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[Other] Client-side GPU load balancing with Redis and Lua

[Other] Detecting AV1-encoded videos with Python

[Other] Show HN: RunMat – runtime with auto CPU/GPU routing for dense math Hi, I’m Nabeel. In August I released RunMat as an open-source runtime for MATLAB code that was already much faster than GNU Octave on the workloads I tried. <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44972919">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44972919</a><p>Since then, I’ve taken it further with RunMat Accelerate: the runtime now automatically fuses operations and routes work between CPU and GPU. You write MATLAB-style code, and RunMat runs your computation across CPUs and GPUs for speed. No CUDA, no kernel code.<p>Under the hood, it builds a graph of your array math, fuses long chains into a few kernels, keeps data on the GPU when that helps, and falls back to CPU JIT &#x2F; BLAS for small cases.<p>On an Apple M2 Max (32 GB), here are some current benchmarks (median of several runs):<p>* 5M-path Monte Carlo * RunMat ≈ 0.61 s * PyTorch ≈ 1.70 s * NumPy ≈ 79.9 s → ~2.8× faster than PyTorch and ~130× faster than NumPy on this test.<p>* 64 × 4K image preprocessing pipeline (mean&#x2F;std, normalize, gain&#x2F;bias, gamma, MSE) * RunMat ≈ 0.68 s * PyTorch ≈ 1.20 s * NumPy ≈ 7.0 s → ~1.8× faster than PyTorch and ~10× faster than NumPy.<p>* 1B-point elementwise chain (sin &#x2F; exp &#x2F; cos &#x2F; tanh mix) * RunMat ≈ 0.14 s * PyTorch ≈ 20.8 s * NumPy ≈ 11.9 s → ~140× faster than PyTorch and ~80× faster than NumPy.<p>If you want more detail on how the fusion and CPU&#x2F;GPU routing work, I wrote up a longer post here: <a href="https:&#x2F;&#x2F;runmat.org&#x2F;blog&#x2F;runmat-accel-intro-blog" rel="nofollow">https:&#x2F;&#x2F;runmat.org&#x2F;blog&#x2F;runmat-accel-intro-blog</a><p>You can run the same benchmarks yourself from the GitHub repo in the main HN link. Feedback, bug reports, and “here’s where it breaks or is slow” examples are very welcome.

[Other] Show HN: Marmot – Single-binary data catalog (no Kafka, no Elasticsearch)

[Other] Nixtml: Static website and blog generator written in Nix

[Other] A deep dive into QEMU: The Tiny Code Generator (TCG), part 1 (2021)

[Other] Show HN: Webclone.js – A simple tool to clone websites I needed a lightweight way to archive documentation from a website. wget and similar tools failed to clone the site reliably (missing assets, broken links, etc.), so I ended up building a full website-cloning tool using Node.js + Puppeteer.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;jademsee&#x2F;webclone" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jademsee&#x2F;webclone</a><p>Feedback, issues, and PRs are very welcome.

[Other] ULID: Universally Unique Lexicographically Sortable Identifier

[Code Quality] Show HN: Cm-colors –I got tired of manually fixing wcag contrast,so I made this I usually look up palettes and the UI comes out nice except some color pairs don&#x27;t pass wcag color contrast and well - just isn&#x27;t readable Ended up writing a tiny library that automatically nudges your text color just enough to pass AA or AAA, while keeping it visually similar to the original along with a color contrast linter so we can use it in CI Thought you guys might find it useful too, it&#x27;s foss :&gt;

[Other] Durin is a library for reading and writing the Dwarf debugging format

[Other] Show HN: An AI zettelkasten that extracts ideas from articles, videos, and PDFs Hey HN! Over the weekend (leaning heavily on Opus 4.5) I wrote Jargon - an AI-managed zettelkasten that reads articles, papers, and YouTube videos, extracts the key ideas, and automatically links related concepts together.<p>Demo video: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;W7ejMqZ6EUQ" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;W7ejMqZ6EUQ</a><p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;schoblaska&#x2F;jargon" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;schoblaska&#x2F;jargon</a><p>You can paste an article, PDF link, or YouTube video to parse, or ask questions directly and it&#x27;ll find its own content. Sources get summarized, broken into insight cards, and embedded for semantic search. Similar ideas automatically cluster together. Each insight can spawn research threads - questions that trigger web searches to pull in related content, which flows through the same pipeline.<p>You can explore the graph of linked ideas directly, or ask questions and it&#x27;ll RAG over your whole library plus fresh web results.<p>Jargon uses Rails + Hotwire with Falcon for async processing, pgvector for embeddings, Exa for neural web search, crawl4ai as a fallback scraper, and pdftotext for academic papers.

[Other] Ghostty compiled to WASM with xterm.js API compatibility

[Other] Show HN: RFC Hub I&#x27;ve worked at several companies during the past two decades and I kept encountering the same issues with internal technical proposals:<p>- Authors would change a spec after I started writing code<p>- It&#x27;s hard to find what proposals would benefit from my review<p>- It&#x27;s hard to find the right person to review my proposals<p>- It&#x27;s not always obvious if a proposal has reached consensus (e.g. buried comments)<p>- I&#x27;m not notified if a proposal I approved is now ready to be worked on<p>And that&#x27;s just scratching the surface. The most popular solutions (like Notion or Google Drive + Docs) mostly lack semantics. For example it&#x27;s easy as a human to see a table in a document with rows representing reviewers and a checkbox representing review acceptance but it&#x27;s hard to formally extract meaning and prevent a document from &quot;being published&quot; when criteria isn&#x27;t met.<p>RFC Hub aims to solve these issues by building an easy to use interface around all the metadata associated with technical proposals instead of containing it textually within the document itself.<p>The project is still under heavy development as I work on it most nights and weekends. The next big feature I&#x27;m planning is proposal templates and the ability to refer to documents as something other than RFCs (Request for Comments). E.g. a company might have a UIRFC for GUI work (User Interface RFCs), a DBADR (Database Architecture Decision Record), etc. And while there&#x27;s a built-in notification system I&#x27;m still working on a Slack integration. Auth works by sending tokens via email but of course RFC Hub needs Google auth.<p>Please let me know what you think!

[Other] Show HN: Rust-based ultra-low latency streaming framework – Wingfoil

[Other] Xlibre is a fork of the Xorg Xserver with lots of code cleanups

[DevOps] Show HN: Flowctl – Open-source self-service workflow automation platform Flowctl is a self-service platform that gives users secure access to complex workflows, all in a single binary. These workflows could be anything, granting SSH access to an instance, provisioning infra, or custom business process automation. The executor paradigm in flowctl makes it domain-agnostic.<p>This initial release includes: - SSO with OIDC and RBAC - Execution on remote nodes via SSH (fully agentless) - Approvals - Cron-based scheduling - Flow editor UI - Encrypted credentials and secrets store - Docker and Script executors - Namespaces<p>I built this because I needed a simple tool to manage my homelab while traveling, something that acts as a UI for scripts. At work, I was also looking for tools to turn repetitive ops&#x2F;infra tasks into self-service offerings. I tried tools like Backstage and Rundeck, but they were either too complex, or the OSS versions lacked important features.<p>Flowctl can simply be described as a pipeline (like CI&#x2F;CD systems) that people can trigger on-demand with custom inputs.<p>Would love to hear how you might use something like this!<p>Demo - <a href="https:&#x2F;&#x2F;demo.flowctl.net" rel="nofollow">https:&#x2F;&#x2F;demo.flowctl.net</a><p>Homepage - <a href="https:&#x2F;&#x2F;flowctl.net" rel="nofollow">https:&#x2F;&#x2F;flowctl.net</a><p>GitHub - <a href="https:&#x2F;&#x2F;github.com&#x2F;cvhariharan&#x2F;flowctl" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;cvhariharan&#x2F;flowctl</a>

[Other] Show HN: GitHits – Code example engine for AI agents and devs (Private Beta) It has been almost 10 years since I started the opencv-python packaging project. Scaling it to more than 100 million downloads as a side project showed me how much ease of installation and proper package distribution matter to users. It gave the computer vision ecosystem a noticeable boost. Now I have a new idea that I hope can help even more people across the broader software engineering world.<p>A while ago, I realized I kept giving the same advice to teammates and friends when they ran into a programming issue they couldn&#x27;t easily solve: go to GitHub and look at how others solved it.<p>There is a huge pool of underused example material across open source. Most problems developers face are not that novel. With enough digging, someone has already solved the same issue in code or at least posted a workaround to an issue or discussion thread.<p>The trouble is that GitHub search is limited and works only when you already know the right keywords. You also need the time and patience to go through and read all the results, connect information across files, repositories, issues, discussions, and other metadata, and then turn that into a working solution. The same limitations apply to Stack Overflow and other search tools.<p>LLMs changed a lot, but they did not change this. They do not perform equally well across all programming languages, and their training data is always stale. They cannot reliably show how to combine multiple libraries in the way real projects do. For these and many other cases, they need a real, canonical code example rather than an outdated piece of documentation written for humans.<p>That is why I started building GitHits. It is designed to handle the work that humans and AI coding agents struggle with: finding real solutions in real repositories and connecting the dots across the open source ecosystem.<p>GitHits searches millions of open-source repositories at the code level, finds real code and surrounding metadata that match the intent of your blocker, and distills the patterns it finds into one example.<p>The initial product is in private beta, with MCP support to connect GitHits to your favorite coding agent IDE or CLI.<p>What makes it different from Context7 and other generic documentation search tools:<p>- It is built around unblocking, not general search<p>- It does not require manual indexing jobs<p>- It works for humans through the web UI and for agents through the MCP<p>- It clusters similar samples across repositories so you can see the common path real engineers took<p>- It ranks the sources using multiple signals for higher quality: the selected sources might be, for example, a combination of code files, issues, and docs<p>- It generates one token-efficient code example based on real sources<p>It is not perfect yet. Right now, GitHits supports only Python, JS, TS, C, C++, and Rust. More languages and deeper coverage are coming, and I would appreciate early feedback while the beta is still taking shape. If you have ever lost hours stuck on a blocker you knew someone else had solved already, I would love to hear what you think.

[Other] Stacked Diffs with git rebase —onto

[Other] Show HN: Identifiy test coverage gaps in your Go projects

[Other] Langjam Gamejam: Build a programming language then make a game with it