Show HN: A physically-based GPU ray tracer written in Julia https://ift.tt/ML4H6jO

Show HN: A physically-based GPU ray tracer written in Julia We ported pbrt-v4 to Julia and built it into a Makie backend. Any Makie plot can now be rendered with physically-based path tracing. Julia compiles user-defined physics directly into GPU kernels, so anyone can extend the ray tracer with new materials and media - a black hole with gravitational lensing is ~200 lines of Julia. Runs on AMD, NVIDIA, and CPU via KernelAbstractions.jl, with Metal coming soon. Demo scenes: github.com/SimonDanisch/RayDemo https://ift.tt/eBFyWdJ February 19, 2026 at 07:55AM

Show HN: Potatometer – Check how visible your website is to AI search (GEO) https://ift.tt/wja1ZQ5

Show HN: Potatometer – Check how visible your website is to AI search (GEO) Most SEO tools only check for Google. But a growing chunk of search is now happening inside ChatGPT, Perplexity, and other AI engines, and the signals they use to surface content are different. Potatometer runs multiple checks across both traditional SEO and GEO (Generative Engine Optimization) factors and gives you a score with specific recommendations. Free, no login needed. Curious if others have been thinking about this problem and what signals you think matter most for AI visibility. https://ift.tt/fXbLDH1 February 19, 2026 at 03:41AM

Show HN: A Lisp where each function call runs a Docker container https://ift.tt/1HuhrAe

Show HN: A Lisp where each function call runs a Docker container https://ift.tt/fJP9MpU February 19, 2026 at 01:19AM

Show HN: I built a fuse box for microservices https://ift.tt/3NATCnO

Show HN: I built a fuse box for microservices Hey HN! I'm Rodrigo, I run distributed systems across a few countries. I built Openfuse because of something that kept bugging me about how we all do circuit breakers. If you're running 20 instances of a service and Stripe starts returning 500s, each instance discovers that independently. Instance 1 trips its breaker after 5 failures. Instance 14 just got recycled and hasn't seen any yet. Instance 7 is in half-open, probing a service you already know is dead. For some window of time, part of your fleet is protecting itself and part of it is still hammering a dead dependency and timing out, and all you can do is watch. Libraries can't fix this. Opossum, Resilience4j, Polly are great at the pattern, but they make per-instance decisions with per-instance state. Your circuit breakers don't talk to each other. Openfuse is a centralized control plane. It aggregates failure metrics from every instance in your fleet and makes the trip decision based on the full picture. When the breaker opens, every instance knows at the same time. It's a few lines of code: const result = await openfuse.breaker('stripe').protect( () => chargeCustomer(payload) ); The SDK is open source, anyone can see exactly what runs inside their services. The other thing I couldn't let go of: when you get paged at 3am, you shouldn't have to find logs across 15 services to figure out what's broken. Openfuse gives you one dashboard showing every breaker state across your fleet: what's healthy, what's degraded, what tripped and when. And, you shouldn't need a deploy to act. You can open a breaker from the dashboard and every instance stops calling that dependency immediately. Planned maintenance window at 3am? Open beforehand. Fix confirmed? Close it instantly. Thresholds need adjusting? Change them in the dashboard, takes effect across your fleet in seconds. No PRs, no CI, no config files. It has a decent free tier for trying it out, then $99/mo for most teams, $399/mo with higher throughput and some enterprise features. Solo founder, early stage, being upfront. Would love to hear from people who've fought cascading failures in production. What am I missing? https://www.openfuse.io February 18, 2026 at 11:04AM

Show HN: I built a "Socratic" AI to stop my daughter from copy-pasting homework https://ift.tt/SOIpJsa

Show HN: I built a "Socratic" AI to stop my daughter from copy-pasting homework Hey HN, I’m a dev and a dad to a 10-year-old. I built this because I caught my daughter using ChatGPT to do her history homework. She wasn't learning; she was just acting as a "middleware" between the AI and the paper. The Backstory: I realized the problem isn't the AI—it's the zero-friction answers. Most "AI for kids" apps are just "parrots"—they mimic intelligence by repeating patterns. What’s Different: Qurio is a "Bicycle" for the mind. It treats the child like a future "Architect" rather than a "Junior Executor." Technically, it wraps an LLM in a strict "Socratic Loop." It detects intent to "cheat," refuses the direct answer, and generates a leading question based on the user's current logic level. It forces "Healthy Friction" back into the learning process. The stack: Next.js 14, Supabase (Auth/DB), Vercel AI SDK. Mods: I've added the backstory and differentiator as requested. Ready for the re-up! Thank you. https://thinkqurio.com/ February 16, 2026 at 12:55PM

Show HN: How I built Timeframe, our family e-paper dashboard https://ift.tt/5rUHdSJ

Show HN: How I built Timeframe, our family e-paper dashboard I'm proud to share the e-paper family dashboard I've been building over the past decade. I think you might find it interesting. It's open source: https://ift.tt/wOhr1zY . https://ift.tt/vuzd23P February 17, 2026 at 11:20PM

Show HN: Windows 98 web desktop with a functional remote browser (no CORS) https://ift.tt/75vgyNz

Show HN: Windows 98 web desktop with a functional remote browser (no CORS) https://ift.tt/AIzWguc February 17, 2026 at 04:24AM