Testing.

Lots and lots of testing. I usually interactively test with the LLM. Now that I'm spending less time, coding, I get to spend more time, testing.

In my experience, I test more, with an LLM, than I did, with my own code. I don't find coding errors, but I do find implementation errors.

On another note: I ran an LLM on a bunch of really mature code that I wrote, and could swear was "perfect," and it found a number of issues.

Nobody's perfect. Trust; but verify.

Technically it's just a role change. You're now the project manager or something, not the developer. It isn't a project manager's job to understand the code, only to ensure it does what it should and doesn't do what it shouldn't. And to ensure the developers, etc str unblocked and doing well. Embrace the new role.

I've been spending a lot of time iterating on feature implementation plans with Claude (at work) and GPT (for personal projects).

I then make sure they write a lot of tests and that if functionality is changed or added/removed then tests are updated alongside.

Generally I try to review changes myself and ask it to explain the changes.

It's not perfect but I feel like the planning step being iterative isn't all too different from my old research -> think about implementation -> implement loop.

At that point, reviewing any code changes goes hand in hand with how I coded before LLMs.

The major difference is I usually have it make any changes for things I don't like.

I’m slowly coming around to the idea that if it works, I’m good with that. I never used to personally check all my colleagues work, either, I just trusted that they’d done a good job.

That’s not to say I blindly trust an LLM - my project is an autovectorising compiler. I have it write a bazillion tests, and I have real, non-trivial programs that it compiled. I have it run a ‘gate’ before releases that takes literally hours on 5 different architectures. To say this code is tested is … masterful understatement.

I think what helps is a long planning session, going into details, working out corner cases, designing test-cases, you know work and effort. Once that’s done it’s easier to let it work through the night and do a major production feature.

I still review the code, but hand on heart I couldn’t say I’d reviewed everything, sometimes I miss something in a complicated check-in. I try to keep checkins small and contained, but sometimes that’s not easy to manage alongside the “don’t break stuff” rule as well.

So it comes down to: I’ve been working on this for 6 months or so, and so far this has worked out for me. You know, kind of like getting used to a colleague’s idiosyncrasies over a period of time.

I will say that in my experience the code quality is fairly high. Maybe that’s down to the effort put in ahead of time in the planning stages, I don’t know, but I don’t seem to suffer from the ‘slop’ that people complain of. YMMV.

     ┌─────────────┬────────────┬──────────────────────┬──────────┐
     │  Benchmark  │ clang -O2  │ xcc --par-auto (GPU) │ Speed-up │
     ├─────────────┼────────────┼──────────────────────┼──────────┤
     │ saxpy       │ 1,546 µs   │ 726 µs               │ 2.1×     │
     ├─────────────┼────────────┼──────────────────────┼──────────┤
     │ Mandelbrot  │ 394,238 µs │ 2,110 µs             │ 187×     │
     ├─────────────┼────────────┼──────────────────────┼──────────┤
     │ nbody       │ 37,090 µs  │ 1,035 µs             │ 36×      │
     ├─────────────┼────────────┼──────────────────────┼──────────┤
     │ perlin      │ 78,016 µs  │ 1,850 µs             │ 42×      │
     ├─────────────┼────────────┼──────────────────────┼──────────┤
     │ mandel_deep │ 73.2 s     │ 55.8 ms              │ 1,312×   │
     └─────────────┴────────────┴──────────────────────┴──────────┘

This is how every product manager feels every day.

If you're an LLM prompter shipping products, you're outputs are more akin to a product managers, not an software engineer's.

It's not too dissimilar to life as a Principal level engineer pre-AI. You technically oversee the work of many engineers on their own systems, but you couldn't possibly review all the code yourself. You appreciate that human code review reduces, but does not eliminate, the number and severity of bugs shipped to production. You don't have the low-level details in your head, just the high-level ones. You design how all these engineers interact with each other, without stepping on any toes.

Time will tell whether we were right to move from a system where we trusted humans to understand the low-level system, to one where we trust fancy calculators. But so far, it's working.

I've been playing with this https://getchronosphere.com. It will (attempt to) rebuild a PR in a coherent journey rather than a massive blob. It's been somewhat helpful up to this point, and still improving.

Test driven development and the testing pyramid.

If the agent has written tests and crucially - witnessed them fail before starting the work to make them pass - you have a lot of confidence.

Review of your own work's easier too - test and production code tend to mirror each other, giving you a much more complete picture. Confused by the production code? Go read its tests for a bit, then come back.

It's also easier to understand the change if you were there for the build, watching the transcript, checking in every so often. Doesn't have to be your primary task - leave it on a second monitor, it'll catch your eye naturally.

And finally, on top of test-driven development, have the agent first (before the first test driven development cycle) build out an end-to-end test covering the work it'll do. Once that test is green (test driven word for "is passing") you're done with your work and can clean up at will.

Play with it before shipping, get it real nice. It's never been easier to clean the tech debt up and make the UI perfect.

Eventually you'll be ready to package it up into a PR!

Now's you can leverage agents and get creative. Make your colleagues froth to review your work. Consider making a Loom video to walk them through it. Consider reworking it into a commit series; it only takes a few second for your agent and you can tell a story for humans. Maybe you want to sequence it according to your struggles; maybe it's better to show the different layers emerge with each commit - it's up to you. Consider also that your agent can easily do one-off visualizations of the change for your colleagues if you think it'll help them, and you can attach those too - mermaid charts or even whimsical AI generated images. All this is because motivation is driven entirely by other humans' motivation and you want them frothing with excitement every time you drop a review. Make it fun, it takes seconds and gets your stuff merged fast while making your teammates smile.

This is a brand new job and we will work out how to do it together :)

I'm miles away from all of this as a non-user of AI and it seems like a mania to me.

I lost my ability to fully explain the systems I work on around the time I transitioned from next-edit-prediction AI running in my IDE (early Copilot) and working primarily via Codex/Claude CLI's.

The further away I got from the implementation details, the fuzzier the semantics became. To the point that now I could not in good faith say I understand the system I have built from the ground-up and worked on the last ~4-5 years.

The solution to this, in my mind, is not to revert back to reviewing implementation details. The economics of that from a business/productivity perspective don't play out well.

The only viable solution in my mind is adapting code/languages themselves to the shift by making programs formally verifiable. That way you can be sure that the code "does what it says on the tin", and provably so.

I feel so strongly about this I've written on it:

https://gavinray97.github.io/blog/design-by-contract-and-eff...

I think the solution is leaning on static types and hard to abuse abstractions to make programs correct by construction. It does mean that you’ll still have to use your brain to design the internal code architecture, and just use LLMs to fill in the boring bits between the safe abstractions.

Well, it's simple. Don't do it.

Formal verification, pick a language that has it integrated and prove relevant post conditions.

AI still does not know how to even write code that makes impossible state unpresentable so testing won’t help.

I was struggling with this today. Working on a feature and AI review flagged something so I need to fix that. I iterated multiple times throughout the day and was completely exhausted cause each iteration was always complicated with the fix being ~1k lines. Finally landed on a one I felt at least a little comfortable submitting for review but a work day like this is super draining and I felt like crap the entire evening (I am about to go to bed, I am probably gonna have nightmares about pressing enter and enabling auto mode on plans that make no sense in a true nightmare sense)

Force to always use TTD, at least validate the tests.

Recommend reading (All though a little self serving): https://www.linkedin.com/pulse/ai-code-quality-jorge-varas-a...

I can think of so many times this has happened before. People getting used to MVC, Dependency Injection, ORMs. Sometimes the code is so large you only work on a part of it, so you have to get used to a lot of not knowing.

So how do we build confidence anyhow when we can't know everything?

I think first, standard checks - does it compile? What do static analysis tools say - code quality, passes SAST analysis? Linters? Style checkers?

Do the tests run? I'm not comfortable with the tests I write let alone the AI.. by adding fault injection (fuzzing, chaos), and mutation testing, I have a better degree of confidence in the tests and code.

Spend even more time getting to know the product instead. Maybe I don't know the code as well as I used to, but I know the product backwards and forwards. That helps me have some new confidence that I didn't really have before. Knowing the greater whole.

It’s very disconcerting. I think this will largely be solved by going all in on AI and use it to test and review the code in an adversarial manner. That won’t be cheap but it’s the only way I see this work. But a lot of companies will probably save money and use AI mainly for writing code and then skimp on review and testing. I bet we will see some huge blow ups with AI written code and hopefully the industry will learn.

It also took the industry quite a while to accept that server code needs to be secure even if that requires a lot of extra effort. AI will probably be the same.

Im currently building a ui framework for unreal. I dont enjoy ui work so I wanted a way to have to write less ui.

For me I dont look too closely at the implementation but I look at the api where if a human was using my framework how is the code they'd have to write and engage look at. Then I also do plenty of performance against a comparable target in my case slate (default unreal) and keep overhead minimal.

So good performance and reasonable public facing api are the 2 main pieces im focused on. Less with the internals.

Care about the seams and interaction and the things you can measure or count.

By taking responsibility of it, maybe by saying "Hey this is not a good idea", and if that doesn't work perhaps by switching jobs if that's an option?

The mental gymnastics developers are willing to go through just so they can avoid standing up for something is quite perplexing.

I'm currently working on a combined Aero sim/CAD/CAM software for designing, simulating, and performing DFM optimization specifically for Group 1 to 3 winged UAVs.

What I do is ask Opus/Gemini to explain the parts I don't understand. And I do some of my own research to verify accuracy of the explanations.

In addition:

* Every new feature has a Feature Design document which I review, modify, and approve. And a corresponding Implementation Plan which I also review and approve.

* Every fix has a RCA document and fix Implementation Doc which I review, modify, and approve.

* There's a global high level Architecture and Design document with additional sub component design documents which are always kept in synch after a new feature or bug fix is done. Synching it is always a step in the Implementation plan.

This does adds some overhead but it allows me to have have check point gates. This has often allowed me to catch misunderstandings and align properly before any code gets edited by the AI agent.

Having the above docs also helps when it comes to referencing them as context to properly ground new features or bug fixes.

I shipped code I didn't understand when I copy pasted from Stackoverflow before. At a level if it works it works.

Product doesn’t care. And most of us will get replaced by agents. I guess around 30% of the engineering workforce will keep their jobs which will be mainly to keep the product up and running, doing whatever it takes (so using agents to fix stuff basically).

The ones who get to keep their jobs will be rebranded as Maintenance engineers with a lower salary. VPs and Heads of will keep their cozy job (bc there are not so many of them), and they are not that dumb to fire themselves.

Code understanding doesn’t matter anymore (or won’t matter soon)

I don't ship code I don't understand. I fully review every line if it's going to prod.

My dumb little utils in ~/.local/bin? Those get merged after a quick functional test without my having read a single line of it. Still, the skill I maintain for such a job directs agents to write such throwaway scripts in anything but shellscript because I might want to extend some day and fuck shellscript

I had same problem, but after a while I just accepted that is inevitable

That's the neat part, you don't.

Or at least, I don't think people should.

To me this completely goes against the whole point of engineering.

IMO If you don't understand the code, then you're just a manager now. Soon your salary may also reflect as such, assuming you get to keep your job.

>This new standard of shipping with AI is moving so fast that if we're going to spend our time checking each line of code, it's going to take forever.

"This new way of piling bricks is so fast that I can no longer afford to check the buildings integrity".

I just don't understand how the fuck we ended up with this narrative. Somehow the machine making things twice as fast forbids us from spending time ensuring quality? Surely if it does things faster you have more time and tools to check!

I'm half convinced this frenzy is the marketing work of AI companies at this point.

>How are you handling this?

easy, don't write code with LLMs. If my job forced me to do so I would quit.

An engineer from Meta not knowing what they're doing is exactly how I imagined engineers at Meta. Thank you for confirming it's an actual reality.

On the other hand, not even surprised anymore most people even try to provide serious answers to this. Welcome to the pure insanity.

You just stop caring. Everyone else did about three months ago. Time to find something new to care about.

It's simple: you don't. You slow down. It's essential to understand what you ship, before you ship, as there are real world consequences for not doing so, ranging from wasting the time and energy of yourself and others, up to failures which cause a great deal of harm to people and the world. To knowingly ship code you don't understand is grossly negligent, regardless of how common it becomes.

Claude to Prod. Every day.

Don't.

By doing so, you debase your own profession.

If you don't understand it, then you haven't built it.

> How to deal with shipping AI-written code you don't understand?

Stop shipping code you don't understand.

Is there really any other solution? You can either ignore it and pretend like it's the new era of software like half the other commenters are, or you can sit down and understand the code and what you're shipping.

You are accountable.

You're going to have to understand it one way or another, either at writing or at debugging. Even if you delegate all debugging to AI, you'll still need to determine what is "right".

If you don't understand the code at writing or debugging, you'll pay the architectural price (where it degrades). That price may be worth paying, but that depends on the system you are building.

Generally speaking I brain storm the feature first and produce a dev plan. This includes asking it to reuse code/libraries we’ve already got! After it’s gone off ah done its thing we go back over and do a QA on what it’s built, why that approach, what can we do better etc etc?

I spend time to add extra observability, cross-checking and alerting at runtime to offset the risk.

It feels like another layer of "conceptual compression". I'm not sure who/when that phrase came around but in context of Rails is where I heard it and its things like ActiveRecord or really any ORM. You aren't writing the SQL directly, something else is and for the most part it works. Then it doesn't work or your database grows too much and you need to optimize table structures with indexes, etc.

My point is if you don't understand the fundamentals of the underlying code (SQL in the example above), you're more likely to have issues in the future. Or maybe not if that app doesn't reach a usage pattern or complexity where the nuances under the hood matter.

It really depends on what you're shipping. Meta ships social media apps where in the grand scheme of things if something is broken software wise it largely doesn't matter. What's the worst case? lost ad revenue? Users can't do things? If that were software for MRI machines or aerospace where software issues/bugs can result in lives lost then it matters much more in understanding what is going on at all levels of the software and potentially hardware.

Anyone who trusts the output of LLMs uncritically, even after the impressive level models and harnesses have gotten to, is borrowing against their future.

Models and harnesses have gotten really good, but they still are hard-capped by the operators that use them. The sycophancy of models is perhaps their greatest weakness, as when a user who might be experienced in one area attempts to use AI to build within an area they are less familiar with, the model does not take a step back and examine the "why" of what is being built from an architectural standpoint, it simply performs the expected task in a direct manner, ultimately creating sloppy work that may be easy to ship but difficult to maintain.

A good example of this was a recent interaction I had with a Dev who was very familiar with how to develop embedded and system level applications. He and every other dev are being asked for increasingly more due to AI, and he had to bring something he had built as a single-user desktop application into a distributed cloud environment. He ended up rsyncing a copy of his working directories including multiple credentials and sensitive internal project documents to a virtual machine binding it to a public ip address. Thankfully our tooling detected this before he could get any farther but it illustrates how the AI model isn't doing any real thinking or engineering at the end of the day. Unless the operator knew to instruct the model about proper cloud architecture and understood best practices around deployment of public systems, the model simply did what it was being told to do by a user who didn't know any better.

Another example of this is the sheer amount of unsophisticated users we've been detecting using Javascript injection into their web browser sessions as a form of automation for web services they may use. These users would never know to ask how to request an API token which may allow them to accomplish things in a much easier and approved way, and instead are relying on the frailest possible automations that expose them to risks of credential exposure.

We've got to be careful with how we engineer things with AI models. Just because we've made boilerplate easier to generate doesn't mean that we can avoid the actual engineering of what we do. And I can't help but think that firms which are fully committed to AI code generation, won't just be introducing an avalanche of slop into their product which will explode the costs of maintenance over time.

If it's important code (and much isn't), then I spend a lot of time using Matt Pocock's /grilling first, and then I get the agent to kick off peer-review with other agents, and surface any disagreements. Some models are better at it than others. This usually forces me to broadly understand the architecture, and I spend most of my time answering the design questions for which there isn't an obvious solution. I have been pretty happy with the code I've launched to production, as measured by the ways it breaks, and by how agents who weren't part of the original committee describe it.

This is the state that founders and decision makers are usually in at a startup. Sure, sometimes you have the coder founder who wrote the first iteration of the product, but many times the founders have no clue how the product works under the hood. They entrust that to the engineers. That was then. Now, much of that engineering layer has been removed, so the founder just needs a smaller team of people who understand how to structure the software and interact with the LLM. LLMs do the rest.

The problem here is that you're trying to do a job that is already being done for you. Maybe you don't yet trust it to do a good job, but that is quickly changing.

We just don't do that, I don't know why people talk like that is a given

Is it a company culture thing or a domain specific thing?

Do not

Replace "AI agents" with "a bunch of human coders" and you'll see the answer. Your product needs decent testing and it's your job as the responsible person in charge to make sure that's in place and working properly. So if anything you probably want to be reviewing the test code. But often that isn't necessary since you can do things like introduce bugs deliberately and see if tests fail; check test logs for information that confirms the tests were run as expected; try some manual testing once in a while; ask for new more advanced tests (e.g. test performance not just functionality); and so on.

> How to deal with shipping AI-written code you don't understand?

Recognise you aren't the one shipping it.

It's Meta. Shipping crap is par for the course. You're fine.

The part that makes me uneasy is when the AI writes both the code and the tests. They might agree with each other and still miss the same thing. How are people here checking for that without doing the whole job twice?

[flagged]

[flagged]

[flagged]

[flagged]

[flagged]

[flagged]

[flagged]

[dead]

[flagged]