The best Hacker News stories from Show from the past day
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Show HN: Holos – QEMU/KVM with a compose-style YAML, GPUs and health checks
I got tired of libvirt XML and Vagrant's Ruby/reload dance for single-host VM stacks, so I built a compose-style runtime directly on QEMU/KVM.<p>What's there: GPU passthrough as a first-class primitive (VFIO, OVMF, per-instance EFI vars), healthchecks that gate depends_on over SSH, socket-multicast L2 between VMs with no root and no bridge config, cloud-init wired through the YAML, Dockerfile support for provisioning.<p>What it's not: Kubernetes. No clustering, no live migration, no control plane. Single host.
Prototype, but I'm running it on real hardware. Curious what breaks for people.
Show HN: Daemons – we pivoted from building agents to cleaning up after them
For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams.<p>The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development.<p>Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness
Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution.<p>Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations.<p>A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements.<p>This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to.<p>An article with more technical detail: <a href="https://mediator.ai/blog/ai-negotiation-nash-bargaining/" rel="nofollow">https://mediator.ai/blog/ai-negotiation-nash-bargaining/</a>
Show HN: GoModel – an open-source AI gateway in Go
Hi, I’m Jakub, a solo founder based in Warsaw.<p>I’ve been building GoModel since December with a couple of contributors. It's an open-source AI gateway that sits between your app and model providers like OpenAI, Anthropic or others.<p>I built it for my startup to solve a few problems:<p><pre><code> - track AI usage and cost per client or team
- switch models without changing app code
- debug request flows more easily
- reduce AI spendings with exact and semantic caching
</code></pre>
How is it different?<p><pre><code> - ~17MB docker image
- LiteLLM's image is more than 44x bigger ("docker.litellm.ai/berriai/litellm:latest" ~ 746 MB on amd64)
- request workflow is visible and easy to inspect
- config is environment-variable-first by default
</code></pre>
I'm posting now partly because of the recent LiteLLM supply-chain attack. Their team handled it impressively well, but some people are looking at alternatives anyway, and GoModel is one.<p>Website: <a href="https://gomodel.enterpilot.io" rel="nofollow">https://gomodel.enterpilot.io</a><p>Any feedback is appreciated.
Show HN: VidStudio, a browser based video editor that doesn't upload your files
Hi HN,
I built VidStudio, a privacy focused video editor that runs in the browser. I tried to keep it as frictionless as possible, so there are no accounts and no uploads. Everything is persisted on your
machine.<p>Some of the features: multi-track timeline, frame accurate seek, MP4 export, audio, video, image, and text tracks, and a WebGL backed canvas where available. It also works on mobile.<p>Under the hood, WebCodecs handles frame decode for timeline playback and scrubbing, which is what makes seeking responsive since decode runs on the hardware decoder when the browser supports it.
FFmpeg compiled to WebAssembly handles final encode, format conversion, and anything WebCodecs does not cover. Rendering goes through Pixi.js on a WebGL canvas, with a software fallback when WebGL is
not available. Projects live in IndexedDB and the heavy work runs in Web Workers so the UI stays responsive during exports.<p>Happy to answer technical questions about the tradeoffs involved in keeping the whole pipeline client-side. Any feedback welcome.<p>Link: <a href="https://vidstudio.app/video-editor" rel="nofollow">https://vidstudio.app/video-editor</a>
Show HN: Alien – Self-hosting with remote management (written in Rust)
Hi HN, I'm Alon, and I'm building Alien, an open-source platform for deploying your software into your customer's environment and keeping it fully managed.<p>In my previous startup, I heard the same question from <i>every</i> single enterprise customer over and over again: "My data is sensitive. Can I deploy your product to my own cloud account?"<p>Self-hosting is becoming very popular because it lets users keep their data private, local, and inside their own environment. Unfortunately, self-hosting breaks down when someone starts paying for your software. Especially if it's an enterprise customer.<p>Customers usually don't actually know how to operate your software. They might change something small — Postgres version, environment variables, IAM, firewall rules — and things start failing. From their perspective, the product is broken. And even if the root cause is on their side, it doesn't matter... the customer is always right, you're still the one expected to fix it.<p>But you can't. You don't have access to their environment. You don't have real visibility. You can't run anything yourself. So you're stuck debugging a system you don't control, through screenshots and copy-pasted logs on a Zoom call. You end up responsible for something you don't control.<p>I think there's a better model of paid self-hosting: the software runs in the customer's environment, but the developer can actually operate it. It's a win-win: for the customer, their data stays private and local, and the developer still has control over deployments, updates, and debugging.<p>Alien provides infrastructure to deploy and operate software inside your users' environments, while retaining centralized control over updates, monitoring, and lifecycle management. It currently supports AWS, GCP, and Azure targets.<p>GitHub: <a href="https://github.com/alienplatform/alien" rel="nofollow">https://github.com/alienplatform/alien</a><p>Getting started: <a href="https://alien.dev/docs/quickstart" rel="nofollow">https://alien.dev/docs/quickstart</a><p>How it works: <a href="https://alien.dev/docs/how-alien-works" rel="nofollow">https://alien.dev/docs/how-alien-works</a><p>Excited to share Alien with everyone here – let me know what you think!
Show HN: Alien – Self-hosting with remote management (written in Rust)
Hi HN, I'm Alon, and I'm building Alien, an open-source platform for deploying your software into your customer's environment and keeping it fully managed.<p>In my previous startup, I heard the same question from <i>every</i> single enterprise customer over and over again: "My data is sensitive. Can I deploy your product to my own cloud account?"<p>Self-hosting is becoming very popular because it lets users keep their data private, local, and inside their own environment. Unfortunately, self-hosting breaks down when someone starts paying for your software. Especially if it's an enterprise customer.<p>Customers usually don't actually know how to operate your software. They might change something small — Postgres version, environment variables, IAM, firewall rules — and things start failing. From their perspective, the product is broken. And even if the root cause is on their side, it doesn't matter... the customer is always right, you're still the one expected to fix it.<p>But you can't. You don't have access to their environment. You don't have real visibility. You can't run anything yourself. So you're stuck debugging a system you don't control, through screenshots and copy-pasted logs on a Zoom call. You end up responsible for something you don't control.<p>I think there's a better model of paid self-hosting: the software runs in the customer's environment, but the developer can actually operate it. It's a win-win: for the customer, their data stays private and local, and the developer still has control over deployments, updates, and debugging.<p>Alien provides infrastructure to deploy and operate software inside your users' environments, while retaining centralized control over updates, monitoring, and lifecycle management. It currently supports AWS, GCP, and Azure targets.<p>GitHub: <a href="https://github.com/alienplatform/alien" rel="nofollow">https://github.com/alienplatform/alien</a><p>Getting started: <a href="https://alien.dev/docs/quickstart" rel="nofollow">https://alien.dev/docs/quickstart</a><p>How it works: <a href="https://alien.dev/docs/how-alien-works" rel="nofollow">https://alien.dev/docs/how-alien-works</a><p>Excited to share Alien with everyone here – let me know what you think!
Show HN: Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac.<p>I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files.<p>Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency.<p><a href="https://github.com/shivampkumar/trellis-mac" rel="nofollow">https://github.com/shivampkumar/trellis-mac</a>
Show HN: Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac.<p>I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files.<p>Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency.<p><a href="https://github.com/shivampkumar/trellis-mac" rel="nofollow">https://github.com/shivampkumar/trellis-mac</a>
Sauna effect on heart rate
Show HN: Faceoff – A terminal UI for following NHL games
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN.<p>The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months.<p>Try it out with `uvx faceoff` (requires uv).
Show HN: Faceoff – A terminal UI for following NHL games
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN.<p>The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months.<p>Try it out with `uvx faceoff` (requires uv).
Show HN: Faceoff – A terminal UI for following NHL games
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN.<p>The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months.<p>Try it out with `uvx faceoff` (requires uv).
Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)
Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)
Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)
Show HN: Shader Lab, like Photoshop but for shaders
Show HN: Shader Lab, like Photoshop but for shaders
Show HN: Shader Lab, like Photoshop but for shaders
Show HN: AI Subroutines – Run automation scripts inside your browser tab
We built AI Subroutines in rtrvr.ai. Record a browser task once, save it as a callable tool, replay it at: zero token cost, zero LLM inference delay, and zero mistakes.<p>The subroutine itself is a deterministic script composed of discovered network calls hitting the site's backend as well as page interactions like click/type/find.<p>The key architectural decision: the script executes inside the webpage itself, not through a proxy, not in a headless worker, not out of process. The script dispatches requests from the tab's execution context, so auth, CSRF, TLS session, and signed headers get added to all requests and propagate for free. No certificate installation, no TLS fingerprint modification, no separate auth stack to maintain.<p>During recording, the extension intercepts network requests (MAIN-world fetch/XHR patch + webRequest fallback). We score and trim ~300 requests down to ~5 based on method, timing relative to DOM events, and origin. Volatile GraphQL operation IDs are detected and force a DOM-only fallback before they break silently on the next run.<p>The generated code combines network calls with DOM actions (click, type, find) in the same function via an rtrvr.* helper namespace. Point the agent at a spreadsheet of 500 rows and with just one LLM call parameters are assigned and 500 Subroutines kicked off.<p>Key use cases:<p>- record sending IG DM, then have reusable and callable routine to send DMs at zero token cost<p>- create routine getting latest products in site catalog, call it to get thousands of products via direct graphql queries<p>- setup routine to file EHR form based on parameters to the tool, AI infers parameters from current page context and calls tool<p>- reuse routine daily to sync outbound messages on LinkedIn/Slack/Gmail to a CRM using a MCP server<p>We see the fundamental reason that browser agents haven't taken off is that for repetitive tasks going through the inference loop is unnecessary. Better to just record once, and get the LLM to generate a script leveraging all the possible ways to interact with a site and the wider web like directly calling backed API's, interacting with the DOM, and calling 3P tools/APIs/MCP servers.