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You can ask Nano Banana Pro to predict clicks heatmap and draw that on top of website screenshot.
CPO at https://t.co/BNZzlkTfVp. Founder of https://t.co/hOAmca8qLm and https://t.co/dRwgbZCSOw. Coffee-making, parenting, building, exploring: RU → CN → NZ → CL → UK → NZ → PL → UK?
It's very telling about the current stage that even articles deboonking AGI/ASI basically concede that AGI, as any previous generation of thinkers would have understood it, is inevitable in short order, and just nitpick about trajectories and unit economics of diffusion.
For my part I think that NPUs can be improved by > OOM. We will have to venture very far from the current paradigm, though. It's economically impossible with humans. It will be instantly pursued with human-level thinking at DeepSeek costs plus closed loop robotic experimentation.
When Apple launched the App Store in 2009 the TAM for a mobile app was 6M iPhones. With the OpenAI launch of Apps SDK you can reach 900M ChatGPT users. ChatGPT will be the AI app store. Start your engines..
AI Apps investing @ A16Z; A1111; Boards of Krea, Deel, Clutch, Titan, Arc Boats, Untitled, Happy Robot + more; If you’re not at the table, you’re on the menu
LLM training and inference in space. It's happening! This is why SpaceX is eyeing a 2026 IPO. This is why Google is sending TPUs into orbit. AI models will be born and will live in space. We can finally call them “aliens.”
Co-founder & CTO @hyperbolic_labs cooking fun AI systems. Prev: OctoAI (acquired by @nvidia) building Apache TVM, PhD @ University of Washington.
几周前，我偶然看到一篇来自 Hacker News 网站 2015 年 12 月的帖子。 那是当时网站首页的截图——文章、评论，应有尽有。 这让我开始思考：如果我们现在回顾过去，看看那些旧评论中哪些预言了未来，会怎么样？ 于是，我提取了那个月首页上的 930 篇帖子及其讨论，并将它们输入到一个人工智能工具（GPT 5.1 Thinking API）中进行事后分析。 搭建这个系统花了几个小时的编程时间，运行则花费了大约一个小时和 60 美元。目标是什么？ 找到那些最具“先见之明”的评论——那些在经历了 8 年的技术发展后，看起来像是预见到了未来发展的评论。 为什么这很重要？有两个原因。首先，事后分析是训练自己“预测未来能力”的好方法。阅读哪些旧观点经受住了时间的考验（哪些没有），会让你思考：*今天的讨论在 2023 年会是什么样子？* 其次，这也是对人工智能强大力量的提醒。未来，这些工具将更便宜、更快、更准确。你在线留下的每一条评论、帖子或推文，几十年后都可能被人工智能系统仔细分析。这听起来可能有点可怕，但它也提醒我们要深思熟虑。在一个数字足迹永存的世界里，诚实、好奇和批判性思维比以往任何时候都更加重要。 现在，让我们来谈谈结果。排名前十的最具先见之明的评论者是谁？他们的名字包括 pcwalton（一位顶级的 Rust 开发者）、tptacek（一位安全专家）和 cstross（一位以技术前瞻性著称的科幻小说作家）。他们在 2015 年的观点不仅仅是对新闻的反应，而是深入探讨了潜在的趋势。例如，一条评论预测了人工智能将如何从狭窄的任务发展到通用问题解决；另一条评论则警告了集中式数据控制的风险。 这不仅仅是关于“正确”与否。这些评论者之所以脱颖而出，是因为他们提出了正确的问题：*这项技术对社会意味着什么？* *它将如何改变我们的工作、交流或创造方式？* 那么，我们能从中学习到什么呢？未来不仅仅关乎新的小工具或应用程序，更关乎创造它们的人以及塑造其用途的理念。保持好奇心，脚踏实地，并以审慎的态度参与讨论，我们或许就能成为那些在未来被人们铭记的声音。 毕竟，最具远见的评论不仅仅是预测，更是启发人们以不同的方式思考。而我认为，这才是回顾过去真正的价值所在。
找工作、找面试题、改简历、模拟面试。关注： 创业（冷启动） ｜ 认知心理学｜智能体 ｜ 强化学习 building：https://t.co/A4YmEz90B8
Disappointed. None of this has anything to do with «AGI» as in «AI able to perform on high human level in novel cognitive problems». It makes sense to pursue economically and it's not clear it's far algorithmically. You'd have better luck arguing about limits of MLPs, a la Minsky
It's very telling about the current stage that even articles deboonking AGI/ASI basically concede that AGI, as any previous generation of thinkers would have understood it, is inevitable in short order, and just nitpick about trajectories and unit economics of diffusion.