tripluca.eth
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@tripluca
Built an Airbnb before Airbnb to fund 10 years of travels. Founder @tripscommunity @tectrisvc tripluca.eth $TRIPS
Moreover I don't want to convince other people to write code with AI. They can keep their old way, what's the problem? The only thing that is mildly disturbing is that the average programmer does not have a solid idea of what LLMs are, so they hallucinate a lot (the people).
ethereum isn't a fucking company and you don't "fix" it by building a $1b command center with a board & a mandate to pump eth. the moment you route staking or fee revenue into a central org "aligned with price", you've built a capture machine and called it accountability. that's _not_ alignment, that's governance gravity - power concentrates, others defer, and it slowly becomes the system's real control layer. cypherpunk systems don't win by optimising some retarded balance sheet, they win by resisting control, staying permissionless, and never creating a single point of pressure, bribery, or capture. period.
最近Andrej Karpathy @karpathy 结束了他的AI教育创业，去了Anthropic。有人说这是背刺OpenAI，也有人说他是AI教育创业失败。 抛开这些八卦，作为普通人，我想见贤思齐，看看能从他身上学到什么。 首先说说，他的哪些事情是我们学不到的？ 第一，英语区里的文化语感。 英语本不是他的母语。他是捷克斯洛伐克人，但是他15岁去了加拿大，整个高中和大学都在英语环境里度过，英语对他来说是有文化感和语感的语言。我们这种博士才来美国的人，很难达到那个程度。缺的不是英语水平，是那种高密度的浸泡环境，以及从青春期开始就和英语母语者建立的深层学习关系。这一层补不上。 第二，顶尖的学术和职业履历。 他在加拿大的资源其实一般，但是后来去到斯坦福，就开始获得顶级资源。先是成为OpenAI的co-founder，又在Tesla最重视自动驾驶的那几年加入并主导FSD项目。顶着这两个title可以吃一辈子，这种成长背景和行业机遇，可遇不可求，普通人完全无法复制。 再来说说，什么是我们可以学习的。 第一，Building in Public。 他从19岁就开始这件事了。本科期间在YouTube开了一个叫badmephisto的频道，做魔方教程。读博期间他手搓了ConvNetJS，一个用纯JS写的深度学习库，打开浏览器就能看到神经网络在训练。之后每隔一两年，他就出一个从零手搓的小项目。2020年micrograd，2022年nanoGPT，from scratch重现GPT-2。2024年 llm.c，纯C训练LLM。2026年microgpt，200行无依赖跑通整个GPT。 二十年里没停过。每个项目都放在GitHub，配博客或者视频。这就是Building in Public的实质，做完一件事就留下一个公开的工件。 第二，Learning in Public。 这一点其实更值得学，因为门槛更低，但大部分人不好意思做。 他写过一篇博客叫《What I learned from competing against a ConvNet on ImageNet》。当时他自己亲手给ImageNet图片做人类标注，跟神经网络比赛准确率，然后把整个过程写下来。他还写过一篇《A Recipe for Training Neural Networks》，本质上是把自己训练神经网络踩过的坑列成 checklist。 他的YouTube系列Neural Networks: Zero to Hero也是一样。两个小时一个视频，他坐在电脑前边写代码边出声思考，包括卡住的地方、调试的过程，不修饰，不剪辑炫技。学生看到的不是结果，是一个真人怎么搞懂一件事。 Learning in Public还包括Teaching in Public。他读博期间主导设计了CS231n 这门深度学习课，从第一届150人涨到第三届750人，成了斯坦福最大的课之一。但更关键的是，他把整套课程的 slides、笔记、作业、视频，全部免费放到网上。 Building in Public和Learning in Public这两件事，是每个人都可以做的，而且完全可以现在中文区做起来。我们现在说做个人IP，其实Andrej Karpathy是最好的做个人IP的例子。 至于如何变现个人IP，不要太指望你直接通过在自媒体平台做in public系列就可以赚钱。Karpathy自己也没靠YouTube广告或者卖课吃饭，他的钱来自Tesla股票、OpenAI股权这些真正的工作。Eureka Labs想直接卖AI教育课程，最后也没真正做起来。 个人IP真正的价值在于给你选择权。它可以让你卖课，卖产品，但是更能让你被人记得，被人主动找到，让原本你够不到的机会自己来找你。可能是一个好工作的offer，可能是一个合伙人，可能是一个客户，可能是一笔投资。这些东西的回报可能超过你自己的预期。
I attended the University of Arizona commencement ceremony, where Eric Schmidt @ericschmidt faced boos throughout his speech. If you don’t know how young graduates feel about AI, this post is for you. The message is clear: it reflects growing skepticism toward AI narratives coming out of Silicon Valley. I keep coming back to one question for anyone building AI: are you building for humans? Build responsible and ethical AI. AI governance and compliance matter. (Speech excerpt.)
tested out @antirez' ds4.c this morning. so impressive and delivers. on a M3 max, 128GB, stock ds4 settings: - 14–15 t/s at 62K pre-filled actual coding conversation - memory usage was flat during gen ~85GB res - disk cache is ~8GB for a full 100K context window - thermals were normal, light fan activity - inference server is rock solid so far biggest constraint: anytime there's a compact, we pay the wait-time price of a fresh prefill (~1min per 10k context) before we are back in action. sequential inference + multiple agents in parallel performance is unclear, will report back. I'm so amped.
Subsidies in Europe are generally a net negative because they change incentives People won't compete to become the best at their job, they'll just compete who can win the most subsidies So you get below average people doing below average work but getting paid for it
The "europoor" discourse migrating from terminally online Twitter to the WSJ op-ed is actually a big tell. When a narrative stops being a meme and becomes establishment messaging, it means the establishment needs it. You simply don't reach for "but Europe is poor!" unless your domestic numbers have become very hard to spin for the citizenry. Millennials and Gen Z have no memory of American prosperity - you can't revive the American dream, because they never lived it. And if they ever compare their median household situation to, say, Denmark or Belgium, the math is simply not mathing. Just go with the truth (for once!) and admit the U.S. has been run as an economic extraction zone for a narrow class of people, and the bill is now coming due. Pointing at Europeans won't make average Americans grocery bill or insurance premiums go lower.
Such an important point!
