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Pull a hot steel bolt out of a furnace and quench it in oil, and a fair question is: does the bolt cool from the outside in, with a sharp temperature difference between its skin and its core, or does the whole thing drop in temperature more or less together? The answer is not obvious from the part itself. A thin copper washer and a thick ceramic block behave very differently in the same bath, even at the same starting temperature. The Biot number is the small calculation that settles this question before you commit to any heavy analysis. It tells you, in a single dimensionless figure, whether an object can be treated as having one uniform temperature or whether you must resolve a temperature gradient inside it. That distinction changes the math from a one-line exponential decay to a partial differential equation. Why this calculation matters Transient heating and cooling problems show up everywhere: heat-treating metal parts, quenching forgings, cooling electronics, baking or chilling food, warming up an engine block. In every one of these, the engineer wants to know how the temperature changes over time. The hard version of that question requires solving the heat conduction equation across the body, with position and time as variables. The easy version is the lumped-capacitance model, which treats the whole object as a single point at one temperature. It reduces the problem to a simple first-order exponential. The catch is that the lumped model is only valid when internal conduction is fast compared with surface convection. The Biot number is exactly the check that tells you whether that condition holds. Skip the check and apply the lumped model where it does not belong, and you can badly mispredict cooling times, residual stresses, and the risk of cracking from thermal gradients. The core formula The Biot number compares two thermal resistances. One is the resistance to conducting heat through the inside of the solid. The other is the resistance to carrying heat a

n8n is the open-source workflow automation tool that competes with Zapier and Make. I have been running it for saas.pet's content pipeline for 6 months. Here is my honest take on self-hosting n8n versus paying Zapier, and whether it is worth the hassle. What n8n does that Zapier cannot n8n is an open-source workflow automation platform with 400+ built-in integrations. You connect nodes on a visual canvas: when X happens in one app, do Y in another. The killer difference from Zapier: you control where it runs. Self-host on a $5/month VPS or run on n8n Cloud at $20/month. No per-task pricing, no 'you hit your zap limit' emails. For high-volume workflows, the cost difference is dramatic. I run n8n on the same $6/month HK server that hosts my proxy. 12 workflows handle saas.pet's entire content pipeline: daily data fetch from GitHub Trending API, transform JSON, write to data files, trigger build, push to git, notify me on Telegram. The same workflows on Zapier would cost $73.50/month (Professional plan with 2,000 tasks). On n8n, $6/month for the server plus $0 for the software. The self-hosting overhead is real—updates, SSL certs, monitoring—but for 12+ active workflows, the savings are $800+/year. If you only have 2-3 simple zaps, stay on Zapier's free tier. If you have 5+ workflows with volume, n8n pays for the hosting in month 1. My 6-month setup for saas.pet I run n8n in Docker on the HK server. The initial setup: install Docker, pull n8n image, configure nginx reverse proxy, set up SSL via Certbot. That took about 2 hours the first time. Now I can deploy n8n in 15 minutes on a fresh server. The 12 workflows: (1) Daily GitHub trending fetch via saas.pet/api/trending, (2) data transform to unified JSON, (3) write to data/YYYY-MM-DD.json, (4) trigger build-ci.mjs, (5) git add + commit + push, (6) Telegram notification with commit SHA, (7) weekly sitemap health check, (8) monthly backup of reviews/ JSONs to S3, (9) uptime ping every 15 minutes, (10) DNS health check,

i use claude code daily and measured what pages cost it while doing research. an average wikipedia article, for instance, is 68,240 tokens of raw html (tiktoken); nike's homepage is 353,000. claude code's built-in webfetch handles the easy case well. it summarizes wikipedia to about 950 tokens and clears cloudflare on some sites like indeed and ticketmaster. but, and there's always a but, on js-rendered and some anti-bot pages it returns nothing. quotes.toscrape.com/js gives "no quotes found"; n

On Thursday, the Instagram account for a lecture series in Newport Beach, CA posted a photo of what appeared to be a cease and desist letter from the surveillance technology company Flock Safety. Flock has received significant backlash over its technology and work with law enforcement agencies, and this letter kicked off yet another wave […]

Quando voce cria um container e depois outro, eles podem nao se enxergar. O motivo quase sempre e a rede. Entender os tipos de rede que o Docker oferece resolve isso de uma vez. Comece listando as redes que ja existem no seu Docker. docker network ls A rede bridge e a rede host vem de fabrica. Nenhuma delas oferece resolucao de nomes entre containers. Para isso voce precisa criar a sua propria rede. docker network create minha-rede Agora os containers que entrarem nessa rede conseguem se comunicar pelo nome do container. docker run -d --name app1 --network minha-rede nginx docker run -d --name app2 --network minha-rede alpine sleep 3600 Teste a comunicacao. docker exec app2 ping app1 -c 2 O ping funciona. O DNS interno do Docker resolve app1 para o IP interno do container. Isso resolve 90% dos problemas de comunicacao. A rede bridge padrao nao tem esse recurso. Crie sua propria rede sempre que precisar que containers se enxerguem. Agora a rede host. Ela elimina o isolamento de rede. O container usa a placa do host diretamente, sem NAT e sem mapeamento de porta. docker run -d --name web-host --network host nginx O nginx fica acessivel em http://localhost:80 direto. Sem -p 80:80. Mas so um container pode usar cada porta, porque a porta e a do host de verdade. Para ambientes com mais de uma maquina, existe a rede overlay. Ela precisa do Docker Swarm ou de um cluster. docker swarm init docker network create -d overlay rede-overlay docker service create --name api --network rede-overlay --replicas 3 nginx Os containers em maquinas diferentes se enxergam pelo nome como se estivessem na mesma maquina. No dia a dia, a rede bridge personalizada resolve quase todo caso de uso. Host e para performance ou casos especificos. Overlay e para quando voce escala para mais de uma maquina. That's all for now. Thanks for reading!

Notes from bringing up a PCIe WiFi module on i.MX8MQ; symptoms and how to diagnose them. Phy link never came up — what does this mean? This message is typically seen in dmesg as shown below. [ 3.828121] imx6q-pcie 33800000.pcie: iATU: unroll T, 4 ob, 4 ib, align 64K, limit 4G [ 4.807241] imx6q-pcie 33c00000.pcie: Phy link never came up [ 4.841482] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.821279] imx6q-pcie 33c00000.pcie: Phy link never came up [ 5.830481] imx6q-pcie 33c00000.pcie: PCI host bridge to bus 0001:00 [ 5.854997] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.862205] imx6q-pcie 33800000.pcie: PCI host bridge to bus 0000:00 It means one of the following The PCIe peripheral is not powered up. PCIe reset is not deasserted, so the chip is in reset. This could be because the DTB is deasserting an incorrect GPIO. Reference clock is not enabled Using the incorrect PCIe controller in the device tree. As we can see that both the PCIe controllers can report this. So first determine which controller is the peripheral hooked to. More on this in the next section. Which PCIe controller is my device on? ( &pcie0 vs &pcie1 ) The rule here is to match by address and not by label/name. If the schematic calls out controllers as PCIE1 and PCIE2, and the device tree lists pcie0 and pcie1, understand the mapping. The DTS label is arbitrary - match by register base ( @address in the node name), which is the same in the DTS reg and the reference memory map. For definitive addresses look in the .dtsi , as sometimes the manuals are misleading. Given below is a mapping table for i.MX8MQ DTS. | ADDRESS (in .dtsi) | Silicon (RM) Label &pcie0 | 0x33800000 | PCIe1 &pcie1 | 0x33c00000 | PCIe2 The addresses are listed in the chip’s memory layout are from processor reference manual. Below is snapshot from the i.MX8MQ reference manual, where the layout for the core A-53 is listed. Start Address | End Address | Size | Description 3381_0000 | 3381_3FFF | 4MB | PCIe-2 << inco

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T

Meta told Dylan Byers, of Puck News, that it had nixed the feature after backlash from its user base.

Following significant backlash, Meta is turning off the feature it announced this week that let users generate AI images based on content from public Instagram accounts just by tagging them. The feature, as originally set up, meant that content from any public Instagram account could be used in AI creations without the account owner's permission. […]

Data centers are driving up the company’s use of electricity—and carbon pollution.

Reinforcement learning uses error information to adjust control algorithms.

More police and firefighters use drones to catch and deter illegal fireworks.

Last year, we told you about Xtra, the company that lets DJI sneak its popular cameras into the US, and Skyrover, a brand seemingly selling DJI drones in disguise. They're just two of the many firms DJI is suspected of starting to skirt the United States' foreign drone ban. But it appears the FCC is […]