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A developer discovers a decades-old bug in Knuth's Algorithm D for long division, leading to a new theorem added to the TAOCP errata, and also uncovers a related bug in LLVM's implementation.
An essay arguing that NP-hard problems are often not as intractable in practice as commonly believed, citing modern solvers and algorithmic advances that handle real-world instances efficiently.
A deep-dive educational article exploring dynamic programming as a unifying principle behind shortest-path algorithms, neural network training, and context-free grammar parsing, tying together automata, optimal control, and linear programming.
In his talk, Carson Gross discussed the impact of AI on university computer science education, arguing that in the AI era, students still need to be taught to write and read code, while also noting that AI brings an assessment crisis and opportunities for pedagogical change.
A tweet recommending Jeff Erickson's free online algorithms textbook, highlighting its great illustrations and overall quality.
Computer science enrollment is declining at U.S. universities, but schools like Colby College are integrating AI education across disciplines to prepare students for a changing workforce.
OpenAI's internal next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.
OpenAI announces ten results on long-standing open problems in mathematics and theoretical computer science, achieved by an internal version of its next model Astra, with proofs formalized in Lean.
An article explaining why floating point numbers are a leaky abstraction, covering precision limits, subtraction issues, and overflow/underflow, with references to Goldberg's classic paper.
Turing Award laureate Gilles Brassard visited Peking University's Center on Frontiers of Computing Studies for a lecture on Alan Turing's legacy, and was appointed as a PKU Turing Mentor.
A detailed overview of Coding Interview University, a free open-source GitHub repository that provides a self-directed 8-month study plan covering data structures, algorithms, and system design to help prepare for technical interviews at major tech companies.
This article introduces functional programming from first principles, covering the mathematical definition of functions and the classification of programming language paradigms. It is the first part of a series aimed at imperative programmers.
MIT offers the free textbook 'Structure and Interpretation of Computer Programs' (SICP) for learning computer science fundamentals like recursion, abstraction, and language design.
The article examines the trend of computer science professors leaving academia for lucrative positions at AI companies like Anthropic, OpenAI, Meta, and DeepMind, which is leading to a concentration of research in industry and a decline in open academic research.
Professor Emeritus Dimitri Bertsekas, a pioneering computer scientist known for his work in optimization, reinforcement learning, and AI, and author of over 20 influential books, has passed away at age 83.
A Peking University undergraduate compiled high-quality open-source course resources (from MIT, Stanford, Berkeley, etc.) accumulated over four years of self-taught computer science into an open-source document arranged in a practical order, aiming to help beginners learn programming systematically from scratch.
Promotional tweet claiming a single article is all you need to learn about State Machines.
A free PDF of the textbook 'Mathematics for Computer Science' by Eric Lehman, F. Thomson Leighton, and Albert R. Meyer is available via MIT OpenCourseWare, providing essential discrete mathematics foundations for computer science.
Berkeley CS162 offers a free comprehensive online operating systems course with lectures, projects, and assignments covering key OS concepts like processes, virtual memory, scheduling, and distributed systems.
The article explores the unsolved problem in computer science of finding the fastest possible multiplication algorithm, tracing its history from the grade-school O(n²) method to modern breakthroughs like Karatsuba's 1960 discovery and the recent 2024 result, and explains why this matters for AI, encryption, and other digital tasks.