The Speed of Prototyping in the Age of AI
darylcecile.net
darylcecile.net
For example, for personal projects, I've been wondering if it's possible to automatically create RSS feeds for pages that don't have them (yes), what are the challenges when building an archive-style page dumping system (need to dump CSSOM alongside getOuterHTML, remove/rewrite remote content, walk iframes, automate Chrome, scroll to load lazily loaded content, etc.), and if training a model to remove native ads from markdown coming from readability is possible (no, at least not with my current approach, but using the dom might work).
I also think there are a few interesting things you can explore that go beyond a simple carbon copy of what's on the Internet. Ideas that I've implemented are things like automatic extraction of audio tracks, transcription, and summarization, loading a page or podcast transcript into the context window of a LLM to discuss the arguments or factuality of the claims being made, automatically turning articles to reader view using readability/trafilatura, etc.
Directions I'd like to explore would be things like multimodal search ("that page I read six months ago about computer security with neon green text on a black background", or give me a list of fitness related pages I've read in the last twelve months), personal statistics (how is the mix of topics I've been reading about changing over time), annotating pages instead of just passively reading them, maybe even P2P archiving or discussions about pages, and all kinds of other things.
But installing archivebox would be easier indeed.
> the "cost" of other people producing garbage is?
Lemon Markets really require one important thing: at time of purchase, the average consumer is unable to differentiate the quality of the product.
Consumers are "rational"[1], so with "all other things being equal"[2], will make their purchases based on price. Therefore, the product that is cheaper but is also _in reality_ lower quality wins. This then pushes out any competition who is trying to differentiate their product through quality. Thus all products in that category decrease in quality and it becomes a race to the bottom, maximizing profits.
I want to stress that this doesn't require that the quality of products are distinguishable by experts, but only by the average consumer. You can probably look around at tech and notice this pretty quickly. The average consumer is not really tech literate[3]. They can't tell the difference. Hell, my parents don't even know the difference between the internet speeds from their ISP, even with the numbers displayed. The numbers mean nothing to them. Do they want 1GBps? 100 MBps? They don't know!
> Prototypes are generally shop jigs
[0] https://en.wikipedia.org/wiki/The_Market_for_Lemons
[1] Economics uses this term differently than what we use colloquially. Read "consumers make decisions based on the information available to them" not "consumers are geniuses and making perfect decisions"
[2] i.e. the only distinguishing purchase criteria is price
[3] If you think I'm wrong, please go spend a month outside Silicon Valley. Hell, go try a different country, and not in the major metro areas. We're nerds here. Every single person on HN is above average in this respect.
Or maybe you're making a very different point, which I have entirely missed.
I guess I'm curious what you mean by this, I don't particularly see either of those companies doing this, certainly not in the way this article describes, and not really in any way that's impacted substantially by AI.
What "product that doesn't exist" is Apple selling? Google? Who is paying for it?
> What "product that doesn't exist" is Apple selling? Google? Who is paying for it?
>>> My Google Pixel phone still can't schedule a haircut for me or book a reservation at a restaurant, despite multiple promises
8 years ago: https://m.youtube.com/watch?v=D5VN56jQMWM
You're telling me you haven't seen the same promise for 8 years?
Everyone understands that a wireframe isn’t done yet and it’s easy to change at that phase.
Once mockups became easy, that little bit of vocational gate (as in gate-keeping) that was holding the wall for UX work went away. Execs and PMs could make decent looking rectangles, so designers became the people who could make especially nice looking rectangles. So you got a lot of product/UX designers that were much more visual designers. That matters, but the prior part was bigger in that the product processes and sprints often started to have little design in them at all. What was a two or three phase process was one, designer got requirements and made design—often they didn't even really get requirements before design. The design was the impetus for the requirements not the other way around.
This is what became standard: Leader would give something vague because they didn't have much idea or vision yet. They probably had something blue-sky-ish, meaning they had a bunch of ideas, which in amorphous abstract blue skies come together. Once those things appear side by side on paper/screen, they're off putting and contradicting. There are problems not just with how to fit the pieces, but with the pieces. The visual the designer provides triggers this. Designers being visual people can see a lot of that in their head beforehand, but won't be heard until they show "bad work." It's pretty common though to see the PM or the leader look at it and say it wasn't the vague requirements, it's that the designer didn't get it. Anyways, it's that design that then kicks off some assessment against reality. Then you have a little bit of a shot at real requirements starting to leak out.
I remember discussions over relative easy of use of gray box wireframes... and that led to better products.
Now I've got designers vibing monstrosities that would have fit right in in the Flash era, I guess in order to draw even nicer rectangles now that execs than wave at AI and get a design.
If I need to think about the solution that hard, I may just as well take it all the way, remove the middleman and get some efficiency back.
There's still a way out of this mess though. All it takes is for folks to take shared ownership for hitting the goal and bring their expertise to the table to help draw the path to the goal.
If I need to think about the solution that hard, I may just as well take it all the way, remove the middleman and get some efficiency back.
Leaders (and companies) should be doing less horizontally. It runs the normal risk of stretching to thin, but their tenth and eleventh ideas are also filtered towards worse. Perhaps more fundamentally, the bar for adding a product/project should be higher. When the leader can "just" add another leader to take on the work—particularly one by nature with less power, ability, autonomy, and perhaps accountability—it's getting set up for a lower chance of success. In order: (1) the project probably shouldn't start at all; (2) if "started" at all, it shouldn't really look like starting, it should be a person validating for the leader who will then start it (many product teams pretend this is what they're doing); (3) if started, it makes quite a bit more sense for the higher (and likely more capable, but especially more empowered) leader to take on that project and hand the existing, stable, better trodded project where the team has institutional knowledge and support to the other leader.
In practice, sharing ownership is superficial, whether between leaders at different levels or between project team members. Why share? Own it or have someone else own it. Or if there isn't trust in the other person, cut it. If the leader doesn't have the time/interest and doesn't think they have someone capable of doing it, it should be as much a sign as a low quality idea.
It's not hard to find problems with middle managers—many are with the sorting that picks them, many are with the conditions of the middle. Structures and strategies to remove them, as you suggest, are a great idea. The main way is for the leaders, when faced with the scenario of "if I need to think hard", to not come away with "let's do it, but not me." We should be default no. Green lighting some work to validate it to a Yes works, but these are tasks such that the leader knows what kind of validation is needed—and is assigned to a person with those skills. An engineer, marketer, business analyst, user researcher, data scientist is going to validate things that a PM or a director have less or no training in. Leaders tend to appoint the latter though, sometimes for empire-building reasons, but I think more basically out of fear of control and legibility. Those are counter productive reasons if understandable.
very low fidelity - but describe ux flows.
One solution is to keep all mockups / prototypes strictly grey scale using bare bones vectors until every stakeholder has weighed in / signed off.
I haven't seen these in at least a decade in the industry!! Everywhere I used to work was always "PM wanted" or similar and the validation was always just QA making sure the thing works/does the bare minimum!!! Customer input was just for bugs.
I hope that with AI speeding up prototyping we can actually go the other way long term, where we go back to ACTUALLY talking to a customer and then quickly prototyping it to see if it is what they wanted. Figuring out what the customer wants remains the hardest part of software engineering, but at least right now its mainly because we just dont talk to the customer.
The project has tracked your imaginative state, and perhaps the states of your beta testers as they imagine things. It's a power armor suit tailored to specifically you. Nobody else will ever fit it because it's evolving too fast, all to implement your every whim.
I've seen this take 1.0 projects that are intentionally wildly scope-limited and great at that, and balloon until the project is the Everything Machine, doing everything but send email. I guess in the new era, every project expands until it becomes alive and devotes itself to your service… or at least, does its level best to be that for you and your beta team.
These things are not approachable. They're fever dreams, unparsable by outsiders. Discipline is lacking.
This has always existed. The ability to rapidly prototype has not changed it in any way.
An extremely experienced UX researcher once told me that, having been doing field research and user research for 3 decades now, every time it's a Fortune 500 company, after presenting mountains of research, it comes down to what color the CEO liked in the moment.
I don't understand the proclivity to latch onto whatever the new thing is and blame it for shitty decision-making that has existed as long as humans have existed.
One of the second order effects of AI collapsing the cost of building things is that product management is much more important now. A Product Owner/Manager who lacks the taste and insight (or data) to know what they should put in front of users and what they should just put in the bin will cause a company real harm, especially if the company moves to a "there's zero effort in building something, so we'll try everything!" model.
The only part that's really collapsed in effort is the translation from requirements into code. If you're using AI to generate requirements you're effectively building things based on what a 'random' requirements generator says. If that's as good as the requirements a Product Owner was writing then that person needs to improve.
I'm using this specifically in the context of concepts/features that are hard to explain/sell without some working visual or prototype, but which aren't immediately evident needs or features requested by our users. Some of them go nowhere, but I think the net result is an increased ability for me to get my experiments from the "lab" to production.
In this new world, the ability to say "no" is more important than ever. It has never been so easy to burn time, money and energy. The fact that you can try anything now can be modeled as a disadvantage. The space of possible solutions got a lot bigger. Unless you have good taste you could wander and get lost very quickly in this vast new expanse. A true expert that has more paths to work with can arrive at higher quality solutions faster. A novice will get into trouble faster.
It often takes 10k hours suffering through an idea with a live customer before we deeply learn why something is a bad/good approach. None of this painful wisdom is available in the models. You can easily change the mind of ChatGPT with a single adjective. You cannot so easily persuade the person who has successfully cast the ring into the volcano already. They know what it actually feels like to get there.
In the browser javascript console I see "Uncaught TypeError: can't access property "ordinal", r is undefined"
High quality ensued. Usually ;)
Either you would get chastised for wasting time with prototypes, or worse, your prototype would end up in production
I think the software industry really needs a cultural reset to embrace slower and deliberate development to build quality, but unfortunately AI has us racing recklessly in the wrong direction
I am so tired of it. Are there any companies out there that actually give devs time to build quality software anymore? I'm so burned out of the "move fast and break everything" grind
Has this (for me, normal) process really been that arduous in your past jobs? It's a slam dunk to leadership, as we do this to corral time wasted.
Quality must come from engineering. If you’re depending on a product manager to ask you that you can improve the quality of the code, you already lost.
So it requires soft skills, proper framing and ability to iterate quickly on quality-related tasks without leaving junk and multiple-versions behind.
But I completely understand push back for “doing improvements developers want to do”: A lot of developers confuse quality with familiarity or even complexity/verbosity. So business people have a reason to be reluctant.
And as an engineering manager I also had to push back several times. The thing that makes money is not the place to learn new skills, for example.
If they have the soft-skills to do it, then by all means.
If not, they need to upskill their soft skills before tackling anything big.
Prototyping already existed - how do you think the iPhone came into existence?
Also seeing a lot of managerial class bypassing the PR system entirely and just committing to main “because it’s faster”.
In software it is now possible to test ideas instantly, this is great for people like me that have so many ideas.
I’ve felt that any non trivial amount of code not written myself tends to be hard to own. And like the author said, need to keep skills sharp also
I chuck some of them on my public tools.simonwillison.net collection, others in their own GitHub repos with GitHub Pages enabled, and some I just share in a Gist (served via gisthost.github.io) or stick in a public S3 bucket so I have a URL for them.
If a prototype is against an existing project sometimes I'll leave it to silently rot in a branch on GitHub.
We’ve been seeing Claude artifacts sometimes come out as JSX or TSX
Never use React in artifacts - always
plain HTML and vanilla JavaScript and CSS
with minimal dependencies.
CSS should be indented with two spaces
and should start like this:
```
<style>
* {
box-sizing: border-box;
}
```
Inputs and textareas should be font size
16px. Font should always prefer
Helvetica.
JavaScript should be two space indents
and start like this:
```
<script type="module">
// code in here should not be indented at
the first level
```
Prefer Sentence case for headings.
I avoid any build steps because those make it harder to copy and paste code in and out of LLMs.
I'm experimenting with different ways to standardize some aspect of this process in a lightweight way so an Agent and I can "communicate with each other over rendered html".
A simple script or cli the agent can run to to serve an html app, act as a sink for interactions (can just submit a button+form to the runner port), and then close the page when done can work.
A little farther out in this direction would be something like a persistent client+server via web or electron. It's always on and you iterate in a loop, streaming diffs/file edits back and forth to each other.
A little farther out and you can load extensions that contain templates to generate the html, custom server code to serve htmx interactivity, and agent functionality.
Just two days ago I started on this idea https://github.com/hank-bond/uix
I was working on two ideas
1. "highlight a web page a-la obsidian web clipper and then intake that information into a personal wiki of concepts" (my third prototype in a row of this concept)
2. "visualize the code review process and organize discussion in a non-linear branching conversation"
And I realized both of them are basically chat pane on the left, agent with custom tools, and html "app" pane on the right to support interactivity.
The project as of this comment doesn't have any functionality yet its basically just the panes and a simple agent messaging channel, but if you (or anyone) are interested in the idea comment here and I will reach out when its a bit farther along and actually useful for building things.
Likewise please share your experiences with this concept, I would love to learn what others are doing with this type of workflow!
I think what people ARENT doing is interesting.
Usability isn't even in your list, it is not something most people even think of.
The best thing you can do with a prototype is give it to (potential) users and observe what they do with it. Just because you think that you're clearly communicating the intent of your system does not mean you are.
I find this is where AI is genuinely useful, it lets us prototype an idea a lot faster, make no bones about the fact that it is a buggy proof of concept but lets people see the potential and get an idea for what the final product might look like.
The flip side is, nobody cares. I've put some of these things up on Github and ... nothing. It seems even my pre-AI projects have dropped sharply in eyeballs judging by issues, prs, stars, etc. People are too busy doing their own things to bother looking at other people's stuff. And rightfully so. There's nothing magical about my prompting to what people can prompt themselves. The value of these prototypes just dropped. Except op course for people still doing things the old fashioned way.
So, you can ship your prototype. But there's very little point to doing so. Even if it isn't slop, it's just very hard to stand out from the masses of other people's prototypes. The value of custom applications just dropped by an order of magnitude. Everybody is going to expect things to be tailored to them now.
These are the questions everyone seems to be ignoring and saying “only LLMs can make projects quickly” but ignoring everything those LLMs are built on (your llmis probably calling a code gen tool).
For the at work side, I personally haven’t experienced any disadvantages or missed any project deadlines because I didn’t use an LLM, so what does velocity get me? Thumb twiddling time?
I was thinking the other day how much better Drupal is. Want a online store? A few commands and bam, online store. Want a newspaper? A few commands and bam, newspaper with publishing workflows, user management, and caching.
Using coding agents isn't much different. There are several things the models are trained to do very well and a few commands will get something. If the developer wants to move the project beyond that, it requires domain knowledge and a lot of hacking.
I wonder if the coding agents will move towards the Drupal model where they create interchangeable components with common interfaces. Like Drupal the coding agents never provide anything truly inovative that hasn't been done before.
Reminds me a bit of this blog post[0].
I remember doing a Drupal project around that time and being astonished at how powerful it was.
I also remember feeling more like a technician connecting various components than like a software engineer, writing code.
I totally saw the value for the client but I really disliked my experience, so I avoided it afterwards.
0: https://www.rickmanelius.com/p/the-website-rfp-and-the-impos...
Yes, very much so. Our team was fast with those tools and created many of our own before this LLM AI (we used other AIs though to go faster), however it still took weeks to months from idea to launch; the same complexity now takes days, including everything. We already had rigorous processes and those really help now moving at speed. No way anyone can beat this except better AI.
What did the time savings gain you? A quicker release date? How can you prove that? “This would have taken weeks” is the old problem of project time estimation. How can I take any engineer seriously that they think they know it saved weeks?
Because we have experience doing things pre-AI.
For example, most projects had no tests before AI because tests are very time intensive and take a lot of forethought since you also need to engineer the code to be testable. Yet now tests are trivial. Every project I delegate to AI has tests. Good tests too. How do I know? Because I have 20 years of experience and I looked.
Or, fixing a bug in my codebase is as simple as copy and pasting the user's bug report email into Claude Code. The LLM verifies that the bug exists, writes a red test to ensure the bug exists, then proposes the best fix that will turn the test green. Meanwhile it did all this while I was doing something else.
Or, the projects I've built that would have taken me a lot of time because they are in domains I don't have much experience in. I built a macOS app around libghostty which involved bridging Swift to Zig, something I've never done before. And when Ghostty has new versions, I ask AI to look at the diff and find new features and APIs I can take advange of in my own project. I didn't write any code in this project myself. It would have taken me a lot of time because everything takes a lot of time. It makes progress on the project while I'm doing something else, yet I use the project as my main terminal every day, it's so good.
It's very wishful thinking to assume that nobody knows whether AI is helping them or not, probably coming from an understandable place where you hope all of this a fever dream and we'll go back to the old way any day now. But you owe it to yourself to believe others here so that you can take it seriously.
So you do no validation of the code that’s generated? Just asking because you didn’t state that as a step in your process. You’re prototyping to running then you’re missing a big step that will most likely cost you later.
Why does it matter how much time I spent writing code for a project I’m most likely either not sharing or if I am sharing it can be obtained for free? Which market am I rushing to? Bluntness doesnt seem to be an advantage other than bragging.
I work as a DevOps engineer and have been using agents exclusively to code since the beginning of the year. Agents are really nice to quickly craft utilities to speed up planning. For instance I had it create a small cli for me that'll pull my cards from azure DevOps, load them as json, markdown and csv, and push updates once I'm done. Then I'll load into context transcripts of meetings and other written requirements, cross with current state of repos, to have meaningfully conrextualized work items without me having to implement these myself. I'll just have a long chat with the agent exploring these cards and defining the necessary refinements for description and acceptance criteria than I jusr push them all at once. Anything you can think of you just ask for the agent, so for me I don't trust code, so I'll have all my clis be no-op by default, so they will first print all they'll do and if I think the changes make sense I approve them and let the script commit to the canonical board.
Working with cloud consoles like Aws in general is a huge hassle, so crafting quick inventory utilities and tools for correlating data is a breeze.
Now the work itself is mainly ci pipelines, terraform files and automation. For these I'll base the agents on the specified work items and enrich them with my own understanding of the problem. I then launch the agents and read the agent output attentively. This is very important. You can't just prompt and leave, you need to be present all the time so you can steer the agent into solving the right problems. At the very least you need to review all the changes after an implementation session is done when you came back from making coffee. Many times it tries to create meaningless abstractions or very complicated solutions that I know can be done better. Or I have a different idea of how to organize the project so I do many follow-up sessions to refactor code.
In my personal projects I do a lot of small utilities. I spent some weeks designing and polishing a replacement for zurg and debridmediamanager the way I like it to be, simple and to the point, also tightly integrating them with jellyfin https://gitlab.com/gabriel.chamon/buzz
I have my own micro desktop environment on top of hyprland called Archie which recently I've been redesigning and improving a lot with agents https://gitlab.com/gabriel.chamon/archie
I have my own agile based methodology for creating and managing work items with tight integration with gitlab https://gitlab.com/gabriel.chamon/orisun
I have been improving my fork if gamma-launcher so that installing and managing the game on bazzite is simpler and more automated than relying on workarounds for workflows intended for windows https://gitlab.com/gabriel.chamon/gamma-launcher
Now for how I approach developing with agents. I think it's really important to get your constraints sorted out as soon as possible, so have your agent create a CI pipeline for code quality testing, like with ruff, pyright and pytest, to control style, code consistency and cyclomatic complexity. Put in the AGENTS.md explicit instructions that the agent must run these tools at the end of every coding session. If adopting a new project, use the agent to explore the code and see which refactoring points are worth tackling. Agents really thrive on good codebases, so this first code quality improvement pass is a must.
To sum it up, with agents you give up writing code manually for reading lots of code, exploring the domain with the help of the agent and architecting the solution at a strategic level. You trust the agent but you also verify. And lots and lots of manual testing. My personal take is that I'm infinitely productive now, only constrained by how much code and agent terminal output I can read, and also by the rate limits of the model providers and mental fatigue.
I am in a similar professional position to you, and I make a lot of small things in my spare time. I have found using agents very tedious and frustrating to workflow. Initial prototyping can be ok, but when you start to get serious with code it falls apart quickly. If you don't tell the agent literally exactly what to do to the letter, it will guess some things. Usually some of those things are wrong, and dont match the functionality you expected. I find this a very frustrating place to be, trying to tell the agent what is the wrong functionality, and what I expect instead. Usually at this point I enter what I refer to as a doom spiral, where everything I tell the agent just takes me further from what I want, until I eventually have to revert everything it has done and try again.
This gets worse with bugs, where a inevitably a code bug will appear, and trying to tell the agent what the bug is and what is expected instead usually results in more broken functionality elsewhere. When I have written the codebase manually myself, I can usually pinpoint and fix bugs in a few minutes after diagnosing them. I have literally spent hours trying to get an agent to fix a bug without breaking something else.
I thought maybe refactoring code might be a strong point for LLMs, so I tested taking a monolith codebase and asked various agents to refactor into reusable module structures with exposed api endpoints so that I could split apart functions into modular chunks whilst retaining full functionality. They all failed miserably at this, breaking everything and never managing to make a working example.
LLMs and their agents certainly are cool, and they are great at writing emails for people and summarising meeting notes. They can even create very small coded programs well. But let loose on serious production codebases and they can cause much more frustration than they solve. I will come back and try another day when LLMs have evolved again to the next level, but for now they can stay coding my toy projects and dictating my teams meeting notes.
And I don't doubt you have had you agro with LLMs, because I've also had my fair share of issues with them, I just think we have different emotional responses to the workflow with agents. They don't work the first time and they aren't very good at sweeping large sets of loosely related changes. They need to focus on one feature only and crunch it to the end.
Honestly though I've didn't have the chance to work in large codebases, but with those projects I had lots of success and I found the workflow very stimulating, reading the solutions the LLM come up with, some very interesting and some comically bad, but more often than not I'll pick up a technique or an approach I didn't think about. Worse case it's something I can bounce ideas off of.
About bugs, I have the opposite impression. I find it really interesting to get a functionality wrong, provide the agent with the logs and context and explain in detail the issue and have it help me explore the codebase to identify and fix the issue. I've never had an instance until now that I couldn't fix the bug or that I left the session in a worse mental state than I entered.
I'll take buzz, for instance. Before using zurg I had to use Plex because jellyfin would only detect a single file in a folder with multiple files. Codex created the presentation layer I described in a single go and it worked first time. That was really impressive I have to say. The project also has it's own WebDAV server, it integrated with debrid, has a persistent catalogue of media that is independent of debrid and can be used to restore previously deleted media. It has a logging UI, a config UI and a nice event system for waiting for different independent services that it needs to orchestrate. I don't think it's a large codebase, but it's nowhere near a toy project. It also has a very capable CI pipeline that supports the development. The only part I couldn't get the agent to do well for nothing was frontend implementation, maybe because I refused to use a framework and defaulted to plain JavaScript and CSS embedded in jinja2 templated html files. I have picked up a couple of techniques when I did full stack work when I was an intern so I was cabaple of using the browser to inspect and refine the Dom elements. One thing that it did poorly for instance was to create all elements in block display, however planning a refactor to use flexbox throughout the code really improved the UI resilience and it was really effortless to deploy. In buzz I haven't touch most of the code, just some adjustments in the htmls to serve as an example for the agent of how to do it correctly, prompts not being the only way to interact with them, but I read most of the code and validated most of the functionality in merge requests, just like you'd do in a team work.
In a nutshell I think agents are really capable since November last year of working in large code bases, but I don't trust them to just be let loose. They need lots of hand holding and steering, but for me once I got the hang of it I really feel like I'm extremely productive.
My hypothesis is that people are more likely to have success with agents the more they enjoy writing in natural language and reading code, while people that prefer coding and dislike writing text will usually prefer handcrafting their programs.
Reminds me of a conversation I had with Kelsey Hightower where he suggested that using agents to build utilities and software was a smarter way to proceed than using agents to do the work. It is almost like the software artifacts are a cached version of your understanding of the problem and can be used over and over again until the problem (or your understanding of it) changes.
Imagine if instead of f AI generated code, we all just started copying and pasting code from open source repos.
Imagine my velocity! I cloned the Linux kernel in seconds!
Instead we're basically doing exactly that, except through an AI remixer.
It leaves a very sour taste in my mouth
You do not, never EVER, start from a blank slate.
Step 0 is to actually challenge the value. Before you even start you spend a LOT of time with the person with a need to narrow down what they actually need. Not what they think they want but what's genuinely problematic for that. Again, NOT how to solve it but what's a thorn that's painful for them, not for what you imagine it might be. This honestly often get uncomfortable quickly because you have to ask "Ok but have you tried this? What about this quirky thing?" because it challenges their own attempts. If you don't spend a significant amount of time in that space you WILL implement faster, that's obvious, but you are very likely to efficient "solve" the wrong problem. You will solve what you can solve easily. Think about it like looking for keys where there is light, not where you lost them.
So... that's before one has even started to code, it's mostly uncomfortable discussions. Only once there is some confidence from both parties that the problem is identified can implementing might make sense. Then you don't! You do NOT touch a line of code. Instead you take whatever you can, post-it notes, Lego bricks, existing software, you tape all that together and you ask "Would THAT (very ugly barely working monster) kind of solve it for you?". So you do not build anything new, you ONLY stick together the BIGGEST existing parts.
Only then you might eventually build something but STILL you don't start from a blank slate. You are going to find the highest level of abstraction you can find. They want something related to the Web? You don't freaking build a Web browser, or a even PWA, you paste a code snippet in the console.
You always look for the way with the MINIMUM amount of new code. It's never about implementing faster. It's about NOT implementing faster.
Now the fun part (arguably) actually begins when you have done all that but it's still not enough. You use a CMS or a browser or whatever large exiting verified code base and the need is not solved. Then you rely on the built-in extension system of that code! Guess what, there might even be an existing plugin that does what you thought was novel.
Finally, finally you did find no extension for that existing quality open-source large code based so you HAVE to build it. Well, not so fast, is there another piece of code from another software that does it? Does it have an API? Can you connect to that API to get that functionality in that extension?
Then you have done it, you brought together 2 large pieces of software but you only coded the connector between the two.
Your prototype is, in practice, 10 lines of code.
TL;DR: yes, good prototypists code very little and yet still end up with genuinely novel work.
I also use AI, not for muh agents but for asking questions. Even when I’m not forced to, unfortunately.
> I still don't think AI is magic, and I'm still cautious about the broader picture; the environmental, financial, and social questions haven't gone anywhere. But for me, right now, the day-to-day reality is that I can move faster, think bigger, and ship more than I could before. And that's been genuinely fun.
Three categories of concern, two of which are relevant for the well-being of commoners, and you still go ahead with it? Why? Because being productive and having fun is more important than the environment and driving people into social crises?
Now the quality of what comes after the iteration depends on the reviewer, and that's no AI's fault.
That's where everyone is overconfident. And that's where mature companies like ours are starting to get customers come back again after jumping ship for some vibe coded startup and getting screwed.
LLMs, minimum advantages aside, are merely amplifiers for the worst characteristics of the human race.