pretty big. About 80% of the time in manual coding would have been that. Little typos, little adjustments to get the formatting right.

The other nice benefit is that I don't have to trust the LLM. I can evaluate each snippet right there and typically the machine does a good job of picking out syntactic style and semantics from the rest of the codebase and file and applying it to the completion.

The snippet, if it's not obvious, is from a bit of compiler backend code I'm working on. I would never have even _attempted_ to write a compiler backend in my spare time without this assistance.

For experienced devs, autocomplete is good enough for massive efficiency gains in dev speed.

I still haven't warmed to the agentic interfaces because I inherently don't trust the LLMs to produce correct code reliably, so I always end up reviewing it, and reviewing greenfield code is often more work than just writing it (esp now that autocomplete is so much more useful at making that writing faster).

It works with low-level C/C++ just fine as long as you rigorously include all relevant definitions in the context window, provide non-obvious context (like the lifecycle of some various objects) and keep your prompts focused.

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.

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 specifically hired to be a "wildcard" in their usual operations. Which is too say, maybe 10 of us in a company of 3000 know what we're doing regarding software (but that's being generous because I don't really have visibility into half of the company). So, that means 99.7% of the company doesn't have the experience necessary to tell what good software development looks like.

The stuff the people using the AI are putting out is... better than what the MilOps analysts pressed into writing Python-scripts-with-delusions-of-grandeur were doing before, but by no means what I'd call quality software. I have pretty deep experience in both back end and front end. It's a step above "code written by smart people completely inexperienced in writing software that has to be maintained over a lifetime", but many steps below, "software that can successfully be maintained over a lifetime".

Same. It’s amazing for frontend.