Prannay Hebbar
@Pran_Ker
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ai researcher @ agi inc. / @stanford /
Palo Alto, CA
Joined May 2019
The Math Academy Way is just really good! .spent most of my time these nights reading it, also started upskilling. Top tier mental models.Ty @justinskycak
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RT @teortaxesTex: Congratulations to Yang Zhilin, he looked at the sun and did not blink. Congratulations to Wenfeng, he clearly has a rea….
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- (anonymous) friend went to Founders inc.- we cloned his key with a flipper0.- used to chill in the robo room.- got found out.- they changed to Face Auth.
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RT @jay_azhang: Things I'm extremely bullish on:. - Simulation software.- Multi-agent RL.- Open-ended learning.- RLVR (towards superhuman d….
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The future is going to be so amazingly interesting
This is super cool.
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Experience and comparison of different Rl libraries, very good
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In my experience, Principle decomposition and handling vectors mentally as automatisms are useful. Probability would have really high impact cross domains, makes sense on improving this to the point of automacity
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Good blog.
New post! . Reinforcement learning, deterministic chaos, and scaling simulation. I think this is my favorite post so far, do check it out!. Shoutout @robertghrist for teaching an awesome course on dynamic systems. I took it 4 years ago but I think about these ideas all the time
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RT @LisaShmulyan: Every ambitious 20 something year old builder should live in a hacker house. @SurbhiTodi and I are moving out of our hac….
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Gold.
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Lot of good insights, read on if you’re interested in investments and capital markets.
This is Bill Gurley. He is a former GP at Benchmark, a leading venture capital firm, and in his most recent interview, he provides his latest view on the market - Long🧵. 1) Mega Venture Fund Rise.2) Misaligned Incentives & Zombie Unicorns.3) Permanent Privates.4) Capital
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Well written.
The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. Let me show you how!
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Neuroscience explained really well. I feel like there is a lot of cross learnings.
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