wisdomHunt
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Observations and thoughts of a curious troublemaker. MuhammadAli Sayfiddinov Student Researcher at Stanford Machine Intelligence master's at ETH 📍Zurich, Switzerland
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Case for building a startup
Building a startup, if you can, beats almost anything else on the table. The Cursor guys make the case clean: co-founder Sualeh Asif's stake is now worth $2.7 billion, four years after starting Anysphere. Do the math and you see he was doing $56 million per month. Compare that to a $20k capped salary at Google. This is a 3000x difference.
Sualeh is Pakistani. He did math olympiads before MIT. So, he has a lesson to share with us, and the lesson is not - become the hustling, talkative, self-proclaimed Steve Jobs who talks a vision without substance. It is - study, build real depth in something hard. Bring an actual skill to the table, math, engineering, research, whatever it is, and pair with people who have equally strong skills. A startup is real skills compounding into something big, not a personality performance. That's the trade worth making.
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A person is a city
We grew up hearing tales of evil characters that do everything only with bad intentions. But as we grow - we realize that people are not white or black; a person is never singular. Rather he is a city of multiple characters. City where many entities take over in different times. City where both good and bad coexist. City where the bad vies for power and pays cheap tribute to the revolting good, and yet - always claims the title of nobility. City where the good is always present and never disappears. Like a lantern that might get covered in dust. But continues to shine in the hopes that one day - it will light all the city.
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Oh mani yorim dardi-darmonim,
So’ra jonim berayin borim.
Oh mani yorim san beg’uborim,
Mani doim senda hayolim.
--‐----------‐-‐-----------------------
Oh my beloved, the cure of my pain,
Ask, and I'll give you my life, all I have.
Oh my beloved - my pure one, you,
Always, my thoughts are with you.
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Repost from Grad School with Abdurashid
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It is very hard to beat a person who is doing it for fun.
Peter Steinberger
Developer of OpenClaw
p.s. Peter built solo what a battery of startups were trying to build - an agent present everywhere in your devices, and his explanation for why the startups couldn't beat him: "They take themselves too seriously".
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Repost from Research Hub🔬
🎙️ Research Hub Webinar Series
Join us for an exclusive webinar with a Professor at Carnegie Mellon University (CMU).
Ex-VP of Research at Meta.
Ex- director of AI Research at Apple
This session will cover: 🔹 Research opportunities for students 🔹 Academic pathways to top universities 🔹 Artificial Intelligence and emerging technologies 🔹 Tips for aspiring researchers and future leaders📅 Date: July 4 💻 Place: Zoom 🕘 Time: 9:00 PM (Tashkent, Uzbekistan) Don't miss this opportunity to learn from a researcher working at one of the world's leading institutions. Register now and prepare your questions! #ResearchHub #Webinar #Research #CMU #ArtificialIntelligence #Education #StudentDevelopment@YouthResearchlab
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Research opportunity
I am starting a research project and am looking for assistants. The topic is on AI for education. If you are an undergraduate or even high school student looking to publish as a first author, this is your chance.
Traits we are looking for: critical thinking; grit (in Uzbek "tishlaganini uzib oladigan"😁); some math, tutoring and coding background are nice to have.
Interested - apply here:
https://forms.gle/BadWxohKuaUY39G26
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Intuition is a compressed way of looking at some phenomenon, an easy way to understand something. You observe again and again that people who do real innovations often have very robust intuition on the topic. So you wonder how could one get more of it?
Many physicists think of Feynman when the word intuition comes up. He was very good at giving intuitions for complex phenomena. One wonders how Feynman did it. But you barely find anything from his own words, except that he was good at imagination.
So, what to do? There is a scientist called Stephen Wolfram (I was listening to his appearance on Lex Fridman's podcast). It turns out Wolfram was a younger colleague and friend of Feynman's at Caltech. He gave some insights about Feynman's intuition that you cannot find elsewhere:
- First, Feynman knew that you cannot compress everything into simple intuitions — not everything can be reduced to a child-level analogy. (This is similar to the line often attributed to Einstein: everything should be made as simple as possible, but not ever simpler.)
- Then, Feynman was very good at calculating. He would do complex calculations/simulations on paper or in his head, fast, then see the overall picture, then come up with a post-hoc intuition for the phenomenon. BUT because he thought calculation was easy, he would simply not mention the many days he spent calculating.
So the important point: to get intuition you need to work in the weeds / fight in the trenches, then you understand what is happening, then you get the intuition. Usually there is no shortcut, and often the intuition you end up with is not child-level easy.
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"Study hard what interests you the most in the most undisciplined, irreverent and original manner possible."
Richard Feynman
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Had the first meeting with my Stanford supervisors.
Impression - overwhelmed with their thought lead. You feel the difference.
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Burn the forest until it works
Am I the only one finding out about this so late? There was a source code leakage from Claude Code, 2 months ago. It is quite interesting to see the inner details of what makes Anthropic's Claude persistently smarter than OpenAI's Codex. Most important takeaways:
- Claude Code was written, yes, by Claude Code itself. A random walkthrough shows how the system is cumbersome and lacks logical precision - signature of LLM generated code. Anthropic's way to build: Generate the code. If it doesnt work, generate again. So, burn the forest until the code works. Have a look at this article for more details.*
- The whole system is not pure deep learning. Some parts are old-school symbolic AI. And this is important. Apparently, it is a proof that making the models ever bigger is not gonna solve problems of LLMs.
*it is ironic how the company that talks most about safety is not taking safety seriously
