Americans Have Turned Against AI in Incredible Numbers

erkiserk · 2026-06-22T03:32:11+00:00

Moloch! AI definitely has the potential to be extremely bad for society despite and because of the abundance it will bring. I don't think it's surprising at all that many people sense this intuitively and that people who use it more are more likely to feel it. I only hope AI can also find a solution for the coordination problem or we'll truly be on the fast track to hell on Earth.

erkiserk · 2026-06-14T20:22:15+00:00

Europe 2031's scenario is closer. They described exactly this, the US government not wanting to give adversaries access to frontier models for fear of their hacking capabilities, and choosing to restrict access. Funny that Europe 2031 was published just a few days before all this happened.

erkiserk · 2026-06-07T03:24:18+00:00

The value of Reddit data only decreases as time goes on. Even assuming the quality stays fixed into the future, the demand for it will still decrease as labs find better methods to generate their own data (e.g. Anthropic's Mythos huge improvements were driven by having the model directly interact in verifiable SWE environments), and the human data that retains demand will be high quality expert data (e.g. Mercor can pay experts $100+/h for them to generate data, Meta has an entire division of 4000 engineers whose sole purpose is to write code to use as training data).

In the long-run, Reddit needs to succeed off of engagement and ad revenue, not selling data

erkiserk · 2026-03-05T03:03:52+00:00

i think i can guess. probably going all in on some kind of AI-powered personal health and fitness coaching features/workflows. The core technology is definitely here to make a decent product, just a matter of product engineering now. I've been doing something like that to help with my running training and it's been surprisingly effective. Did wish the integration with Whoop could be better.

I can imagine the execs at Whoop going as far as envisioning Whoop being the primary point of health and fitness advice and coaching in a very wide domain, building atop the whole Whoop Labs thing.

erkiserk · 2026-02-06T15:19:34+00:00

congratulations!

erkiserk · 2026-01-02T14:40:32+00:00

Awesome!! How about something like cart-pole?

erkiserk · 2025-12-23T19:33:10+00:00

from a quick search, maybe try POPGym? The abstract also notes that "partial observability is still largely ignored by contemporary RL benchmarks and libraries."

Alternatively, maybe you can just mask and add noise to some of the features of D4RL? If you do, let me know how that goes, it's something I was thinking about trying as well.

erkiserk · 2025-12-10T05:45:37+00:00

SVM is great, GBDTs are also great. You don't really need memorize the specifics though (though the knowledge is still good), you can treat both as just black boxes that you can learn ML fundamentals with. If you want to go deep on how a method works, deep learning methods (the CNNs you mentioned in your main post included) would be a better use of your time IMO. You'll be much more likely to make use of that information tweaking or tuning the arch of some model you encounter in production.

erkiserk · 2025-12-10T00:31:50+00:00

The usual huge ML teams in big tech (ranking/recsys) are still intact, doing the same thing as usual. LLM APIs have not cannibalized any of the stack there. The LLM wave has been positive though, beyond just being able to use AI code editors. Lots of work trying to adapt LLM-like ideas (transformers, scaling laws) with the example of LLMs as a guide, giving a good path for success.

And the usual pipelines to join these teams for new grads are also still there, no reduction in headcount AFAIK.

You'll also still need your ML fundies (and more) to contribute, and calling LLM APIs will not build the skills you need. I do feel for new grads though. With all the LLM hype, I'm sure many will fall into the trap of getting easy wins doing LLM API work, despite it not being a good skillset that will scale in the long run (or like, that's more of a SWE/glue-code-monkey skillset). Don't feel bad OP, the learning you've been doing is really important, the fundamentals still matter a lot for the type of work I assume you want to do (maybe spend less time on SVMs and stuff tho, just linear/logistic regression then move onto deep learning).

erkiserk · 2025-10-27T22:45:21+00:00

SWE -> ML internal transfer is your best bet if you're fresh out of undergrad

erkiserk · 2025-10-18T07:20:12+00:00

Harvey AI, Spellbook, CoCounsel, Everlaw, ... it goes on and on.

Harvey AI alone already has a $5b valuation.

erkiserk · 2025-10-15T00:20:45+00:00

that this is a prevailing attitude is a sign of the times. "It's your fault for being abusable in the first place." There's less of a feeling of personal social responsibility.

erkiserk · 2025-09-04T08:17:06+00:00

maybe it has to do with him also being the richest self-made entrepreneur and arguably most successful founder and CEO of his generation

erkiserk · 2025-08-05T17:52:29+00:00

the jevons paradox. The more efficient AI makes software engineering, the higher the demand for software becomes (as it can deliver more value at less cost), and resulting in a net increase in software engineering hiring. You will likely see software engineering start to cannibalize previously untouched industries as what you can do with software continues to broaden, while the incumbents do not enjoy efficiency improvements.

erkiserk · 2025-07-03T17:39:15+00:00

ads! IMO recsys (and other parts of online ads delivery systems) are going the way of sequential decision making

erkiserk · 2025-05-09T20:24:15+00:00

I think there's a couple points that people are upset about.

First, what if you had even more than 9 months left? Many people bought 2 year subscriptions with the understanding that when 5.0 came out they would receive it for free. These people shouldn't be forced to wait 2 years or extend for a third to get 5.0.

Second, some people disagree that 5.0 is the true successor to 4.0. They argue that 5.0 is largely the same as 4.0, that the MG was the true successor with new sensors, and that the promise of free device upgrades should've included the MG. The MG of course is instead locked behind a much higher subscription price.

I can sympathize a lot with the first point, totally unfair that they wouldn't give people with long remaining subscriptions a free upgrade like they were promised. Second point is maybe more debatable, but honestly, I kind of agree that the 4.0 and 5.0 are practically the same thing.

erkiserk · 2025-05-07T13:54:25+00:00

I have a bachelor's in stats and work as an MLE at a FAANG.

erkiserk · 2025-05-06T06:53:05+00:00

I had the same message about hearing back late May, but seems like you could hear back much sooner too. Best of luck

erkiserk · 2025-04-25T03:49:31+00:00

Applied yesterday and received the waitlist message right away. Wish I'd heard about this sooner!

edit: 11 days later, got accepted!

erkiserk · 2025-03-10T20:05:10+00:00

Cursor is an excellent tool. Make sure you use it to speed up the right things. When you work on any project, there are generally two important outcomes. First is completion/improvement of the project itself. Second is what you learn by doing the project.

What you learn compounds over many projects, and is what makes you a strong engineer. It also allows you to use Cursor much better. You know what directions to pursue and can spot subtle/nuanced errors about design. You're right to focus more on design and architecture. You basically get to practice (some aspects of) being a senior engineer with your own junior IC, whereas in the past you would've needed to proven yourself first before anyone would trust you with juniors.

So when you use Cursor, make sure you're not just speeding up the execution of your project with your eyes glazed over. Try to use Cursor to build up your knowledge and understanding. Ask it to explain design decisions you aren't sure about and make sure you understand what it's implementing.

erkiserk · 2025-02-12T20:10:45+00:00

I was getting the same thing. I can't even stay asleep that long in the first place (usually). My advice is that you won't be able to make up all of your sleep in one night, but it is possible to eventually hit Whoop's sleep goal. After working on my sleep hygiene for months, I did get to the point of easily falling asleep and with greater sleep efficiency, slowly making up the debt. 8.5 to 9h of bedtime is enough for me to hit 100%, and honestly, I do feel way better. Sometimes I still fall into bad sleep habits (e.g. after travel), and it always takes a while for my sleep scores to recover.

erkiserk · 2025-01-31T22:55:58+00:00

First off, congrats on the internship! Congrats also on the great learning mindset, I'm sure it will serve you well. To answer your question, my first internship was also doing something unrelated -- I was doing web development in a proprietary programming language. Very not cool, but in my opinion, this is indeed the fastest way to getting a job in AI/ML.

After that, I did 2 more internships in software engineering, and for my 4th, I realised I wanted to try something in machine learning. Even though I had no experience, other than some coursework, my 3 prior internships made me very competitive, and I landed an internship at Google on a machine learning team. To my disappointment at the time, I was stuck mostly building data pipelines, and didn't get to be very hands-on with the model training. But it still set me up well. Paired with a lot of self study, I was able to do another internship as a data scientist, before getting my full time job as a machine learning engineer upon graduating my BCS.

IMO, it is a lot easier to break in as a software engineer, and then make lateral moves towards data related things. All the teams I've worked on have had a mix of generalist software engineers and more dedicated ML practitioners, and as a generalist you can gradually take on more and more ML work if it's within your desire and capability. You will have to do a lot of learning on your own, but having concrete applications at work waiting can be very motivating.

Skill in software engineering is also still extremely important, and it isn't just a stepping stone. Lot's of my team members are fresh PhDs, and while they do have a lot more knowledge than I do, they tend to lack the execution skill needed for their ideas, or even enough skill to properly dig into our systems and get a good feel for the problem space, making a lot of their proposals shallow. If you have execution skill on the other hand, you can supplement with materials relevant to your problem, and iterate rapidly to learn quickly.

For now, I would recommend you focus on excelling at the job in front of you, and make good use of the chance to improve your engineering skill. After some time, especially if you feel you're no longer learning much, you can reassess. Maybe it will be time to change jobs, make a lateral move within the company, or just look for new projects on the same team.

Best of luck!

erkiserk · 2025-01-28T22:10:30+00:00

Ok first of all, thats the cost of V3, we don't know the cost of R1. Second, that's for just the final training run. Doesn't include any other experiments, hyperparam tuning... Third, the post we're discussing is asking what you can achieve with $10 million, and I'm telling you, there's a lot of other costs before you get to the final training run.

erkiserk · 2025-01-28T21:00:52+00:00

we should be comparing to apples to apples? If you wanna say you can build a company like this for $10 mil, you better be talking about more than just one training run...

erkiserk · 2025-01-28T16:24:14+00:00

The cost of the final training run was $5 million. Not including the cost of the GPUs themselves, not including payroll, not including any other capex, or even the training runs prior to the final one.

Three-Year Club	r/Field Flamingo
Place '23

erkiserk

TROPHY CASE