Best of Algorithms — January 2026

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    Article
    Avatar of freecodecampfreeCodeCamp·18w

    Learn Dynamic Programming Through Dynamic Visuals

    A comprehensive 2-hour video course teaches dynamic programming through visual patterns rather than memorization. Created by an ex-Google engineer, it covers six fundamental DP patterns including constant transition, grid patterns, two sequences, interval DP, non-constant transition, and knapsack problems. Each pattern is explained with logic and Python code examples, using classic problems like Climbing Stairs, Longest Common Subsequence, and Coin Change to build intuition for optimization.

  2. 2
    Article
    Avatar of programmingdigestProgramming Digest·18w

    I got paid minimum wage to solve an impossible problem.

    A computer science student turned a supermarket floor sweeping job into an optimization problem using simulated annealing and the traveling salesman problem. The initial solution minimized distance but created an impractical path with excessive turns. Adding a turn penalty to the cost function produced a more realistic, human-friendly route. This experiment illustrates how optimizing for easily measurable metrics (distance, engagement, profit) instead of actual goals (usability, wellbeing, sustainability) leads to technically correct but practically useless or harmful outcomes in algorithms, social media, AI, and business.

  3. 3
    Article
    Avatar of physPhys.org·18w

    Building the world's first open-source quantum computer

    Researchers at the University of Waterloo launched Open Quantum Design (OQD), a non-profit organization offering the world's first open-source, full-stack quantum computer. The platform uses ion-trapping technology and provides shared access to hardware, electronics, and software layers. With over 30 software contributors and partnerships with organizations like Xanadu and the Unitary Foundation, OQD aims to accelerate quantum computing development through collaboration rather than competition. The initiative addresses bottlenecks in algorithm testing by providing real hardware access to developers and theorists, while training the next generation of quantum experts.

  4. 4
    Video
    Avatar of youtubeYouTube·20w

    Seriously watch if you're a computer science student | Brilliant

    AI tools like ChatGPT are enabling students to complete assignments without truly learning, diminishing the value of CS degrees. To build genuine programming skills, students should struggle with problems for at least 30 minutes before using AI, build fundamental data structures and algorithms from scratch, explain AI-generated code line by line, and regularly solve problems without AI assistance. These strategies require discipline but help develop real problem-solving abilities and coding intuition that passive learning through tutorials cannot provide.

  5. 5
    Article
    Avatar of muratbuffaloMetadata·20w

    The Sauna Algorithm: Surviving Asynchrony Without a Clock

    A creative analogy explains how distributed systems can coordinate without synchronized clocks by using causal relationships between events. The "sauna algorithm" demonstrates the happened-before relationship: by tracking when someone enters after you and leaving when they leave, you ensure a safe duration without measuring time. This mirrors how asynchronous distributed systems use logical clocks and causal ordering to maintain consistency. The approach highlights the importance of anchoring to events that occur after your entry point, avoiding stale state, though it introduces potential deadlock risks if universally adopted.

  6. 6
    Article
    Avatar of bytebytegoByteByteGo·17w

    How to Write High-Performance Code

    Performance optimization requires developing intuition about where it matters most. Start by estimating operation costs across different speed tiers (cache, RAM, disk, network) to make informed architectural decisions. Always measure with profiling tools before optimizing, as intuition about bottlenecks is often wrong. The biggest wins come from better algorithms and data structures (O(N) vs O(N²)). Memory layout matters significantly due to CPU cache behavior—prefer contiguous storage and sequential access. Reduce allocations through pre-sizing containers, reusing objects, and moving instead of copying. Avoid unnecessary work through fast paths for common cases, caching, lazy evaluation, and early bailouts. Focus optimization efforts on the critical 20% of code that accounts for 80% of runtime, balancing performance gains against code complexity and maintainability.

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