Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
Yes. Most people I've played with do some variant of the super stacker ruleset.
All draw cards function as a skip your own turn and add to the draw stack.
Skip and reverse can also function as passing the stack to someone else.
This adds an additional element of risk/reward, do you play a scarce action card to pass the large stack at the risk of it backfiring and you possibly receiving an even larger draw stack, or do you just take the draw now?
The variants were popular enough that the UNO Facebook page has officially suggested them, for what it's worth.
Nemotron Ultra is a open source and already gives DeepSeek R1 performance (admittedly not that good anymore). NVIDIA has open sourced the entire training process, including raw datasets and synthetic data generation. The raw data is mostly curated web crawl data.
Amazon mechanical turk, Outlier.ai, scale ai, are all gig platforms that pay people to produce real human content for LLM training.
At first they were paying people to actually produce content, like worked solutions to maths problems or translations, then they pivoted more to rating LLM output.
And as with every enshittification cycle, wages and work rapidly dried up, so people responded by asking LLMs for the answer just to meet deadlines and get by.
Roommate finding platforms already have user profiles and allow other people to rate them. Browsing profiles and reviews in Facebook groups is the best argument against this.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
No, Chinese Is Not More Token-Efficient Than English for LLMs | markhuang.ai - https://markhuang.ai/blog/chinese-token-myth
AI: Australia Inverted
Conservative MP Craig Kelly
Yes. Most people I've played with do some variant of the super stacker ruleset.
All draw cards function as a skip your own turn and add to the draw stack. Skip and reverse can also function as passing the stack to someone else. This adds an additional element of risk/reward, do you play a scarce action card to pass the large stack at the risk of it backfiring and you possibly receiving an even larger draw stack, or do you just take the draw now?
The variants were popular enough that the UNO Facebook page has officially suggested them, for what it's worth.
Rules for Stacking Draw Cards in UNO | Uno Variations - https://www.unovariations.com/uno-variation-stacking
Stirring mac and cheese does sound eerily similar to sex tbh
The song is called Walking on Sunshine but its music video was filmed in the UK and doesn't have any sunshine.
For sale: brand new door stop in packaging, never used.
No low ballers, I know what I've got.
Damn we got fate grand order memes now
Yeasty goodness
I can't laugh at this joke anymore because its founder resurrected the Enron brand started shilling an Enron crypto currency.
Birds aren’t real, and neither is the Enron relaunch - Sherwood News - https://sherwood.news/crypto/behind-the-weird-enron-relaunch/
Nemotron Ultra is a open source and already gives DeepSeek R1 performance (admittedly not that good anymore). NVIDIA has open sourced the entire training process, including raw datasets and synthetic data generation. The raw data is mostly curated web crawl data.
"Linux is too hard, there's too many config files" all while Microsoft requires people to learn what databases and Group policy are.
Already happening for years.
Amazon mechanical turk, Outlier.ai, scale ai, are all gig platforms that pay people to produce real human content for LLM training.
At first they were paying people to actually produce content, like worked solutions to maths problems or translations, then they pivoted more to rating LLM output.
And as with every enshittification cycle, wages and work rapidly dried up, so people responded by asking LLMs for the answer just to meet deadlines and get by.
Are you serious? Right in front of my salad?!
Roommate finding platforms already have user profiles and allow other people to rate them. Browsing profiles and reviews in Facebook groups is the best argument against this.