Show HN: Music player for big local collections with mpd support https://ift.tt/OAmXupe

Show HN: Music player for big local collections with mpd support mpz is a C++/Qt music player focused on UX, with derectory tree and playlists management. Version 2 got experimental https://musicpd.org support. https://ift.tt/nXyU37J December 20, 2025 at 02:25AM

Show HN: MCPShark Viewer (VS Code/Cursor extension)- view MCP traffic in-editor https://ift.tt/Rx2LK7X

Show HN: MCPShark Viewer (VS Code/Cursor extension)- view MCP traffic in-editor A few days ago I posted MCPShark (a traffic inspector for the Model Context Protocol). I just shipped a VS Code / Cursor extension that lets you view MCP traffic directly in the editor, so you’re not jumping between terminals, logs, and "I think this is what got sent". VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=MCPShark... Main repo: https://ift.tt/GRbfe97 Feature requests / issues: https://ift.tt/53vOdM6 Site: https://mcpshark.sh/ If you’re building MCP agents/tools: what would make MCP debugging actually easy—timeline view, session grouping, diffing tool args, exporting traces, something else? I’d be thankful if you could open a feature request here: https://ift.tt/53vOdM6 December 17, 2025 at 11:49PM

Show HN: Stickerbox, a kid-safe, AI-powered voice to sticker printer https://ift.tt/5AOwj8Z

Show HN: Stickerbox, a kid-safe, AI-powered voice to sticker printer Bob and Arun here, creators of Stickerbox. If AI were built for kids, what would it look like? Asking that question led us to creativity, and more specifically, the power of kids’ imaginations. We wanted to let kids combine the power of their ideas with AI tools but we needed to make sure we did it safely and in the right way. Enter Stickerbox, a voice powered sticker printer. By combining AI image generation with thermal sticker printing, we instantly turn kids' wildest ideas into real stickers they can color, stick, and share. What surprised us most is how the “AI” disappears behind the magic of the device. The moment that consistently amazes kids is when the printer finishes and they are holding their own idea as a real sticker. A ghost on a skateboard, a dragon doing its taxes, their dog as a superhero, anything they can dream of, they can hold in their hand. Their reactions are what pushed us to keep building, even though hardware can be really hard. Along the way the scope of the project grew more than we expected: navigating supply chains, sourcing safe BPA/BPS free thermal paper, passing safety testing for a children’s product, and designing an interface simple enough that a five year old can walk up and just talk to it. We also spent a lot of time thinking about kids’ data and privacy so that parents would feel comfortable having this in their home. Stickerbox is our attempt to make modern AI kid-safe, playful, and tangible. We’d love to hear what you think! P.S. If you’re interested in buying one for yourself or as a gift, use code FREE3PACK to get an extra free pack of paper refills. https://stickerbox.com/ December 20, 2025 at 01:14AM

Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude) https://ift.tt/uIb37Xq

Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude) Hi HN, Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this. My Workflow: I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem. The Tech: Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No accounts required. Team Memory: Index knowledge so teammates' LLMs get context automatically. Visual Map: See file dependencies and refactor "blast radius." MCP-Native: Supports Cursor, Zed, and Claude Desktop. Linggen saves me hours. I’d love to hear how you manage complex system context! Repo: https://ift.tt/KOI3Fmp Website: https://linggen.dev https://ift.tt/KOI3Fmp December 19, 2025 at 11:24PM

Show HN: TinyPDF – 3KB PDF library (70x smaller than jsPDF) https://ift.tt/IE40lMO

Show HN: TinyPDF – 3KB PDF library (70x smaller than jsPDF) I needed to generate invoices in a Node.js app. jsPDF is 229KB. I only needed text, rectangles, lines, and JPEG images. So I wrote tinypdf: <400 lines of TypeScript, zero dependencies, 3.3KB minified+gzipped. What it does: - Text (Helvetica, colors, alignment) - Rectangles and lines - JPEG images - Multiple pages, custom sizes What it doesn't do: - Custom fonts, PNG/SVG, forms, encryption, HTML-to-PDF That's it. The 95% use case for invoices, receipts, reports, tickets, and labels. GitHub: https://github.com/Lulzx/tinypdf npm: npm install tinypdf https://github.com/Lulzx/tinypdf December 19, 2025 at 12:29AM

Show HN: Explore Prometheus /metrics endpoints from your terminal https://ift.tt/wBApo73

Show HN: Explore Prometheus /metrics endpoints from your terminal https://ift.tt/69tSwyG December 18, 2025 at 11:40PM

Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system https://ift.tt/AzOTbi2

Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system I've spent the last few months architecting a RAG system for a regulated environment. I am not a developer by trade, but I approached this with a strict "systems engineering" and audit mindset. While most tutorials stop at "LangChain + VectorDB", I found that making this legally defensible and operationally stable required about 40+ additional components. We moved from a simple ingestion script to a "Multi-Lane Consensus Engine" (inspired by Six Sigma) because standard OCR/extraction was too hallucination-prone for our use case. We had to build extensive auditing, RBAC down to the document level, and a hybrid Graph+Vector retrieval to get acceptable accuracy The current architecture includes: Ingestion: 4 parallel extraction lanes (Vision, Layout, Text, Legal) with a Consensus Engine ("Solomon") that only indexes data confirmed by multiple sources Retrieval: Hybrid Neo4j (Graph) + ChromaDB (Vector) with Reciprocal Rank Fusion Performance: Semantic Caching (Redis) specifically for similar-meaning queries (40x speedup) Security: Full RBAC, Audit Logging of every prompt/retrieval, and PII masking. I documented the complete feature list and gap analysis https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125... My question to the community: Looking at this list – where is the line between "robust production engineering" and "over-engineering"? For those working in Fintech/Medtech RAG: what critical failure modes am I still missing in this list? https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125601945ceeb December 17, 2025 at 11:20PM