Jane Street’s January 2026 puzzle¹, “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 simpler approach that revealed a key insight: pairing corrections unlock cascading improvements in ordering.

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 actually works by tracing the code and architecture step by step. I’ll also cover the important follow-up critiques that question some of these claims.

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 BrushNet with language understanding to turn natural instructions into image edits.

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.

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.

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.

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” — and not very deep obscurity at that.

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.

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.

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。

尽管困难重重，作者心有所往，逆流而上，不畏险阻，最终得偿所愿。主线之余，作者夹叙一年挂职生活所遇的形色人等，有的让人牙关紧咬，有的让人唏嘘感慨，说尽人情冷暖。西安的美食，官场中的本色不改，选书朋友们的人生故事，对弱势群体的关照，五味杂陈，乒乓作响，读者吃到的是酸辣爽口的一餐。