The pixel shows on screen an icon whenever he microphone is being used. The voice alert I got was from Zoom. This seems like a bug in their end, just a scary one
You're right, it's not tech support, and I didn't use it as such. HN is a tech forum, and I found this incident strange enough to warrant warning others. If you're not interested, you can simply ignore it
Last night at 4:00 AM, my Pixel 7 woke me up with Zoom's native voice prompt "Recording in progress", with the green mic indicator active on screen.
My phone was idle on my nightstand out of reach and no Bluetooth device was connected. No Zoom account signed in and no active meeting. I uninstalled the app immediately after taking the screenshot, so I don't have access to any logs. After waking up this morning I realized I shouldn't have uninstalled. Is there any way to get logs of what happened now?
I don't know what caused this, but it got me more paranoid about what runs on my phone. Has anyone experienced anything similar?
* The most recent one: I was doing an "AI assisted coding interview". The problem itself was simple. I gathered clear specs, I explained what I planned to do. I was supposed to use AI so I wrote down the main function signatures I expected (the API boundary) and wrote in the prompt what I wanted Claude to do. I wrote no code myself other than editing the output. When I got rejected, I was told it was because "I wrote too much code myself".
* Once I was asked a brain teaser. I solved the initial problem, but one of the follow-ups made it significantly more challenging. I wrestled with the problem for a few minutes, and realizing I was going around in circles I stated so and told the interviewer I wasn't sure how to proceed. I was expecting a tip or at least an acknowledgement, but I heard nothing. Blank silence with the interviewer staring at the screen. Since it was a zoom call, I thought my internet was down, but when I asked "hey, can you hear me?", he replied yes, and went back to radio silence. This was a pattern of the interviewer throughout the interview. Later on, after this question I implemented an algorithm and was asked for its time complexity. I mistakenly said O(n) (I forgot the initial sort), and the interviewer literally just stared at the screen and said nothing until after 10 seconds or so when I realized my mistake and corrected it - at which point he acknowledged and moved to the next question.
* Another one that happened two times (at different companies) is getting asked a very vague question, like "how do you fix a bug in production" (to which I reply with 'I try to replicate it locally, I go through logs, etc') and then being told by the recruiter the interviewer didn't like that my responses were "too generic".
Regarding the first you posted, it sounds very much like a "feature" sweatshop. Meaning that they have some product with customers, and will implement absolutely every single request they get from customers - and really just want prompt engineers that will ship out updates as quick as possible.
IME, there's been a rise of those for the past 1-2 years. They not only embrace AI/slop coding, it is a core part of their business model.
Unfortunately I fear this is going to become the standard in our industry. Everyone will be forced to rely on AI to "ship faster", to trade learning for short term speed.
Author here. I expect most to be ironed out too, regardless of whether they're publicized or not. The engineers working on these models are aware of the problems
Cool method. Pre deep learning there was plenty of interesting research on sparse methods. What do you think we're missing to have more widely used neural+sparse approaches?
I think the lack of efficient GPU kernels was the main problem. It is much, much easier to get a real speedup and memory reduction from quantization from fp16 to fp8 than from 50% sparsity. For sparsity you needed structure (which makes your model worse) and special hardware support.
> Does this mean Google will be releasing OSS LLMs? They could justify it as "commoditizing your competitors business".
That’s what this piece argues for. I predict it will not be reflected in Google’s strategy in the next, say, six months, or morw to the point until and unless the apparent “Stable Diffusion” moment in LLMs becomes harder to ignore, such as via sustained publicity on concrete commercially significant non-demonstration/non-research use.
