maxrmk

I feel very fortunate to have worked other jobs before my first in tech. My last was hanging drywall and I will never forget how awful it was. I haven’t loved every part of every tech job I’ve worked, but I’ve always chosen to be there.

I think the author is missing perspective on what the alternatives are like, and seems to have a lack of agency when they say things like:

> How many of us have been forced to work on projects that make us sick to our stomachs - surveillance tech, data mining tools, algorithms that reinforce social biases - because we don’t have the power to say no?

There’s incredible mobility within tech - more so than almost any other industry. Vote with your feet! I’ve taken major pay cuts to have more choice over my work, and have never regretted it.

If it's Mustafa vs Sam Altman, I know where I'd put my money. As much as I like Satya Nadella I think he's made some major hiring mistakes.

I think this probably isn't as big of a deal as people are making it out to be. But I find a certain kind of joy in Mozilla being judged on the worst possible interpretation of their terms of service, since they do that to others _all the time_ [1].

[1] https://foundation.mozilla.org/en/privacynotincluded/

Ok this is way too long to read in one sitting, but it looks incredible? I've been looking for resources like this for """real""" model training at scale. The authors have worked on BLOOM (the first >100B parameter model), starcoder and fineweb so they actually know this space.

I wish it were not a single giant webpage, but I guess I can deal with that.

I think this misses the mark. We know LLMs can learn facts. There are lots of other benchmarks full of facts, and I don't expect that saturation of this benchmark will mean we have AGI.

The missing capabilities of LLMs tend more in the direction of long running tasks, consistency, and solving a lot of tokenization and attention weirdness.

I started a company that makes evals though, so I may be biased.

I'm not sure what they intended this to apply to. LLM based systems don't change their own operation (at least, not more so than anything with a database).

We'll probably have to wait until they fine someone a zillion dollars to figure out what they actually meant.

I _think_ that's a factor of using rule based reward functions and not actually a feature of GRPO? The original formulation of GRPO from deepseek math uses a neural reward model that is trying to predict human rankings of responses, and in that configuration won't see 0 gradient updates.

Flipping it around, if you swapped out the neural reward model in PPO with a reward function that can return zero, I thiiinnkkk it would be able to produce zero (or very low) gradient updates.

I'll be the first to admit that I don't know enough about the space to say though. I'm still a beginner here.

There’s some disagreement over whether or not GRPO is the important part of deepseek or not. I’m personally in camp “it was the data and reward functions” and that GRPO wasn’t the key part, but others would disagree.

I read this to get up to speed on RL for LLMs. If you have limited time, I’d recommend reading the entire first chapter covering the basics and some terminology and then section 5.4 on RL for LLMs.

I struggled a lot with the first chapter, and had to look up a lot of terms that weren’t defined. But ultimately it was one of the most worthwhile things I read, and has helped me follow along with other important papers.

Seconding the other commenter, I’d love a more detailed version of how you do this. I looked into doing something similar with voice memos and couldn’t figure it out.

Could be the case, I’m not familiar with their specific tokenizers. IIRC llama 3 tokenizes in chunks of three digits. That seems better than arbitrary sized chunks with BPE, but still kind of odd. The embedding layer has to learn the semantics of 1000 different number tokens, some of which overlap in meaning in some cases and not in others, e.g 001 vs 1.

Cool! Contrary to some of the other posters I think this definitely counts as deanonymization, or at least is close enough. How anonymous would satoshi be today if we had his location to within 250 miles?

Repeated applications of this attack (maybe disguised somehow?) could let you track someone’s travel over time, and it is usually only takes 4-5 zip code sized locations to uniquely identify someone.

Yeah that’s my understanding of the root cause. It can also cause weirdness with numbers because they aren’t tokenized one digit at a time. For good reason, but it still causes some unexpected issues.

This is clever, and I got a good laugh out of their example video. The demo UI of "Double click here" isn't very convincing - I bet there's a version of this that gets people to double click consistently though.

I'm vaguely amused by the headline of "requires no external power" right above the image of it sitting on top of (and plugged in to) a giant portable battery.

As much as I hate it, I use twitter to follow a bunch of people who work at fair/openai/etc and that's been a pretty good source. There's also a "daily papers" newsletter from huggingface, but it's pretty hit or miss.

sure, but once it's trained there isn't a running maintenance cost

I was wondering about this one too...

> At best, they say, Orion performs better than OpenAI’s current offerings, but hasn’t advanced enough to justify the enormous cost of keeping the new model running.

wdym "keep it running"?

This is really interesting. Based on their wikipedia I can see they collect a lot of RF traffic - are IMEIs identifiable with the raw data captured that way? I'm surprised they are not encrypted. I say this as someone who knows nothing about the space.

Totally– This was in the early days of open source .net, when it was still called .net core. We had just moved onto GitHub and I don’t think microsoft had quite figured out how to manage that organizationally. There is a point of time there where the fastest way to reach an engineer was not to go through a tier one contract that you pay microsoft millions of dollars for, but instead to just open an issue on the GitHub. I doubt that’s the case anymore.

I thought linus had lost it for a second there, until I saw it's just named after him and not something he created. I generally disagree - I think that having many developers with a shallow understanding of the whole codebase scales less-than-linearly with the number of devs.

It's probably less actively harmful than new product development, but I'd still say a team of 50 is probably larger than is necessary for the amount of usage flutter gets.

That ethos runs through everything the WA team does. I learned a bit about it when I was at fb and I was beyond impressed by how few servers WA used for its core infrastructure. It's a really well engineered product.

> That's 50 people serving the needs of 1,000,000. Doing a little bit of division, that means that every single member of the Flutter team is responsible for the needs of 20,000 Flutter developers! That ratio is clearly unworkable for any semblance of customer support.

Back when I worked on .net we had fewer than 50 people maintaining a product that shipped to over a billion machines. If you opened an issue on github we'd usually reply that day.

It's getting a bit long in the tooth, but I feel like 'the mythical man month' should still be required reading for software devs. More devs != better.

Tried it! I really like the idea, but I think the clue generation could use some work. Every clue ended in "in games", and honestly most of them were not really game related to start with. For example the clue "Place in games where characters go to rest and replenish health or mana" had the solution "bar"... which I wouldn't describe as right. Similarly "The name of a popular character who may need rescuing in some games" was "Emily".

I think it might be worth working on prompting to make sure the answer is a unique solution to the hint (or at least closer to unique). What model are you using here?

There's a newer version of this model that takes a really cool RL based approach: https://arxiv.org/pdf/2408.08152

other talc founder here. ask us anything!

Can't wait for all the spam calls I'll get.

This reads like something generated by AI. Or at least heavily using it in the writing process.

I’d consider it a web browser but that’s a vague enough term that I can understand seeing it differently.

I’d be disappointed if it became common to block clients like this though. To me this feels like blocking google chrome because you don’t want to show up in google search (which is totally fine to want, for the record). Unnecessarily user hostile because you don’t approve of the company behind the client.

The author has misunderstood when the perplexity user agent applies.

Web site owners shouldn’t dictate what browser users can access their site with - whether that’s chrome, firefox, or something totally different like perplexity.

When retrieving a web page _for the user_ it’s appropriate to use a UA string that looks like a browser client.

If perplexity is collecting training data in bulk without using their UA that’s a different thing, and they should stop. But this article doesn’t show that.