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- Hacker News
- Elephant in the room : how many of these bugs were written by LLMs in the first place ?
Because creating 100x more bugs and fixing 100x more isn't something to be proud of.
by goldenarm - That is a stat you invented out of thin air, based upon nothing but pejorative hope, against all logic or rational consideration of the timeline or the scale of the project, and you declare it the "elephant in the room"?
HN is so weird on the topic of AI, and so many desperately are trying to contrive a reality. Odd stuff.
by llm_nerd - If you assume that AI can produce code with less bugs, your 100 times increase in fresh bugs means you also increased your speed for new functionality by more than 100 times.
If models can spot 13 years old critical bugs, I think they can also produce fresh code without those bugs. The skillset is the same.
by buzzin__ - Look at the git stats for the project. Lines of code submitted haven't changed dramatically. Not everyone is just merging slop into the codebase. There are many ways to use AI, and most established players are not merging vibe coded nonsense.by surajrmal
- If people are handing off the coding to AI more and more then they'll be less skilled at spotting potential bugs in the first place. AI, course of, could be run to find potential bugs in the AI-coded features before commit. But then you're getting further and further towards a world where code, and code that runs infrastructure, is incomprehensible. We'll have to start sacrificing the first-born to the silicon in the hopes of a good build for code that controls your electrical sub-station.by tarkin2
- Can't have been much, as the Chrome project is 20+ years old and LLMs have only recently become a thing; they did not slacken their code review and testing practices with the advent of LLM code generators. And I doubt they do much development on the affected areas at the moment, they mention a 13 year old issue for example.
But it's an open source project, you can go and figure out whether your assertion is correct.
by Cthulhu_ - This is a tacit admission it was Mythos, not Gemini, despite alluding to experiments with Gemini earlier on:
> Added support for model interoperability to leverage the unique strengths of both open-weights and proprietary models
by iknowstuff - Weird, why would they use open weight models.by naveen99
- I'm also curious if they used Mythos. Google definitely had access to it via Project Glasswing. However, your logic doesn't hold. That sentence only tells us they were using multiple models, but offers no real clues as to which proprietary model(s) they were using.by dbmnt
- Neither Mythos nor Gemini are open-weight models (right?) so I'm not sure what model(s) this is actually referring to.
- I've recently been using AI a lot for performance optimisation during a particularly busy period at work. I would say it was almost completely useless at the high-level direction - it would point out suspicious parts of SQL queries for example but on back to back testing these almost never resulted in any performance change.
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.
by VBprogrammer - You're using it wrong. No really you are.
Give Fabe 5 access to a test database with some data, or even restricted access to your live DB and tell it to optimize then.
I've had stunning success optimizing for performance this way. In a single day, I made the core part of our app 2-3x faster.
by aurareturn - Then you're not grounding it to reality properly. LLMs get bad pretty quickly when you just tell them to pull the answer out of thin air. Pin them to reality with actual performance tests to run to test theories and the results will be much better.
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.
- Which model/agent/harness tool did you use? I've found what you describe was my exact experience some ~6-8 months ago, but since about a month or so the game has completely changed. Using 5.6 Sol with highest reasoning setting in Codex or Fable in Code, the models come up with a list of possible improvements from static analysis (ranked by complexity/benefit), write and run their own custom profilers and deliver significant performance improvements with barely any input needed from my side. So this is no longer a model issue, it's a user toolchain issue.by sigmoid10
- I've consistently found that I see performance issues that the AI misses. It often says "that's not going to be what improves performance, it's noise" and then I get it to do it and it's like a global 30% throughput win lol. I think a lot of performance guidance it'll be trained on is shit - I see devs consistently misunderstand performance too and downplay the impact of anything other than "IO".by insanitybit
- People in the comments sound like they're literally asking claude: "hey claude, can you find areas for performance improvements." Nah, it doesn't work like that. You have to be more like: "can you profile [...] and identify hot spots." then looks at results that are suspicious. if you don't ground it to the real world reality of your project then don't be surprised when it makes up its own.
that's the first mistake i made as a new claude user. i only knew what prompts people were sharing on forums. but since they had been written by vibe coders it was all complete non-sense. also think about this: software engineering is filled with highly specialised tooling that can easily improve software quality. the thing is, most developers dont know how to use them, and if they do -- using them is so slow that its almost not worth it.
any tool you can name claude can use. obscure academic proof verifiers, memory corruption scanners, debuggers, profilers, coverage tools, backdoor supply chain pattern scanners... it can run these and have results in seconds. really is like living in the future.
by Uptrenda - The mistake there is to point it at code to figure out performance optimizations.
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.
- The useless suggestions are exhausting. One of my co-workers uses Claude for all asynchronous communication, including Slack messages, Jira comments, code reviews, and emails.
Every single message from him, literally every time he communicates, it's a massive wall of text, overflowing with scope-creep suggestions like nothing I've ever seen before. It's impossible to ask him a simple question and get a simple answer.
And this seems to be a broader trend with AI in general. It's getting more and more difficult to retain some semblance of brevity with AI tools. I'm constantly asking them to "be brief," and "only answer the question I asked," and "don't provide more information than I requested," but they keep churning out more than I want in their responses.
It looks like a subtle attempt to use up more tokens.
by stack_framer - The thing that makes it work really well is to make sure it has all the tooling to verify its hypotheses. If you allow it to run the full lifecycle in loops you will be surprised how well it works.by herrkanin
- A lot of people here seem to be living in a different universe than me or simply don't know how to work with AI. I think detractors believe you should just let AI do the job blindly instead of leveraging it as a tool to accelerate you. They get mad at Excel for the poor investment returns. At this point, this is such a strawman, it isn't worth counter arguing.
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.
by glimshe - There is an argument that there is a lot propaganda in this space - because the issue of AI is now an issue of international security and politics.
I see a lot of the disagreement here has the same form as many of the political arguments from 10 years ago.
by qarl2 - Reminds me those Bitcoin threads where I would explain a use case it solved for me and everyone was like "nope that's impossible, there's no use case". And before the inevitable replies come in: no, I'm not saying AI is like Bitcoin.by olalonde
- LLMs are the first tech topic I’ve seen that attracts as much wishcasting as political debates. Some people get attached to the conclusion they want to believe and get angry at anyone who disagrees with them.
Most of the tech people I talk to outside of HN, Reddit, Twitter, and other debate heavy spaces have a more nuanced view. In group chats we share stories about the amazing things we got out of an LLM today, or the hilarious failures or mistakes they made. We share tips and battle stories. We talk about the pros and cons of different models. We discuss the hard things we’re doing that LLMs can’t handle yet.
Go on HN or LinkedIn or Lobsters and there are some comments from people who seem like they’re coming from a different universe or timeline. Some people claim LLMs are doing everything in their life perfectly. They’re posting thought leader Tweets about working 2 hours per day and doing everything with ChatGPT voice so they don’t even need to be at their computer. Others are claiming that LLMs are useless and they can’t get anything usable out of them at all, or that they create more problems than they solve.
It’s weird. I can’t recall a time when people in tech were having hot takes that disagreed with such polarized extremes. Like I can look at LLM output all day long and see that both of the extreme takes are not right, but they’re all steadfast in their ideas.
by Aurornis - It appears to me that this is the same pattern you often see when junior employees are given one or more resources intended to help them succeed. Despite having access to those resources, there is frequently no shortage of complaints about how incompetent people are, that they "should know" esoteric home-built sowtware patterns or business knowledge without being told or that they failed to perform a task in the one specific way that someone considers the "correct" approach.by andrehacker
- This (fixing bugs, making code better, refactoring), is the best fit for AI.
I think a lot of us were excited that we will finally be able to polish our old software.
Many of us were never given the opportunity and were tasked to produce new features at an ever increasing pace. Depending on where you landed you can be either elated or jaded.
by yoz-y - Did you think for a second that maybe other people work with other languages, domains, tools and work approaches than you?
It's so bizarre that noone realizes that there's a bit of a difference between banging together JavaScript into websites, writing firmware for widgets, herding servers or fixing medical devices?
by izacus - I agree with you.
> I think detractors believe you should just let AI do the job blindly instead of leveraging it as a tool to accelerate you.
However this is how it’s marketed. C-suite is telling people eventually you won’t need to read code, frontier labs saying programmers won’t exist etc.
The reality is like you said, a tool to accelerate you.
by cautiouscat - > A lot of people here seem to be living in a different universe than me
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.
by maccard - Worrying. Extrapolating a (speculative) future, this means (Google will feel) that soon the chromium base will no longer need the crowd-sourced bug hunting that is open-source. I expect Google to eventually stop working on chromium (in the open) and all current chromium-flavours will become de-facto forks of the last published version of chromium. These forks won't be equally easy to maintain given that the groups running them do not have access to the same level of subsidy as Chrome does with Gemini.by Phemist
- Conversely, it has never been more feasible for alternative web browsers to be created and maintained outside of big tech.