How a simple Linux kernel memory corruption bug can lead to complete system compromise
In this case, reallocating the object as one of those three types didn't seem to me like a nice way forward (although it should be possible to exploit this somehow with some effort, e.g. by using count.counter to corrupt the buf field of seq_file). Also, some systems might be using the slab_nomerge kernel command line flag, which disables this merging behavior. Another approach that I didn't look
MAI-Thinking-1: Building a Hill-Climbing Machine The Microsoft AI Team 1 Abstract Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes
Python 3.15’s interpreter for Windows x86-64 should hopefully be 15% faster
Ken Jin's Blog Python 3.15’s interpreter for Windows x86-64 should hopefully be 15% faster 24 December 2025 Some time ago I posted an apology piece for Python’s tail calling results. I apologized for communicating performance results without noticing a compiler bug had occured. I can proudly say today that I am partially retracting that apology, but only for two platforms—macOS AArch64 (XCode Clan
Large Text Compression Benchmark Matt Mahoney Last update: Mar. 25, 2026. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compress
This December I once again did the Advent of Code, in Rust. If you are interested, my solutions are on Github. I wanted to highlight one particular solution to the day 2 problem as it is both optimized completely beyond the point of reason yet contains a useful technique. For simplicity we’re only going to do part 1 of the day 2 problem here, but the exact same techniques apply to part 2. We’re go
はじめに 株式会社ファースト・オートメーションCTOの田中(しろくま)です！ 生成AIによる文章生成が発展してく中で、入力に関して文章だけでなく様々な入力を扱えるようになってきています。音声や画像だけでなく、 GoogleのGemini 1.5 Proでは最大で1時間程度の動画を入力として扱うことができます。 弊社では製造業向けの生成AIを活用したプロダクト・サービス展開を行っており、この動画入力の技術を用いて、 製造現場での作業動画から作業手順書 を作るということを今回試してみたいと思います。 以下の2つのモデルで作業手順書の作成を行います。 Google Gemini 1.5 Pro Exp 0806 LLaVa-NexXT 前者はGoogleが提供しているクラウド環境で動く生成AIモデルです。 後者はByteDanceなどの研究者たちによって作られた オープンなマルチモーダル生成AI
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MAI-Thinking-1: Building a Hill-Climbing Machine
Large Text Compression Benchmark
The World's Smallest Hash Table | orlp.net
LLaVA-NeXTで作業動画から画像入りの手順書を作成してみる