The Speed of Prototyping in the Age of AI(darylcecile.net) |
The Speed of Prototyping in the Age of AI(darylcecile.net) |
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.
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.
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.
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.
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?
I work as a DevOps engineer and have been using agents exclusively to code since the beginning of the year. Agents are really nice to quickly craft utilities to speed up planning. For instance I had it create a small cli for me that'll pull my cards from azure DevOps, load them as json, markdown and csv, and push updates once I'm done. Then I'll load into context transcripts of meetings and other written requirements, cross with current state of repos, to have meaningfully conrextualized work items without me having to implement these myself. I'll just have a long chat with the agent exploring these cards and defining the necessary refinements for description and acceptance criteria than I jusr push them all at once. Anything you can think of you just ask for the agent, so for me I don't trust code, so I'll have all my clis be no-op by default, so they will first print all they'll do and if I think the changes make sense I approve them and let the script commit to the canonical board.
Working with cloud consoles like Aws in general is a huge hassle, so crafting quick inventory utilities and tools for correlating data is a breeze.
Now the work itself is mainly ci pipelines, terraform files and automation. For these I'll base the agents on the specified work items and enrich them with my own understanding of the problem. I then launch the agents and read the agent output attentively. This is very important. You can't just prompt and leave, you need to be present all the time so you can steer the agent into solving the right problems. At the very least you need to review all the changes after an implementation session is done when you came back from making coffee. Many times it tries to create meaningless abstractions or very complicated solutions that I know can be done better. Or I have a different idea of how to organize the project so I do many follow-up sessions to refactor code.
In my personal projects I do a lot of small utilities. I spent some weeks designing and polishing a replacement for zurg and debridmediamanager the way I like it to be, simple and to the point, also tightly integrating them with jellyfin https://gitlab.com/gabriel.chamon/buzz
I have my own micro desktop environment on top of hyprland called Archie which recently I've been redesigning and improving a lot with agents https://gitlab.com/gabriel.chamon/archie
I have my own agile based methodology for creating and managing work items with tight integration with gitlab https://gitlab.com/gabriel.chamon/orisun
I have been improving my fork if gamma-launcher so that installing and managing the game on bazzite is simpler and more automated than relying on workarounds for workflows intended for windows https://gitlab.com/gabriel.chamon/gamma-launcher
Now for how I approach developing with agents. I think it's really important to get your constraints sorted out as soon as possible, so have your agent create a CI pipeline for code quality testing, like with ruff, pyright and pytest, to control style, code consistency and cyclomatic complexity. Put in the AGENTS.md explicit instructions that the agent must run these tools at the end of every coding session. If adopting a new project, use the agent to explore the code and see which refactoring points are worth tackling. Agents really thrive on good codebases, so this first code quality improvement pass is a must.
To sum it up, with agents you give up writing code manually for reading lots of code, exploring the domain with the help of the agent and architecting the solution at a strategic level. You trust the agent but you also verify. And lots and lots of manual testing. My personal take is that I'm infinitely productive now, only constrained by how much code and agent terminal output I can read, and also by the rate limits of the model providers and mental fatigue.
I am in a similar professional position to you, and I make a lot of small things in my spare time. I have found using agents very tedious and frustrating to workflow. Initial prototyping can be ok, but when you start to get serious with code it falls apart quickly. If you don't tell the agent literally exactly what to do to the letter, it will guess some things. Usually some of those things are wrong, and dont match the functionality you expected. I find this a very frustrating place to be, trying to tell the agent what is the wrong functionality, and what I expect instead. Usually at this point I enter what I refer to as a doom spiral, where everything I tell the agent just takes me further from what I want, until I eventually have to revert everything it has done and try again.
This gets worse with bugs, where a inevitably a code bug will appear, and trying to tell the agent what the bug is and what is expected instead usually results in more broken functionality elsewhere. When I have written the codebase manually myself, I can usually pinpoint and fix bugs in a few minutes after diagnosing them. I have literally spent hours trying to get an agent to fix a bug without breaking something else.
I thought maybe refactoring code might be a strong point for LLMs, so I tested taking a monolith codebase and asked various agents to refactor into reusable module structures with exposed api endpoints so that I could split apart functions into modular chunks whilst retaining full functionality. They all failed miserably at this, breaking everything and never managing to make a working example.
LLMs and their agents certainly are cool, and they are great at writing emails for people and summarising meeting notes. They can even create very small coded programs well. But let loose on serious production codebases and they can cause much more frustration than they solve. I will come back and try another day when LLMs have evolved again to the next level, but for now they can stay coding my toy projects and dictating my teams meeting notes.
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
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.