151–160 of 490 posts
You know, typing fast and accurately is kind of important.
The new speed skill that developers now need is speed reading. LLMs just make copious amounts of output (from tests, documentation, diagnostics). They also produce code so quickly that a skill for focusing on weak points is so important.
I think when LLMs first came out people thought they could just say something like, "Make a Facebook clone". But now we're realizing we need to be more exact with our requirements and define things better. That has always been the bottle neck in software. When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored,…
In what I've seen, tickets are much richer in detail now because PMs are using AI (connected to the codebase itself, like Claude Code or Codex) to fill out a template as to what and why the problem is (ie X field exists in the backend not frontend), how and where to get any data (query the backend), and what acceptance criteria is needed (frontend should have the field exposed and "submit" should push the field's dat…
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In what I've seen, tickets are much richer in detail now because PMs are using AI (connected to the codebase itself, like Claude Code or Codex) to fill out a template as to what and why the problem is (ie X field exists in the backend not frontend), how and where to get any data (query the backend), and what acceptance criteria is needed (frontend should have the field exposed and "submit" should push the field's dat…
lol Just lol. Is this what you guys mean by productivity boost? Comical. LLM’s aren’t all that great - it’s more that most orgs are horribly inefficient. Like it’s amazing how bad they are. That’s why Elon succeeded with spacex - he saw how horrible inefficient the industry was. And used that thinking to take a gamble and it’s paid off.
Considering that that’s been a running complaint for like 50 years, it doesn’t seem like project management is going to get better on its own at this point. So, yes, an LLM does represent a productivity boost in that area.
This article assumes that AI only has an impact on the development phase which is certainly not true. It can speed up every part of the step. Including ideation, legal, documentation, development, and deployment. Ideation: Throw ideas back & forth, cross reference with knowledge bases, generate design documents. Documentation: Generate large parts of docs. Development: Clear. Deployment: Generate deployment manifests…
In fact, these disagreements and disbeliefs create opportunities and salients in the market.
This article assumes that AI only has an impact on the development phase which is certainly not true. It can speed up every part of the step. Including ideation, legal, documentation, development, and deployment. Ideation: Throw ideas back & forth, cross reference with knowledge bases, generate design documents. Documentation: Generate large parts of docs. Development: Clear. Deployment: Generate deployment manifests…
This post makes it sound like an engineers role is only the collection and filling of feature gaps, but leaves completely out that an engineer is also responsible for the feasibility of a feature. If you get a request for a feature, but you are aware of the current system's limitations, it is your job do come up with a solution which fits into the business sides given frame. But nowadays engineers have been so much d…
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The PMs validate it, why do you think they don't read over it to make sure it fits what they want? You might say "well they're lazy, look why they didn't write enough detail to start off with" but for lots of people, reviewing something to make sure it's close to what they want and then tweaking it is much easier than writing it from scratch. It's the equivalent of writer's block and is why a common advice given to w…
> The PMs validate it, why do you think they don't read over it to make sure it fits what they want? The PM has historically often not had a detailed enough mental model of the implementation to spot the hard parts in advance or a detailed enough mental model of the customer desires to know if it's gonna be the right thing or not. Those are the things that killed waterfall. You can use LLM tools to help you improve b…
Normally waterfall works where the scope is extremely-well defined and articulated in design plans. Which shortens dev time because prior to AI code was mostly deterministic. Here we have to do waterfall level of documentation while iterating on a non-deterministic solution (code gen) to non-deterministic requirements (per usual).
It's bonkers.
I still think the technology is cool though.
And to answer the questioner.. Have you worked with a PM? Most of the ones I've worked with try to be simultaneously in charge yet not responsible for anything. Validating something implies skill and responsibility.
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This failure is human laziness, not an issue with the technology. People who use AI because they are trying to avoid doing work fall into a completely different category than people using AI as a force multiplier and for skills/capabilities enhancements / quality improvement.
This is very much a "you're holding it wrong" response. If your technology relies on humans using it in ways that go against the ways they are inclined to use them, then that is an issue with the technology.
This isn’t actually an argument for or against anything, I don’t know why people say this. It is entirely possible that people are using this brand new, historically unprecedented tool wrong.
Cars have been a huge success in spite of requiring people to learn a bunch of new things use them.
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This is very much a "you're holding it wrong" response. If your technology relies on humans using it in ways that go against the ways they are inclined to use them, then that is an issue with the technology.
I don't think that works as a critique of LLMs because it's far too broadly applicable to well-accepted tools. Are advanced calculators bad because a student could use the CAS to ace calculus homework, exams or the SAT without actually learning the material? Is copy/paste bad because a person could use it to copy/paste code from one place to another without noticing some of the areas they need to update in the new lo…
ok, so for some of the jobs we're doing plausible sounding goo is just fine. and that's kinda sad. but the 'just playing around' case is fine for PSG, this isn't a serious effort but just seeing how things might work out without much effort.
taking the remainder, where understanding and intent are important, the role of the ai is produce PSG, but the intentional person now goes through everything and plucks out all the nonsense. this may take more or less time than simply writing it, but we should understand this is resulting in less real engagement by the ultimate author. where this is actually interesting is a parallel to Burrough's cutup method - where source text and audio were randomly scrambled and sometimes really clever and novel stuff pops out.
but to say the current model of vibe coding has much to offer in the second case is really quite unclear. to the extent to which coding is the production of boilerplate is really a problem with APIs and abstraction design. if we can get LLMs to mitigate some of that I the short term without causing too much distraction, that's fine, but we should really be using that to inform the solution to the fundamental problem.
so for me what's missing in your model is how LLMs are supposed to be used 'properly'. I don't think laziness is really the right cut here, make-work is make-work, and there's plenty of real work to be done. but in what sense does LLM usage for code actually improve our understanding of these systems and get us more agency?