6 ms·

The Speed of Prototyping in the Age of AI

201 points by mooreds 5mo ago

zenai666 5mo ago
[flagged]
righthand 5mo ago
But is it really any faster than using an already existing code generator/scaffolding tool? How do you know your project isn’t just a regurgitation of another repository? Would it be just as fast to clone some existing project and hack on it? 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?
- dataviz1000 5mo ago
  It reminds me of Drupal circa 2009. 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.
  - mooreds 5mo ago
    > If the developer wants to move the project beyond that, it requires domain knowledge and a lot of hacking. 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-impossible https://www.rickmanelius.com/p/the-website-rfp-and-the-impos...
  - righthand 5mo ago
    It might centralize around a specific framework but I think part of the problem is that people want to generate their own framework or at least not care about what the framework is/does/can do. They treat the LLM as the framework which can be non-deterministic and structureless.
  - anonzzzies 5mo ago
    Drupal and WP etc all have plugins to switch stuff on in minutes, however, customising and making it as your client wants would take a lot of time. WP shops we work with for clients (we need to integrate some times) take weeks to get some plugin to do what they want by adding tags and config options.
- anonzzzies 5mo ago
  > But is it really any faster than using an already existing code generator/scaffolding tool? 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.
  - righthand 5mo ago
    But “are you really moving at speed after you generate the majority of your application?” is my other point. If you were to start working somewhere with an existing product the changes you would apply are more than likely incremental. What is the advantage of using LLMs to change 1-10 lines of code on average? How do you measure the ROI for that? 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?
    - snoman 5mo ago
      Considering that engineer never reliably estimated anything beyond a few days remotely accurately before… but now they can…
      - righthand 5mo ago
        Can they? Or are they just estimating the time it takes to generate a working prototype?
    - hombre_fatal 5mo ago
      > 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.
- hparadiz 5mo ago
  Yea cause it's done while you're still reading the docs for your code generator. lol
  - righthand 5mo ago
    Then how much time do you spend debugging and fixing the generated code? lol
    - hparadiz 5mo ago
      Usually I know exactly what I want before hand. What structs. What protocols. How I want the event bus layered and what threads need to exist. And what make targets I want. So generally the generated code is strictly bound to my design pattern. Then it's all a matter of running it. To put it bluntly I'm running benchmarks and testing it while you're still deciding what to name your files.
      - righthand 5mo ago
        And? What advantage does that have for you over me when it comes to a personal project? What does that velocity get me? More time to foolishly rewrite/regenerate the already built software from scratch? I don’t spend a lot of time naming my files personally. 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.
  - dawnerd 5mo ago
    Code generators are usually one short command. It’s less typing than a prompt would take.
    - hparadiz 5mo ago
      That statement tells me you have less experience with code generators than me and I'll just leave it at that.
- gchamonlive 5mo ago
  Your tone makes me think you already decided that agents aren't worth your time, but I'll give it a try anyways. 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 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 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 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 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.
- bluefirebrand 5mo ago
  I think you're right 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
- utopiah 5mo ago
  Yes... and in fact I'm a professional prototypist (as in I get paid to do that) and this is 100% the process. 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.
rossjudson 5mo ago
I'm truly hopeful that AI will open a new of prototyping. Back in the day, prototyping was how you figured out what to build, you'd very deliberately toss the entire first (or second!) version, and you'd plan to do that. High quality ensued. Usually ;)
- bluefirebrand 5mo ago
  Most places I've worked, devs were basically afraid to prototype 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
  - wyre 5mo ago
    All these companies want devs with top-engineering talent and coding skill, but then fire them because they aren't using LLMs enough.
  - lelandfe 5mo ago
    Ask on sprint planning if time can be set aside to spike out a proof of concept, and then you go do that prototyping in the sprint. 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.
  - whstl 5mo ago
    I understand both sides. 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.
tim-projects 5mo ago
Prototype? Why stop there..
- amelius 5mo ago
  Most managers rename the prototype into the product anyway.
kadhirvelm 5mo ago
What are people doing with prototypes afterward? Do you end up shipping it as is to production? What about at work? Are the prototypes useful in that context?
- coffeeaddict1 5mo ago
  I use AI mostly to prototype features in my existing projects. If I have an idea, I use the AI to implement it and try out different ways in which it could be implemented. Then I throw away the code and mostly write the code manually, with AI used primarily for review or docs.
- malwrar 5mo ago
  You know how when you finish prompting some code generator to build something, and you look over what it has built and feel a sense of emptiness even if it does what you want? I think about what I wish the prototype looked like, and basically start describing details that I expect to exist (think longer versions of e.g. “this should be using our internal graph library, and I figure we can model this task as a traversal, how far have you strayed from this and why?”) and let the agent analyze what it built against my expectations. I’ve spent hours in conversation just “refining the context” this way, and then I channel that into an update process. I figure the prototype is just about proving out behavior, and this next phase is about refining it into the pieces I’ll use elsewhere. It’s kinda fun, I’d absolutely burn out a coworker if I grilled their PRs the way I roast AI contributions :P
- simonw 5mo ago
  I've started making most of my prototypes single HTML documents with inline CSS and JavaScript, because a single file is a lot easier to store somewhere and share and will probably keep on working forever (browsers are really good at backwards compatibility). 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.
baisampayans 5mo ago
While the speed of prototyping and even shipping to production has increased, I have been asking myself at what cost? I see a lot of garbage being shipped. Not because the code quality is bad, because execution has become cheap now. Ideas even though crap, are getting prototyped. Things which look effective on the surface, but has real UX problems in the underneath, are getting prioritised because someone in the room can talk better and enrol a leader to align with the idea. Good old user research or talking to users to validate ideas, iron out issues in the user flows has become too slow for the new process!!
- KaiShips 5mo ago
  [flagged]
- speff 5mo ago
  Similar experience here, however my feeling is that this isn't necessarily a bad thing. Garbage being made is indicative of a gap in the currently-available tools. User research should shift towards analyzing these prototypes and enhancing existing tools to fill this need.
- jfim 5mo ago
  Prototypes aren't only for UX though, sometimes they're for exploring whether something is technically possible, or what are the unknown unknowns in a particular area. 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).
  - dakolli 5mo ago
    Why wouldn't you use Archive Box? https://github.com/archivebox/archivebox https://github.com/archivebox/archivebox
anssip 5mo ago
AI makes it possible to ship a lot of junk really fast
- luka2233 5mo ago
  You can argue that's the point of prototyping. Iterate fast. Getting a "junk" prototype in 2 days is better than getting the same in 2 months. Now the quality of what comes after the iteration depends on the reviewer, and that's no AI's fault.
  - anssip 5mo ago
    I think creating prototypes is now so easy that people are creating them for ideas that are not even worth of prototyping.
- cryo32 5mo ago
  Well the worst thing is it allows you to make a really convincing pile of junk and get people to pay for it. Then you can worry about the details when you have your first paying customers. 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.
Sasisundar09 5mo ago
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deleted 5mo ago
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jdw64 5mo ago
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vladsiu 5mo ago
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meaningofdesign 5mo ago
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rmnclmnt 5mo ago
For the past few months, many times i’ve tried this workflow: 1. Ask a coding agent to think and implement a feature that is non trivial 2. This leads to really understand pros and cons for many possible solutions and see it happen end to end 3. Revert all changes and implement it myself when i’m settled on a solution i’m satisfied with 4. At this point the agent is just an iterative reviewer 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
Songjinhao 5mo ago
[flagged]
blackarck 5mo ago
Productivity has increased only for people who knows what they are doing. I have been able to increase my productivity to build and turn around things faster and in a much polished manner. One problem is too many things goes on in your head and I see using tools like Jira or notion are very handy to capture all edge scenarios, integrations need to be captured. Taking break from AI is very very essential for this to work for me.
- stringfood 5mo ago
  AI is like a really fast car - a good driver can cross a state a day but a bad driver can go the wrong way fast
keybored 5mo ago
Standard disclaimer from me that if you are forced to do it by people who have power over you then that is different from doing things voluntarily. You can still “just quit your job” but you have less agency. 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?
acamerer 5mo ago
[flagged]
jillesvangurp 5mo ago
Prototyping is a lot easier indeed. I've experienced this as well. And many of the prototypes are kind of shipable even after a only a bit of iteration. Mostly it's super easy to go from prototype to something shippable. I hate the term vibe coding actually. Is it still vibe coding when the thing has end to end tests and I've been trying to break it for a few days? 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.
bob1029 5mo ago
I think it is mostly accurate to say that the cost of execution has dropped to zero. 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.
- alansaber 5mo ago
  Very well said. The relative importance of prioritisation has shot up massively (and it was already essential before).
peter_retief 5mo ago
I created three throwaway android apps in an afternoon to test my code. (All fairly well written) In software it is now possible to test ideas instantly, this is great for people like me that have so many ideas.
AIOperator2026 5mo ago
[flagged]
ArneCode 5mo ago
I think there are costs beyond having to sacrifice writing code yourself. When prototyping yourself you learn a lot about the problem, see what design decisions lead to what tradeofs. While you write code your brain is always running in the background, giving you thoughts about how things could break, where the structure could be simplified, where the code could be extended. I feel this is lost or at least reduced a lot when an LLM writes code because you have a lot less contact with the software.
aseemrajvanshi 5mo ago
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claud_ia 5mo ago
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imdsm 5mo ago
that moving background is terrible, i can't read it properly
- jmkni 5mo ago
  document.querySelectorAll('div').forEach(x => x.classList.remove('jumbo'));
Tepix 5mo ago
The page shows up however briefly and then there's a full screen error message saying "This page couldn’t load" In the browser javascript console I see "Uncaught TypeError: can't access property "ordinal", r is undefined"
- gorszon 5mo ago
  I guess they prototyped the site too quickly.
samvrit 5mo ago
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cml123 5mo ago
The approach I've been trying to use at work is making heavy use of AI for the generation of experimental prototypes, but with little intention of keeping all that code verbatim. I can get something to the demo stage much more quickly than before, allowing me to show it to coworkers and users to get feedback on random possibilities. From there, I make a case to properly delegate time for "doing it the right way" as a slotted piece of work. That's resulted in a number of high-impact features being added to our app in the last few months, that in the past would have taken much longer or never came to fruition. 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.
brammertottens 5mo ago
There definitely is the danger of a lot of garbage being shipped, but with both the models getting better and better, and more tools and ways of working being discovered, I believe the quality of what is being outputted is going up as well. Right now, a tool like claude code is more like a junior dev. you really need to steer it well, and know what you are doing, to make sure it doesn't output slop. but with the ability to create agents that work together, implementing design agents, coding agents, review agents, even a non-technical person will be able to design a workflow that spits out quality code, and I think that time isn't far away.
- collyw 5mo ago
  Are the models really getting better and better? They tell us they are but I haven't noticed much difference in the last year or two.
  - brammertottens 5mo ago
    It goes a bit up and down, compared to two years ago, in my feeling, both anthropic and open ai coding models have made massive jumps. In between big releases I do feel the quality of the models varies over time. 2 years ago I got annoyed with the models being crappy autocompletes, right now I'm just managing them more rather than coding myself

201 points by mooreds 5mo ago

[flagged]

But is it really any faster than using an already existing code generator/scaffolding tool? How do you know your project isn’t just a regurgitation of another repository? Would it be just as fast to clone some existing project and hack on it? 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?

dataviz1000 5mo ago
It reminds me of Drupal circa 2009. 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.
- mooreds 5mo ago
  > If the developer wants to move the project beyond that, it requires domain knowledge and a lot of hacking. 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-impossible https://www.rickmanelius.com/p/the-website-rfp-and-the-impos...
- righthand 5mo ago
  It might centralize around a specific framework but I think part of the problem is that people want to generate their own framework or at least not care about what the framework is/does/can do. They treat the LLM as the framework which can be non-deterministic and structureless.
- anonzzzies 5mo ago
  Drupal and WP etc all have plugins to switch stuff on in minutes, however, customising and making it as your client wants would take a lot of time. WP shops we work with for clients (we need to integrate some times) take weeks to get some plugin to do what they want by adding tags and config options.
anonzzzies 5mo ago
> But is it really any faster than using an already existing code generator/scaffolding tool? 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.
- righthand 5mo ago
  But “are you really moving at speed after you generate the majority of your application?” is my other point. If you were to start working somewhere with an existing product the changes you would apply are more than likely incremental. What is the advantage of using LLMs to change 1-10 lines of code on average? How do you measure the ROI for that? 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?
  - snoman 5mo ago
    Considering that engineer never reliably estimated anything beyond a few days remotely accurately before… but now they can…
    - righthand 5mo ago
      Can they? Or are they just estimating the time it takes to generate a working prototype?
  - hombre_fatal 5mo ago
    > 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.
hparadiz 5mo ago
Yea cause it's done while you're still reading the docs for your code generator. lol
- righthand 5mo ago
  Then how much time do you spend debugging and fixing the generated code? lol
  - hparadiz 5mo ago
    Usually I know exactly what I want before hand. What structs. What protocols. How I want the event bus layered and what threads need to exist. And what make targets I want. So generally the generated code is strictly bound to my design pattern. Then it's all a matter of running it. To put it bluntly I'm running benchmarks and testing it while you're still deciding what to name your files.
    - righthand 5mo ago
      And? What advantage does that have for you over me when it comes to a personal project? What does that velocity get me? More time to foolishly rewrite/regenerate the already built software from scratch? I don’t spend a lot of time naming my files personally. 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.
- dawnerd 5mo ago
  Code generators are usually one short command. It’s less typing than a prompt would take.
  - hparadiz 5mo ago
    That statement tells me you have less experience with code generators than me and I'll just leave it at that.
gchamonlive 5mo ago
Your tone makes me think you already decided that agents aren't worth your time, but I'll give it a try anyways. 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 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 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 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 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.
bluefirebrand 5mo ago
I think you're right 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
utopiah 5mo ago
Yes... and in fact I'm a professional prototypist (as in I get paid to do that) and this is 100% the process. 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.

rossjudson 5mo ago

I'm truly hopeful that AI will open a new of prototyping. Back in the day, prototyping was how you figured out what to build, you'd very deliberately toss the entire first (or second!) version, and you'd plan to do that. High quality ensued. Usually ;)

bluefirebrand 5mo ago
Most places I've worked, devs were basically afraid to prototype 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
- wyre 5mo ago
  All these companies want devs with top-engineering talent and coding skill, but then fire them because they aren't using LLMs enough.
- lelandfe 5mo ago
  Ask on sprint planning if time can be set aside to spike out a proof of concept, and then you go do that prototyping in the sprint. 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.
- whstl 5mo ago
  I understand both sides. 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.

tim-projects 5mo ago

Prototype? Why stop there..

amelius 5mo ago
Most managers rename the prototype into the product anyway.

kadhirvelm 5mo ago

What are people doing with prototypes afterward? Do you end up shipping it as is to production? What about at work? Are the prototypes useful in that context?

coffeeaddict1 5mo ago
I use AI mostly to prototype features in my existing projects. If I have an idea, I use the AI to implement it and try out different ways in which it could be implemented. Then I throw away the code and mostly write the code manually, with AI used primarily for review or docs.
malwrar 5mo ago
You know how when you finish prompting some code generator to build something, and you look over what it has built and feel a sense of emptiness even if it does what you want? I think about what I wish the prototype looked like, and basically start describing details that I expect to exist (think longer versions of e.g. “this should be using our internal graph library, and I figure we can model this task as a traversal, how far have you strayed from this and why?”) and let the agent analyze what it built against my expectations. I’ve spent hours in conversation just “refining the context” this way, and then I channel that into an update process. I figure the prototype is just about proving out behavior, and this next phase is about refining it into the pieces I’ll use elsewhere. It’s kinda fun, I’d absolutely burn out a coworker if I grilled their PRs the way I roast AI contributions :P
simonw 5mo ago
I've started making most of my prototypes single HTML documents with inline CSS and JavaScript, because a single file is a lot easier to store somewhere and share and will probably keep on working forever (browsers are really good at backwards compatibility). 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.

baisampayans 5mo ago

While the speed of prototyping and even shipping to production has increased, I have been asking myself at what cost? I see a lot of garbage being shipped. Not because the code quality is bad, because execution has become cheap now. Ideas even though crap, are getting prototyped. Things which look effective on the surface, but has real UX problems in the underneath, are getting prioritised because someone in the room can talk better and enrol a leader to align with the idea. Good old user research or talking to users to validate ideas, iron out issues in the user flows has become too slow for the new process!!

KaiShips 5mo ago
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speff 5mo ago
Similar experience here, however my feeling is that this isn't necessarily a bad thing. Garbage being made is indicative of a gap in the currently-available tools. User research should shift towards analyzing these prototypes and enhancing existing tools to fill this need.
jfim 5mo ago
Prototypes aren't only for UX though, sometimes they're for exploring whether something is technically possible, or what are the unknown unknowns in a particular area. 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).
- dakolli 5mo ago
  Why wouldn't you use Archive Box? https://github.com/archivebox/archivebox https://github.com/archivebox/archivebox

anssip 5mo ago

AI makes it possible to ship a lot of junk really fast

luka2233 5mo ago
You can argue that's the point of prototyping. Iterate fast. Getting a "junk" prototype in 2 days is better than getting the same in 2 months. Now the quality of what comes after the iteration depends on the reviewer, and that's no AI's fault.
- anssip 5mo ago
  I think creating prototypes is now so easy that people are creating them for ideas that are not even worth of prototyping.
cryo32 5mo ago
Well the worst thing is it allows you to make a really convincing pile of junk and get people to pay for it. Then you can worry about the details when you have your first paying customers. 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.

Sasisundar09 5mo ago

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deleted 5mo ago

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jdw64 5mo ago

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vladsiu 5mo ago

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meaningofdesign 5mo ago

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rmnclmnt 5mo ago

For the past few months, many times i’ve tried this workflow: 1. Ask a coding agent to think and implement a feature that is non trivial 2. This leads to really understand pros and cons for many possible solutions and see it happen end to end 3. Revert all changes and implement it myself when i’m settled on a solution i’m satisfied with 4. At this point the agent is just an iterative reviewer 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

Songjinhao 5mo ago

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blackarck 5mo ago

Productivity has increased only for people who knows what they are doing. I have been able to increase my productivity to build and turn around things faster and in a much polished manner. One problem is too many things goes on in your head and I see using tools like Jira or notion are very handy to capture all edge scenarios, integrations need to be captured. Taking break from AI is very very essential for this to work for me.

stringfood 5mo ago
AI is like a really fast car - a good driver can cross a state a day but a bad driver can go the wrong way fast

keybored 5mo ago

Standard disclaimer from me that if you are forced to do it by people who have power over you then that is different from doing things voluntarily. You can still “just quit your job” but you have less agency. 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?

acamerer 5mo ago

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jillesvangurp 5mo ago

Prototyping is a lot easier indeed. I've experienced this as well. And many of the prototypes are kind of shipable even after a only a bit of iteration. Mostly it's super easy to go from prototype to something shippable. I hate the term vibe coding actually. Is it still vibe coding when the thing has end to end tests and I've been trying to break it for a few days? 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.

bob1029 5mo ago

I think it is mostly accurate to say that the cost of execution has dropped to zero. 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.

alansaber 5mo ago
Very well said. The relative importance of prioritisation has shot up massively (and it was already essential before).

peter_retief 5mo ago

I created three throwaway android apps in an afternoon to test my code. (All fairly well written) In software it is now possible to test ideas instantly, this is great for people like me that have so many ideas.

AIOperator2026 5mo ago

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ArneCode 5mo ago

I think there are costs beyond having to sacrifice writing code yourself. When prototyping yourself you learn a lot about the problem, see what design decisions lead to what tradeofs. While you write code your brain is always running in the background, giving you thoughts about how things could break, where the structure could be simplified, where the code could be extended. I feel this is lost or at least reduced a lot when an LLM writes code because you have a lot less contact with the software.

aseemrajvanshi 5mo ago

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claud_ia 5mo ago

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imdsm 5mo ago

that moving background is terrible, i can't read it properly

jmkni 5mo ago
document.querySelectorAll('div').forEach(x => x.classList.remove('jumbo'));

Tepix 5mo ago

The page shows up however briefly and then there's a full screen error message saying "This page couldn’t load" In the browser javascript console I see "Uncaught TypeError: can't access property "ordinal", r is undefined"

gorszon 5mo ago
I guess they prototyped the site too quickly.

samvrit 5mo ago

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cml123 5mo ago

The approach I've been trying to use at work is making heavy use of AI for the generation of experimental prototypes, but with little intention of keeping all that code verbatim. I can get something to the demo stage much more quickly than before, allowing me to show it to coworkers and users to get feedback on random possibilities. From there, I make a case to properly delegate time for "doing it the right way" as a slotted piece of work. That's resulted in a number of high-impact features being added to our app in the last few months, that in the past would have taken much longer or never came to fruition. 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.

brammertottens 5mo ago

There definitely is the danger of a lot of garbage being shipped, but with both the models getting better and better, and more tools and ways of working being discovered, I believe the quality of what is being outputted is going up as well. Right now, a tool like claude code is more like a junior dev. you really need to steer it well, and know what you are doing, to make sure it doesn't output slop. but with the ability to create agents that work together, implementing design agents, coding agents, review agents, even a non-technical person will be able to design a workflow that spits out quality code, and I think that time isn't far away.

collyw 5mo ago
Are the models really getting better and better? They tell us they are but I haven't noticed much difference in the last year or two.
- brammertottens 5mo ago
  It goes a bit up and down, compared to two years ago, in my feeling, both anthropic and open ai coding models have made massive jumps. In between big releases I do feel the quality of the models varies over time. 2 years ago I got annoyed with the models being crappy autocompletes, right now I'm just managing them more rather than coding myself