aenis

Except that in this case it's no longer 'collect money, move on to the next one'. It's 'collect money, become dynastically-rich, retire in 3 quarters from now'. After that, who cares.

As for the $400 a year, for years I'd cancel before the end of the subscription, ignore them for 2 weeks, and get a great offer for the next year. I haven't done it this year, though, forgot to set a reminder.

Great value anyway. One of the only news sources focused on facts and meaningful stuff. Its been my go to news source for at least 15 years. I don't work in finance.

Wow, this is very nice! I was thiniking about porting it myself this year, but glad I dont have to!

Omg. Mercenary was a game that left a huge mark on me. I played it on Commodore PLUS/4 when I was 9. The freedom to explore! The hidden elevator far outside the city. The destructible buildings. The satellite!

Surely, that can't be just one chip. Something needs to watch the watchers.

That was the old Dario. He now has automations for that sort of thing.

I am not sure how putting anyone out of work is criminal, or even particularly morally wrong. The whole history of the world is about discovering productive things -> doing them manually -> automating them. The article makes it sound like its some sort of taboo.

But its also a bit funny to read the 2023 people seriously consider that their slop machines will replace writers. Software devs, sure, but writers? Nop.e

I know it's slightly offtopic, but the brand I am using - ExposureLights - offers a battery diagnosis/replacement service for their bicycle lights.

Those are on the expensive side, but still, most brands would rather sell you a new one.

Spot on. Or because of addictiveness. Or impact on democracy. Or ruining people's ability to focus on anything. Those attention grabbing algos havent been designed by non-technical people.

Agree. I spent so much time with it that it infected my own command of English. I hope something new comes and bears the load.

That, plus blocking all updates from the 'smart' devices; in the last few years, I dont remember anything getting better from updates, everything is actually getting way worse.

I have a Sonos soundbar. It used to be nice. Now it automatically goes to sleep after a few minutes of tv inactivity. I now hate the device and the company.

My old M1 mbp used to feel super fast. A number of MacOs updates later it feels sluggish. I don't see anything better about MacOs of today vs. 3 years ago, literally NOTHING.

My apple tv now routinely drops airplay -- it used to work flawlessly.

Etc. etc. All those companies push shit updates to keep their existing engineering teams busy.

Call me cynical, but I dont remember interacting with well engineered software, outside of pure, mainstream opensource repos.

Most of proprietary software is just crap, and always has been. The agents are not producing worse code than typical, demotivated, i-dont-care-what-i-am-building-i-wont-try-using-it corporate development teams have over the years. I'd even bet that because now making changes and fixes is so much easier, the user perceived quality will trend upwards for popular stuff.

I do have a 128gb mac, and the experience is, for my use cases, hobby grade. I use models on a daily basis for design work (chiefly), and sometimes implementation work. I can't justify a drop in output quality and significantly increased waiting time. Sure, if frontier did not exist, I'd use them, but way differently than fable or astra.

Its also pretty telling that the release of the new Chinese chip wasn't met with an intense discussion here on HN. And it's arguably way, way more important than a version bump on some benchmaxxed model or two. The advances in Chinese chipmaking are super exciting.

The irony here is, thats hobby hardware. You spend 20k and can run slow hobby models that are barely capable of anything unsupervised.

Entry level serious hardware starts at 100k, and a bit better but still almost-useful grade is 200k (8x rtx pro, plus a nice epyc pairing). Thats the sort of thing a salaried expert lets their employer buy them for sort of serious work.

Anything really serious is well north of 1M - not including the housing and commercial grade mains connection. And at best that buys fast Kimi K3 or GLM.

Subscription. Translated to Around $90 in tokens.

Did you forget to tell Claude to make it run fast?

(I am not joking. I did a port of an old utility, Dos Navigator, and I gave Claude+Codex a set of KPIs: cold start under 100ms, 30MB RAW image preview under 50ms, and a few others. I went to bed, and I woke up to a file manager written in Swift that is a joy to use, its so fast, esp. when previewing images. If I haven't requested it, it would likely be way slower).

The details of the Apple vs. OpenAI dispute over leaked data show their security is not quite that tight.

Loss of life and other calamities were not enough to discourage humanity from things like cars, alcohol, nicotine, chainsaws, fast food, etc. etc. I am not sure if there is an example of in-some-ways-useful technology where humanity took a measured approach, weighed the pros and cons, and decided to go back. And AI is useful in so, so, so many ways. People I know already seem to have defaulted to letting AI do most of their thinking, on matters big or small. Nope, loss of life won't change a thing.

The question is: isn't the cat out of the bag already? With what is already in the public domain, and the compute available to motivated, deep-pocketed actors, will they be able to carry on the research without the key researchers? And will those people be able to recruit key researchers with sufficient motivations?

Unlike with nuclear proliferation, there is no heavy industrial base requirement. No time consuming, visible uranium enrichment. All it takes is for someone to buy sufficient amount of compute and try to get it past the RSI gate. Or bribe people with access to model weights to existing frontier - the asymmetry between what it takes to bribe a bunch of geeks vs. what is at stake is staggering.

Best thing I saw on hacker news in a while.

In case of my company, the port was planned for this year, with a 9 month timeline and about 10 FTE team. It ended up being done in 3 months, with 2 engineers. (Sure, plus testers and internal bureaucracies, but that 2 FTE for 3 months vs. 10 FTE for 9 forecasted is apples-to-apples). Sure its eating up SWE jobs.

We did the same with our apps used by a few hundred thousand people a day. We are a slow, boring company, so the port took about 4 weeks of engineering work, and maybe 3 months taking into account release process and change management. But in fairness, we had the first working version after a day as well. This was in February, too, with substantially less capable models, unable to do overnight runs.

That it was a dick move, I think there is no doubt about that. OpenAI wanted to scoop Anthropic, and the two guys working on the problem got caught in the crossfire.

Both OpenAI and the researchers know if the sessions in questions were subject to data sharing. Why neither the scientists nor OpenAI is clear about that is weird - it would seem at least one party has the incentive to report that. But even if their sessions were in training data sets its hard to tell whether it influenced the outcome. Those models are big, but are they big enough to preserve subtle, niche techniques enough to draw from them while solving a related problem? Probably nobody knows.

There is nothing even close to a proof. A lot of accusations, a lot of people ready with pitchforks and torches (sadly, also here on HN), but not a lot of facts.

Did the researches opt out from data sharing on subsidised subs?

Did anyone prove that their methods enabled OpenAI models to produce the solution?

For a discussion about science, there is almost no scientifical method applied to proving anyone stole anything.

And its reasonable to assume that if they did, in fact, opt out, they'd make it clear.

Most people do not understand that the main reason for the subscriptions is to give OpenAI and Anthropic the priceless, unique data that shows how the models are used, what people are building, how they are building, which solutions they consider OK, which they consider bad -- they purchase this data with cheap tokens. This is their only moat, really. If some really proprietary IP gets swept in the training data set its not really OpenAI's fault -- its the researchers'. Have something secretive? Dont fricking paste this into chatgpt. Duh!

(I'd definitely not think OpenAI/Anthropic ignore the opt outs, or ZDRs. All it would take is one whistleblower to get them into terminal troubles. And why would they do it? They are not in the business of scooping unique IP -- they are in the business of understanding how AI is used across a variety of mundane, day to day work of individuals and companies. Useless math problem is good (or bad, as in this case) PR, but otherwise entirely worthless for the labs.

8x RTX PRO 6000 or 4x Spark? Or 1x M5 Ultra 512GB.

The model is theoretically FP8, but really internally its mostly FP4 already, so there won't be a cut-in-half-but-almost-just-as-good quant coming for this one.

Yes, this is getting ridiculous. On both OpenAI and Anthropic.

Simple example. I am a CTO, and I want to upgrade our capabilities to perform automated pentesting. We see automated attacks of growing sophistication against our infra, and I want to be able to do the same to find vulnerabilities before the bad guys do. I asked GPT 5.6 Sol and Fable to give me a summary of options. No dice, in both cases I was told I need to be an accredited researcher to get anything. A fricking summary of commercially available options is getting censored. WTF.

No, I don't think so.

I have opted out from data sharing, and when Claude asks me for feedback on a session it then asks if its OK to share that data with Anthropic.

I'd assume an opt out is an effective opt out. An opt out that is ignored by Anthropic is a breach of contract, not something they would do casually, esp. given the high turnaround and animosities between their own employees and ex-employees - and the labs. All it takes is one pissed off whistleblower to open a can of worms.

Occam's razor applies. The mathematician did not opt out from data sharing. OpenAI vacuums up all such data into training data sets. If OpenAI genuine does not easily know if a given session went into the actual training data set its probably due to the complexity of the data pipelines - not everything ends up impacting the model weights, after all.

Its not "in the future", its already here, right now. Sensitive IP gets processed on rented or local GPUs running open weight models. Sometimes its required due to data privacy laws, sometimes its because the people running those shops are not trusting OpenAI/Anthropic ZDR policies being observed. Normal, boring, US and EU-domiciled enterprises do this on a regular basis, and there is an industry of companies helping said boring companies set things up.