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traders are becoming famous outside the charts too. people now follow them for their trades, lifestyle and opinions. but are they really worth listening to, or is it all just social media noise? what do you think? w: @shivst3r

50 Sites That Show What Billionaires Buy and Read for Free🤯 1.) → Current portfolios of famous investors 2.) → Quarterly fund trades 3.) → Stock transactions of US politicians 4.) → Executives' own company stocks 5.) → Making fund filings readable 6.) → Companies' official reports 7.) → Buffett's letters since 1977 8.) → Video archive of Buffett meetings 9.) → Howard Marks' investor memos 10.) → Research from Ray Dalio's firm 11.) → Dalio's principles for free 12.) → Jeremy Grantham's market notes 13.) → Quantitative fund research 14.) → Damodaran's valuation data 15.) → Valuation professor's blog 16.) → Morgan Housel's money writings 17.) → Mental models for decision-making 18.) → JPMorgan's free market guide 19.) → Innovation-focused fund research 20.) → Venture capital essays 21.) → Value investing lecture notes 22.) → Columbia's value investing archive 23.) → Fed's economic data archive 24.) → Fed decisions and statements 25.) → European Central Bank data 26.) → Reports from the central bank of central banks 27.) → Countries' economic indicators 28.) → Companies' long-term charts 29.) → Company financials for free 30.) → Companies' market cap rankings 31.) → Stock screener and heat map 32.) → Portfolio backtesting 33.) → Charts and market analysis 34.) → Professional market screener 35.) → Company activity reports archive 36.) → SEC's investor education site 37.) → Passive investing encyclopedia 38.) → Financial terms dictionary 39.) → Free finance guides 40.) → CFA research publications 41.) → Fund and stock evaluations 42.) → Companies' visual analysis 43.) → Archive of selected investment theses 44.) → Seeing what's inside funds 45.) → European fund backtests 46.) → Investment writings with data 47.) → Market history notes 48.) → Investor psychology writings 49.) → Explaining the economy with charts 50.) ourfiniteworld? → (see note) The rich don't hoard information. They just don't tell you where it is. Save it, follow @Liyana_Coder you'll need it. Not investment advice. @Liyana_Coder

overheard from friends talking about AI in China, and it’s very alarming - AI short films went massive in China market because of how good the video models are now - the first wave of people who mastered using AI to generate short films and comics made quite some money because they could produce a lot more content than traditional workflows - but within just a few months, everybody and their mom could make equally good short films because all the expertise could be written in a markdown file - when everyone’s more productive, no one actually has an advantage and it’s a race to the bottom. very quickly it became impossible to make money, and everyone’s busier than they were without AI - the only one making money now is companies like bytedance who owns user demand and distribution. they sell ads to creators who pay to get traffic, and eventually ad spend became 80% of those creators’ operating expense, while AI tokens is less than 5% so what are the problems here? 1. there’s no moat if you are creating anything with AI if a human is really good at something, and it’s hard to find more humans who can do the same, they can leverage the scarcity of their skill to get paid very well but if AI is good at something, it will be infinitely replicated, so there’s no scarcity hence you can’t really expect a sustainable business by creating anything with AI 2. more creators created more content people actually liked. that’s a good thing but the economical benefit of that all got absorbed by the distribution platforms there’s a famous chinese poem 遍身罗绮者，不是养蚕人 - it basically says people who dress well aren’t the people making dresses. that’s exactly what’s happening here if our society doesn’t figure out a better way to distribute wealth, AI will help billionaires become trillionaires while average people become more exhausted than ever

Also new, Taiwan had been monitoring Ms. Huang, including when she appeared publicly with Jensen in some of the now-famous videos. Taiwan began watching her on the suggestion of US government. She made trips beyond her public appearances with Mr. Huang.

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works

Boston, known as the "City on the Hill," was famously described by John Winthrop in his 1630 sermon, "A Model of Christian Charity." His sermon emphasized moral responsibility and the need to uphold a covenant with God. Matthew 5:14🕯️✨🙇‍♂️🙏

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