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[CLI Tool] Show HN: leaf – a terminal Markdown previewer with a GUI-like experience

[Other] Spinel: Ruby AOT Native Compiler

[Other] Show HN: How LLMs Work – Interactive visual guide based on Karpathy's lecture All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.

[Other] Show HN: Gova – The declarative GUI framework for Go

[Other] Running Bare-Metal Rust Alongside ESP-IDF on the ESP32-S3's Second Core

[Other] Dear friend, you have built a Kubernetes (2024)

[API/SDK] DeepSeek v4 <a href="https:&#x2F;&#x2F;api-docs.deepseek.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;api-docs.deepseek.com&#x2F;</a><p><a href="https:&#x2F;&#x2F;huggingface.co&#x2F;deepseek-ai&#x2F;DeepSeek-V4-Pro&#x2F;blob&#x2F;main&#x2F;DeepSeek_V4.pdf" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;deepseek-ai&#x2F;DeepSeek-V4-Pro&#x2F;blob&#x2F;main...</a>

[CLI Tool] Show HN: Stash – CLI to search over your team's coding agent sessions

[Other] Show HN: Tolaria – Open-source macOS app to manage Markdown knowledge bases Hey there! I am Luca, I write <a href="https:&#x2F;&#x2F;refactoring.fm&#x2F;" rel="nofollow">https:&#x2F;&#x2F;refactoring.fm&#x2F;</a> and I built Tolaria for myself to manage my own knowledge base (10K notes, 300+ articles written in over 6 years of newslettering) and work well with AI.<p>Tolaria is offline-first, file-based, has first-class support for git, and has strong opinions about how you should organize notes (types, relationships, etc).<p>Let me know your thoughts!

[Other] Show HN: Chestnut – The antidote to AI-induced skill atrophy I come from a machine learning background - PyTorch code, leaving a training job running overnight, and Jupyter Notebooks. I hadn&#x27;t touched much frontend before diving deep into start-ups. It was similar for my co-founder Nick, who spent time working on semiconductors.<p>I started building, and noticing patterns in AI outputs. Enough to be able to understand how a hook works, how to manage state and why Typescript is great. But whenever it came to optimising a piece of code, debugging state issues or designing a codebase from scratch, my mind went blank. I went to ChatGPT Study Mode to seek wisdom.<p>I found learning with a chat-based interface frustrating. Unstructured conversation with a super smart colleague, who occasionally talks rubbish, would often lead to rabbit holes and surface-level understanding - not true wisdom. I basically became my own teacher, and unless I checked myself - I wrecked myself.<p>This is why we&#x27;ve been building the best interface for learning programming in the AI era, we called it Chestnut.<p>We believe interactive, personalised courses, focusing on high-level systems thinking and in-depth understanding, not syntax, are the best way to stay sharp while the world of programming changes. Not hours of passive tutorial hell, browsing the internet for nuggets of wisdom, or endless conversations with coding agents that never quite click.<p>Give it a spin and let us know what you think!

[Other] An update on recent Claude Code quality reports

[Other] MeshCore development team splits over trademark dispute and AI-generated code

[Other] Show HN: Run coding agents in microVM sandboxes instead of your host machine Hi HN, we built SuperHQ, an open source app that runs AI coding agents in isolated microVM sandboxes instead of directly on your machine. Each agent gets its own VM with a full Debian environment. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a diff view to accept or discard changes. API keys never enter the sandbox. We also just launched remote.superhq.ai which acts as a remote control for SuperHQ, allowing you to access your workspaces and agents from anywhere.

[Other] Incident with multple GitHub services

[Other] Show HN: LocalLLM – Recipes for Running the Local LLM (Need Contributors) I built localLLLM: a small community project for running local models.<p>Live: <a href="https:&#x2F;&#x2F;locallllm.fly.dev" rel="nofollow">https:&#x2F;&#x2F;locallllm.fly.dev</a><p>The goal is simple: if someone has model + OS + GPU + RAM, they should get steps that actually work (ideally one liner)<p>I need help populating and validating guides.<p>If you run local models, please submit one working recipe (or report what failed). Would love to hear general feedback as well!

[Other] Bitwarden CLI compromised in ongoing Checkmarx supply chain campaign

[Other] UK Biobank health data keeps ending up on GitHub I&#x27;m a researcher studying privacy, and I started tracking the DMCA notices that UK Biobank sends to GitHub. I tracked 110 notices filed so far, targeting 197 code repositories by 170 developers across the world.<p>The exposure of Biobank data on GitHub is the latest in a long series of governance challenges for UK Biobank. (My colleague and I have an editorial in the BMJ about this: <a href="http:&#x2F;&#x2F;bmj.com&#x2F;cgi&#x2F;content&#x2F;full&#x2F;bmj.s660?ijkey=dEot4dJZGZGXeG1&amp;keytype=ref" rel="nofollow">http:&#x2F;&#x2F;bmj.com&#x2F;cgi&#x2F;content&#x2F;full&#x2F;bmj.s660?ijkey=dEot4dJZGZGXe...</a>). The latest is today, with information of all half a million members listed for sale on Alibaba.<p>Looking at the takedown notices, we often see specific files being targeted rather than entire repositories (possibly to justify the copyright infringement as required for a takedown notice, not a copyright expert; although it is clear that they only use DMCA notices as a last resort, for GitHub users they cannot identify, and who were likely not given access in the first place). A quarter of the files are genetic&#x2F;genomics. Tabular data account for another large share and could contain phenotype or health records.

[Other] Optimizing Datalog for the GPU

[Other] Writing a C Compiler, in Zig

[Other] Arch Linux Now Has a Bit-for-Bit Reproducible Docker Image