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'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
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.
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.
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?
Prototype? Why stop there..
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 ;)
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
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?
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.
> 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-impos...
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?
Yea cause it's done while you're still reading the docs for your code generator. lol
Code generators are usually one short command. It’s less typing than a prompt would take.
Then how much time do you spend debugging and fixing the generated code? lol
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.
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 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.
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