Adversarial testing, checking developer assumptions, refactor suggestions, small dev tools and even some guided coding all sit on the other side of the spectrum of what you can do with coding and AI. For larger and larger codebases even simple things like tracing dependencies or behavior might be greatly aided.
And the critiques reserved for that narrow bucket on the other end, blindly generating code, are too easily conflated with the rest.
2. Code reviews and security reviews happen quicker and produce more findings.
I would think that (m)any team(s) using AI might also be seeing a higher rate of finding and fixing issues.
Even the Linux Kernel (I'd say Windows and Apple too) are seeing the same phenomenon.
Do you import new more bugs?
That's AI for you.
At Amazon we have many forums to share our AI wins, but none to share AI failures or disappoinments.
No wonder execs make bad decisions regarding AI, they only hear completely one-sided stories.
In fact, if it wasn't for the fact that it made making the actual changes I identified much easier (move these joins into a CTE etc) it would have been a detriment. Not only did I get sidetracked by a bunch of useless suggestions but I also had to put up with others dumping their raw AI output at me as if it was somehow a meaningful contribution.
Performance optimization involves simulating real world loads and measurable results. Give them the opportunity to actually have a closed loop if you want more than trivial improvements.
The place to find them would be performance profiles, query plans, telemetry. The guidance for perf still applies, measure before and after change.
The issue is that the code often does not contain the information to do a perf optimization. Eg. you can't tell your cache size, the volumes of data in your DB or the latency of your network through just the text. Should you provide this context, you can get better results.
I think I'll abandon this discussion and keep using AI quietly while exchanging tips with like-minded people who are interested in using it properly and efficiently.
I feel this way on this topic too.
> I think detractors believe you should just let AI do the job blindly instead of leveraging it as a tool to accelerate you.
The problem is; how _should_ I use AI? On a previous thread, I had two replies to the same comment, one saying "provide the LLM all the context it needs and let it go ham", and the other saying "Carefully guide and craft it and review everything". Both subthreads had people agreeing.
My experience with LLMs is leaving them unattended gives very poor results within a handful of iterations, and that carefully guiding them can be a value add, but the effort ot do that is very often as much as just writing the damn code myself, particularly if there's a few iterations that go on.
I'm wondering what the Alphabet employees still commenting on this forum have to say about it? I guess for the right comps they can keep their mouths shut no matter the high level of idiocy involved.
Google.
We really need an alternative to this greedy and evil corporations de-facto controlling a huge portion of the modern www stack. All decision-making processes are here subjugated to what fits Google's adEmpire. This is a perpetual system of control amplifying abusive systems in place. We already see this with the recent age sniffing; Google just yesterday announced that all android users must hand over their age to everyone else. Before that google kicked out or locked out open source alternatives to android (or, at the least, made their life significantly harder than before). These are not isolated ways of abuse - this is systematic abuse.
We really need alternatives to Google here. It can not be that one tyrant company dictates so much of people's open lives (and no, Firefox is no alternative; Mozilla is basically a nerfed and bribed entity that has given up on firefox many years ago already; ladybird may become an alternative at one point in time, but right now is not and there are questions over how Kling handles the overall project, but these complaints are significantly below what Google is doing globally here).
Fixing more bugs in the adChromium code base does not change the underlying problem at hand.
I've mostly only used Codex for reviews, for Godot/GDScript code, and it helped me catch bugs that would've taken me ages to even notice on my own; games be tricky like that.
Claude was mostly useless or annoying up until the last time I tried it (about 3 months ago)
You have to be vigilant though: Codex often picks out obscure edge cases and suggests adding multiple new functions and flags to avoid issues that would be better off left as fast-failure crashes. Maybe that's because of the 5.6 Sol Max I leave it on.
Because creating 100x more bugs and fixing 100x more isn't something to be proud of.
I got Codex to find and apply a bunch of settings I didn't know about and in the future I hope to use AI to make real changes in performance that venders probably don't have time or resources for.
that being said, what I've learned so far is essentially WhyIsItAlwaysHN's entire comment "The place to find them would be performance profiles, query plans, telemetry."
I just find it so frustrating that for a user interface that was basically solved in the 90s, Windows GUI still struggles to survive on hardware exceptionally more powerful than what we had 30 years ago
0 - https://www.lacksan.com/updates/ 1 - https://github.com/Lacksan-Dev/HP-ZBook-Performance
Tbh, once this information is available (which is the tricky part), in 99% of cases there's really no AI needed to analyze the data, since the 'low hanging fruits' will usually stand out anyway. And once you get into the area where optimization hotspots are no longer obvious, you're already deep in the diminishing returns area and optimizations for one use case or hardware configuration may degrade performance on others. That's my experience anyway.
Most people are not arguing that problems are too tricky for a human to solve once presented with it, but AI can look at 1000 things at the same time across a whole code base and then just come back with the results a human can review.
E.g. a good code analysis tool needs to work predictably at button press on any code base.
Still better than nothing of course, e.g. I actually think bug scanning / code analsysis is indeed the one area where LLMs are actually useful, but it suffers from the same 'diminishing returns' problem as traditional approaches, if not worse (e.g. still no silver bullet, but a mostly useful additional tool in the toolbox).
The trend I've noticed is that AI struggles to think outside the box when making optimisations, which exactly what's needed when you've made all of the practical DB and logic optimisations to the existing code.
Often you need to take a step back and question how the system is working and if there would be better ways to design it so the bottlenecks you're hitting wouldn't exist in the first place. Caching things, adding indexes, tweaking logic – these can help, but you'll quickly hit diminishing returns once you've done all of the obvious stuff.
I've seen people here say how AI is great at optimising code though, but I'm not sure if that's because they're giving it optimisation problems with a lot of low hanging fruit or if they're successfully getting AI to rework their systems to remove bottlenecks. This one area I find AI to still be particularly bad at.
I was able to optimize a physical water simulation that was almost unrunnable on browsers to a buttery smooth 60fps version.
That's uninteresting as long as you don't specify the model you used. For example, Mythos was far better at finding security bugs than previous models.
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