Jane Street’s January 2026 puzzle 1 , “Dropped a Neural Net” , presents a deceptively simple premise: a neural network was “dropped” and its 97 pieces scattered. Your job is to put them back together. Behind this simple framing lies a deep combinatorial optimization problem that I solved two different ways — first with gradient-based permutation learning and combined swaps, then again with a…
What if you could build a model that solves complex Sudoku puzzles, navigates mazes, and tackles abstract reasoning — all with just 27 million parameters and 1,000 training examples? No pre-training on massive datasets, no Chain-of-Thought prompting, no language at all. That’s the claim behind the Hierarchical Reasoning Model (HRM) from Sapient Intelligence. In this post, I’ll walk through how HRM…
You’ve probably seen AI tools that can erase objects from photos and fill in the gap seamlessly. But how does the model know what to put there — and how does it figure out where to edit when you just say “remove the dog”? In this post, I’ll break down two papers: BrushNet , a clever architecture that adds inpainting ability to any diffusion model, and BrushEdit , an agent pipeline that wraps…
If you’ve explored image generation, segmentation, or diffusion models, you’ve probably heard of U-Net. But what exactly is it, and why is it so widely used? In this post, I’ll break down U-Net step by step with concrete examples and visual diagrams. What is U-Net? U-Net is a neural network architecture designed for tasks where you need an image in and an image out of the same size. It was…
After building a text-only transformer for name generation, I wanted to tackle something more ambitious: teaching a model to describe images. This post documents my journey building a minimal image captioning transformer that learns to generate captions like “a dog runs through the snow” from raw pixels. Try the live demo! - The model runs entirely in your browser using ONNX Runtime Web. The…
After months of reading about transformers and LLMs, I finally decided to build one from scratch. Not by copy-pasting code, but by incrementally adding each architectural component and measuring its impact. The result was a character-level name generator trained on 32,033 names, and the journey taught me more than any paper or tutorial could. Preparation: Standing on the Shoulders of Giants Before…
Have you ever worked on a Guitar Pro tab, saved it, and then realized you couldn’t edit it anymore because it was “locked”? Or perhaps you downloaded a tab that was perfect but needed just one small tweak, and the author had locked it? I recently went down a rabbit hole reverse-engineering this “protection” mechanism in Guitar Pro 8. What I found was a classic case of “security through obscurity”…
Cross Gate (魔力宝贝) was one of the most influential MMORPGs in Taiwan and China during the early 2000s. As someone who spent countless hours collecting pets in this game during my childhood, I recently embarked on a nostalgia-driven project: extracting all the pet sprites from the game files and building a modern web viewer to browse them. The Challenge Game resources from the early 2000s are…
After 15+ years of note-taking, I finally said goodbye to Evernote. Here’s the technical journey of migrating 4,330 notes—with all their attachments, tables, and formatting—to Apple Notes. The Problem Evernote had been my digital brain since the late 2000s. But with each passing version, the app became slower, more bloated, and increasingly expensive. Apple Notes, meanwhile, has quietly evolved…
最近读到的一本文字流畅，内容清爽的小书。书里描述了大学教授杨素秋，在西安市碑林区文化旅游局挂职一年，筹办区图书馆的经历。这是个繁杂、具体，有时甚至需要挑战权威的工作： 区里提供的馆址是个地下空间，需要在有限的预算内，找到合适的装修公司，把这个地下空间改造成舒适的阅读空间。 在图书采购过程中，供应商惯于提供劣质的，滥竽充数的图书，为采购者支付回扣。作者不屑于收受回扣，一心为公，希望图书馆里都是经历了时间检验的好书。 为一个图书馆选书，工程浩大，无法仅凭一己之力完成。作者发动自己的人脉，联系了诸多好友帮忙选书。选书缘由，荐者心路，作者缓缓道来，推卷而述，好不痛快。…
纳瓦尔·拉维坎特（Naval Ravikant）在《纳瓦尔宝典》中不仅分享了他关于财富和幸福的智慧，还推荐了大量影响他思维的优质书籍和博客。这些推荐读物构成了一个完整的知识体系，涵盖科学、哲学、商业、灵修等多个领域。 《纳瓦尔宝典》提及书籍与博客索引（含博客链接） 以下列表依照在《The Almanack of Naval Ravikant》中首次出现顺序整理，并补充中文译名及 Naval 的一句话点评。博客及博文已附可点击链接。 序 英文原名（含链接） 中文译名 类 型 Naval 一句点评 1 The Beginning of Infinity 无穷的开始：世界进步的本源 书籍 不算易读，却真正把我读聪明了。 2 Sapiens: A Brief History of Humankind 人类简史：从动物到上帝 书籍 近十年读过的最佳著作，洞见满页。 3 The Rational…
每个人都是一座冰山。当你与人交谈，想象你是在和冰山交谈，目之所及的只是水面之上的部分。如果你希望达成交流，你必须具备耐心，从身体和情绪感受出发，逐层递进，弄清原委。
I tried Claude Code this week, and instantly felt the empowerment from the tool, and was stunned by how naturally it blends into developer workflows. It demonstrated how easy the LLM model makers can disrupt the application makers (Cursor in this case). This reminds me of the analogy Andrej Karpathy made in Software Is Changing (Again) presentation that LLM has strong analogies to operating…
为了增加用户活跃度，微信读书团队开发了一个微信小游戏——问答 PK。这是一个双人对决形式的知识问答天梯，题目内容主要基于常识，比如成语填字，古诗词接上下句。 玩了几天后发现，光靠我的知识储备和记忆力，很难持续提升段位。答案在网上一搜就能找到，但是 10 秒钟的答题时间来不及搜索，于是我想到借助 DeepSeek 来自动答题。说干就干，Vide-Coding 了一个 Python 脚本，自动化了整个答题过程，并最终达到了最高等级。本文记录在开发过程中，遇到的问题与一些观察。 技术难点与观察 OCR 错误率导致的复杂度 我首先想到的是将窗口截图转为文字，这一步涉及图片到文字的模态转换： macOS 自带的 OCR 中文识别准确率并不完美。有些中文字符在不同帧中会被错误识别为相似字形。 为了判断题目是否更新，程序需要实现较复杂的题目刷新检测逻辑。 在存储与提取已答题目上，也因此增加了额外复杂度。…
Sundar Pichai: CEO of Google and Alphabet | Lex Fridman Podcast : Sundar Pichai views “moonshot” projects as crucial for several reasons: Driving Innovation: He believes that aiming for audacious, seemingly impossible goals, like the original moon landing, forces radical rethinking and leads to breakthroughs that wouldn’t happen with incremental improvements. It’s about finding “10X” improvements…
To monitor our baby from other rooms, we purchased a Nanit Baby Monitor. Using image recognition, Nanit provides insights into our baby’s nighttime sleep patterns through its app. Each state transition point includes a video for review. However, the display isn’t very intuitive — the chart doesn’t show the exact timestamps for each transition. For example, the start and end times of the two longer…
独立思考的人， 笃定真理一定存在， 但可能不是他心中的模样。 世界上大部分的问题悬而未决， 小部分我们以为的答案， 也随着时间的推移不断演变。 观点就像流过身体的水， 并不属于某一个人。 保持质疑一切的态度， 保持开放， 倾听不同的观点。 听到一个观点之后， 不急着相信或者否定， 而是尝试理解观点背后的事实与逻辑， 然后再做出独立的判断。 做好随时修正持有观点的准备， 因为对事实的认知会改变， 行动之后也会得到了更多的事实。 论辩不是为了输赢， 而是共同探索不同观点的根源， 是价值排序的不同， 还是你我看见了不同局部的世界。 放下偏见和自傲， 做一个理智，独立思考的人。
使用了一整天 MacWhisper 之后的感受： 语音输入文字本身并不是什么新鲜的功能，但就像 iPhone 键盘 的诞生一样，它背后仿佛存在着一道无形的界限——在跨越这道界限之前，一切都显得繁琐笨重；而一旦突破，用户才能真正感受到那种 Magic Moment，仿佛一切变得自然、顺畅，甚至有些神奇。
《思辨力35讲：像辩手一样思考》是最近读到的干货满满的一本书。 这本书前两章系统地整理了分析问题的逻辑框架和常见的逻辑谬误，对于如何提高思辨能力能有帮助。第三章辩论实战部分讲如何应用在辩论中，对于不直接参与辩论的读者不如前两章实用。 塑造理论的整体结构（第二章的内容） MECE（Mutually Exclusive, Collectively Exhaustive） 定义：相互独立、完全穷尽。这些点与点彼此不重合，叫相互独立；它们加在一起能够完整地覆盖对这个问题的分析，叫完全穷尽。 MECE这个概念对我比较有启发，工作中的一些讨论缺乏对问题的总体上的思考。 明确定义是讨论的开始 明确定义，达成共识，挖掘更深洞见 有标准，才有意义 比较标准是建立论证的关键因素 比较标准的公开是建立共识的前提。选择辩论队员上场的例子。 检视标准是发现分歧、明确重点的方式…
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