Comment by Quarrelsome

Comment by Quarrelsome 9 days ago

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This is extremely fascinating and finally something that feels extremely tangible as opposed to vibes based ideas around how AI will "take everyone's jobs" while failing to fill in the gaps between. This feels extremely gap filling.

I find it quite interesting how we can do a very large chunk of the work up front in design, in order to automate the rest of the work. Its almost as if waterfall was the better pattern all along, but we just lacked the tools at that time to make it work out.

skizm 9 days ago

Waterfall has always been the best model as long as specs are frozen, which is never the case.

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  • thisdougb 9 days ago

    When I first started in dev, on a Unix OS, we did 'waterfall' (though we just called it releasing software, thirty years ago). We did a a major release every year, minor releases every three months, and patches as and when. All this software was sent to customers on mag tapes, by courier. Minor releases were generally new features.

    Definitely times were different back then. But we did release software often, and it tended to be better quality than now (because we couldn't just fix-forward). I've been in plenty of Agile companies whose software moves slower than the old days. Too much haste, not enough speed.

    Specs were never frozen with waterfall.

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    • PaulDavisThe1st 9 days ago

      The difference between agile and waterfall only really matters at the start of a project. Once it is deployed/released/in-use, the two approaches converge, more or less.

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  • Quarrelsome 9 days ago

    sure, but if you're generating the code in a very small amount of time from the specs then suddenly its no longer the code that is the source, its the specs.

    That's what waterfall always wanted to be and it failed because writing the code usually took a lot longer than writing the specs, but now perhaps, that is no longer the case.

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    • datadrivenangel 8 days ago

      Specs aren't done until the product is retired, thus, code ain't done either.

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  • kristiandupont 8 days ago

    Only if you don't learn anything while developing. Which is also never the case.

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reneherse 9 days ago

Great observations.

As a frontend designer, not a developer, I'm intrigued by the techniques presented by the author, though most devs commenting here seem to be objecting to the code quality. (Way above my pay grade, but hopefully a solvable problem.)

As someone who loves to nerd out on creative processes, it's interesting indeed to contemplate whether AI assisted dev would favor waterfall vs incremental project structure.

If indeed what works is waterfall dev similar to the method described in TFA, we'll want to figure out how to use iterative process elsewhere, for the sake of the many benefits when it comes to usability and utility.

To me that suggests the main area of iteration would be A) on the human factors side: UX and UI design, and B) in the initial phases of the project.

If we're using an AI-assisted "neo waterfall" approach to implementation, we'll want to be highly confident in the specifications we're basing it all on. On regular waterfall projects it's critical to reduce the need for post-launch changes due to their impact on project cost and timeline.[1] So for now it's best to assume we need to do the same for an AI-assisted implementation.

To have confidence in our specs document we'll need a fully fledged design. A "fully humane", user approved, feature complete UX and UI. It will need to be aligned with users' mental models, goals, and preferences as much as possible. It will need to work within whatever the technical constraints are and meet the business goals of the project.

Now all that is what designers should be doing anyway, but to me the stakes seem higher on a waterfall style build, even if it's AI-assisted.

So to shoulder that greater responsibility, I think design teams are going to need a slightly different playbook and a more rigorous process than what's typical nowadays. The makeup of the design team may need to change as well.

Just thinking about it now, here's a first take on what that process might be. It's an adaptation of the design tecniques I currently use on non-waterfall projects.

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::Hypothesis for a UX and UI Design Method for AI-assisted, "Neo-Waterfall" Projects::

Main premise: Designers will need to lead a structured, iterative, comprehensive rapid prototyping phase at the beginning of a project.

| Overview: |

• In my experience, the DESIGN->BUILD->USE/LEARN model is an excellent guide for wrangling the iterative cycles of a rapid prototyping phase. With each "DBU/L" cycle we define problems to be solved, create solutions, then test them with users, etc.

• We document every segment of the DBU/L cycle, including inputs and outputs, for future reference.

• The USE/LEARN phase of the DBU/L cycle gives us feedback and insight that informs what we explore in the next iteration.

• Through multiple such iterations we gain confidence in the tradeoffs and assumptions baked into our prototypes.

• We incrementally evolve the scope of the prototypes and further organize the UX object model with every iteration. (Object Oriented UX, aka OOUX, is the key to finding our way to both beautiful data models and user experiences).

• Eventually our prototyping yields an iteration that fulfills user needs, business goals, and heeds technical constraints. That's when we can "freeze" the UX and UI models, firm up the data model and start writing the specifications for the neo-waterfall implementation.

• An additional point of technique: Extrapolating from the techniques described in TFA, it seems designers will need to do their prototyping in a medium that can later function as a keyframe constraint for the AI. (We don't want our AI agent changing the UI in the implementation phase of the waterfall project, so UI files are a necessary reference to bound its actions.)

• Therefore, we'll need to determine which mediums of UI design the AI agents can perceive and work with. Will we need a full frontend design structured in directories containing shippable markup and CSS? Or can the AI agent work with Figma files? Or is the solution somewhere in between, say with a combination of drawings, design tokens, and a generic component library?

• Finally, we'll need a method for testing the implemented UX and UI against the USE criteria we arrived at during prototyping. We should be able to synthesize these criteria from the prototyping documentation, data modeling and specification documents. We need a reasonable set of tests for both human and technical factors.

• Post launch, we should continue gathering feedback. No matter how good our original 1.0 is, software learns, wants to evolve. (Metaphorically, that is. But maybe some day soon--actually?) Designing and making changes to brownfield software originally built with AI-assistance might be a topic worthy of consideration on its own.

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So as a designer, that's how I would approach the general problem. Preliminary thoughts anyway. These techniques aren't novel; I use variations of them in my consulting work. But so far I've only built alongside devs made from meat :-)

I'll probably expand/refine this topic in a blog post. If anyone is interested in reading and discussing more, I can send you the link.

Email me at: scott [AT] designerwho [DOT] codes

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[1] For those who are new to waterfall project structure, know that unmaking and remaking the "final sausage" can be extremely complex and costly. It's easy to find huge projects that have failed completely due to the insurmountable complexity. One question for the future will be whether AI agents can be useful in such cases (no sausage pun intended).

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