111–120 of 668 posts
Real question is: how much those "fixed" bugs actually bothered the users to begin with? But since Google cares mostly about its investors, the numbers and a mention of Gemini in such a blog post are more important.
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Exactly, also it lacks a lot of context as to why they did that. Possible (probable?) scenario: - Marketing: "we found and fixed lots of bugs thanks to AI" - Reality: the KPI is now to fix as many bugs as possible with the help of AI, so they used AI to search old and easy bugs in the backlog, and then fixed it manually
Huh? Maybe a third scenario is that AI helped fix the bugs? Like described in the actual post you are replying to? This level of conspiracy theory is getting a bit ridiculous.
If Google are tackling lower value bugs with AI the number is in a way inflated compared to some utility measure (fixing a smaller number of worse bugs could be preferable) but it’s still things fixed.
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 qu…
> 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…
I do not know what the secret sauce is.
But I have a responsibility to the team I lead. I need to protect our velocity. Which means that my hiring practices now include specific checks that people are able to work with ai.
In the end, it might be a step thing. Some people are just inherently incapable of working with this technology...
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 e…
> I've recently been using AI 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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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.
What tooling makes this go?
How many of those automated fixes were reverted? How many introduced a new bug? What's the false positive rate on the finding agents? The post has counts for everything that went right and nothing for what could go wrong.
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Tests! Unit tests, integration tests, random adhoc scripts. You know - TDD! I’ve been working on UI component improvements and it was doing a lousy job until i specifically told it to test in a headless browser to validate it works. I think somewhere in an AGENTS.md i have an instruction to “don’t state your guesses as fact - validate findings and results”.
If the agent can write the tests its supposed to pass and we are worried that agent produces quite a lot of slop (which is why we are doing the tests), then what is the defense against test slop? Test the tests?
I work on user facing applications, and since the models do not have good taste, testing the UX is essential.
If you spot a bug, usually the model will attempt to reproduce it in a new test case that does cover the actual issue.
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 e…
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.