178 karma · joined March 14, 2024
There is a key difference: the laws are formally verified, as in Lean or Rocq (but much faster). So it's like writing a unit test or property-based test, but when it passes, you have a mathematical proof that you will get the expected output given ANY input in the infinite space of possible inputs. In traditional TDD, you make up some test case, write some asserts, and it passes if you get the expected outputs from those inputs and only those inputs. So you have to make multiple test cases for the same thing, and you still don't have any formal guarantee of your code's correctness.
> Also, why the compiler is not written against and with LAWS.md so that no audit is required at all?
Because it is mathematically impossible due to to Gödel’s second incompleteness theorem, which states: any consistent formal mathematical system strong enough to harbor basic arithmetic cannot prove its own consistency
fwiw despite decades of research into nicotine, we have very little data on how bad it actually is at low dosages and via delivery methods other than tobbacco. The vast majority of research on nicotine by itself is in the context of smoking cessation. A lot of the risks people warn about wrt nicotine (dopamine spikes, vasoconstriction, dependence, etc.) is also present in any stimulant, including ADHD medication and even coffee. Also, again, very limited research on this, but some of it suggests nicotine could be helpful for ADHD.
That said, none of this really detract from your point about risks in children, and the trendy delivery methods like vapes and pouches seem to be extremely addictive.
not the person you replied to, but it mostly would for me. Factory farms are among the closest things to hell on earth.
Nothing to add other than I really, really like this. Keep us posted on future updates!
What's with the hostility in these comments? OP straight up said he just doesn't like the damn job. Grinding leetcode is not gonna change that. Is everyone just choosing to ignore this?
Google's DeepMind has been at the forefront of AI research for the past 11+ years. Even before that, Google Brain was making incredible contributions to the field since 2011, only two years after the release of Go.
OpenAI was founded in response to Google's AI dominance. The transformer architecture is a Google invention. It's not an exaggeration to claim Google is one of the main contributors to the insanely fast-paced advancements of LLMs.
With all due respect, you need some insane mental gymnastics to claim AI companies are "unquestionably cancer" while an adtech/analytics borderline monopoly giant is merely a "mixed bag".
This is way off base. Altman is basically a mentee of his, like Zuckerberg. They go way back, and they've both publicly praised each other quite recently. They're very close friends.
I just googled "Moonshot AI" and got the information right away. Not sure what's confusing about it, the only other "Moonshot" I know of is Alphabet's Moonshot Factory.
> This company is far from "open source", it's had over $1B USD in funding.
Since when does open source mean you can't make any money? Mozilla has a total of $1.2B in assets. The company isn't open source nor claiming to be.
This model was released under a "modified MIT-license" [0]:
> Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2" on the user interface of such product or service.
Which sounds pretty fair to me.
[0] - https://huggingface.co/moonshotai/Kimi-K2-Thinking/blob/main...
I'm pretty sure this isn't what happened. DeepSeek just hasn't released a big model upate. But in the meantime, Qwen, Bytedance, Ziphu and Moonshot AI have released extremely impressive models, some of which are SOTA or close to it. The open source/open weight world is still in love with Chinese labs as they keep releasing cool stuff and filling the void left by Meta and Mistral.
the latest Gemini models have a very low hallucination rate in benchmarks [0]. Feel free to try it yourself. Just go to gemini.google.com, choose Deep Research, and ask it to write a report about a topic that you're intimately familiar with.
Check out the MTEB leaderboard:
Feeling like you don't belong in your own country is maddening and difficult to accept.
I can't think of any time in history, under any economic system, where humans who create tools and art were the majority. Most people just want to have families and enjoy life.
Yeah, most of which are rehashes of the same thing, and most of them on the same ~10 websites.
I suspect it would be useful as a fast "context collector" for LLMs as well. Maybe framing it like that could catch HN's attention a bit more.
Most of the others are a 2-3 npm installs away in the frontend, and a few are not used by extremely popular sites like archive.org, Wikipedia nor this very site.