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Silicon Valley has a science fiction problem

Show HN: Brolly, a plain-text weather forecast site The UK MET office recently redesigned their site, adding a lot of additional whitespace, scrolling, and animations. This significantly reduced its usability for me, and left me wanting an ‘at a glance’ weather site.<p>I made <a href="https:&#x2F;&#x2F;brolly.sh" rel="nofollow">https:&#x2F;&#x2F;brolly.sh</a>, a minimalist, plain text weather forecasting site. You can use it to view weather from around the world, with: 7 day forecast; Previous day log (so you can confirm it definitely was cooler &#x2F; hotter &#x2F; wetter &#x2F; drier yesterday!); Hourly rain, wind, temperature, conditions; Hourly UV, air quality and pollen, including pollen type specific forecasts within the EU &#x2F; UK; Location search and last 5 locations; Location specific units.<p>I mostly made the site for myself, if anyone else also benefits from it that’s an added advantage.<p>You can check out the weather in York, UK at <a href="https:&#x2F;&#x2F;brolly.sh&#x2F;forecast&#x2F;RWFP2qW8" rel="nofollow">https:&#x2F;&#x2F;brolly.sh&#x2F;forecast&#x2F;RWFP2qW8</a>, or search for a location at <a href="https:&#x2F;&#x2F;brolly.sh" rel="nofollow">https:&#x2F;&#x2F;brolly.sh</a><p>The site is deliberately styled as a single long scrollable column, to work on mobile phones. You can view it on desktop too, there&#x27;s just a lot of horizontal padding. I naturally took a lot of inspiration from plaintextsports.com. Despite not being a sports fan, I love its aesthetic. But, you&#x27;ll hopefully see that this site isn&#x27;t a rip off, and has its own deliberate look and feel.<p>Visualisations are really important to showing information at a glance. I spent a lot of time designing the different visualisations and making them work with only characters. My favourite is the hourly heat map used for pollen count.<p>It&#x27;s also frustrating to have an interactive site, where you can&#x27;t share a page with a friend and have them see what you&#x27;re seeing. To solve this, all page state (i.e. location, selected day, expanded &#x2F; collapsed sections), is stored in the URL. You can share or bookmark the specific view, and know that you&#x27;ll always be able to come back to it.<p>The site uses PocketBase. It’s written in Go and plain HTML&#x2F;JavaScript&#x2F;CSS. All pages are backend rendered, with light JavaScript to handle re-loading content without page jumps when using interactive features like next &#x2F; previous day navigation. Weather forecasts are fetched from open-meteo.com, which has a very generous free tier. However, I also built a custom LRU cache on top of PocketBase’s SQLite DB to cache forecasts for 5 minutes, and avoid putting unnecessary pressure on the open-meteo API.

Engineering management after the cost of code collapsed

Open-weight AI is having its Kubernetes moment

Task-centered iproute2 user guide

Scanwheel is a drum style mechanical television you can build yourself

My web version of Mars MIPS, now has built-in C compiler

Show HN: Cygnus – A fast, lightweight self-hostable serverless runtime and PaaS I built Cygnus because of a long standing frustration with the compromises needed to be made when choosing a deployment option for web applications.<p>The ecosystem is fragmented into a few distinct camps, each sacrificing user experience or runtime compatibility to balance isolation, startup latency, and their own profit margins.<p>Docker: Heavier and slower because it has to supervise more than web apps. Paying overhead you don&#x27;t need. MicroVM&#x27;s: Good isolation, but huge maintenance surface area and substantial overhead. Great for untrusted code, overkill for your own apps. Workerd: Tries to dance around hardware isolation by enforcing an in-process V8 isolate model. Neutered runtime to prevent arbitrary syscall execution. Personally I still find the tradeoff worthwhile for their edge footprint, but I still find myself complaining about it from time to time. Vercel&#x2F;Managed serverless: Delivers a great developer experience, uses microVM&#x27;s which is good for compatibility but has a business model that eventually hands you a six-figure bill with a straight face ($0.15&#x2F;GB bandwidth when budget providers charge cents per TB) and makes you sacrifice statefulness.<p>These constraints make sense for hyperscalers or enterprises, but I don&#x27;t have those kinds of needs.<p>I just wanted something as light and fast as workerd(not exactly but closest i guess), but more rigid than just running them as bare userspace processes. Containers are just standard Linux processes wrapped in namespaces, cgroups, seccomp, and netns. You can do the same with systemd. And with Bun mature enough for production, combining an all-in-one JavaScript runtime directly with native Linux kernel primitives becomes a no-brainer.<p>So I combined them into Cygnus, a single Rust daemon that turns raw kernel primitives and Bun into a self-hosted, scale-to-zero application platform.<p>[ CLIENT: HTTP&#x2F;1.1 · HTTP&#x2F;2 · HTTP&#x2F;3 (QUIC) ] | v [ Cygnus Daemon ] ├─ TLS termination (rustls) + ACME manager ├─ H1&#x2F;H2&#x2F;H3 front, normalized to H1 upstream ├─ Routing: SNI&#x2F;Host → ArcSwap&lt;HashMap&gt; ├─ io_uring proxy loop (splice UDS↔TCP) ├─ Cage supervisor (spawn, health, drain, reap, crash backoff) └─ Admin API (root-only UDS) + Tenant-0 bridge (typed commands) | | HTTP&#x2F;1.1 over per-app UDS (pooled, keep-alive) v ========== CAGE — per app, warm, reused ========== userns · mntns · pidns · ipcns · utsns · netns cgroup v2 (mem&#x2F;cpu&#x2F;pids) · seccomp allowlist<p><pre><code> bun (text shared via page cache, per version) ├─ preload shim: listen()&#x2F;Bun.serve → UDS └─ artifact: bundle.js + bundle.jsc (RO mount) egress: veth ─ nftables policy ─ host NAT DNS: host-side forwarder at gateway IP </code></pre> What you get:<p>- ~50ms cold starts with 0 guest kernel overhead - Page-cache shared runtime(low resource usage) - 100% native compatibility(it&#x27;s literally running bun) - Zero-config setup(injects a listener shim to serve the app, no manual changes needed) - Dogfooded control plane with a great UX and dashboard running as tenant 0<p>Cygnus uses cages, a shared-kernel process isolation model. The way it currently is, it&#x27;s designed for trusted code(your apps or apps you trust). It provides defense-in-depth against buggy code, supply-chain attacks, and SSRF. However, it is not designed for untrusted, anonymous multi-tenancy (yet).<p>I&#x27;d love to hear your thoughts on the architecture, kernel primitive choices, and seccomp filtering strategy!<p>Trying it out is a 1 liner, it works on macOS too but without any of the Linux isolation benefits, for testing it out or running locally.<p>curl -fsSL <a href="https:&#x2F;&#x2F;cygnus.run&#x2F;install.sh" rel="nofollow">https:&#x2F;&#x2F;cygnus.run&#x2F;install.sh</a> | bash<p>Github repo has a demo gif if you want to see what it looks like!<p><a href="https:&#x2F;&#x2F;cygnus.run" rel="nofollow">https:&#x2F;&#x2F;cygnus.run</a> <a href="https:&#x2F;&#x2F;github.com&#x2F;0xchasercat&#x2F;cygnus" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;0xchasercat&#x2F;cygnus</a>

Show HN: Nostalgia – A shareable Windows 98 desktop builder

SpaceX Starship Flight 13 livestream [video]

Show HN: Open-weight OCR got so cheap I had to share it This was not supposed to become a product.<p>When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn&#x27;t one ready for production that I would trust.<p>So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive.<p>It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages.<p>The nearest competitors are either much lower quality (Azure Read) or absurdly expensive (Extend or Reducto). Even Mistral OCR 4 which is really good and pretty cheap is still 4x more expensive.<p>So I had to share it. I made the endpoitns public, and vibe-coded a simple dashboard.<p>Let me know what you think!

Opus 5 is currently #1 on Artificial Analysis Intelligence Leaderboard

Show HN: Twigg – open-source big-tech-like version control and software forge We&#x27;ve been building Twigg for quite a while now and open sourced all of it.<p>Its goal is to provide a similar developer experience to the Google&#x2F;Meta&#x2F;Phabricator one.<p>The project consists of not only the version control (the &quot;Git&quot; analogous) but also the software forge (the &quot;GitHub&quot; analogous) and the CI&#x2F;CD server (the &quot;GitHub actions&quot; analogous).<p>You can self host it or use our hosted instance (<a href="https:&#x2F;&#x2F;twigg.vc" rel="nofollow">https:&#x2F;&#x2F;twigg.vc</a>).<p>Come check it out, and leave us a star on GitHub!

Don't Take the Black Pill [video]

Show HN: Jixp, a Lisp DSL for describing Jax neural nets This is a side project I&#x27;ve been working on while learning Jax. I noticed that a bunch of the neural net math looked like it would work well in a lisp syntax because most of the data flows through layers in a &quot;functional&quot; manner. Data is threaded through one layer at a time, each layer composing with the previous layer.<p>Jixp is designed as a learning tool for myself while learning Jax to toy around with different shapes&#x2F;styles of models. I used this to train a 4m parameter model on my Obsidian vault to see if I could use it for recall which partially worked. ```lisp (let-dim (d 256) (heads 8)<p>(define transformer-block (chain (residual (layernorm d) (attention d heads #:causal)) (residual (layernorm d) (mlp d [4d] d #:bias)))) ```<p>The Jixp compiler reads the (racket style) DSL and outputs python which is then consumed via your normal python training stack. The advantage here is you get a pretty simple interface to describe your model and the implementation details are largely hidden in the generated python. I definitely do not recommend using this for anything besides learning.<p>One day I can see models being specified in terms of a DSL like this which would allow different implementations of inference&#x2F;training to load the same model. You could write one model definition, then vLLM, PyTorch, and your custom inference stack could all use the same definition. Sort of like a `.safetensor` file for the model.

The front end framework for correctness: built on Effect, architected like Elm

Codeberg Divides

Government orders GitHub to remove Bluetooth-based chat app Bitchat: Jack Dorsey

Preventing line breaks in <code> elements

Writing a Debugger from Scratch