Cthulhu_
Joined 14 years ago
Joined 14 years ago
I had Claude build me a map tool to calculate travel times from one or two points to every street in the city; not only did it do that, it added various scenarios, speed limits, usage of bus lane toggles, different visualizations based on thresholds, and a tool to scan for optimal starting points based on a configurable grid size. etc.
This overcompleteness is a mixed blessing though, in this case it was great and convenient, but if it was e.g. documentation intended for human consumption then it wouldn't be so great, and if it was a letter written to the county that they should consider another location for a fire station it'd actually be pretty ineffective.
Boring reports are also important for historians, although granted, not everything is actually important in the long run.
(this isn't a new observation / thing, see https://randsinrepose.com/archives/nadd/ originally from somewhere in the 2000s)
But now with AI it's easy to generate a lot of prose. I'm thinking for example of the merge request descriptions my colleague submits. And here's the thing, it's not even bad or incorrect - I read it thinking, if I were to really sit down for it, get into The Zone etc, I could crank this out myself.
But I don't because honestly the MR is relatively trivial and can be described in just a sentence or two. Style and accuracy aside, my current take is that LLMs write things that humans wouldn't bother with, because it's too much effort and there's not enough value.
I mean it's an MR for an enterprise library project, the chances that anyone will ever read that MR description again once it's merged is close to zero.
That said: LLMs themselves don't get that, they will read everything you tell them to with the same amount of attention. Granted, their context windows are limited (and performance declines the more you put in it), so they too will use some tools to skim and pick out the important parts, but I think that's a temporary thing. The huge amount of LLM prose will end up being consumed by other LLMs. I don't know enough about the subject whether that's a good or a bad thing though, right now I think it just adds to the noise until it becomes noise in, noise out.
But a lot (generalizing here) don't like giving up access "in case they need it". I haven't worked backend in ages, but years ago there was one developer that went to the ops guys and said "can I just ssh to production I need to check some env vars to debug my software" (or something to that degree). Ops team was good and it was a hard no - "just add some logging, you can deploy a new release quickly enough".
What's the market demand? I'm confident there are plenty of alternatives being churned out today but nobody's looking or asking for any. If only because they settled on k8s, they have it on their CV, they have certifications, and switching is a huge investment and risk.
Right now to me it feels like the innovation is in using LLMs to (finally?) clean up existing code, find vulnerabilities, do maintenance, optimize, and if necessary rewrite.
I can't afford a pay cut, and AI isn't making my job worth more.
We'll see the tools get adjusted over time to use more and more tokens, or at the very least they get optimized to use every paying customer's tokens for whatever period so that they are on the one side thinking they're getting their money's worth, or on the other that they should upgrade to a higher tier plan. Because the companies are investing trillions into thousands of datacenters and if they sit unused it'd have been a waste of money.
(the datacenters won't go unused I'm sure; any unused capacity will end up on a spot market, with cryptocurrency generation as a constant baseline. I'm pretty confident that crypto will make another small resurgence after the AI / datacenter investment bubble pops. This is a random internet comment, not financial advice)