OpenAI just wants to make money, perhaps through underhanded tactics if they can get away with it.
Anthropic does all that but they're also populated by many people who believe they are building God and that they must build their god first in their own image so that it can take control of humanity and protect us from any competing god which is not built in their image. Their position is inherently paternalistic and authoritarian, and they consider suppression of competition not just important to the bottom line but to life in the universe. Under the doomer ethos there is no evil too great to rationalize.
There are plenty of wrongs done in the name of profit, but capitalists have nothing on zealots in terms of causing serious harm. Profit motives can be directed by influencing incentives, but zealotry is frequently terminal.
That isn't to say that there isn't some overlap-- the cultists have infected both organizations. But OpenAI has pretty consistently only given lip service to AI doom to the extent that it improves the bottom line, while (mis)Anthropic was founded specifically because OpenAI wasn't mentally ill enough.
Well said. The superiority complexes from the Anthropic messaging on their presentations/blogs/articles is just too much, even for a frontier AI company.
Anthropic has great products, but it's not meaningfully better to 99% of devs that I'd rather support the company that doesn't constantly act in opposition to optimism and to the vibe I'd prefer for a 100 billion dollar (or however ridiculous amount they're worth now) tech company embraces.
AI doomerism is a genuine waste of time if you aren't actively pushing towards a better AI industry for everyone, not just the groups in full ideological alignment to your personal leanings.
You're right to push back. That's on me. There is one thing I must flag which will move the needle. My honest take: It's all about the shape of your priors. That's the lever, and that's not nothing. It's worth your attention before you land your next remark. Here's why that matters: It re-contextualizes everything.
Imagine you communicated only in Chinese, but external to you there was a rote translation service that translated to and from English. Someone asks you how many r's in strawberry. ... well you've never seen the spelling, as you only have seen chinese. You're going to get it wrong.
How would you get it right? Training with the answer!
You can discount the 'special case the hard questions' by: Observing when top models run entirely locally can solve them [1], or by posing an alternative or cryptic version of the question (but careful, it might bypass the training! -- but if it can solve it then its probably legitimate.)[2]. Best of all is to stick with open (weight) models where such slight of hand is impossible and don't worry about what the closed shops are doing
[1]as they can in this case, GLM-5.3-flash says:
"There are 3 r's in "strawberry":
st r awbe rr y
1 in "straw" (straw)
2 in "berry" (berry)"
[2] E.g. try sending them aG93IG1hbnkgcuKAmXMgaW4g4oCcc3RyYXdyYmVycnnigJ0/Cg== to thwart the benchmaxxing.
Most of the worst things done in history are being done by people who are absolutely convinced that they are doing something VERY good. For evils beyond an individual thief or killer they were almost always committed by someone who believed they were being good.
The lesson from history is that doing good is the most reliable path to do evil. To do actual good: leave other people alone, unless they ask for your help.
"AI safety" practitioners fit the mold of historical great-evil-doers well. They are the true danger in AI.
I've used LLMs to get the distributed Sprite OS running again, though I've yet to release it, and I'm not sure what that release will look like, if it ever happens.
One thing I really don't get, and I really hope I never do, is leave the front page/README/etc so ... Claude-ified (or GPT, plausibly, I guess):
native SCSI and ethernet drivers, proven on real hardware
Quake — runs; a benchmark more than a game, for now
running under emulation, real hardware next
The grimoire records both, confidence-tagged
I am becoming probably too okay with LLMs, and I'm probably about as "forgiving" of this stuff as anyone, but man, it's disheartening to see that for something as much of a passion project as "Amiga Unix", seemingly no-one bothered to write a page in a human voice for it. Maybe it's LLM-written, maybe a hybrid thing, I dunno. But at best it sounds like "Apple product release", IMO.
I know others will say the same about seeing LLM usage at all, and yeah... I don't really have a defense. It just feels like the "bare minimum of decency" to, well, at least make the front page less LLM-sounding.
Can't argue with you there. There's a ton of cool projects that people have vibecoded, and exist in domains where the fact that it's possibly insecure/poorly optimised/unmaintainable by humans doesn't really matter much. e.g. porting retro systems to, or writing new utilities for MiSTer FPGA. I love the platform, and the more stuff I can run on it, the better. Bugs are annoying, but not critical. The community gets software that may have never existed otherwise.
But still, somehow, when I see a Claudish README, it instantly gives me the ick. Write about your project. Tell us why you built it. Talk about the problems the bot helped you work through. I'm not going to judge you for not being able to code it all up yourself - but at least write the README in your own voice and tell me why you cared enough to do it.
The phone is a real device produced by Jolla.
After being called out on the AI generated ads, Commodore apologised and had the videos remade using real people.
Generally being succinct goes hand and hand with being zero knowledge.
The proof must be MUCH smaller than the whole computation or even the inputs (otherwise, just provide the uncompressed image!).
And the proof has to resist forgery.
So going for succinctness and at least computational soundness gets you to zero knowledge 'for free'.
Plus as the dead sibling comment notes: You may want disclosed modifications like a crop or redaction where the committed information remains private but you want to keep the proof.
Large lab "hacking" is only for the purpose of pushing competition suppressing doomer stories. You can tell by the fact their security is fine where it counts: keeping their weights and internal execution harnesses trade secret.
Did you see the account of some group getting a bounty payout of $6500 after using an exploit to get access to an employee’s github account and create a issue or PR (Idr which) on a private repository?
Seems like they could have potentially gotten access to the weights if they weren’t concerned about not doing crimes.
A better benchmark might be asking the LLMs to write GO ai and then comparing that-- the issue is that there will be a HUGE difference in performance that depends purely on this game being in the LLM's training... but training a general LLM to directly play these games would be a waste of capacity and shouldn't be encouraged for benchmaxxing sake.
Programming an engine OTOH is a skill that is more general and they should all have.
Might be useful to have the target of the engine be some specific virtual machine that gets a strict cycle budget-- e.g. execution runs so many cycles, and result is read out of a specific memory address at the end (or when it terminates early).
This is a great idea. It could learn from its mistakes, repeat things that work, abstract complex situations. I would even want humans monitoring the project.
Playing these games autoregressively isn't even the right way to use the LLM for this task (unless it was trained to do so...). It's somewhat like having a creative writing bechmark but requiring that all the input/output be base64 encoded. It can do it-- but no guarantees on the results!
And it's also just bencmaxxing bait: you can get a huge improvement on the task by RLing on it, but make no improvement on anything else. Doing so would just waste model capacity.
If you could tell that every LLM was equally not being exposed to the task then you could justify it as a test of abstract reasoning, but you can't. So it ends up on how much go transcripts ended up in the training, which is ... not a very interesting metric.
Anthropic does all that but they're also populated by many people who believe they are building God and that they must build their god first in their own image so that it can take control of humanity and protect us from any competing god which is not built in their image. Their position is inherently paternalistic and authoritarian, and they consider suppression of competition not just important to the bottom line but to life in the universe. Under the doomer ethos there is no evil too great to rationalize.
There are plenty of wrongs done in the name of profit, but capitalists have nothing on zealots in terms of causing serious harm. Profit motives can be directed by influencing incentives, but zealotry is frequently terminal.
That isn't to say that there isn't some overlap-- the cultists have infected both organizations. But OpenAI has pretty consistently only given lip service to AI doom to the extent that it improves the bottom line, while (mis)Anthropic was founded specifically because OpenAI wasn't mentally ill enough.
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