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!!
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).
> 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
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.
Can you help me understand what the "cost" of other people producing garbage is? Prototypes are generally shop jigs. You'd feel weird gold-plating a stop block.
> the "cost" of other people producing garbage is?
Sure can! It's a well known phenomena, won the researchers a Nobel, and explains a lot of the American economy and "lack of taste". The Market of Lemons[0].
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
The problem is people are shipping prototypes. We may disagree what is a prototype and what is a shippable product, but that disagreement in itself is worth noting as part of the problem. I mean FFS we in the tech industry love selling things with the promise of future improvements. The last few iPhones shipped with the promise that they were going to get better with AI (did Apple intelligence really pan out? Did it pan out anywhere near what they promised? My Google Pixel phone still can't schedule a haircut for me or book a reservation at a restaurant, despite multiple promises).
[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.
The premise here is that people are selling these prototypes, and they are being bought. I mean, fine, that's bad, but when we discuss "prototypes", I assume uninformed cash transactions are off the table.
I'm in an industry where I can really see this, executed by honestly talented people able to interpret what the LLMs produce. It's bikeshedding hell. If you pursue every possible idea and get to implement all of them and it actually works, in the best possible scenario with no technical debt because you're able to stay on top of it (presumably in the window you have before you just burn out), you end up with all the ideas at once.
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.
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.
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.
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
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.
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?
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.
The single file is interesting, we’ve been observing something similar too. Do you have a specific prompt that you load in by default to make this work? Are these react files, or just pure HTML/JS/CSS? Aka do you compile it via Esbuild or webpack or something, or are you asking the model to generate something that works out of the box?
We’ve been seeing Claude artifacts sometimes come out as JSX or TSX
I have been doing the same thing, creating small one file "apps". The problem that I have currently is that I often want my Agent to be able to present me with something like a report on a code change, have me mark it up (comments, choices), and then present those interactions back to the model.
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.
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!
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've been using those for a couple of years, there's a good chance they're not necessary against more recent models. I've found that just saying "use Vanilla JavaScript" is enough to skip React / other build steps.
I avoid any build steps because those make it harder to copy and paste code in and out of LLMs.
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
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.
I used Claude extensively during an internal hackweek at my company to prototype a new data analysis application. Probably would have never attempted the project without AI. Now it’s in production with more than 20k weekly users. Almost never use Claude to dev on it now, but it definitely helped me get off the ground.
> What are people doing with prototypes afterward?
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.
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?
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.
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.
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.
> 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.
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?
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.
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.
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
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 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.
> Agents are really nice to quickly craft utilities to speed up planning.
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.
Your tone makes me think you have already fallen in love with agents and you think they are the best thing since sliced bread, but let me give you my experience.
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.
My general experience is that LLMs are both really good and extremely bad. It's so easy to get into a hole of "No, not like that, like this" and it just never getting better (including with new sessions).
I find it fascinating the wildly different experiences people have with LLMs, and honestly I think it's a good thing. We will need code crafters and technomancers, I don't think having either one or the other is healthy, which is why I'm very critical of mandatory LLM use in corporations.
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.
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.
Everyone understands that a wireframe isn’t done yet and it’s easy to change at that phase.
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).
https://github.com/archivebox/archivebox
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.
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!
The problem is people are shipping prototypes. We may disagree what is a prototype and what is a shippable product, but that disagreement in itself is worth noting as part of the problem. I mean FFS we in the tech industry love selling things with the promise of future improvements. The last few iPhones shipped with the promise that they were going to get better with AI (did Apple intelligence really pan out? Did it pan out anywhere near what they promised? My Google Pixel phone still can't schedule a haircut for me or book a reservation at a restaurant, despite multiple promises).[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.
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.
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
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.
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.
Prototyping already existed - how do you think the iPhone came into existence?
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
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 avoid any build steps because those make it harder to copy and paste code in and out of LLMs.
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.
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?
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.
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.
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.
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
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.