The goal of software engineering is not to write code faster. Coding is itself a translation task (and a learning workflow, as you can’t keep everything in your head). What you want is the power of decision, and better decision can be made with better information. There’s nothing in the setup that helps with making decision.
There are roughly six steps in software engineering, done sequentially and iteratively. Requirements gathering to shape the problem, Analysis to understand it, Design to come up with a solution, Coding to implement it, Testing to verify the solution, and Maintenance to keep the solution working. We have methods and tooling that help with each, giving us relevant information based on important parameters that we need to decide upon.
LLMs are example generators. Give it a prompt and it will gives the answer that fits the conversation. It’s an echo chamber powered by a lossy version of the internet. Unlike my linting tool which will show me the error when there’s one and not when I tell it to.
ADDENDUM
It's like an ivory tower filled with yes-men and mirrors that always reply "you're the fairest of them all". My mind is already prone to lie to itself. What I need most is tooling that is not influenced by what I told it, or what others believe in. My browser is not influencing my note taking tool, telling it to note down the first two results it got from google. My editor is not telling the linter to sweep that error under a virtual rug. And QA does not care that I've implemented the most advanced abstraction if the software does not fit the specs.
That just really depends on your situation. Here's a case I had just last week: we had artists in residency who suddenly showed up with a new, expensive camera that didn't have any easy to use driver but requires the use of their huge and bulky custom SDK.
Claude whipped a basic working c++ proprietary-camera-sdk-to-open-video-sharing-protocol in, what, 2 minutes? From the first go with a basic prompt? Without that it'd have been at least a couple days of development, likely a day just to go through the humongous docs -- except I had at most two hours to put on this. And I already have experience doing exactly this, having written software that involves realsenses, orbbec, leapmotion, Kinect, and all forms of weird cameras that require the use of their c++ SDK.
So the artists would just not be able to do their residency the way they wanted because they only have 3 days on-site to work too.
Or I'd have spent two days for some code that is very likely to only ever being used once, as part of this residency.
Thus in my line of work, being able to output code that works, faster than humans, is absolutely game changer - this situation I'm describing is not the exception, it's pretty much a weekly occurrence.
That's basically what I said. They are example generators. Their creators have not published the source of the data that goes in their training so we can assume that everything that is accessible from the web (and now from places that use their tools) was used.
So if you're already know the domain to provide the right keywords, and can judge the output to see if it's good enough, it's going to be fine. Especially, as you've said, it's something that you're used to do. But do you need the setup mentioned in TFA?
Most software engineering tasks involved more than getting some basic prototype working. After the 80% work done by the prototype, there's the other 80% to have reliable code. With LLMs, you're stuck with the first 80%, and that already require someone experienced to get there.
I was writing some automated infra tests with Terraform and Terratest and I wanted to deploy to my infra. My tests are compiled into a binary and shipped to ECS Fargate as an image to run.
Instead of doing docker in docker to pull and push my images and before googling for an existing lib for managing images directly I asked Claude to write code to pull the layer tarballs from docker hub and push them to my ECR. It did so flawlessly and even knew how to correctly auth to dockerhub with their token exchange on the first try.
I glanced at the code and surmised it would have taken me an hour or two to write and test as I read the docs on the APIs.
I am sure there is a lib somewhere that does this but even that would have likely taken more time than the code gen I got.
It absolutely is a game changer.
Now the game for you is to deal with whatever equipment they throw at you, because nobody is going to bother consulting you in advance.
Just use AI, bro.
Good luck next time they show up with gear that Claude can't help you with. Say, because there's no API in the first place, and it's just incompatible the existing flow.
>So the artists would just not be able to do their residency the way they wanted because they only have 3 days on-site to work too.
That, to me, sounds like the good outcome for everyone involved.
It would have been their problem, which they were perfectly capable of solving by suddenly showing up with supported equipment on the job site.
Wanting you to deal with their "suddenly showing up" is not the right thing to want.
If want that, they shouldn't be able to do the residency the way they want.
Saying this as a performing musician: verifying that my gear will work at the venue before the performance is my responsibility, not the sound tech's. Ain't their job to have the right cables or power supplies. I can't fathom showing up with a setup and simply demanding to make it work.
IDK what kind of divas you work with, but what you described is a solid example of a situation when the best tool is saying "no", not using Claude.
The fact that it's a weekly occurrence is an organizational issue, not a software one.
And please — please don't use a chatbot to resolve that one either.
Writing proper code including tests and refactorings takes substantial time.
It is definitely worth it to do this faster, if only to get faster feedback to go back to the first phase; requirements and analysis.
I have experienced this myself, using CC it took me a few hours less to realise i was on the wrong track.
Much of the criticism of AI on HN feels driven by devs who have not fully ingested what is going with MCP, tools etc. right now as not looked deeper than making API calls to an LLM
"All our critics are clueless morons who haven't realised the one true meaning of things".
Have you once considered that critics have tried these tools in all these combinations and found them lacking in more ways than one?
all these are just tools. there is nothing more to it. there is no etc.
I think I’ve become disgruntled with the anti-llm crowd because every objection seems to boil down to “you are doing software engineering wrong” or “you have just described a workflow that is worse than the default”.
Stop for a minute and start from a different premise. There are people out there who know how to deliver software well, have been doing it for decades and find this tooling immensely productivity enhancing. Presume they know as much as you about the industry and have been just as successful doing it.
This person took the time to very specifically outline their workflow and steps in a clear and repeatable way. Rather than trying it and giving feedback in the same specific way you just said they have no idea what they are doing.
Try imagining that they do and it’s you who are not getting the message and see if you get your a different place.
Workflows are personal and the only one who can judge them are the one who is paying for the work. At most, we can compare them in order to improve our own personal workflow.
My feedback is maybe not clear enough. But here are the main points:
- Too complicated in regards to the example provided, with the actual benefits for the complication not explained.
- Not a great methodology because the answer to the queries are tainted by the query. Like testing for alcohol by putting the liquid in a bottle of vodka. When I search for something that is not there, I expect "no results" or an error message. Not a mirage.
- The process of getting information, making decisions, and then acting is corrupted by putting it only at some irrelevant moments: Before even knowing anything; When presented with a restricted list of options with no understanding of the factors that play in the restriction; and after the work is done.
"Refactor: We are replacing FlogSnarble with FloozBazzle. Review the example usage below and replace all usage across the codebase. <put an example>"
"In the browser console I see the error below. The table headers are also squished to the left while the row contents are squished to the right. Propose a fix. <pasted log and stack trace>."
"Restructure to early exit style and return an optional rather than use exceptions."
"Consolidate sliceCheese and all similar cheese-related utility functions into one file. Include doc comments noting the original location for each function."
By construction the resulting changes pass tests, come with an explainer outlining what was changed and why, and are open in tabs in VS Code for review. Meanwhile I can spend the time reading docs, dealing with house keeping tasks, and improving the design of what I'm doing. Better output, less RSI.
We at Octomind use a mix of augmented screenshots and page representation to guide the agent. If Playwright MCP doesnt work on you page, give our MCP a try. We have a free tier.
GitHub has gh, there's open source jira-cli, Cloudflare has wrangler and so on. No configuration needed, just mention on the agent doc that this kind of tool is available. Likely it will figure out the rest.
And if you have more complicated needs, then you can combine the commands, add some jq magic, put to package.json and tell agent to use npm run to execute it. Can be faster than doing it via multiple MCP calls.
It really feels magical when the AI agent can browse and click around to understand the problem at hand
Also, sometimes an interactive command can stop agents from doing things. I wrote a small wrapper to always return so agents never stop from working
Copilot has a very direct advantage in my eyes - it has a plenty of models available: Sonnet 3.7, 4, GPT, Gemini, etc. That's something you won't get with the latter two.
