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Is your snoring just a nuisance, or is it a health warning? Obstructive Sleep Apnea (OSA) affects nearly 1 billion people worldwide, yet most remain undiagnosed due to the high cost of clinical polysomnography. Today, we are pushing the boundaries of AI Healthcare by repurposing OpenAI Whisper from a speech-to-text powerhouse into a clinical screening tool. In this tutorial, we will explore how to leverage Audio Signal Processing , Hugging Face Transformers , and Librosa to detect breathing patterns. By fine-tuning Whisper on non-speech acoustic events, we can transform a standard smartphone recording into a high-precision OSA screening device. Pro-Tip : If you're looking for more production-ready examples and advanced architectural patterns for AI-driven health monitoring, be sure to check out the deep-dives over at WellAlly Tech Blog . The Architecture: From Raw Audio to Clinical Insight To build an OSA screening algorithm, we don't just need to hear the sounds; we need to understand the rhythm and absence of sound. We use Whisper's robust encoder to capture the spectral features and a custom classification head to identify Apnea-Hypopnea events. graph TD A[Raw Sleep Audio .wav] --> B[Preprocessing: Librosa] B --> C[Noise Reduction & VAD] C --> D[Segmenting: 30s Windows] D --> E[OpenAI Whisper Encoder] E --> F{Event Classification} F -->|Normal| G[Healthy Breathing] F -->|Snore| H[Snore Phase Analysis] F -->|Silence/Choke| I[Apnea Event Detected] I --> J[AHI Index Calculation] J --> K[Final OSA Risk Report] Prerequisites To follow this advanced guide, you'll need: Tech Stack : Python 3.9+, transformers , librosa , torch , and evaluate . Dataset : Ideally, the UCD Snore Database or similar PSG-synchronized audio data. Step 1: Audio Preprocessing with Librosa Before feeding audio into Whisper, we need to clean the signal. Sleep environments are noisy (fans, traffic, etc.). We use librosa to normalize the audio and detect "Voice" (or in our case, Breath) Activity. im

The Silent Disruptor The silence in the room was absolute, broken only by the rhythmic scraping of pens on paper during a high-stakes meeting. Then, it happened. My pocket erupted into a frantic, brassy ringtone that seemed to last an eternity before I could fumble to silence it. My face turned crimson as the room’s focus shifted from the presentation to my vibrating trouser pocket. I had remembered to check my calendar, but I had completely forgotten to toggle my phone to silent mode. That moment of pure, concentrated embarrassment was the catalyst for me building Muffle. The Friction of Manual Control We live in an age of automation, yet our phones—the very devices meant to assist us—remain stubbornly manual when it comes to basic social etiquette. Every day, millions of people walk into mosques for prayer, classrooms for lectures, or medical offices for consultations, and every day, a percentage of them forget to silence their devices. This isn't just a minor annoyance; it is a persistent source of social friction. Before I started building Muffle, I looked for existing solutions. Most apps were either bloated with unnecessary permissions, required invasive cloud accounts, or simply failed to trigger at the right time. The fundamental problem wasn't just the lack of features like GPS-based prayer times or calendar-specific automation; it was the lack of reliability. If an automation app fails once, the user loses trust in it forever. If I am in a meeting, I cannot afford for the app to 'sleep' because the system decided to save battery at the expense of my configured routine. I needed something that could handle these state changes consistently, regardless of whether the phone was in my pocket, sitting on a desk, or buried in a bag. Architecting for Reliability When I began writing the core logic for Muffle, I immediately hit the wall that every Android developer eventually faces: Doze Mode. Android’s aggressive power management is designed to preserve battery by

submitted by /u/IamMichaelSalim [link] [留言]

This article is an English translation of the original Japanese article. In my club management app, I use the following invite URL for both web and iOS app: https://squad-note.com/invite/{orgId} If the app is installed, Expo Router opens the invite screen in the app. If not, the web page displays. Using Universal Links lets me share a single URL rather than splitting it into web and app versions. Expo Router File Structure I place the invite screen as a dynamic route. apps/mobile/src/app/invite/[orgId]/index.tsx The screen retrieves the orgId from the URL via useLocalSearchParams . import { useLocalSearchParams , useRouter } from " expo-router " ; export default function InviteScreen () { const { orgId } = useLocalSearchParams < { orgId : string } > (); const router = useRouter (); const { data : org , isLoading } = api . organization . getPublic . useQuery ( { id : orgId ! }, { enabled : !! orgId }, ); // Display invite content and execute join process } When opened with /invite/abc , orgId receives abc . I also provide a page with the same path on the web side. Setting a Custom Scheme To handle app-specific URLs, I set a scheme in the Expo config. export default ({ config }: ConfigContext ): ExpoConfig => ({ ... config , scheme : " squadnote " , }); This allows handling URLs like the following during development and authentication callbacks: squadnote://invite/abc However, I use HTTPS for the invite URLs shared with users. Because custom schemes can be declared by different apps with the same scheme, I use Universal Links as the entry point to securely associate normal web URLs with the app. iOS Associated Domains In app.config.ts , I separate domains for production and development. ios : { bundleIdentifier : IS_PROD ? " com.squadnote.app " : " com.squadnote.app.dev " , associatedDomains : IS_PROD ? [ " applinks:squad-note.com " ] : [ " applinks:dev.squad-note.com " ], } Adding the configuration alone does not make it work. I also serve apple-app-site-association

Environment Variables the Safe Way Environment variables are the standard way to configure applications without hardcoding secrets or environment-specific details. But they're easy to misuse. I've seen API keys committed to repos, configs that crash when a variable is missing, and defaults that silently override production settings. Here's how I handle them safely. Never Commit Secrets The most important rule: never put real secrets in your code or commit them to version control. That includes .env files. Add .env to your .gitignore immediately. If you're using a framework like Laravel or a tool like Vite, the default .env.example is your friend. Commit that, but never the real one. For local development, you can generate a .env from the example and fill in your own values. For production, set variables through your hosting provider's dashboard or a secrets manager like AWS Secrets Manager or HashiCorp Vault. Read Variables Explicitly Don't access process.env directly all over your codebase. Instead, centralize your configuration. Create a config.js (or config.ts ) that reads and validates all the variables you need. // config.js const required = [ ' DATABASE_URL ' , ' JWT_SECRET ' , ' PORT ' ]; const missing = required . filter ( key => ! process . env [ key ]); if ( missing . length ) { throw new Error ( `Missing required environment variables: ${ missing . join ( ' , ' )} ` ); } module . exports = { databaseUrl : process . env . DATABASE_URL , jwtSecret : process . env . JWT_SECRET , port : parseInt ( process . env . PORT , 10 ) || 3000 , }; Now your app imports config and uses config.port . This has several benefits: Fail fast: if a required variable is missing, the app crashes at startup, not later when you try to use it. Type safety: you can parse and validate values once. Easy to mock in tests. Use Defaults Carefully Defaults are convenient, but they can hide problems. For example, if you default PORT to 3000 in production, you might accidentally run on the w

Cada vez más gente arranca la búsqueda de un proveedor preguntándole a un modelo en vez de a un buscador. Y no pide diez opciones para comparar: pide una recomendación y recibe dos o tres nombres. Si tu empresa no está ahí, no quedaste octava. No estás en la respuesta. La pregunta que sigue es obvia: cuánto tarda en cambiar eso. Pero antes hay un problema más aburrido y más importante, que es cómo se mide. Lo escribo porque es la parte que casi nunca se cuenta y es donde se rompen los informes. Una captura de pantalla no es una medición Es el error más común y el más difícil de discutir, porque la captura parece prueba. La respuesta de una app conversacional depende del historial de la cuenta, de la sesión, del ruteo interno del proveedor, de si esa consulta activó búsqueda web o no, y de la región desde donde se pregunta. Dos personas preguntando lo mismo el mismo día reciben respuestas distintas. La misma persona preguntando dos veces también. O sea: la salida no es determinista y el instrumento no es estable. Una captura te dice qué pasó una vez, en un contexto que no podés reconstruir. Como métrica de seguimiento no sirve para nada. Lo que sí sirve es una serie: la misma consulta, literal, contra el mismo motor, con el mismo criterio de clasificación, repetida en el tiempo. El valor absoluto de un punto importa poco. Lo que importa es la diferencia entre puntos. Fijar el texto de la consulta, no la etiqueta Este es un bug de proceso que da resultados verosímiles y falsos. Si guardás en la planilla una etiqueta como "consulta de chatbot" en vez del texto exacto que preguntaste, dentro de dos meses nadie se acuerda del wording. Y el wording cambia el resultado: preguntar "quién hace X en Argentina" y "mejores empresas de X en Argentina" devuelven listas distintas. Cuando el texto se corre entre rondas, la serie deja de ser comparable, pero el gráfico sigue dibujándose igual de lindo. Guardá el string literal, versionado. Si tenés que cambiar una consulta, empezá u

Specifically, the video is about developing a game where all the textures and sounds are generated at runtime using Sine waves. I thought it was high-quality and explained complex math concepts well. submitted by /u/TheBrokenRail-Dev [link] [留言]

Run your hiring process from Claude or ChatGPT Discussion | Link

Musk kept inflating the already-big promises being made by SpaceX CFO Bret Johnsen and Gwynne Shotwell on the company's first call.

Lucid's new CEO Silvio Napoli listed four must-win priorities, including the successful launch of its midsize EV, finishing a factory in Saudi Arabia, cutting expenses, and robotaxis.

We’ve partnered with hotels around Moscone West during Disrupt, taking place October 13 to 15, to secure the best prices available for attendees.

Pavel Durov claims there's "a potential systemic risk" for platforms with user-generated content.

The Trump administration shared the details of its plan with OpenAI, Anthropic, and other AI labs on Tuesday. For now, the public remains in the dark.

You can link more devices with one phone number on Signal now, including an Android phone or iPhone. Signal already supported linking PCs and iPads, but not additional phones. When you link a device on Signal, you can check and respond to new messages across all of your linked devices. You can also choose whether […]

Make private company knowledge usable for AI Discussion | Link

Fire experts warn such markets could incentivize arson.

Your AI bill tripled last quarter. Your CTO forwarded you an article about companies saving 70% by switching to open models. Now someone is asking you to figure out what that would actually look like. I spent the last few weeks digging into this. The numbers, the hardware, the real trade-offs. Here's what I found, with enough specifics that you can actually make a decision rather than just nodding along to another "open source is the future" think piece. What "Open Models" Actually Means When someone says "open model" they mean an AI model where the weights (the learned parameters that make the model work) are publicly downloadable. You grab the file, run it on your hardware, and you don't pay anyone per request. The big names right now: Meta's Llama 4, DeepSeek V4, Zhipu's GLM-5.2, Moonshot's Kimi K3, Alibaba's Qwen 3.5, and Google's Gemma 4. These aren't toys. Some of them genuinely compete with the frontier models on real benchmarks. Chinese open models now handle over 30% of enterprise traffic on OpenRouter, up from 4.5% in early 2025. That's a massive shift in barely a year. The Architecture: What You Actually Need You want your team to use an open model. Here's the stack, from bottom to top. Hardware (The Expensive Part) A model is a giant file. We're talking anywhere from 4 GB (a small 7B model, quantized) to 1.5 TB (Kimi K3, full weights). That entire file needs to sit in GPU memory to run fast. Why GPU memory specifically? Because generating each word in a response requires billions of multiply-and-add operations. GPUs do thousands of these in parallel. A CPU does them one at a time. The practical difference: a 7B model on a CPU generates 2-5 tokens per second (painfully slow for interactive use). The same model on a GPU generates 30-80 tokens per second (feels instant). For one person on a CPU, it might be tolerable. For a team of 10 all hitting the same endpoint? Unusable. Requests queue up and everyone waits 30-60 seconds for responses. Think of it like

$1.25 billion program restored, but judge ruled race provision unconstitutional.

Wide Events is a Rails gem that puts the production context a coding agent needs onto one OpenTelemetry root span per request or job. In one production search request, the root event showed 30.0 seconds total duration, 446 ms of Postgres time, and 29.4 seconds of outbound HTTP time. That was enough to focus the investigation on an external dependency. The trace then identified a POST that took 28.9 seconds. The trace contained 82 spans and 20,261 bytes of attribute JSON. The root event contained 40 attributes and 1,420 bytes. This is not a token benchmark, but it shows why the root event is a more compact starting point for an agent. Agents can read the code, but not the running system A coding agent starts with an unusual advantage: it can search every model, controller, job, migration, and test in a few seconds. It also starts with a serious blind spot. The repository cannot tell it: which account experienced the problem which build was running which feature-flag variant was active how many queries the request issued whether a semantic-search leg degraded how much an LLM call cost whether the same symptom appears in one tenant or every tenant Those answers often exist somewhere, but “somewhere” might mean a trace waterfall, application logs, a feature-flag service, product analytics, and a database console. Pulling all of that into a context window is expensive and usually requires several joins that were never designed in advance. A wide event changes the starting point. The app accumulates the context it learns while processing one unit of work, then attaches the completed flat map to the OpenTelemetry root span. The span is marked main=true , so every request or job can be queried as one row. request or job -> Rails and domain context accumulate -> child spans contribute dependency counts and timings -> one flat map is flushed onto the root span -> ClickHouse stores one queryable row The trace still exists. Wide Events gives it an application-shaped index. If y

A status page has a blind spot. It can tell you that your API is returning 502s. It can tell you that a TCP port stopped accepting connections. It can tell you when the incident started. It usually cannot tell you why . Was the application host out of memory? Was disk I/O saturated? Did load climb for 40 minutes before users noticed? Was the server completely healthy and the real problem somewhere else? Those answers often live in a separate monitoring product, disconnected from the incident timeline and disconnected from the status page. That is why I built Servers for StatusPage.me. It is a small, customer-installed host metrics agent and dashboard. You install it on a machine you operate, and it reports CPU, memory, swap, load, disk, and network metrics back to your account. The important part is not “now there are more graphs.” The important part is seeing an outage and the host evidence around it on the same timeline. External checks answer one question. Host metrics answer another. Regular uptime monitoring is still the right tool for the outside-in view: Can users reach the website? Is the API returning the expected response? Does DNS resolve correctly? Is the database port open? Did a scheduled job run? But those checks do not run inside your infrastructure. A healthy HTTP response does not prove that a background worker is about to run out of memory. A timeout does not prove that the app server is overloaded. And an incident can start with a slow disk or growing swap usage long before an endpoint is fully unavailable. The distinction is simple: External monitoring tells you what users can see. Host metrics help explain what the machine was doing when they saw it. You need both. What Servers includes Each registered host gets a dedicated dashboard page with: CPU user, system, and I/O wait utilization Memory use Swap use Load averages Disk use and read/write throughput Network inbound and outbound throughput A human-readable OS description for account owners