How Container Filesystem Works: Building a Docker-Like Container from Scratch
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Show HN: Lume 0.2 – Build and Run macOS VMs with unattended setup
Show HN: Lume 0.2 – Build and Run macOS VMs with unattended setup Hey HN, Lume is an open-source CLI for running macOS and Linux VMs on Apple Silicon. Since launch (<a href="https://news.ycombinator.com/item?id=42908061">https://news.ycombinator.com/item?id=42908061</a>), we've been using it to run AI agents in isolated macOS environments. We needed VMs that could set themselves up, so we built that.<p>Here's what's new in 0.2:<p>*Unattended Setup* – Go from IPSW to a fully configured VM without touching the keyboard. We built a VNC + OCR system that clicks through macOS Setup Assistant automatically. No more manual setup before pushing to a registry:<p><pre><code> lume create my-vm --os macos --ipsw latest --unattended tahoe </code></pre> You can write custom YAML configs to set up any macOS version your way.<p>*HTTP API + Daemon* – A REST API on port 7777 that runs as a background service. Your scripts and CI pipelines can manage VMs that persist even if your terminal closes:<p><pre><code> curl -X POST localhost:7777/lume/vms/my-vm/run -d '{"noDisplay": true}' </code></pre> *MCP Server* – Native integration with Claude Desktop and AI coding agents. Claude can create, run, and execute commands in VMs directly:<p><pre><code> # Add to Claude Desktop config "lume": { "command": "lume", "args": ["serve", "--mcp"] } # Then just ask: "Create a sandbox VM and run my tests" </code></pre> *Multi-location Storage* – macOS disk space is always tight, so from user feedback we added support for external drives. Add an SSD, move VMs between locations:<p><pre><code> lume config storage add external-ssd /Volumes/ExternalSSD/lume lume clone my-vm backup --source-storage default --dest-storage external-ssd </code></pre> *Registry Support* – Pull and push VM images from GHCR or GCS. Create a golden image once, share it across your team.<p>We're seeing people use Lume for: - Running Claude Code in an isolated VM (your host stays clean, reset mistakes by cloning) - CI/CD pipelines for Apple platform apps - Automated UI testing across macOS versions - Disposable sandboxes for security research<p>To get started:<p><pre><code> /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)" lume create sandbox --os macos --ipsw latest --unattended tahoe lume run sandbox --shared-dir ~/my-project </code></pre> Lume is MIT licensed and Apple Silicon only (M1/M2/M3/M4) since it uses Apple's native Virtualization Framework directly—no emulation.<p>Lume runs on EC2 Mac instances and Scaleway if you need cloud infrastructure. We're also working on a managed cloud offering for teams that need macOS compute on demand—if you're interested, reach out.<p>We're actively developing this as part of Cua (<a href="https://github.com/trycua/cua" rel="nofollow">https://github.com/trycua/cua</a>), our Computer Use Agent SDK. We'd love your feedback, bug reports, or feature ideas.<p>GitHub: <a href="https://github.com/trycua/cua" rel="nofollow">https://github.com/trycua/cua</a> Docs: <a href="https://cua.ai/docs/lume">https://cua.ai/docs/lume</a><p>We'll be here to answer questions!
Show HN: Figma-use – CLI to control Figma for AI agents
Show HN: Figma-use – CLI to control Figma for AI agents I'm Dan, and I built a CLI that lets AI agents design in Figma.<p>What it does: 100 commands to create shapes, text, frames, components, modify styles, export assets. JSX importing that's ~100x faster than any plugin API import. Works with any LLM coding assistant.<p>Why I built it: The official Figma MCP server can only read files. I wanted AI to actually design — create buttons, build layouts, generate entire component systems. Existing solutions were either read-only or required verbose JSON schemas that burn through tokens.<p>Demo (45 sec): <a href="https://youtu.be/9eSYVZRle7o" rel="nofollow">https://youtu.be/9eSYVZRle7o</a><p>Tech stack: Bun + Citty for CLI, Elysia WebSocket proxy, Figma plugin. The render command connects to Figma's internal multiplayer protocol via Chrome DevTools for extra performance when dealing with large groups of objects.<p>Try it: bun install -g @dannote/figma-use<p>Looking for feedback on CLI ergonomics, missing commands, and whether the JSX syntax feels natural.
Show HN: SnackBase – Open-source, GxP-compliant back end for Python teams
Show HN: SnackBase – Open-source, GxP-compliant back end for Python teams Hi HN, I’m the creator of SnackBase.<p>I built this because I work in Healthcare and Life Sciences domain and was tired of spending months building the same "compliant" infrastructure (Audit Logs, Row-Level Security, PII Masking, Auth) before writing any actual product code.<p>The Problem: Existing BaaS tools (Supabase, Appwrite) are amazing, but they are hard to validate for GxP (FDA regulations) and often force you into a JS/Go ecosystem. I wanted something native to the Python tools I already use.<p>The Solution: SnackBase is a self-hosted Python (FastAPI + SQLAlchemy) backend that includes:<p>Compliance Core: Immutable audit logs with blockchain-style hashing (prev_hash) for integrity.<p>Native Python Hooks: You can write business logic in pure Python (no webhooks or JS runtimes required).<p>Clean Architecture: Strict separation of layers. No business logic in the API routes.<p>The Stack:<p>Python 3.12 + FastAPI<p>SQLAlchemy 2.0 (Async)<p>React 19 (Admin UI)<p>Links:<p>Live Demo: <a href="https://demo.snackbase.dev" rel="nofollow">https://demo.snackbase.dev</a><p>Repo: <a href="https://github.com/lalitgehani/snackbase" rel="nofollow">https://github.com/lalitgehani/snackbase</a><p>The demo resets every hour. I’d love feedback on the DSL implementation or the audit logging approach.
Show HN: Open-Source 8-Ch BCI Board (ESP32 and ADS1299 and OpenBCI GUI)
Show HN: Open-Source 8-Ch BCI Board (ESP32 and ADS1299 and OpenBCI GUI) Hi HN, I recently shared this on r/BCI and wanted to see what the engineering community here thinks.<p>A while back, I got frustrated with the state of accessible BCI hardware. Research gear was wildly unaffordable. So, I spent a ton of time designing a custom board, software and firmware to bridge that gap. I call it the Cerelog ESP-EEG. It is open-source (Firmware + Schematics), and I designed it specifically to fix the signal integrity issues found in most DIY hardware.<p>I believe in sharing the work. You can find the Schematics, Firmware, and Software setup on the GitHub repo: GITHUB LINK: <a href="https://github.com/Cerelog-ESP-EEG/ESP-EEG" rel="nofollow">https://github.com/Cerelog-ESP-EEG/ESP-EEG</a><p>For those who don't want to deal with BGA soldering or sourcing components, I do have assembled units available: <a href="https://www.cerelog.com/eeg_researchers.html" rel="nofollow">https://www.cerelog.com/eeg_researchers.html</a><p>The major features: Forked/modified OpenBCI GUI Compatibility as well as Brainflow API, and LSL Compatibility. I know a lot of us rely on the OpenBCI GUI for visualization because it just works. I didn't want to reinvent the wheel, so I ensured this board supports it natively.<p>It works out of the box: I maintain a forked modified version of the GUI that connects to the board via LSL (Lab Streaming Layer). Zero coding required: You can visualize FFTs, Spectrograms, and EMG widgets immediately without writing a single line of Python.<p>The "active bias" (why my signal is cleaner): The TI ADS1299 is the gold standard for EEG, but many dev boards implement it incorrectly. They often leave the Bias feedback loop "open" (passive), which makes them terrible at rejecting 60Hz mains hum. I simply followed the datasheet: I implemented a True Closed-Loop Active Bias (Drive Right Leg).<p>How it works: It measures the common-mode signal, inverts it, and actively drives it back into the body. The result: Cleaner data<p>Tech stack:<p><pre><code> ADC: TI ADS1299 (24-bit, 8-channel). MCU: ESP32 Chosen to handle high-speed SPI and WiFi/USB streaming Software: BrainFlow support (Python, C++, Java, C#) for those who want to build custom ML pipelines, LSL support, and forked version of OpenBCI GUI support </code></pre> This was a huge project for me. I’m happy to geek out about getting the ESP32 to stream reliably at high sample rates as both the software and firmware for this project proved a lot more challenging than I expected. Let me know what you think!<p>SAFETY NOTE: I strongly recommend running this on a LiPo battery via WiFi. If you must use USB, please use a laptop running on battery power, not plugged into the wall.
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