ismailmaj

I bought the M4 air given that the consensus was also that it was the best value for 1k, but I ended up returning it and went for pro base model for a few reasons (still valid for M5 AFAIK from a bit of research):

- pro motion (120hz screen).

- better display brightness which is important when there is a bright sun outside.

- 1 more USB-C port and HDMI port (no dongle hell).

- 20% more battery life.

- This is more personal, but 13" is too small and 15" is too big, so 14" MBP worked best for me (~25 HFOV with a stand + KBM).

It's hard to justify saving 400 bucks given the gap between the models, but the decision is closer since the air has 16GB memory by default since M4 AFAIK.

Semantic end of turn being 300ms of silence is horrible because I ended up intentionally um-ing to finish my thoughts before getting answer.

It was difficult to detrain and that made me stop using voice chat with LLMs all together.

3.1 feels to me like 3.0 but that takes a long time to think, it didn't feel like a leap in raw intelligence like 2.5 pro was.

Cannot trust those damn Americans during on call.

Warp specialization is an abomination that should be killed and I'm glad this could be an alternative.

I hope they can minimize the bookkeeping costs because I don't see it gain traction in AI if it hurts big kernels performance.

top 10 elo in codeforces is pretty absurd

What I'm getting from you is that, given competence (and here it's a manager being able to make himself dispensable) things "work out".

I tend to agree but I have a feeling that it takes a certain number of bad apples min-maxing KPIs to destroy the system.

When the manager evaluates you based on how many lines of code you submitted because the VP decided that the teams with the most code submitted deserve a bigger bonus, then you stop doing the rational thing and start playing the game, it may be possible for the middle manager to play around a bad VP but from an IC level it's rarely worth it, you might invest your small political capital only to be burned afterwards.

I've seen way too often in my career benevolent IC ignoring the politics, getting things done only to be last to be recognized when it's clear things would be falling apart without them. Even worse, doing the right thing might keep/hide the dysfunctional system for longer.

Sorry for being overly negative, I like the theory but failure points while rare are common enough for me to have seen them a few times, if things are not rotten it probably holds water.

I see your point, this reminded me of the incompetent/malicious section.

Yes, leadership could do the right thing, but the right thing is difficult to measure by shareholders and can only grasped by shareholders until it is too late (Disney for example), but why would they?

I agree, people are not malicious but the system currently selects for people that ignore the difficult to measure right thing to do (in SWE it’s doing the grunt platform work that no one will appreciate) and skip to things that are rewarded (cool feature tm that users hate by gives a cool slide to the CEO), it might seem like people can do both but it’s not in practice due to having limited energy.

Also stream of thought answer, but I feel like we are back to the trust thing, the right thing to do is impossible to measure, so we can only hope they do it because they care about doing the right thing, but incentives punishes them and makes them scarce at the top and the only solution is to go back to a high trust society (maybe like Japan where CEOs care and the incentives are set so people are often doing the right thing).

I agree that culture cascades but leadership is not the top of the food chain, their decisions or culture is often influenced by the capital.

The meta recently has been to ignore everything and only focus on maximizing the stock price, which is aligned with their incentives (stock based compensation, bonus based on stock performance) and the modern investors (money quick now).

I wonder if the rotten feeling I have on management is linked to western societies moving from high trust to low trust and from patient to impatient.

How do you scale a more human management system when both the manager or the managee can be incompetent or malicious.

I'm aware that incentives are often counterproductive or insulting for white collar workers where there are elements of creativity in the job, but I feel like to get it to work you need trust which IMO cannot be scaled to big companies like Google.

FYI, I'm paraphrasing, perhaps badly "The puzzle of motivation | Dan Pink | TED". https://www.youtube.com/watch?v=rrkrvAUbU9Y

Only after voting I understood it was related to duckduckgo....

Even if true, high rep is impractical, otherwise we'd see people doing body weight exercises only reach high levels of bodybuilding.

Even around 1900, it didn't matter if you were a genetic freak, you needed a barbell to win competitions.

undefined behavior on steroids be like:

Ironically, if your setup is too niche (e.g. browsing privacy configuration) you can be easily tracked, though no one will bother, but captcha's will certainly not miss you.

My next low hanging fruit is certainly to make my LLM usage local, my queries contain much more sensitive information than what is mentioned by this post.

In the past I dropped off privacy when it was too inconvenient. For example I dropped protonmail because of bad search, left Linux desktop for Windows due to missing software, etc, I still haven't found the sweet spot for LLMs yet.

For the rest, I'm currently running the full macOS, iOS, safari, Apple passwords and I'm decently happy with this middle ground.

fails on Safari macOS, works on chrome though.

-> expects reasoning

-> runs it in Gemini fast instead of thinking

....

Nothing against the author, the content is good. I just wish this wasn't fluffed out with AI.

Maybe an extremely inefficient way to do money laundering?

"Oh sorry guys, we made the mistake again that saves us X% in compute cost, we will fix it soon!"

I find it a bit too introspective, living in a specific period creates urgency, there is still a (opportunity) deadline.

My concerns are more practical:

- How do we make sure power-wrestling bad leaders don't halt generations, currently old age gives us a soft reset.

- How do we cope with suffering? With no clear end in sight I expect more mental health problems. And I'm unsure we can remove those issues without striping the humanity out of people, I would for sure hate to be turned into a calculator with qualia.

For the lazy, he says this on repeat using 2000 words:

...

In the CPB Digital Cosmos, the system first locked into a strange ratio: two thirds consciousness, one third physics.

...

That anomaly appeared as the missing 0.1 spark.

For the first time the system stabilized. Life emerged.

I don’t think it was ever an option since it had ties with the french government early on (Cédric O) and Macron’s party is quite pro EU

Just in time for the monthly EU bashing

Don't think so, I expect that system to use Spanner, so my best guess is that the user generated an image at the end of the credit reset window (which is around noon EST).

I’m guessing the argument is that LLMs get worse for problems they haven’t seen before, so you may assume they think for problems that are commonly discussed in the internet or seen on github, but once you step out of that zone, you get plausible but logically false results.

That or a reductive fallacy, in either case I’m not convinced, IMO they are just not smart enough (either due to lack of complexity in the architecture or bad training that didn’t help it generalize reasoning patterns).

Probably refers to jobs that are unrelated to his interest like cashier, just to get by for now.

To make matters worse, the RTX3090 was released during the crypto craze and so a decent amount of the second hand market could contain overused GPUs that won’t last long, even if 3xxx to 4xxx performance difference is not that high, I would avoid the 3xxx series totally for resell value.

Typically for matrix multiplications there is a wide range of algorithms you could use to compute it, on one extreme end you could use numerically stable summation and the other extreme you could have tiled matmul with FP8, the industry trend seems to go further away from numerical stable algorithms without much quality drop it seems. My claim is probably unfair since it ignores the scale you gain from the speed/precision tradeoff, so I assumed numerical stability is not that beneficial compared to something precision heavy like physics simulation in HPC.

The way I explained it to myself in the past why so much of the CUDA algorithms don't care much about numerical stability is that the error is a form of regularization (i.e. less overfitting over the data) in deep learning.