nsagent

Reminds me of Incredibuild.

The sun's radiation causes cancer, yet placing a blanket warning stating the sun causes cancer likely leads many to wholesale avoid the sun, which would be folly (humans need some amount of sunlight).

Similarly, imagine if nutrition facts labels only had to state whether the food could be potentially unhealthy, without any other nuance.

A sensible version of Prop 65 would acknowledge the specific substance, its dosage, and the best known safe dosage. Equipped with that information a consumer can make an informed decision.

The this is much ado about nothing. Every single entry on the page you linked goes to its product page with the exact Unix standard it is certified as. Once you select a filter, you cannot get back to unfiltered list without the back button on the browser.

This is just a site rendering issue. Rather than silently removed, it's more like some bug was introduced. macOS 26 is still Unix 03 certified.

I see macOS 26 listed under the Unix 03 standard. Was macOS 27 listed and then removed? The submission title + site contents combined do not give enough info to assess what exactly was silently removed.

> I also think this "paper then code" approach is now obsolete. The modern way, in AI-assisted workflows

To call the approach obsolete and refer to a "modern way" to use an LLM seems like a stretch when critiquing the approach used by a frontier lab a month ago.

Again, really sorry to derail the conversation. What you've created looks amazing! I do typical use the command line for mail (mblaze), but I'd certainly consider giving your client a spin if I wasn't on macOS.

The ad was for a 32TB portable SSD for $200...

https://wccftech.com/customer-buys-32tb-external-hard-drives...

Went to watch the Youtube intro [1] for Penguin Mail and was greeted by the most egregious scam video. Didn't click the ad, but simply went to the domain (usbest1.com) and it's just chock full of AI generated content hoping to scam you out of your money. Going through the checkout flow, it even asks you if you want to tip your driver (presumably for delivery).

Sorry for the off-topic, but Google (and all ad networks) should be legally required to vet their advertisers. That's gotta be the worst I've seen.

[1]: https://www.youtube.com/watch?v=0PyJCsw1FSE

See this recent paper: Quantization Degradation in Large Language Models: A Signal–Noise Perspective [1].

  We observe that such degradation varies substantially across these factors: 4-bit quantization usually preserves performance, 2-bit often causes broad degradation

This repo uses 2-bit quantization and removes some of the experts for its smallest fastest model. Make of that what you will.

[1]: https://arxiv.org/abs/2608.08188

Not sure why you are being downvoted, but this is definitely a limitation of current models and difficult to optimize for. Anything you are not specifically optimizing for is essentially unconstrained: the model could learn it by pure chance, but the likelihood is exceedingly low.

Techniques, like the newly announced RL-XAR from Meta [1] are being developed that will likely improve reward models and guide RL training to optimize for metrics like software architecture that are hard to verify otherwise.

[1]: https://facebookresearch.github.io/RAM/blogs/unslop/

I truly think it's safetyism. People seem to have a lower and lower tolerance for risk as society gets safer.

Since governments are run by people, this safetyism has become enshrined in law, typified by nonsense like criminalizing free-range parenting and mass surveillance.

It's pretty telling that even with RL post-training the big labs have essentially made little progress on the hallucination rate of models. The issue is fundamental to the current paradigm, contrary to humans.

GPT-6 Astra (max) has a hallucination rate of 51% and Claude Opus 5.5 (max) has a rate of 59% according to Artificial Analysis [1].

  AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted)

Full speed ahead like an idiot savant trying a thousand different possibilities, though half of which are without basis in reality.

[1]:https://artificialanalysis.ai/evaluations/omniscience#omnisc...

> So if you happen to be able to make ␛]8;;https://example.com/"/...␇ appear in the job logs —4 which you can, either without even having an account, by sending a patch to a public mailing list with continuous integration turned on

The fact that this could be triggered so easily — just sending a malicious patch to a mailing list — is pretty insane.

Here are a few:

* Forming a long lasting memory from a single encounter

* Speed of adaptation to new environments

* Deft manual dexterity, like playing the violin and needlework

* Competing in a triathlon (requires running, biking, and swimming)

* Having a single agent that can do ALL of those things and more

Humans understand and perform tasks in many more than just the few modalities current model providers have focused on. I'm not discounting the utility of current models in the limited domains they operate in, but they are no where close to being generalists like a human or other animals.

They are simply looking to steer AI policy discussions. It's basically an in-house think tank. It has all the trappings, especially with ominous prognostications like:

  Today’s AI systems have impressive capabilities and the rapid pace of innovation suggests we’re now approaching artificial general intelligence (AGI), a system that exhibits all the cognitive capabilities of the human brain.

Not sure we are anywhere close to all the cognitive capabilities of the human brain... not even a severely lobotomized one.

I'm sure they would: https://tbreak.com/omarchy-m-apple-silicon-linux/

As another student who did the same, I similarly regret it, but also realize if I were put in the exact same scenario I would have behaved identically.

I literally had to retake trigonometry, while simultaneously being advanced into pre-calc, because my teacher saw that I aced the exams, but said I had to do the homework.

I ended up doing well enough: I dual-majored in CS and Math in undergrad and then completed a PhD in CS later in life, but I do sometimes think I could have achieved even more had I known better.

Can you speak to why in both cases, the problems OpenAI's models solved used the same techniques the mathematicians were exploring, which also happened to be niche approaches to the problem. As an NLP researcher myself, I find that coincidence highly suspect unless the models focused most of their attempts on the predominant approaches (they are trained for MLE after all).

It's not. This article is weirdly light on details. The original source article from The Information apparently mentions that companies like OpenAI are buying them up to run RL. So it's yet another case of AI companies buying all the compute.

* 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...

I moved because I found the Proton service to be poor. That includes reaching out to support for issues with my email and not really getting a good resolution.

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.

I'm in the process of leaving Proton and moving to Fastmail. I'm now paying for Mullvad and Fastmail, but haven't had a chance to port over a bunch of Simplelogin aliases I have over to Fastmail yet (my fault for not using a custom domain for those, which I've now rectified with Fastmail).

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.

After listening to Dario's interview with Dwarkesh, I really started wondering if Dario's been drinking too much of Claude's kool-aid.

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.

What a paternalistic response. Apple provides a better deal than the typical telecom — not gouging the user on a lease-to-own program, and your response is the sheep are too dumb, best to take the option away for everyone?

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.

Gaming Commissions oversee gambling machines and will audit code to ensure the RNGs are accurate, return to player meets the expected requirements, and so forth.

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

If anything, I think the terms for this are almost too good. You never end up paying more than the cost of purchasing the device outright if you decide to keep the device. And considering it can have a term of up to 30 months (24 month lease, with 6 months more before you're charged the remaining cost of the device), you've just gotten a zero percent interest loan for 30 months. That's actually cheaper than buying it outright at the start of the term due to inflation. I seriously cannot see a downside to this program on the consumer's end if they end up keeping the device.

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.

I'm a researcher in the field and I definitely take AGI seriously, but think all the major labs and most of the academic research is not helping achieve it any serious way. The field is seriously delusional (and has been ever since GPT 3 was released).

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.

Considering Hassabis co-founded Deepmind to pursue AGI, I take this pivot as a tacit understanding on his part that Deepmind isn't on a path towards AGI in the near future. They might at some point, but they cannot be close in his estimation, otherwise why leave now?

> As it can run in the background doing “chores” while I sleep.

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

Sadly, the major ML/AI/NLP conferences are being inundated with AI slop papers. That will arguably have a bigger impact on the quality of research moving forward.

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

I did that earlier this year ahead of the April earnings reports. I was a bit too early to the punch, but I prefer that versus being too late.

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...