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Show HN: Porting xv6 to HiFive Unmatched board
Show HN: Porting xv6 to HiFive Unmatched board Hi HN,<p>I ported the teaching OS xv6-riscv to HiFive Unmatched and got it running on real hardware, including passing usertests.<p>I've been self-studying OS internals using the MIT 6.1810 materials. After finishing most of the labs, I was eager to see what it's like to run the OS on bare metal, rather than QEMU.<p>The Unmatched may not have the latest RISC-V features, but it's well-documented, and the Rev B release has made it more affordable, which makes it a good learning platform.<p>The porting process involved several interesting challenges:<p>- Hardware Quirks: Handling things like enabling A/D bits in PTEs (the hardware doesn't set them automatically, causing page faults), proper handling of interrupts, and instruction cache synchronization.<p>- Boot Flow: xv6 expects M-mode on startup, but standard RISC-V boot flows (typically via OpenSBI) jump to S-mode. To bridge this gap, I created a minimal U-Boot FIT image that contains only the xv6 kernel. This way, U-Boot SPL handles the complex CPU/DDR initialization, then hands control to xv6 in M-mode (skipping OpenSBI).<p>- Drivers: Ported an SPI SD card driver, replacing the virtio disk driver.<p>I wrote up implementation notes here: <a href="https://github.com/eyengin/xv6-riscv-unmatched/blob/unmatched/doc/NOTES.md" rel="nofollow">https://github.com/eyengin/xv6-riscv-unmatched/blob/unmatche...</a><p>Hopefully, this is useful for others who are learning OS internals and want to try running their code on real RISC-V hardware.
Show HN: DeepDream for Video with Temporal Consistency
Show HN: DeepDream for Video with Temporal Consistency I forked a PyTorch DeepDream implementation and added video support with temporal consistency. It produces smooth DeepDream videos with minimal flickering, and is highly flexible including many parameters and supports multiple pretrained image classifiers including GoogLeNet. Check out the repo for sample videos! Features:<p>- Optical flow warps previous hallucinations into the current frame<p>- Occlusion masking prevents ghosting and hallucination transfer when objects move<p>- Advanced parameters (layers, octaves, iterations) still work<p>- Works on GPU, CPU, and Apple Silicon
ASCII-Driven Development
ASCII-Driven Development
Show HN: HyprMCP – Analytics, logs and auth for MCP servers
Show HN: HyprMCP – Analytics, logs and auth for MCP servers Hi HN, my name is Philip, I’m the co-founder of Glasskube and one of the creators of HyprMCP.<p>This project started when we did what everyone was doing — building a remote MCP server and launching it. Building the first local MCP server for testing was quite simple, and we had our first tools ready within a day. The next step was turning that into a production-ready remote MCP server.<p>As we exposed the MCP server to our users, we wanted to authenticate them with our existing authentication methods. We dove deep into authentication. Our approach was to build an auth proxy and plug it in front of our MCP. It took a while to figure out Dynamic Client Registration (DCR) and the OAuth spec, and especially the gaps between existing OIDC IDPs and what LLM clients needed.<p>We thought authentication would be the hard part — but it wasn’t. When we shared the MCP server with a few friendly startups, we realized that different MCP clients behave differently. Especially if something didn't work, it was hard to figure out the root cause. We ended up storing all the raw gRPC method calls to see if the initialization and subsequent requests worked. This is especially useful if you are on a serverless environment with limited debugging functionality, like Cloudflare Workers.<p>Once we solved auth and compatibility, we launched to a small customer base — done, right? Unfortunately, not quite. Technically everything was working, but when we started talking to users, they told us the MCP server didn’t always respond with the right tools for their prompts. We had a working enterprise-grade MCP server — but it wasn’t very smart. After talking to some startup friends, we realized we needed an evaluation layer. That’s when we added prompt analytics — letting us see which prompts triggered which tools and how well they performed. That alone dramatically improved our MCP’s behavior and overall user experience.<p>After building all of this into our proxy, we realized that everyone building a remote MCP was facing the same challenges. So we decided to package it all up and release it to the community.<p>We’re thrilled to launch and open-source HyprMCP. It acts as a proxy that you can plug in front of your MCP server(s) with zero code changes. You get authentication, logging and debugging, prompt analytics, and an MCP connection instructions generator.<p>Under the hood, HyprMCP leverages dynamic Kubernetes Operators (Metacontroller) to automate infrastructure provisioning.<p>On the roadmap: MCP aggregation — combining multiple MCP servers under one single remote URL for large organizations running servers with different lifecycles. All of it without storing end user credentials on the server and connecting the MCP to the organizations existing authentication methods.<p>You can check the project out on GitHub: <a href="https://github.com/hyprmcp/jetski" rel="nofollow">https://github.com/hyprmcp/jetski</a><p>For testing, we also have a hosted version here: <a href="https://app.hyprmcp.com" rel="nofollow">https://app.hyprmcp.com</a><p>We even created a demo video on YouTube: <a href="https://www.youtube.com/watch?v=m2-YyfjXap4" rel="nofollow">https://www.youtube.com/watch?v=m2-YyfjXap4</a><p>We’d love to get your feedback, hear what features are missing, and learn how you’re building and running your own MCP servers.
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