AWS Cloud Control API, a Uniform API to Access AWS & Third-Party Services | Amazon Web Services
AWS News Blog AWS Cloud Control API, a Uniform API to Access AWS & Third-Party Services Today, I am happy to announce the availability of AWS Cloud Control API a set of common application programming interfaces (APIs) that are designed to make it easy for developers to manage their AWS and third-party services. AWS delivers the broadest and deepest portfolio of cloud services. Builders leverage th
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: PyTorch で Apple Silicon GPU を使ってみる - CUBE SUGAR CONTAINER
PyTorch v1.12 以降では、macOS において Apple Silicon あるいは AMD の GPU を使ったアクセラレーションが可能になっているらしい。 バックエンドの名称は Metal Performance Shaders (MPS) という。 意外と簡単に使えるようなので、今回は手元の Mac で試してみた。 使った環境は次のとおり。 GPU が 19 コアの Apple M2 Pro を積んだ Mac mini を使用している。 $ sw_vers ProductName: macOS ProductVersion: 14.4.1 BuildVersion: 23E224 $ sysctl machdep.cpu.brand_string machdep.cpu.brand_string: Apple M2 Pro $ pip list | grep -i tor
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
Reflections on 2 years of CPython’s JIT Compiler—The good, the bad, the ugly
Ken Jin's Blog Reflections on 2 years of CPython’s JIT Compiler: The good, the bad, the ugly 5 July 2025 This blog post includes my honest opinions on the CPython JIT. What I think we did well, what I think we could have done better. I’ll also do some brief qualititative analysis. I’ve been working on CPython’s JIT compiler since before the very start. I don’t know how long that is at this point …
Eliciting Reasoning in Language Models with Cognitive Tools Brown Ebouky IBM Research - Zurich ETH Zurich Brown.Ebouky@ibm.com Andrea Bartezzaghi IBM Research - Zurich abt@zurich.ibm.com Mattia Rigotti IBM Research - Zurich mrg@zurich.ibm.com Abstract The recent advent of reasoning models like OpenAI’s o1 was met with excited spec- ulation by the AI community about the mechanisms underlying these
Ken Jin's Blog Python 3.15’s JIT is now back on track 17 Mar 2026 (JIT performance as of 17 March (PST). Lower is better versus interpreter. Image credits to https://doesjitgobrrr.com/). Great news—we’ve hit our (very modest) performance goals for the CPython JIT over a year early for macOS AArch64, and a few months early for x86_64 Linux. The 3.15 alpha JIT is about 11-12% faster on macOS AArch64
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
R ユーザーのための Pandas 実践ガイド II: siuba と datar - ill-identified diary
概要 以前にも書いたように Python の pandas は参照透過性に欠けるため, 何度も書き換えて使用するような使い方に向いていない. これは pandas の用途と合わない. pandas をもっと快適にデータハンドリングする方法がないか探したところ, siuba, datar というパッケージを見つけたので紹介する. これらのパッケージの特徴を挙げ, 実験によるパフォーマンス比較してみた. 個人的には siuba のほうが信頼できると思うが, 現時点ではどちらも発展途上のパッケージである. 以前の続きということでタイトルを踏襲したが, 実は私がこれらのパッケージを知ったのは昨日なので「実践」的かどうかは少し疑わしい タイトルの通り R を知っている pandas ユーザーを想定読者としているが, R ユーザでなくても再利用のしやすい書き方は知っていて損はないと思う. その場合は実
The future of Python web services looks GIL-free | Hacker News
C code needs to be updated to be safe in a GIL free execution environment. It is a lot of work! The pervasive problem is that mutable data structures (lists, dict etc) could change at any arbitrary point while the C code is working with them, and the reference count for others could drop to zero if *anyone* is using a borrowed reference (common for performance in CPython APIs). Previously the GIL
Source code: Lib/profiling/sampling/ The profiling.sampling module, named Tachyon, provides statistical profiling of Python programs through periodic stack sampling. Tachyon can run scripts directly or attach to any running Python process without requiring code changes or restarts. Because sampling occurs externally to the target process, overhead is virtually zero, making Tachyon suitable for bot
Performance of Python programs has been a major focus of development for the language over the last five years or so; the Faster CPython project has been a big part of that effort. One of its subprojects is to add an experimental just-in-time (JIT) compiler to the language; at last year's PyCon US, project member Brandt Bucher gave an introduction to the copy-and-patch JIT compiler. At PyCon US 20
Published as a conference paper at ICLR 2026 AGENTIC CONTEXT ENGINEERING: EVOLVING CON- TEXTS FOR SELF-IMPROVING LANGUAGE MODELS Qizheng Zhang1∗ , Changran Hu2∗ , Shubhangi Upasani2 , Boyuan Ma2 , Fenglu Hong2 , Vamsidhar Kamanuru2 , Jay Rainton2 , Chen Wu2 , Mengmeng Ji2 , Hanchen Li3 , Urmish Thakker2 , James Zou1 , Kunle Olukotun1 1 Stanford University 2 SambaNova Systems, Inc. 3 UC Berkeley {q
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MAI-Thinking-1: Building a Hill-Climbing Machine
Eliciting Reasoning in Language Models with Cognitive Tools
Python 3.15’s JIT is now back on track
Large Text Compression Benchmark
The World's Smallest Hash Table | orlp.net
profiling.sampling — Statistical profiler
Following up on the Python JIT
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models