Thats why those Engineering fields have strict rules and often require formal education and someone can even end up in prison if screw up good enough.
Software is so much easier and safer, till very recently anonymous engineering was the norm and people are very annoyed with Apple pushing for signing off the resulting product.
Highly paid software across the board must have been an anomaly that is ending now. Maybe in the future only those who code actually novel solutions or high risk software will be paid very well - just like engineers in the other fields.
(A thing I think is under-explored is how much LLMs change where the value of tests are. Back in the artisan hand-crafted code days, unit tests were mostly useful as scaffolding: Almost all the value I got from them was during the writing of the code. If I'd deleted the unit tests before merging, I'd've gotten 90% of the value out of them. Whereas now, the AI doesn't necessarily need unit tests as scaffolding as much as I do, _but_ having them put in there makes future agentic interactions safer, because they act as reified context.)
The tests I have for systems that keep evolving while being production critical over a decade are invaluable. I cannot imagine touching a thing without the tests. Many of which reference a ticket they prove remains fixed: a sometimes painfully learned lesson.
Writing, art, creative output, that's nothing at all like code, which puts the software industry in a more particular spot than anything else in automation.
What I notice is that Claude stumbles more on code that is illogical, unclear or has bad variable names. For example if a variable is name "iteration_count" but actually contains a sum that will "fool" AI.
So keeping the code tidy gives the AI clearer hints on what's going on which gives better results. But I guess that's equally true for humans.
LLM has very high chance of on shotting this and doing it well.
So eventually it gets to the point where I'm basically explaining to it what interfaces to abstract, what should be an implementation detail and what can be exposed to the wider system, what the method signatures should look like, etc.
So I had a better experience when I just wrote the code myself at a very high level. I know what the big picture look of the software will be. What types I need, what interfaces I need, what different implementations of something I need. So I'll create them as stubs. The types will have no fields, the functions will have no body, and they'll just have simple comments explaining what they should do. Then I ask the LLM to write the implementation of the types and functions.
And to be fair, this is the approach I have taken for a very long time now. But when a new more powerful model is released, I will try and get it to solve these types of day to day problems from just prompts alone and it still isn't there yet.
It's one of the biggest issues with LLM first software development from what I've seen. LLMs will happily just build upon bad foundations and getting them to "think" about refactoring the code to add a new feature takes a lot of prompting effort that most people just don't have. So they will stack change upon change upon change and sure, it works. But the code becomes absolutely unmaintainable. LLM purists will argue that the code is fine because it's only going to be read by an LLM but I'm not convinced. Bad code definitely confuses the LLMs more.
I tend to use a shotgun approach, and then follow with an aggressive refactor. It can actually take a lot of time to prune and restructure the code well. At least it feels slow compared to opening the Claude firehose and spraying out code. There needs to be better tools for pruning, because Claude is not thorough enough.
This seems to work well for me. I write a lot of model training code, and it works really well for the breadth of experiments I can run. But by the end it looks like a graveyard of failed ideas.
(If the analogy, in the first paragraph, of a Roomba dragging poop around the house didn't convince you)
If 100% code coverage is a good thing, you can't tell me anyone (including parallel AI bots) is going to do this correctly and completely for a given use case in 60 seconds.
I don't mind it mind it being fast, but to sell it as 60 second fast while trying to give the appearance you support high quality and correct code isn't possible.
At Qlty, we are going so far as to rewrite hundreds of thousands of lines of code to ensure full test coverage, end-to-end type checking (including database-generated types).
I’ll add a few more:
1. Zero thrown errors. These effectively disable the type checker and act as goto statements. We use neverthrow for Rust-like Result types in TypeScript.
2. Fast auto-formatting and linting. An AI code review is not a substitute for a deterministic result in sub-100ms to guarantee consistency. The auto-formatter is set up as a post-tool use Claude hook.
3. Side-effect free imports and construction. You should be able to load all the code files and construct an instance of every class in your app without a network connection spawning. This is harder than it sounds and without it you run into all sorts of trouble with the rest.
3. Zero mocks and shared global state. By mocks, I mean mocking frameworks which override functions on existing types or global. These effectively are injecting lies into the type checker.
Should put to tsgo which has dramatically lowered our type checking latency. As the tok/sec of models keeps going up, all the time is going to get bottlenecked on tool calls (read: type checking and tests).
With this approach we now have near 100% coverage with a test suite that runs in under 1,000ms.
With no contention for shared resources and no async/IO, it just function calls running on Bun (JavaScriptCore) which measures function calling latency in nanoseconds. I haven't measured this myself, but the internet seems to suggest JavaScriptCore function calls can run in 2 to 5 nanoseconds.
On a computer with 10 cores, fully concurrent, that would imply 10 billion nanoseconds of CPU time in one wall clock second. At 5 nanoseconds per function call, that would imply a theoretical maximum of 2 billion function calls per second.
Real world is not going to be anywhere close to that performance, but where is the time going otherwise?
But the weird thing is: those things have always been important to me.
And it has always been a good idea to invest in those, for my team and me.
Why am doing this 200% now?
Including using more rigidly typed languages, making sure things are covered with tests, using code analysis tools to spot anti patterns and addressing all the warnings, etc. That was always a good idea but we now have even less excuses to skip all that.
>CEO of an AI company
Many such cases
Surely they know 100% code coverage is not a magical bullet because the code flow and the behavior can differ depending on the input. Just because you found a few examples which happen to hit every line of code you didn't hit every possible combination. You are living in a fool's paradise which is not a surprise because only fools believe in LLMs. You are looking for a formal proof of the codebase which of course no one does because the costs would be astronomical (and LLMs are useless for it which is not at all unique because they are useless for everything software related but they are particularly unusable for this).
I use devenv.sh to give me quick setup of individual environments, but I'm spending a bit of my break trying to extend that (and its processes) to easily run inside containers that I can attach Zed/VSCode remoting to.
It strikes me that (as the article points out) this would also be useful for using Agents a bit more safely, but as a regular old human it'd also be useful.
I don't think anyone wants production code paths that have never been tried, right?
That's a negative correlation signal for me (as are all the other weird TLDs that I have not seen besides SEO spam results and perhaps the occasional HN submission.) On the other hand, .com, .net, and .org are a positive signal.
100% coverage for AI generated code is a very different value proposition than 100% coverage for human generated code (for the reasons outlined in the article).
some (many?) devs don't want agents. Either because the agent takes away the 'fun' part of their work, or because they don't trust the agent, or because they truly do not find a use for it in their process.
I remember being on teams which only remained functional because two devs tried very hard to stay out of one another's way. Nothing wrong with either of them, their approach to the work was just not very compatible.
In the same way, I expect diverse teams to struggle with finding a mode of adoption that does not negatively impact on the existing styles of some members.
i was thinking it was more like llms when used personally can make huge refactorings and code changes that you review yourself and just check it in, but with a team its harder to make sweeping changes that an llm might make more possible cause now everyone's changes start to conflict... but i guess thats not much of an issue in practice?
It's a footnote on the post, but I expand on this with:
100% coverage is actually the minimum bar we set. We encourage writing tests for as many scenarios as is possible, even if it means the same lines getting exercised multiple times. It gets us closer to 100% path coverage as well, though we don’t enforce (or measure) thatBut what I care about is code breaking (or rather, it not breaking). I'd rather put effort into ensuring my test suite does provide a useful benefit in that regard, rather than measure an arbitrary target which is not a good measure of that.
Seems like even if people could potentially die, industry standards are not really 100% realistic. (Also, redundancy in production is more of a solution than having some failures and recalls, which are solved with money.)
def fragile(x):
lst = [None]
lst[x - 42]
return "ok"
def test_fragile():
assert fragile(42) == "ok"jmo, but tests should be a chance to improve the implementation of functions not just one-off "write and forget" confirmations of the happy path only... automating all that just short-circuits that whole process... but maybe i'm missing something.
"Ship AI features and tools in minutes, not weeks. Give Logic a spec, get a production API—typed, tested, versioned, and ready to deploy."
Seems actively harmful, and the AI hype died out faster than I thought it would.
> Agents will happily be the Roomba that rolls over dog poop and drags it all over your house
There it is, folks!
How are LLMs going to stay on top of new design concepts, new languages, really anything new?
Can LLMs be trained to operate "fluently" with regards to a genuinely new concept?
I think LLMs are good for writing certain types of "bad code", i.e. if you're learning a new language or trying to quickly create a prototype.
However to me it seems like a security risk to try to write "good code" with an LLM.
Especially with the massive context windows modern LLMs have. The core idea that the GPT-3 paper introduced was (summarizing):
A sufficiently large language model can perform new tasks it has never seen using only a few examples provided at inference time, without any gradient updates or fine-tuning.This is true now, but it can't stay true, given the enormous costs of training. Inference is expensive enough as is, the training runs are 100% venture capital "startup" funding and pretty much everyone expects them to go away sooner or later
Can't plan a business around something that volatile