rishabhaiover

Nope, there is a categorical degradation in quality of output, especially with medium to high effort thinking tasks.

The downtime forces me to relook at my utterly dependent relationship with agentic assistance. The inertia to begin engaging with my code is higher than it has ever been.

OAuth is failing, I can't login via claude code.

If one can't find their true purpose in life, they resort to seek purity in morals and virtue (in themselves and people around them).

> We are now generating $2B in revenue per month

What??

I find it bizarre than no one here seems to be commenting on the insane amount of capex redirected towards AI infrastructure build out as a reason for such decisions. I only hear bad product or covid over hiring but they seem like cope to my cynical mind.

because the damage done is relatively objective?

I strongly disagree. I've gone through a similar education system and it's soul crushing to not perform well in those singular events that define your career and identity.

Therein lies the argument that SWEs could become operators (in a much reduced capacity) between AIs and the world.

I wonder if this move is to grab customer base as much as they can before the supply chain crunch dents Apple manufacturing for the first time ever

A kind person with humility would never say this.

But at what stage are we asking for that RAM? if it's the inference stage then doesn't that belong to the GPU<>Memory which has nothing to do with the CPU?

I did see they have the unified CPU/GPU memory which may reduce the cost of host/kernel transactions especially now that we're probably lifting more and more memory with longer context tasks.

I'm assuming this is for tool call and orchestration. I didn't know we needed higher exploitable parallelism from the hardware, we had software bottlenecks (you're not running 10,000 agents concurrently or downstream tool calls)

Can someone explain what is Vera CPU doing that a traditional CPU doesn't?

I'm in a CS program right now, I've seen wild shifts from ChatGPT 3.0 to the current models:

1) I've seen students scoring A grades in courses they've barely attended for the entire semester

2) Using generative AI to solve assignments and take-home exams felt "too easy" and I was ethically conscious at first

3) At this point, a lot of students have complex side-projects to a point where everyone's resume looks the same. It's harder to create a competitive edge.

I'm curious, what was the algorithm problem?

So many more products are competing for finite attention now. And the solution to that problem is not to productize your commodity imo, art created for the sake of selling is not art.

Naive question but do people really value certifications like these?

Google (and almost all other BigTech) is spending on scaling compute (data centers/securing power generation/chip contracts). My comment was not related to AI producivity and its impact on reduction of workforce. I believe a company spending nearly all its free cash flow on scaling compute (or borrowing money to do so) would have a different opinion on the economics of human capital.

Google's projected AI capex spend is $170-180 billion for this year. It's unreasonable to think AI would not be a reason for companies to consider layoffs.

These kind of HN submissions test how fair discussions can be here:

> Please don't use Hacker News for political or ideological battle. It tramples curiosity.

Reference: https://news.ycombinator.com/newsguidelines.html

How is this related to the current discussion at hand?

I can't tell if this is a bot or human response.

How do you get local sandboxing with a permission based model? I thought wasmtime was the answer!

Damn, the research moves fast. I was wrong again: https://arxiv.org/abs/2507.11768

Human brain's prediction loop is bayesian in nature.

I used to think it was the quadratic complexity of attention but I guess that's not a concern anymore as they've made more hardware aware kernels of attention? The other I remember is continual learning but that may be solved in near-term future. I am not completely confident about it.

I don't agree. The recent emergent behavior displayed by LLMs and test-time scaling (10x YoY revenue for Anthropic) is worth some hype. Of course, you are correct that most people who rally behind AGI do not understand the fundamental limitations of next-token prediction.

> The impotence of naive idealism in the face of economic incentives

A great point. I saw blinding idealism during the early days of GPT era.

I'd be curious to see the shift in numbers since December, 2025.

> There's suggestive evidence that hiring of young workers (ages 22–25) into exposed occupations has slowed — roughly a 14% drop in the job-finding rate

There goes my excuse of not finding a job in this market.