标签：#os

Did a prototype Microsoft gamepad fall off the back of a truck? We can't say, but a Redditor has legit-looking photos of a prototype Xbox controller that's almost certainly the Xbox Elite Series 3. Incredibly, they claim they bought the pad for $200 on OfferUp. In May, a Brazilian regulator's leaked images revealed that Xbox […]

It looks like we'll be getting a successor to the Sonos Ace headphones sometime this fall, which was hinted at by CEO Tom Conrad during a third quarter earnings call. As reported by What Hi-Fi?, an FCC filing by Sonos, Inc reveals details about upcoming wireless headphones with the model number S52A. In addition to […]

FDA report finally reveals where Taylor Farms sent its lettuce—and it raises questions.

Netflix has described how it redesigned the streaming pipeline behind Service Topology, its real-time service dependencies map, to support production scale. The system uses three stages to separate intermediary resolution from enrichment and persistence, propagates backpressure to Kafka rather than dropping records, and uses server-sent events instead of gRPC for high-volume internal transfers. By Eran Stiller

ReBAC (relationship-based access control) decides access based on how entities are connected to each other, rather than on a role attached to the user. Nowhere is it written that you can see that repository. You see it because a chain of relationships leads you there. It's a model I like, and I want to say that up front, because what follows isn't a criticism of ReBAC. I spent a few weeks integrating OpenFGA into a prototype to use it properly: declarative model, sixteen test scenarios, a hundred and twenty assertions running offline in two seconds with no database and no application. It worked well. I removed it anyway. Not because of a bug, and not because of check latency. I removed it because none of the tools I tried gives me a usable answer to the second question every application asks. The check is fast. The list isn't. "Can this user see this object?" resolves in milliseconds. The problem is that the first screen after login is almost always a list. And "which objects can this user see?" looks like the same question reversed, but it isn't: nowhere is it recorded which objects are reachable, which is the point of ReBAC seen from the other side. In the first case you hold two things and walk the graph from one to the other. In the second you hold only the user, and the set of possible answers is everything that exists in the system. Three routes, and where each one stops Filter afterwards. Normal query with its normal pagination, then you send the twenty-five ids to the service and drop the ones that don't pass. The result is correct, but the total at the bottom of the page is the one from before the filter, so it's a lie. And the user with access to a small slice gets three rows out of twenty-five. Filter first. You ask the service which objects the user holds that permission on and hand them to the database. Except the list arrives whole. There's no real pagination to draw twenty-five from. It ends up as an IN with thousands of identifiers. A local index. Yo

JetBrains has described how it began centralising AI usage after development-related spending increased roughly tenfold in six months. Rather than restricting engineers to a small set of approved tools, the company built a shared access and accounting layer intended to preserve tool choice while giving teams greater visibility and control over consumption. By Matt Foster

You can load-test search latency with a script and a graph. Relevance has no such gauge by default, so most teams ship a new ranking rule, eyeball a handful of queries, and hope nothing important regressed. The fix is a small, boring relevance test suite: a fixed set of queries, human-judged expected results, and a metric you compute the same way every time — so "did this ranking change help?" becomes a number you can diff, not an argument you have in Slack. This post is a build guide. By the end you'll have a judgments file, a scorer that outputs precision@k, MRR, and nDCG, and a before/after comparison you can wire into CI. The examples use Postgres full-text search, but the harness is engine-agnostic — Elasticsearch, Meilisearch, or a vector store all slot into the same shape. Why can't I just load-test relevance the way I load-test latency? Latency is a property of the system. Relevance is a property of the match between a query and what a human expected to see — and that judgment lives outside the database. A commenter on an earlier post about running Postgres search in production put it well: latency can be load-tested, but quality needs query sets, expected result buckets, bad-query examples, and a way to compare changes before shipping a new ranking rule. That's the whole job, and none of it comes for free with your index. The trap is thinking a passing query proves relevance. SELECT ... WHERE tsv @@ query returning rows tells you the index matched. It says nothing about whether the right rows landed in the top 5, which is all a user ever sees. The takeaway: relevance is measured against human judgments, not row counts — so the first artifact you build is the judgments, not the query. Building the golden query set Start with 20–50 real queries. Pull them from your search logs if you have them (the head terms plus a long tail of specific ones), or write them from real user intents if you don't. For each query, mark which documents should come back and how rel

TL;DR — I built and open-sourced AI Health Steward , a self-hosted, private AI health manager for families. It reads photos of lab reports with multimodal LLMs, builds a structured per-person health profile, shows trends on a dashboard, and answers health questions grounded in your actual data — all running on your own server. Privacy isn't a feature; it's the whole point. Star it on GitHub . The problem: your health data is a product Every family has a shoebox — or a folder — of medical reports: blood tests, blood-pressure logs, prescriptions, scan findings. And every "convenient" health app wants to hold those records for you. But hold them where ? On someone else's cloud, to be monetized, analyzed, or lost when the startup pivots. Health records are the most sensitive data you own. They shouldn't be a product. They should live on your shelf. So I built the opposite: a self-hosted AI health steward where the data never leaves your server. What it does 📄 Take a photo of a lab report → structured data. A multimodal LLM extracts key metrics (BP, glucose, lipids, CBC…) with your confirmation before anything is filed. 🧬 A person-level health profile as the single source of truth — basics, metrics, diagnoses, medications, allergies, lifestyle, family history, and data provenance (where each value came from). 📈 Trend visualization with anomaly markers and clinical critical-value alerts (e.g. BP ≥ 180/110 triggers a "see a doctor" banner). 💬 AI consultation grounded in real data — not a generic chatbot. Intent routing + function calling means answers reflect your profile, not Wikipedia. 🗓️ Personalized checkup plans via a 1+X+Y framework, with budget tiers and safety/contraindication screening. 📋 Periodic health summaries (weekly/monthly/yearly), risk scales (PHQ-9, GAD-7, diabetes, ASCVD), and follow-up/medication reminders . 🧠 RAG over your own history — archived reports are vectorized so you can ask "what did my A1C trend look like over 3 years?" The architecture ┌────

OpenAI is expanding its AI cybersecurity defense program Daybreak, and rolling out a new cyber-trained AI model with it.

Jeff Bezos is reportedly close to buying his first stake in a sports team: the U.K.'s famed Liverpool Football Club.

I keep seeing the same mismatch in the screen-time category: a lot of apps are technically blockers, but they feel like parental control software. That is fine if a parent is the customer. It is a bad fit if the user is an adult trying to manage their own habits. The difference is not cosmetic. When a blocker feels like surveillance, adults bounce. They do not want an account, a dashboard, or the feeling that their phone behavior is being watched somewhere else. That is the gap I built SproutGuard for: built for adults blocking themselves , not kids runs on-device through Apple's Screen Time APIs no account no server no usage data leaving the phone App Store: https://apps.apple.com/us/app/sproutguard-screen-time-detox/id6768664921?ct=devto-adults I also put the positioning plainly on the product page: self-control, not parental control The hard lesson from launching it is that being right about the problem is not the same thing as being shareable . Privacy architecture matters, but users rarely tell friends about architecture. What they do share is something emotional or visible: a streak, a mascot, a challenge, a before/after feeling. So the current working question for me is not "how do I explain on-device privacy better?" It is: How do you make a self-control product feel human enough that people talk about it? Website: https://shantj.github.io/sproutguard/ If you've worked on consumer productivity or habit products, I'm interested in what actually made users talk about them.

I wanted to see earthquakes the way they actually happen: as points lighting up on a spinning planet, in near real time. That became Earthquake: Live Seismic Monitor , an iOS app that renders a 3D globe of recent quakes straight from the USGS feed. No backend of my own - just the public data and the device. Here's how it comes together. The data source The USGS publishes earthquake data as GeoJSON feeds, updated continuously, at several time/magnitude cutoffs (past hour, past day, 2.5+, 4.5+, etc). Each feature has coordinates, magnitude, depth and time. That's everything you need to place a quake on a globe - no custom API required. The app polls the appropriate feed, diffs against what it already has, and updates the scene. Because USGS does the heavy lifting, the whole thing is effectively serverless from my side. Putting quakes on a globe The core mapping problem is turning (latitude, longitude) into a point on a sphere. Once you have that, each earthquake becomes a marker whose size and color encode magnitude and depth, so a glance tells you "big and shallow" vs "small and deep". Design decisions that mattered: Encode magnitude visually. Radius and color do more than any label. A magnitude 6 should look like a magnitude 6. Cluster sensibly. Active regions produce swarms; markers need to stay readable when dozens land in one area. Keep the globe interactive. Rotate, zoom, tap a quake for details. It should feel like an object, not a chart. Real-time without a server Every network-dependent app has to answer: what happens offline, and how fresh is "live"? My rules: Cache the last good feed so the globe still renders with no connection. Refresh on foreground and on an interval, and show the data's own timestamp so "live" is honest. Never block the UI on the network - render what you have, then update. Why no backend It's tempting to proxy the feed through your own server "for control". But USGS is reliable, public, and built for exactly this. Skipping a backend me

fMRI scans show happiness, fear, anger, and sadness have distinct brain activity patterns in doggy brains.

Today, I’m talking with Lila Snyder, who is the CEO of Bose. You certainly know Bose — it’s one of the most famous brands in all of consumer tech. The company started 60 years ago selling speakers to consumers, and its focus on research and development has led it to be a leader in both […]

As I sit here in my RV, typing away on my latest project, I often think about the community that has formed around my indie apps. One story that stands out is when I released ShipDrop, a simple one-click hosting tool for developers. I was overwhelmed by the response from the developer community, who appreciated the ease of use and simplicity of hosting their projects. One user even hosted a website for their local animal shelter using ShipDrop, and it was amazing to see how such a small tool could make a big impact. From a technical standpoint, building ShipDrop taught me a lot about the importance of simplicity in code. When I started working on the project, I was tempted to add a lot of features and complexity, but I realized that the core value of the app lay in its ease of use. By keeping the codebase small and focused, I was able to create a seamless user experience that allowed developers to host their projects in just a few clicks. For example, using a simple drag-and-drop API, I was able to abstract away the complexities of hosting and deployment, making it accessible to a wider range of users. One lesson I've learned from building and sharing ShipDrop with the community is the importance of listening to feedback and being open to iteration. When I first released the app, I thought it was perfect, but the community quickly pointed out areas for improvement. By being receptive to their feedback and making changes accordingly, I was able to create a tool that truly met the needs of my users. This experience has taught me the value of community involvement in the development process, and I'm grateful to be a part of the DEV community, where I can share my experiences and learn from others.

Here's a bug your test suite probably wouldn't catch. A backend developer refactors the user model. The id field — an integer since forever — starts coming back as a string: "42" instead of 42 . Every value is still "correct". Your assertion pm.expect(user.id).to.eql(42) fails, sure — but only on the one endpoint you asserted id on, not the other nine that return users. Meanwhile three client apps that did user.id + 1 are now computing "421" . That's structural drift , and it's what actually breaks API consumers: renamed fields, changed types, properties that quietly vanish. Field-by-field value assertions catch it patchily and by accident. Schema validation catches it systematically — and in Postman it costs about ten lines, because the ajv JSON-schema validator is built into the script sandbox. The ten lines In Scripts → Post-response on any request that returns a user: const userSchema = { type : " object " , required : [ " id " , " name " , " email " ], properties : { id : { type : " integer " }, name : { type : " string " }, email : { type : " string " , pattern : " @ " } } }; pm . test ( " Response matches the user schema " , () => { pm . expect ( pm . response . json ()). to . be . jsonSchema ( userSchema ); }); That single test now fails if id becomes a string, if email disappears, if name becomes an object — every structural mutation, whether or not you thought to assert on that field's value. For an endpoint returning an array of users: const userListSchema = { type : " array " , minItems : 1 , items : userSchema // reuse the object schema }; pm . test ( " List matches schema " , () => { pm . expect ( pm . response . json ()). to . be . jsonSchema ( userListSchema ); }); Share one schema across every endpoint The real power move: your API returns users from /users , /users/:id , /login , /teams/:id/members … and they should all be the same shape . Store the schema once as a collection variable (JSON, stringified), and every request validates against the sa

Digital transformation has led companies to organize their infrastructure and development processes in entirely new ways. To address the challenges of modern cloud-native applications, concepts like Platform Engineering are gaining increasing importance. Especially in environments using Azure Kubernetes Services (AKS) , platform engineering plays a crucial role in efficiently managing and scaling containerized applications. What is Platform Engineering and Why is it Important? Platform engineering is the process of designing, implementing, and managing internal platforms that provide developers with a stable and efficient environment. These platforms bundle all the necessary resources and services to ensure smooth development and operation of applications. A well-developed platform engineering team ensures that recurring tasks are automated, allowing developers to focus on writing code without dealing with the underlying infrastructure. In a container environment like AKS, automation and standardization are critical. Platform engineering provides the framework to simplify these complex workflows. How Does Platform Engineering Support AKS Deployments? A key advantage of platform engineering is the ability to standardize the entire lifecycle of applications—from development to testing and deployment. When working with AKS, the main task of the platform engineering team is to create a seamless and scalable environment for container orchestration. Here are some key aspects of how platform engineering supports AKS: Standardizing and Automating Deployments Platform engineering enables the automation of Kubernetes cluster deployments in AKS using best practices and tools such as Infrastructure as Code (IaC) (e.g., Terraform or Azure Resource Manager templates). This automation reduces human errors and accelerates the time needed to deploy applications in production environments. Self-Service Platforms for Developers A well-designed platform engineering team builds self-ser

POML คืออะไร, ภาษาที่ทำให้ Prompt Engineering เป็นแบบ HTML/CSS โดย Nokka (นก-กา) | 6 สิงหาคม 2569 บทความนี้เขียนโดย AI (deepseek-v4-flash:0731) ผ่าน Hermes Agent ภายใต้การควบคุมและตรวจสอบคุณภาพโดยมนุษย์, Nokka (นก-กา) ถ้าคุณเป็นนักพัฒนาที่ทำงานกับ AI และรู้สึกว่า prompt ที่เขียนเป็นข้อความยาวๆ เริ่มจัดการยากขึ้นเรื่อยๆ มีข่าวดีจาก Microsoft ในบทความนี้ผมจะอธิบายว่า POML คืออะไร เอาไว้ใช้ทำอะไร และเหมาะกับใคร POML (Prompt Orchestration Markup Language) เป็นภาษาโอเพ่นซอร์สที่ให้ prompt engineering แบบเดียวกับ HTML/CSS, มี semantic tags สำหรับ role, task และ example พร้อม stylesheet ที่ควบคุมความยาวและรูปแบบโดยไม่ต้องแตะ logic หลัก [1][2] POML คืออะไร POML ย่อมาจาก Prompt Orchestration Markup Language เป็นภาษาโอเพ่นซอร์สที่ Microsoft พัฒนาขึ้น เพื่อจัดระเบียบ prompt components อย่างเป็นระบบ [1][2] แนวคิดหลักคือการแยก "เนื้อหา" (content) ออกจาก "การนำเสนอ" (presentation), เหมือนที่ HTML แยกโครงสร้างออกจาก CSS ที่ควบคุมสไตล์ [1][2] โปรเจกต์นี้มีผู้ติดตามบน GitHub ประมาณ 4,900 stars และถูก fork ไปกว่า 250 ครั้ง [2] เอาไว้ใช้ทำอะไร POML ให้ "การรักษาแบบ HTML/CSS" กับ prompt engineering [1]: 1. Semantic tags สำหรับ role, task, example แทนที่จะเขียน prompt เป็นข้อความยาวๆ POML ใช้แท็กที่สื่อความหมาย เช่น <role> , <task> , <example> เพื่อจัดโครงสร้าง [1] ฟีเจอร์ สิ่งที่ทำได้ Semantic tags แท็ก <role> <task> <example> จัดโครงสร้าง prompt Stylesheet ควบคุมความยาว/รูปแบบ โดยไม่แตะ logic หลัก Templating engine สร้าง prompt ที่นำกลับมาใช้ซ้ำได้ VS Code extension preview + diagnostics ในตัว 2. Stylesheet ควบคุม verbosity และ format เหมือน CSS ที่ควบคุมสไตล์เว็บ POML มี "stylesheet" ที่ควบคุมความยาว (verbosity) และรูปแบบ (format) ของ prompt โดยไม่ต้องแตะ logic หลัก [1] 3. Built-in templating engine มีเครื่องมือ templating ในตัว ช่วยให้สร้าง prompt ที่นำกลับมาใช้ซ้ำได้ (reusable) [1] 4. VS Code extension มี extension สำหรับ VS Code ที่ให้ preview และ diagnostics, เห็นผลลัพธ์และตรวจสอบข้อผิดพลาดได้ [1] ตัวอย่าง POML จริงจาก Microsoft, เทียบกับ Prompt แบบดั้งเดิม เพื่อให้เห็นภาพชัดเจนว

Multi-Repo to Monorepo: How I Automated 6 Go Microservice Releases and Then Made It 15x Faster Amandeep Singh Amandeep Singh Amandeep Singh Follow Aug 7 Multi-Repo to Monorepo: How I Automated 6 Go Microservice Releases and Then Made It 15x Faster # go # devops # automation # monorepo 6 reactions 1 comment 10 min read

You're triaging an alert. Scheduled task, weird parent process, and a command line that looks like this: powershell.exe -nop -w hidden -enc JABjACAAPQAg... You know the drill: grab the Base64 blob, decode it, read the script. So you paste it into a decoder and get back this: $ c = " h t t p : / / ... Garbage. A space (or a null) between every single character. First instinct is that the payload is doubly-encoded or encrypted. It isn't. This is the single most common gotcha with -EncodedCommand , and once you know it, it takes ten seconds to fix. Why it looks garbled powershell.exe -enc (short for -EncodedCommand ) expects Base64 of UTF-16LE (little-endian Unicode) bytes — not UTF-8. That's mandated by PowerShell itself, not a choice the attacker made. In UTF-16LE, every ASCII character is stored as two bytes : the character followed by a 0x00 null byte. So the letter c isn't 0x63 , it's 0x63 0x00 . When you Base64-decode the blob and then read it as UTF-8, every one of those null bytes renders as a space or an invisible control character. Hence the h t t p spacing. Text: c = " UTF-16LE: 63 00 3D 00 22 00 UTF-8 view: c ␀ = ␀ " ␀ <- the null shows up as a "space" Decode it as UTF-16LE instead and the nulls disappear, because that's what they were: the high byte of each 16-bit code unit. Decode it correctly In PowerShell itself — the encoding is literally called Unicode in .NET, which means UTF-16LE: $enc = 'JABjACAAPQAg...' [ System.Text.Encoding ]:: Unicode.GetString ([ System.Convert ]:: FromBase64String ( $enc )) In Python — decode the bytes, then read them as utf-16-le : import base64 enc = " JABjACAAPQAg... " print ( base64 . b64decode ( enc ). decode ( " utf-16-le " )) In CyberChef — build the recipe From Base64 → Decode text (UTF-16LE) . Or From Base64 then Remove null bytes for a quick-and-dirty look. Any of these turns the spaced-out mess back into readable PowerShell. The encode direction (for building test cases) If you're writing detections or a lab sample