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[IDE/Editor] Show HN: VS Code extension to edit the filesystem like a text buffer This is a spiritual adaptation of oil.nvim for vscode. The main idea is you edit the filesystem by editing the current directory listing&#x27;s text buffer. For example, if I want to rename a file, I just rename it in the listing file. This is extremely powerful because it translates all of your text-editing skills immediately into file editing capabilities.<p>Some features:<p>* Create&#x2F;rename&#x2F;move&#x2F;delete files by editing the current directory listing&#x27;s textbuffer<p>* Filter using glob pattern<p>* Trash and undo support<p>* Works even in remote-ssh workspaces<p>* Works across multiple vscode windows

[Other] Atopile – Design circuit boards with code

[Other] Show HN: HNping 'remind me later' for HN via web push HNping lets you set a reminder for a HN post. You get a web push browser notification when it&#x27;s time, and clicking it takes you back to the post. That&#x27;s it.<p>I built HNping because I kept stumbling on HN posts where the discussion still had to get going. Wanted to revisit, but didn&#x27;t want to create even more bookmarks&#x2F;etc I&#x27;ll just forget about. So I created a &#x27;remind me later&#x27; tool (like the reddit bot) to fix this for myself.<p>To use it: go to hnping.com, enable notifications, and drag the bookmarklet to your bookmarks bar. Then click it on any HN post to set a reminder (5 minutes to 1 week). No personal info needed - you just get a UUID that serves as your account.<p>I tried to make it as simple as possible.<p>It&#x27;s built on a Cloudflare Worker with D1 for data storage and uses Firebase Cloud Messaging for push notifications.

[Other] Show HN: We developed an AI tool to diagnose car problems Hey HN,<p>We built AutoAI – an AI tool that tells you what&#x27;s wrong with your car in plain English.<p>Just enter:<p>Your car’s make&#x2F;model&#x2F;year<p>The OBD2 error codes (optional) (like P0420, P0171, etc.)<p>Any symptoms you&#x27;re noticing (e.g. “rough idle” or “weird sound when starting”)<p>And we’ll tell you:<p>The most likely issue<p>How to verify it yourself<p>Whether it’s a DIY fix or shop-worthy<p>No more endless Googling or forum-hopping. Built for car owners, tinkerers, and pros who want fast, reliable answers. Powered by a repair-trained AI using real-world automotive data.<p>We’re trying to make diagnostics smarter, not replace your mechanic – just make you way more informed before spending money.<p>Would love feedback or crazy edge-case inputs to improve it.

[Other] Show HN: FluidAudio – Swift Speaker Diarization on CoreML We needed a speaker diarization solution that could run every few seconds alongside transcription on iOS and macOS. But native Swift support was either limited or locked behind paid licenses. Since diarization is a common need in speech-to-text workflows, we decided to open source our work and give back to the community.<p>We initially tried sherpa-onnx, which works, but running both diarization and transcription models slowed down older devices. CPU-only inference just isn’t ideal for near real-time workloads, so we wanted the option to offload segmentation and speaker embedding to the GPU or ANE. Supporting M1 Macs in particular meant pushing more of the workload to the ANE.<p>Instead of shoehorning the ONNX model into CoreML with C++, we converted the original PyTorch models directly to CoreML. This approach required some monkey-patching in the PyTorch and pyannote code, but the initial benchmarks look promising.<p>We’d love feedback! We&#x27;re currently working on adding VAD and integrating Parakeet for transcription, but still wrestling with CoreML model conversion.

[Other] Show HN: ArchGW – An intelligent edge and service proxy for agents Hey HN!<p>This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again.<p>A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague input so agents don’t make mistakes. You’re routing prompts to the right expert agent based on context or task type. You’re writing integration code to quickly and safely add support for new LLMs. And every time a new framework hits the market or is updated, you’re validating or re-implementing that same logic—again and again.<p>Putting all the low-level plumbing code in a framework gets messy to manage, harder to update and scale. Low-level work isn&#x27;t business logic. That’s why we built archgw - an intelligent proxy server that handles prompts during ingress and egress and offers several related capabilities from a single software service. It lives outside your app runtime, so you can keep your business logic clean and focus on what matters. Think of it like a service mesh, but for AI agents.<p>Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized NLP models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that rule-based, single-purpose tools that handle the work around resiliency, processing and routing prompts should move into a dedicated infrastructure layer for agents, but built on the battle-tested foundational of Envoy Proxy.<p>The intelligence in archgw comes from our fast Task-specific LLMs [3] that can handle things like agent routing and hand off, guardrails and preference-based intelligent LLM calling. Here are some additional details about the open source project. archgw is written in rust, and the request path has three main parts:<p>* Listener subsystem which handles downstream (ingress) and upstream (egress) request processing. * Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard hooks and identifies where to forward the conversation to via its prompt_target primitive. * Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models<p>We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4] Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7].<p>[1] <a href="https:&#x2F;&#x2F;github.com&#x2F;katanemo&#x2F;archgw">https:&#x2F;&#x2F;github.com&#x2F;katanemo&#x2F;archgw</a> [2] <a href="https:&#x2F;&#x2F;www.envoyproxy.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.envoyproxy.io&#x2F;</a> [3] <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;katanemo&#x2F;arch-function-66" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;katanemo&#x2F;arch-function-66</a>... [4] <a href="https:&#x2F;&#x2F;discord.com&#x2F;channels&#x2F;1292630766827737088&#x2F;12926307682" rel="nofollow">https:&#x2F;&#x2F;discord.com&#x2F;channels&#x2F;1292630766827737088&#x2F;12926307682</a>... [5] <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=I4Lbhr-NNXk" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=I4Lbhr-NNXk</a> [6] <a href="https:&#x2F;&#x2F;docs.archgw.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;docs.archgw.com&#x2F;</a> [7] <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;katanemo" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;katanemo</a>

[Other] Aeron: Efficient reliable UDP unicast, UDP multicast, and IPC message transport

[Other] Show HN: I made a JSFiddle-style playground to test and share prompts fast I built this out of frustration as I lead the development of AI features at Yola.com.<p>Prompt testing should be simple and straightforward. All I wanted was a simple way to test prompts with variables and jinja2 templates across different models, ideally somthing I could open during a call, run few tests, and share results with my team. But every tool I tried hit me with a clunky UI, required login and API keys, or forced a lengthy setup process.<p>And that&#x27;s not all.<p>Then came the pricing. The last quote I got for one of the tools on the market was $6,000&#x2F;year for a team of 16 people in a use-it-or-loose-it way. For a tool we use maybe 2–3 times per sprint. That’s just ridiculous!<p>IMO, it should be something more like JSFiddle. A simple prompt playground that does not require you to signup, does not require API keys, and let&#x27;s experiment instantly, i.e. you just enter a browser URL and start working. Like JSFiddle has. And mainly, something that costs me nothing if I&#x27;m or my team is not using it.<p>Eventually I gave up looking for solution and decided to build it by myself.<p>Here it is: <a href="https:&#x2F;&#x2F;langfa.st" rel="nofollow">https:&#x2F;&#x2F;langfa.st</a><p>Help me find what&#x27;s wrong or missing or does not work from you perspctive.<p>P.S. I did not put any limits or restrictions yet, so test it wisely. Don&#x27;t make me broke, please.

[Other] Show HN: BinaryRPC – Lightweight WebSocket-based RPC framework in modern C++ Hi HN,<p>I’m a recent CS graduate. During the past few months I wrote BinaryRPC, an open-source RPC framework in modern C++20 focused on low-latency, binary WebSocket messaging.<p>Why I built it * Wanted first-class session support, pluggable QoS levels and a simple middleware chain (global, specific, multi handler) without extra JSON&#x2F;XML parsing. * Easy developer experience<p>A quick feature list * Binary WebSocket frames – minimal overhead * Built-in session layer (login &#x2F; reconnect &#x2F; heartbeat) * QoS1 &#x2F; QoS2 with automatic ACK &amp; retry * Plugin system – rooms, msgpack, etc. can be added in one line * Thread-safe core: RAII + folly<p>Still early (solo project), so any feedback on design, concurrency model or missing must-have features would help a lot.<p>Thanks for reading!<p>also see &quot;Chat Server in 5 Minutes with BinaryRPC&quot;: <a href="https:&#x2F;&#x2F;medium.com&#x2F;@efecanerdem0907&#x2F;building-a-chat-server-in-5-minutes-with-binaryrpc-qos2-session-management-and-room-plugin-ccb66d722466" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@efecanerdem0907&#x2F;building-a-chat-server-i...</a>

[DevOps] Incus – Next-generation system container, application container, and VM manager

[Other] Show HN: RULER – Easily apply RL to any agent Hey HN, Kyle here, one of the co-founders of OpenPipe.<p>Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it&#x27;s so hard to implement.<p>One of the biggest challenges when adapting RL to a new task is the need for a task-specific &quot;reward function&quot; (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and&#x2F;or significant domain expertise to generate.<p>RULER is a drop-in reward function that works across different tasks without any of that complexity.<p>It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).<p>We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3&#x2F;4 tasks! <a href="https:&#x2F;&#x2F;openpipe.ai&#x2F;blog&#x2F;ruler">https:&#x2F;&#x2F;openpipe.ai&#x2F;blog&#x2F;ruler</a><p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;OpenPipe&#x2F;ART">https:&#x2F;&#x2F;github.com&#x2F;OpenPipe&#x2F;ART</a>

[Other] Show HN: Vibe Kanban – Kanban board to manage your AI coding agents

[Other] Show HN: NodeLoop – Hub for electronics design knowledge and tools Building a free web toolbox for hardware engineers: harness cable diagram generator, connector pinout tools (M.2, JTAG...), microcontroller serial monitor, and various other small utilities.<p>No sign-up required. Designed from my own needs. Feedback and feature suggestions are welcome.

[Other] Show HN: Bedrock – An 8-bit computing system for running programs anywhere Hey everyone, this is my latest project.<p>Bedrock is a lightweight program runtime: programs assemble down to a few kilobytes of bytecode that can run on any computer, console, or handheld. The runtime is tiny, it can be implemented from scratch in a few hours, and the I&#x2F;O devices for accessing the keyboard, screen, networking, etc. can be added on as needed.<p>I designed Bedrock to make it easier to maintain programs as a solo developer. It&#x27;s deeply inspired by Uxn and PICO-8, but it makes significant departures from Uxn to provide more capabilities to programs and to be easier to implement.<p>Let me know if you try it out or have any questions.

[DevOps] Show HN: Pangolin – Open source alternative to Cloudflare Tunnels Pangolin is an open source self-hosted tunneled reverse proxy management server with identity and access control, designed to securely expose private resources through encrypted WireGuard tunnels running in user space.<p>We made Pangolin so you retain full control over your infrastructure while providing a user-friendly and feature-rich solution for managing proxies, authentication, and access, all with a clean and simple dashboard web UI.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;fosrl&#x2F;pangolin">https:&#x2F;&#x2F;github.com&#x2F;fosrl&#x2F;pangolin</a><p>Deployment takes about 5 minutes on a VPS: <a href="https:&#x2F;&#x2F;docs.fossorial.io&#x2F;Getting%20Started&#x2F;quick-install" rel="nofollow">https:&#x2F;&#x2F;docs.fossorial.io&#x2F;Getting%20Started&#x2F;quick-install</a><p>Demo by Lawrence Systems (YouTube): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;g5qOpxhhS7M?si=M1XTWLGLUZW0WzTv&amp;t=723" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;g5qOpxhhS7M?si=M1XTWLGLUZW0WzTv&amp;t=723</a><p>Some use cases:<p><pre><code> - Grant users access to your apps from anywhere using just a web-browser - Proxy behind CGNAT - One application load balancer across multiple clouds and on-premises - Easily expose services on IoT and edge devices for field monitoring - Bring localhost online for easy access </code></pre> A few key features:<p><pre><code> - No port forwarding and hide your public IP for self-hosting - Create proxies to multiple different private networks - OAuth2&#x2F;OIDC identity providers - Role-based access control - Raw TCP and UDP support - Resource-specific pin codes, passwords, email OTP - Self-destructing shareable links - API for automation - WAF with CrowdSec and Geoblocking</code></pre>

[Other] eBPF: Connecting with Container Runtimes

[Other] Show HN: Open source alternative to Perplexity Comet Hey HN, we&#x27;re a YC startup building an open-source, privacy-first alternative to Perplexity Comet.<p>No invite system unlike bunch of others – you can download it today from our website or GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;browseros-ai&#x2F;BrowserOS">https:&#x2F;&#x2F;github.com&#x2F;browseros-ai&#x2F;BrowserOS</a><p>--- Why bother building an alternative? We believe browsers will become the new operating systems, where we offload much bunch of our work to AI agents. But these agents will have access to all your sensitive data – emails, docs, on top of your browser history. Open-source, privacy-first alternatives need to exist.<p>We&#x27;re not a search or ad company, so no weird incentives. Your data stays on your machine. <i>You can use local LLMs with Ollama</i>. We also support BYOK (bring your own keys), so no $200&#x2F;month plans.<p>Another big difference vs Perplexity Comet: our agent runs locally in your browser (not on their server). You can actually watch it click around and do stuff, which is pretty cool! Short demo here: <a href="https:&#x2F;&#x2F;bit.ly&#x2F;browserOS-demo" rel="nofollow">https:&#x2F;&#x2F;bit.ly&#x2F;browserOS-demo</a><p>--- How we built? We patch Chromium&#x27;s C++ source code with our changes, so we have the same security as Google Chrome. We also have an auto-updater for security patches and regular updates.<p>Working with Chromium&#x27;s 15M lines of C++ has been another fun adventure that I&#x27;m writing a blog post on. Cursor&#x2F;VSCode breaks at this scale, so we&#x27;re back to using grep to find stuff and make changes. Claude code works surprisingly well too.<p>Building the binary takes ~3 hours on our M4 Max MacBook.<p>--- Next? We&#x27;re just 2 people with a lot of work ahead (Firefox started with 3 hackers, history rhymes!). But we strongly believe that a privacy-first browser with local LLM support is more important than ever – since agents will have access to so much sensitive data.<p>Looking forward to any and all comments!

[Other] Writing Bounds-Safe Code in C with Arrays

[Other] Measuring the impact of AI on experienced open-source developer productivity

[Other] Show HN: Ten years of running every day, visualized Today marks ten years, 3653 consecutive days, of running at least one mile every day under the USRSA rules [1]. To celebrate, I built an interactive dashboard that turns a decade of GPX files into charts you can explore.<p>Running has truly changed my life: I&#x27;ve made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I&#x27;m starting to see the positive benefits as I get older.<p>The stack is pretty simple: a NextJS app, with a Postgres database to keep all my running data, and all the stats are pre-computed and cached in Redis, so I effectively only hit the database once a day when a new run is ingested. On the fronted, I toyed with the idea of using D3 or pre-existing data viz libraries, but ended up rolling my own using SVGs directly, it gave me more control on the visualizations.<p>I used the Strava bulk export to pre-populate the database, and I&#x27;m using their webhook API to do incremental updates. I have to tap into OpenWeatherMap and OpenCageDate to enrich the running data a little bit.<p>Happy to answer anything about the stack, data pipeline, or how I stayed motivated for 10 years!<p>[1] <a href="https:&#x2F;&#x2F;www.runeveryday.com" rel="nofollow">https:&#x2F;&#x2F;www.runeveryday.com</a> Run Streak Association rules: ≥ 1 mile per day