Stefan Riuki @theoriginalcode

Give me my robe, put on my Crown; I have/ Immortal longings in me. venmail.io Soul. Joined July 2009

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Dr. Nabiha Saklayen @nabihasaklayen

6 days ago

In partnership with Vinod Khosla (@vkhosla), I’m proud to introduce Reservoir. Billionaires have owned their cells for years. Starting today, you can too. Sign up today: withreservoir.com

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Afshin Rattansi @afshinrattansi

2 weeks ago

Burkina Faso’s🇧🇫 Captain Ibrahim Traore: ‘They destroyed Iraq. They created ISIS, just as they created Taliban. All these wars were invented in Iraq, Syria and Libya with the aim of destabilising the world for their own benefit.’

New Order with Afshin Rattansi @NewOrder_TV

2 weeks ago

Col. Lawrence Wilkerson: Why the US🇺🇸 is LEAVING Iraq🇮🇶, and the entire Middle East 'A series of elected members of Iraq's parliament and its leaders, including the current one, have wanted the US to leave, and not been very vocal about it because they know the trouble they

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CHIGO @iamChigoo

2 weeks ago

“G0d is anğry with Black people because we’ve refused to use our brains.” — Ibrahim Traoré

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Stefan Riuki @theoriginalcode

3 weeks ago

@phoeniciachild Yes.

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@jason @Jason

4 weeks ago

My lord, three IPOs are worth more than… all IPOs in the last 45 years of tech 🤯

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♡ @velunehq

a month ago

Midding

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Rohan Paul @rohanpaul_ai

a month ago

Terence Tao: The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models. The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical. A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua. Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle. ---- Video from Prof @Briankeating YT Channel (Link in comment)