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Walrus: A 1 Million ops/sec, 1 GB/s Write Ahead Log in Rust
https://www.reddit.com/r/programming/comments/1o0hdn9/walrus_a_1_million_opssec_1_gbs_write_ahead_log/

<!-- SC_OFF -->Hey r/programming (https://www.reddit.com/r/programming), I made walrus: a fast Write Ahead Log (WAL) in Rust built from first principles which achieves 1M ops/sec and 1 GB/s write bandwidth on consumer laptop. find it here: https://github.com/nubskr/walrus I also wrote a blog post explaining the architecture: https://nubskr.com/2025/10/06/walrus.html you can try it out with: cargo add walrus-rust just wanted to share it with the community and know their thoughts about it :) <!-- SC_ON --> submitted by /u/Ok_Marionberry8922 (https://www.reddit.com/user/Ok_Marionberry8922)
[link] (https://nubskr.com/2025/10/06/walrus.html) [comments] (https://www.reddit.com/r/programming/comments/1o0hdn9/walrus_a_1_million_opssec_1_gbs_write_ahead_log/)
I pushed Python to 20,000 requests sent/second. Here's the code and kernel tuning I used.
https://www.reddit.com/r/programming/comments/1o087dh/i_pushed_python_to_20000_requests_sentsecond/

<!-- SC_OFF -->I wanted to share a personal project exploring the limits of Python for high-throughput network I/O. My clients would always say "lol no python, only go", so I wanted to see what was actually possible. After a lot of tuning, I managed to get a stable ~20,000 requests/second from a single client machine. The code itself is based on asyncio and a library called rnet, which is a Python wrapper for the high-performance Rust library wreq. This lets me get the developer-friendly syntax of Python with the raw speed of Rust for the actual networking. The most interesting part wasn't the code, but the OS tuning. The default kernel settings on Linux are nowhere near ready for this kind of load. The application would fail instantly without these changes. Here are the most critical settings I had to change on both the client and server: Increased Max File Descriptors: Every socket is a file. The default limit of 1024 is the first thing you'll hit.ulimit -n 65536 Expanded Ephemeral Port Range: The client needs a large pool of ports to make outgoing connections from.net.ipv4.ip_local_port_range = 1024 65535 Increased Connection Backlog: The server needs a bigger queue to hold incoming connections before they are accepted. The default is tiny.net.core.somaxconn = 65535 Enabled TIME_WAIT Reuse: This is huge. It allows the kernel to quickly reuse sockets that are in a TIME_WAIT state, which is essential when you're opening/closing thousands of connections per second.net.ipv4.tcp_tw_reuse = 1 I've open-sourced the entire test setup, including the client code, a simple server, and the full tuning scripts for both machines. You can find it all here if you want to replicate it or just look at the code: GitHub Repo: https://github.com/lafftar/requestSpeedTest On an 8-core machine, this setup hit ~15k req/s, and it scaled to ~20k req/s on a 32-core machine. Interestingly, the CPU was never fully maxed out, so the bottleneck likely lies somewhere else in the stack. I'll be hanging out in the comments to answer any questions. Let me know what you think! Blog Post (I go in a little more detail): https://tjaycodes.com/pushing-python-to-20000-requests-second/ <!-- SC_ON --> submitted by /u/Lafftar (https://www.reddit.com/user/Lafftar)
[link] (https://tjaycodes.com/pushing-python-to-20000-requests-second/) [comments] (https://www.reddit.com/r/programming/comments/1o087dh/i_pushed_python_to_20000_requests_sentsecond/)
The childhood game that explains AI’s decision trees
https://www.reddit.com/r/programming/comments/1o0t8ml/the_childhood_game_that_explains_ais_decision/

<!-- SC_OFF -->An engineer recently explored how the classic board game Guess Who? reveals the underlying logic of AI decision trees. In the game, players don’t guess — they ask the question that gives the most information, systematically eliminating possibilities until only one remains. This mirrors how decision trees in machine learning split data: each “question” (feature) aims to reduce uncertainty and create cleaner partitions. The project draws direct parallels between the game’s yes/no mechanics and predictive ML processes, such as feature selection and information gain. Just as a player might ask, “Does your character wear glasses?” to remove half the options, a model might ask, “Is blood pressure high?” to refine its classification. By using a nostalgic, visual example, the engineer illustrates how understanding question efficiency in a simple game can demystify how AI models learn to make accurate predictions with minimal steps. <!-- SC_ON --> submitted by /u/shift_devs (https://www.reddit.com/user/shift_devs)
[link] (https://shiftmag.dev/how-guess-who-logic-shapes-ai-decision-trees-and-predictive-ml-5874/) [comments] (https://www.reddit.com/r/programming/comments/1o0t8ml/the_childhood_game_that_explains_ais_decision/)
So, you want to stack rank your developers?
https://www.reddit.com/r/programming/comments/1o07ghf/so_you_want_to_stack_rank_your_developers/

<!-- SC_OFF -->Something to send to your manager next time some new initiative smells like stack ranking <!-- SC_ON --> submitted by /u/Realistic_Skill5527 (https://www.reddit.com/user/Realistic_Skill5527)
[link] (https://www.swarmia.com/blog/dont-stack-rank-your-developers/) [comments] (https://www.reddit.com/r/programming/comments/1o07ghf/so_you_want_to_stack_rank_your_developers/)
Built a visual Docker database manager with Tauri
https://www.reddit.com/r/programming/comments/1o1398z/built_a_visual_docker_database_manager_with_tauri/

<!-- SC_OFF -->Hey 👋 — Solo dev here. Just launched Docker DB Manager, a desktop app built with Tauri v2 and React. The problem: Managing database containers across projects got tedious—constantly checking available ports, recreating containers to change settings, and hunting for passwords across .env files and notes. What it does: Create and manage containers without terminal commands Detects port conflicts before creating containers Edit configuration (ports, names) without manual recreation Generates ready-to-copy connection strings Syncs with Docker Desktop in real-time Currently supports PostgreSQL, MySQL, Redis, and MongoDB (more databases coming). It's open source and I'd love your feedback:
GitHub: https://github.com/AbianS/docker-db-manager Available for macOS (Apple Silicon + Intel). Windows and Linux coming soon. Happy to answer questions about the architecture or implementation! 🚀 <!-- SC_ON --> submitted by /u/Zukonsio (https://www.reddit.com/user/Zukonsio)
[link] (https://github.com/AbianS/docker-db-manager) [comments] (https://www.reddit.com/r/programming/comments/1o1398z/built_a_visual_docker_database_manager_with_tauri/)
20 hardest to easiest rankings of programming languages to learn in 2025 | BIZMIA
https://www.reddit.com/r/programming/comments/1o12xcz/20_hardest_to_easiest_rankings_of_programming/

<!-- SC_OFF -->20 Hardest Programming Languages for Developers to Learn in 2025 by BIZMIA. <!-- SC_ON --> submitted by /u/waozen (https://www.reddit.com/user/waozen)
[link] (https://www.hellobizmia.com/insights/hardest-programming-languages) [comments] (https://www.reddit.com/r/programming/comments/1o12xcz/20_hardest_to_easiest_rankings_of_programming/)