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Where's the shovelware? Why AI coding claims don't add up

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Re: Where's the shovelware? Why AI coding claims don't add up

#261
post #253
post #115

Earlier quoted context omitted.

> LLMs can be automated to do busy work and although they may take longer in terms of clock time than a human, the work is effectively done in the background. What is this supposed busy work that can be done in the background unsupervised? I think it's about time for the AI pushers to be absolutely clear about the actual specific tasks they are having success with. We're all getting a bit tired of the vagueness and h…

No, you've got it backwards. If anything, people are getting tired of comments like yours.

Nope!

Re: Where's the shovelware? Why AI coding claims don't add up

#262

These claims wouldn't matter if the topic weren't so deadly serious. Tech leaders everywhere are buying into the FOMO, convinced their competitors are getting massive gains they're missing out on. This drives them to rebrand as AI-First companies, justify layoffs with newfound productivity narratives, and lowball developer salaries under the assumption that AI has fundamentally changed the value equation. This is my…

Today a friend of mine connected me with his uncle who wanted to develop an MVP for his company. He claimed he didn’t want to distract his engineers on this project, but that “it shouldn’t take you more than 5hs to do this with vibe coding”. I promptly declined, if it takes 5hs why are you reaching out to me? It would take more than 5hs just to bring me into the loop of what you want vs your own engineers. If vibe co…

[flagged]

Re: Where's the shovelware? Why AI coding claims don't add up

#263

Earlier quoted context omitted.

> My manager told me that the time to deliver my latest project was cut to 20% of the original estimate That's insane. Who the hell pulls a number out of their ass and declares it the new reality? When it doesn't happen, he'll pin the blame on you, but everyone else above will pin the blame on him . He's the one who will get fired. Laying off unnecessary developers is the answer if LLMs turn out to make us all so muc…

> That's insane. Who the hell pulls a number out of their ass and declares it the new reality? Product and Sales?

Why don't we cut sales commissions by 30% and expect double the sales now. Surely LLMs will make them that much more effective and they still make more.

Re: Where's the shovelware? Why AI coding claims don't add up

#264
post #253

Earlier quoted context omitted.

No, you've got it backwards. If anything, people are getting tired of comments like yours.

Nope!

They are. There's no vagueness. It's rare to see people that still don't believe LLMs can do anything at all nowadays. Most other naysayers have moved on to the much more relevant question on whether the perceived productivity gains are real or not.

Re: Where's the shovelware? Why AI coding claims don't add up

#265

Great angle to look at the releases of new software. I, too, thought we'd see a huge increase by now. An alternative theory is that writing code was never the bottleneck of releasing software. The exploration of what it is you're building and getting it on a platform takes time and effort. On the other hand, yeah, it's really easy to 'hold it wrong' with AI tools. Sometimes I have a great day and think I've figured i…

This is the answer. Programming was never the bottleneck in delivering software, whether free-range, organic, grass-fed human-generated code or AI-assisted. AI is just a convenient excuse to lay off many rounds of over-hiring while also keeping the door open for potential investors to throw more money into the incinerator since the company is now “AI-first”.

The point was that "programming" is far more than just "writing code".

Re: Where's the shovelware? Why AI coding claims don't add up

#266
post #19

Most of it doesn't exist beyond videos of code spraying onto a screen alongside a claim that "juniors are dead." I think the "why" for this is that the stakes are high. The economy is trembling. Tech jobs are evaporating. There's a high anxiety around AI being a savior, and so, a demi-religion is forming among the crowd that needs AI to be able to replace developers/competency. That said: I personally have gotten imp…

By "knowing what you're doing" do you mean "have enough experience to it by hand", "have experience with a specific AI tool and its limitations" or a combination?

You don't need sotware engineering to build successful software, until you do.

In my experience you don't need to know a whole lot about LLM's to work them. You need to know that everything they spit out is potential garbage, and if you can't tell the good from the garbage then whatever you're using them for is going to be terrible. In terms of software terrible is fine for quite a lot of systems. One of the first things I build out of university in the previous millennium is still in production today and it's horrible. It's inefficient, horribly outdated since it hasn't been updated ever. It runs 10 times a day and at least 1 of them will need to automatically restart itself because it failed. It's done it's job without the need for human intervention for the past many decades though. I know because one of my old colleagues still works there. It could've been improved, but the inefficiency cost over all those years is probably worth about two human hours, and it would likely take quite a while to change it. A lot of software is like that, though a lot of it doesn't live for so long. LLM's can absolutely blast that sort of thing. It's when the inefficiency cost isn't less than a few human hours that LLM's become a liability if you don't know how to do the engineering.

I use LLM's to write a lot of the infrastructure as code we use today. I can do that because I know exactly how that should be engineered. What the LLM can do that I can't, is that it can spit out the k8s yaml for an ingress point with 200 lines of port settings in a couple of seconds. I've yet to have it fail, probably because those configurations are basically all the same depending on the service. What a LLM can't do, however, is write the entire yaml config.

Similarily it can build you a virtual network with subnets in bicep based on a couple of lines of text with address prefixes. At the sametime it couldn't build you a reasonable vnet with subnets if you asked it to do it from scractch. That doesn't mean it can't build you one that works though, it's just that you're likely going to claim 65534 ip addresses for a service which uses three.

Re: Where's the shovelware? Why AI coding claims don't add up

#267
post #117
post #73

The answer is that we're making it right now. AI didn't speed me up at all until agents got good enough, which was April/May of this year. Just today I built a shovelware CLI that exports iMessage archives into a standalone website export. Would have taken me weeks. I'll probably have it out as a homebrew formula in a day or two. I'm working on an iOS app as well that's MUCH further along than it would be if I hand-r…

> AI didn't speed me up at all until agents got good enough, which was April/May of this year. That was 5 months ago, which is 6 years in 10x time.

> That was 5 months ago, which is 6 years in 10x time.

That's some pretty bad math.

But yes, it isn't making software get made 10x faster. Feel free to blow that straw man down (or hype influencer, same thing.)

Re: Where's the shovelware? Why AI coding claims don't add up

#268
post #264

Earlier quoted context omitted.

Nope!

They are. There's no vagueness. It's rare to see people that still don't believe LLMs can do anything at all nowadays. Most other naysayers have moved on to the much more relevant question on whether the perceived productivity gains are real or not.

> It's rare to see people that still don't believe LLMs can do anything at all nowadays.

I don't think the original comment you responded to made this specific point.

Re: Where's the shovelware? Why AI coding claims don't add up

#269

Earlier quoted context omitted.

You’re assuming how i would respond before i even respond. Please allow inquiries to happen naturally without polluting the thread with meritless cynicism.

With all due respect, with a response like "What AI coding tools/models have you been using?" to a complaint that AI tools just don't seem to be effective, what difference does a reply to that even make? If your experience makes you believe that certain tools are particularly good--or particularly bad--for the tasks at hand, you can just volunteer those specifics. FWIW, my own experiences with AI have ranged from med…

We're still in the early days of LLMs. ChatGPT was only three years ago. The difference it makes is that without details, we don't know if someone's opinion is still relevant, because of how fast things have moved since the original GPT-3 release of ChatGPT. If someone half-assed an attempt to use the tools a year ago, and hasn't touched them since, and is going around still commenting about the number of R's in strawberry, then we can just ignore them and move on because they're just being loudmouths who need everyone else to know they don't like AI. If someone makes an honest attempt, and there's some shortcoming, then that can be noted, and then the next version coming out of the AI companies can be improved.

But if all we have to go on is "I used it and it sucked" or "I used it and it was great", like, okay, good for you?

Re: Where's the shovelware? Why AI coding claims don't add up

#270
I'm not sure what to make of these takes because so many people are using such an enormous variety of LLM tooling in such a variety of ways, people are going to get a variety of results.

Let's take the following scenario for the sake of argument: a codebase with well-defined AGENTS.md, referencing good architecture, roadmap, and product documentation, and with good test coverage, much of which was written by an LLM and lightly reviewed and edited by a human. Let's say for the sake of argument that the human is not enjoying 10x productivity despite all this scaffolding.

Is it still worthwhile to use LLM tooling? You know what, I think a lot of companies would say yes. There are way too many companies whose codebases lack testing and documentation, that are too difficult to on-board new engineers and have too high risk if the original engineers are lost. The simple fact that LLMs, to be effective, force the adaptation of proper testing and documentation is a huge win for corporate software.

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