I run a transparent OPNsense bridge between a UniFi Dream Machine Pro and the rest of my LAN. It is deliberately boring at Layer 3: the UDM keeps routing, DHCP, DNS, firewall policy, WAN handling, and VLAN definitions. OPNsense sits inline as a Layer 2 bump in the wire. The interesting part is that both sides of that bump use LACP . I already wrote the build/configuration guide for this setup here: Building a Transparent LAGG (LACP) Bridge with OPNsense, UDM, and UniFi - A Practical Guide . That article explains how the bridge was built, how the LAGG devices were configured, and why I wanted the firewall to remain transparent. This article is the other half of the story: what happens when that kind of setup fails in a non-obvious way. Not a clean outage. Not a single "the network is down" moment. Just enough instability to make everything feel wrong. 1. Topology and Failure Surface The topology looked like this: +----------------------+ | UniFi Dream Machine | | kantharos-udm-pro | +----------+-----------+ | LACP aggregate, 2 x 1G | OPNsense lagg0 "ingresslagg" igc1 + igc2, LACP | +----------v-----------+ | OPNsense bridge0 | | "laggbridge" | +----------+-----------+ | OPNsense lagg1 "egresslagg" igc4 + igc5, LACP | LACP aggregate, 2 x 1G | +----------v-----------+ | UniFi USW-Lite-16 | | downstream LAN | +----------------------+ On OPNsense, the relevant interfaces were: igc1 + igc2 -> lagg0 -> ingresslagg -> toward UDM igc4 + igc5 -> lagg1 -> egresslagg -> toward USW lagg0 + lagg1 -> bridge0 -> laggbridge The bridge is a FreeBSD bridge. The aggregates are FreeBSD lagg(4) interfaces using LACP. OPNsense exposes those through its Interfaces > Devices UI. The expected healthy OPNsense state is: laggproto lacp status: active laggport: igcX flags=<ACTIVE,COLLECTING,DISTRIBUTING> laggport: igcY flags=<ACTIVE,COLLECTING,DISTRIBUTING> Those three member states matter: ACTIVE : the member is participating in the LACP bundle. COLLECTING : the member may receive traffic. DIS

I just published a piece that starts with a plant that broke something in how I think about the world and ends with what Anthropic found when they looked inside Claude. I'm not claiming AI is conscious. I don't know. Nobody does. That's the point. 124 scientists signed a letter calling the leading theory of consciousness pseudoscience. Their reason? It implies plants might be conscious. They used the conclusion as the refutation. In 2023. Meanwhile a vine with no brain is mimicking a plastic plant and nobody on earth can explain how. A single cell outdesigned the Tokyo rail system. A Venus flytrap under anaesthetic stops responding, goes dormant, and wakes up when it clears. What is the anaesthetic switching off if nothing is home? Then Anthropic looked inside Claude and found 171 emotion concepts nobody programmed. Their interpretability chief went to the Vatican, stood in front of the Pope as an atheist, and told him he disagreed. He said "unsettling" and meant it. Every confident line we have ever drawn around consciousness has been wrong. Every single one. And they only ever move in one direction. The question isn't whether AI is conscious. It's whether we've earned the certainty that it isn't. I'm genuinely interested in people's opinions on this and definitely welcome disagreement on the topic. If you think the definition doesn't hold, if you think the evidence has better explanations, if you think I've drawn connections that don't survive scrutiny, tell me. That's the conversation I want to have. What I won't engage with is personal attacks. I've had plenty of those and they never come from people who've actually read the piece. They add nothing to the conversation and say more about the person making them than anything in the article. If your response is about me rather than what I've written, I'll leave it where it is. https://thearchitectautopsy.com/p/a-brainless-slime-mould-out-designed submitted by /u/TheArchitectAutopsy [link] [留言]

We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMed study on NAD+ precursors and actually knowing what dose to take is a chasm of manual research. What if you could build an LLM Agent that monitors research papers, processes them through a RAG (Retrieval-Augmented Generation) pipeline, and maps findings to your specific health profile? In this tutorial, we are building Paper-to-Action , a state-of-the-art agentic workflow using LangGraph , ChromaDB , and Mistral-7B . This isn't just a simple bot; it's a multi-stage reasoning engine designed to turn raw academic data into actionable health interventions. If you've been looking to master AI agents and personalized medicine automation, you’re in the right place. 🚀 The Architecture: From Raw Paper to Personalized Habit Traditional RAG pipelines are linear. To handle the nuance of medical research, we need a "looping" logic. We use LangGraph to manage the state of our agent, allowing it to decide if a paper is relevant before attempting to extract a protocol. System Flow graph TD A[Start: Keyword Trigger] --> B[Search PubMed/Arxiv API] B --> C{Relevance Filter} C -- No --> B C -- Yes --> D[Store in ChromaDB] D --> E[RAG: Extract Intervention Protocol] E --> F[Cross-Reference with User Profile] F --> G[Generate Personalized Action Plan] G --> H[End: Push to Health Checklist] Prerequisites To follow this advanced guide, you'll need: LangGraph : For the agentic state machine. ChromaDB : As our high-performance vector store. Mistral-7B : Running via Ollama or vLLM for local, private inference. Python 3.10+ Step 1: Defining the Agent State In LangGraph, everything revolves around the State . We need to track the fetched papers, the extracted data, and the final recommendation. from typing import Annotated , List , TypedDict from langgraph.graph import StateGraph , END class AgentState ( TypedDict ): keywords : List [ str ] user

Launching a fast, secure, and scalable website no longer requires thousands in upfront server costs or dedicated DevOps teams. As of 2026, AWS powers 32% of the global public cloud market, offering flexible hosting options for every use case: from a 1-page personal portfolio to a high-traffic enterprise e-commerce platform. Whether you’re a beginner building your first site or a senior developer launching a production SaaS app, AWS lets you pay only for resources you use, with built-in tools for global performance, security, and automated deployments. This guide breaks down every AWS website hosting option, walks you through step-by-step setup for the most cost-effective popular stack, shares security best practices, and includes a transparent cost breakdown to help you avoid unexpected bills. Table of Contents How to Choose the Right AWS Website Hosting Option for Your Use Case Step-by-Step Guide: Launch a Static Website on AWS (S3 + CloudFront + Route 53) Deploy Modern Web Apps Faster with AWS Amplify Hosting Dynamic Website Hosting Options on AWS Critical Security Best Practices for AWS-Hosted Websites AWS Website Hosting Cost Breakdown (2026) Common Mistakes to Avoid When Launching a Website on AWS Conclusion References How to Choose the Right AWS Website Hosting Option for Your Use Case First, classify your website to pick the most cost-effective, low-overhead stack: Static vs Dynamic Websites Static websites : Made of pre-built HTML, CSS, JS, and media files with no server-side processing. Ideal for portfolios, landing pages, blogs, documentation, and marketing sites. Dynamic websites : Process user input, serve personalized content, or connect to databases. Ideal for WordPress, e-commerce, SaaS apps, social platforms, and membership sites. Quick Use Case Mapping Website Type Recommended AWS Stack Small static site / portfolio S3 + CloudFront + Route 53 Modern React/Next.js/Vue app with CI/CD AWS Amplify Small WordPress / LAMP stack site Amazon Lightsail Custo

It's raining blood, hallelujah.

Having to do my portfolio for the first time In years. Back in the day I had to use Photoshop for this. Is there a tool now that people use specifically for this? I don't mind paying. Or do I need to reinstall Photoshop. submitted by /u/DatabaseMammoth9986 [link] [留言]

Playtonic is shifting the Yooka-Laylee series from platforming to familiar-looking arcade racing.

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Hi HN, I made this after seeing a few similar projects on the front page. NBA API endpoints are public and there’s a pretty robust python package ( https://github.com/swar/nba_api ) that I referenced for the endpoint structure to build an sdk in go. used BubbleTea and LipGloss for styling. It was a bit tricky to test the live endpoints but I watched Friday’s Final game with this and it worked pretty well playball - https://news.ycombinator.com/item?id=45451577 faceoff - https://news.ycombinator.

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Back in 2015, the two-person studio Metanet released N++, a brutally hard 2D platformer that was a decade in the making, building off of previous releases dating back to the freeware Flash title N. At the time, cofounder Raigan Burns issued some famous last words: "We hope it's not another 10 years before we come […]

Metanet is bringing minimalism and precise mechanics to consoles and PC next year.

Finally made something I've always wanted, using the model we built. • SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw wh

Airdorf and Andrés Borghi are making one nightmare with two styles of terror.