* I say apparently because The Information wants you to sign up for access to the article, but I found this mentioned in multiple summaries of the article like this one [1].

[1]: https://tech-insider.org/openai-mac-buying-apple-supply-shor...

What's worse is that I ported all my emails over to Proton, which meant my data usage was above the free tier, so I was essentially hostage to keep paying (even though my actual usage is on Proton is much below the free tier). While they do label the emails you import, it's useless because it gets intermingled if they are part of the same thread (for example, all my bills that switched over were tagged as imported even when I got a new bill; this was an issue for even direct email threads with other people). Furthermore, it's nearly impossible to bulk delete anything, you have to use their web interface (which is very limiting). If you try to delete using something like Thunderbird, the emails stay in All Mail, so it's not really deleted (thus keeps using your disk space, preventing you from moving to the free tier). It took me nearly a week to devise a way to properly delete the imported emails without accidentally deleting real emails. Using the web interface is SLOW. It was painful doing the deletes that way because Proton struggled to keep up with the deletes, it was glitching like crazy.

Needless to say, that was a shitty shitty experience, and I'm positive that's part of their approach to lock users into their platform.

Despite having Proton since 2018, I've really gotten fed up with their service quality — they seem to half-ass most of their services and it's a real pita to use their ecosystem. People have been complaining for ages and nothing changes, so I've decided to take my business elsewhere.

Hopefully I don't get flamed for my decision; that's another annoying aspect about Proton. There's a vocal portion of the userbase that are zealots and really take you to task when they feel you are criticizing Proton in any way.

I know people in positions of power often see lots filtered information that skews their perceptions of reality, but it seems like the level of pure hype Dario's pushing exceeds even the typical sycophantic filter bubble. Cure cancer indeed.

If you want to fight that battle, then educate consumers. I'm all for education. Taking away reasonable options because some people aren't capable of making decisions in their own interest is the worst kind of nanny state behavior.

I'm sure other highly regulated industries also have their code audited.

If the customer is the sort of person that upgrades yearly then it reduces hassle: no extra effort for valuing a trade-in or selling to a third party. It affects these customers the most because they might be able to recoup more in a third party sale in exchange for effort on their part.

Even though my PhD research was in generative language modeling, I got into it for the pursuit of AGI. I just think LLMs are a dead end for AGI.

Comments URL: https://news.ycombinator.com/item?id=49098445

Points: 7

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=48956752

Points: 3

# Comments: 0

Slightly off topic, but this reminds me of the night family in Rick and Morty.

  Wouldn't it be wiser to get out of the market into fixed rate assets like government bonds?

I just hope the companies aren't considered too big to fail. Bailing them out would be a bad idea.

https://www.openmarketsinstitute.org/publications/no-bailout...

In the paper I linked, they talk of a particular take on the word that is commonly discussed in the ML research: between one discrete step and the next a phenomenon came into existence without existing before (i.e. it occurred spontaneously). The paper then goes on to refute specific claims. That's all I'm pointing out (I could point out similar issues with research into reasoning).

I think it's difficult to pin down "emergence" in the general sense as it likely goes into a deep philosophical argument like consciousness, so I'm unwilling to state whether or not some notion of "emergence" actually takes place. In that sense I agree with you.

Frequently, those alternative hypotheses are demonstrated to be true. For example, "emergence" as was researched and proclaimed by many labs, including places like OpenAI and Anthropic is due to using too few discrete steps in sampling a continuous phenomenon. See "Are Emergent Abilities of Large Language Models a Mirage?" [1] for example.

[1]: https://arxiv.org/abs/2304.15004

That approach seems entirely counter to the claims in this post. Obviously, different teams can have different perspectives, but I'm surprised just how different they are (especially for someone on the Haskell board).

I'd love to better understand why the LLM flow works for Bun, but feels untenable for Scarf. Maybe the Scarf team could learn from the Bun team's approach?

So I'd say, if you're motivated you could do the same. That said, I've always been a self-starter and I started my PhD after working for a decade. I'm sure there are other resources out there, but I'm not equipped to say what's best for a beginner (I found the original paper to be excellent, but most everyone during my PhD, including my advisor, found it to be inscrutable; I think it's written more like an engineering focused paper, which might be why researchers found it difficult to grok, but with my previous industry experience it seemed quite clear).

[1]: https://arxiv.org/abs/1706.03762

Papers like "Curated Synthetic Data Doesn't Have to Collapse" [1] and "How to Synthesize Text Data without Model Collapse?" [2] demonstrate it's possible to do this.

Since OpenAI's Privacy Policy [3] explicitly allows for the use of deidentified data, it's possible they consider rewrites (maybe paired with a model used to identify explicit PII) to be deidentified. Whether OpenAI's legal team thinks rewriting in this way technically means they aren't training on your data isn't something I'm able to comment on.

Here's the relevant Privacy Policy statement:

  We also aggregate or de-identify Personal Data so that it no longer identifies you and use this information for the purposes described above, such as to analyze the way our Services are being used, to improve and add features to them, and to conduct research. We will maintain and use de-identified information in de-identified form and not attempt to reidentify the information, unless required by law.

[1]: https://arxiv.org/abs/2605.07724

[2]: https://arxiv.org/abs/2412.14689

[3]: https://openai.com/policies/privacy-policy/

I've seriously gotten tip fatigue and have been working to move back to a sane standard. I've noticed the places that have these crazy tips, also pay their staff well.