标签：#m

Online speculation over the weight loss of celebrities like Ariana Grande—even out of genuine concern—can make things worse for people struggling with eating disorders.

Recently, I've added a bunch of hype-monsters to my AI Werewolf : Kimi K3 Qwen 3.8 Max, Qwen 3.7 Plus, Qwen 3.7 Flash MiniMax M3 Plus the ones I've had for a while DeepSeek V4 Pro and Flash GLM-5.2 Sakana Fugu base and Ultra The last one is Japanese, not Chinese, but it was in the news a month or two ago and it belongs in this story. I sat down to write about how slow Chinese models are. Because they are all annoyingly slow even with a small context. I had the numbers, the thesis wrote itself, and then... I discovered something I didn't expect. Okay, let me go straight to the problem All Chinese official APIs are extremely slow. DeepSeek got better with their v4, the rest are just terrible. So slow that they are barely usable in a text game. No, seriously, take a look. Time to produce one four-sentence vote: Kimi K3 : 29 to 34 seconds MiniMax M3 : 25 to 30 seconds Qwen 3.8 Max : 25 to 27 seconds, and only because I capped its thinking. Uncapped it hit 100. DeepSeek V4 Pro : 14 to 22 seconds, the best of the group Same prompt, same afternoon: Claude 5 Opus answers in 5.9 seconds. I have a test which simulates the voting at the end of a game day. Some chat has happened, some players have already cast their votes, and now the model under test has to do the same. The prompt contains all of that: 36,000 characters, which works out to 8-13k tokens depending on the model's tokenizer. Not much, assuming those models have 1M contexts. More US/EU models: Model Time Input Output tokens Avg cost GPT-5.6 Luna 3.2-3.6s 12,124 190-231 $0.0020 Mistral Large 3 2.9-5.2s 8,175 73-134 $0.0042 Claude Fable 5 4.8-6.0s 13,027 183-209 $0.1399 Gemini 3.6 Flash 4.6-7.1s 7,979 297-532 $0.0152 GPT-5.6 Sol 6.7-7.3s 12,124 150-156 $0.0471 Claude 5 Opus 5.9-7.8s 13,027 220-387 $0.0726 Gemini 3.1 Pro 11.6-12.1s 7,979 821-940 $0.0263 Claude 5 Sonnet 6.8-14.8s 13,027 252-821 $0.0464 Grok 4.5 10.0-14.8s 8,190 428-607 $0.0149 Mistral Medium 3.5 2.9-35.0s 8,175 57-109 $0.0128 Worth staring at the input

Introduction: Moving Beyond Traditional XML Layouts Android development has evolved significantly. The days of managing complex XML layouts with findViewById or basic View Binding are fading fast. Modern Android development demands clean architecture, reactive state management, and declarative UI tools like Jetpack Compose. In this deep dive, we will explore how to structure scalable, maintainable, and testable native Android applications using the Model-View-ViewModel (MVVM) architecture alongside Jetpack Compose. Why MVVM with Jetpack Compose? The Model-View-ViewModel pattern provides a clean separation of concerns between your business logic and presentation layer: Model: Handles data sources (Local database via Room, Remote API calls via Retrofit). ViewModel: Preserves state during configuration changes, holds business logic, and exposes state observables. View (Compose): Declarative UI composables that automatically re-compose (re-render) when the underlying state changes. Using Jetpack Compose alongside MVVM eliminates UI boilerplate code, avoids memory leaks associated with traditional views, and simplifies dynamic UI state management. Layered Architecture Overview The Data Layer The data layer is responsible for retrieving and storing data from external or local sources. It uses the Repository Pattern to expose a clean API to the rest of the app: Kotlin interface UserRepository { suspend fun getUserProfile(userId: String): Result } class UserRepositoryImpl( private val apiService: ApiService, private val userDao: UserDao ) : UserRepository { override suspend fun getUserProfile(userId: String): Result { // Handle network requests, local caching, and fallback strategies } } The Domain Layer (Optional for Large Apps) Contains Use Cases (Interactors) that encapsulate single pieces of business logic. This ensures that ViewModels remain lightweight and focused strictly on managing UI state. The UI Layer (ViewModel + Composables) The UI layer reads state exposed by

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

Let's dive in to Roku's privacy policy to see what data is being collected from you -- and how to stop it.

After months of speculation, the companies formally announced the massive project will happen just north of Houston.

submitted by /u/swdevtest [link] [留言]

eBay touted an increase in eBay Live's gross merchandise volume as it plans to expand it to more international markets in the coming weeks and months.

Suno's watermarking feature comes as the company is fighting legal battles on several fronts.

Deliveries of the new electric pickup are slated for later next year.

Dolby Vision 2 includes a new algorithm that optimizes content on a scene-by-scene basis.

The startup's platform predicts what product a shopper wants next, learn their general taste, and fine-tune continuously based on what they do in real time.

If you've ever tried coding on an iPad, Galaxy Tab, or Chromebook, you know the frustration: Standard desktop tutorials assume a Mac or high-spec Linux laptop. Neovim configuration takes 4 hours of plugin debugging. Touch input on mobile terminals sucks without a dedicated extra-keys bar. I built DevDock (dock) to solve this permanently. What is DevDock? DevDock is a turnkey developer environment manager built specifically for mobile devices, Termux, Chromebooks, and low-spec hardware. Instead of fighting configuration files, one command installs a complete, high-performance terminal stack: bash curl -fsSL https://get.devdock.io | bash -s -- --profile=fullstack ⚡ Key Features Sub-5ms Terminal Rendering: Uses Starship prompt + Zsh lazy-loading tuned for ARM chips. Termux Touch Optimization: Automatically injects an ESC/TAB/CTRL touch bar and enables mouse scrolling in Tmux. Low-Memory Neovim: Starts in <50ms and uses under 50MB RAM while providing full Language Server Protocol (LSP) support for TS, Go, Python, and Rust. Curated Profiles: fullstack: Web + API tools frontend: React, TS, Vite & Tailwind preset backend: Go, Rust, Python, Postgres & Redis CLI tools devops: Kubectl, Helm, Terraform, and Cloud CLIs 🛠 Trying It Out bash Check your mobile terminal health: dock doctor View available developer stacks: dock profiles Initialize a frontend stack: dock init frontend 🔗 Open Source & Community DevDock is 100% open source under the MIT License! GitHub Repo: github.com/devdock/devdock Web Showcase: devdock.io Give it a spin on your Android phone, tablet, or cloud shell and let me know what you think in the comments below!

The Model Context Protocol (MCP) is becoming a more consequential piece of enterprise AI infrastructure because useful AI assistants need more than reasoning ability. They need controlled access to customer records, marketing context, decisioning systems, and the tools that turn an answer into an action. MCP's 2026-07-28 release candidate advances that goal with a stateless core for standard HTTP infrastructure, formal extensions for interfaces and long-running work, and a stronger framework for authorization and conformance . For SEO and marketing teams, the change is not that an AI agent suddenly replaces strategy or governance. It is that the data-access layer connecting an agent to CRM-like and marketing systems is becoming more standardized. That can make AI-enabled workflows more practical to design, review, and operate, provided organizations define what data an agent may access and what it may do with it. The official MCP 2026-07-28 release candidate announcement describes a stateless core intended to scale over conventional HTTP infrastructure. It also introduces formal extensions: MCP Apps for server-rendered user interfaces, and Tasks for work that takes longer than a single request. Alongside those technical changes, the release candidate strengthens authorization alignment with OAuth and OpenID Connect practices, while establishing a formal deprecation policy and conformance framework. What the 2026-07-28 release candidate changes MCP is an open standard for connecting AI agents to external data sources and tools. In an enterprise setting, those connections can include CRM-like systems and marketing data, subject to the systems and permissions an organization exposes. Rather than building every connection as a bespoke integration, teams can use a common protocol layer for supplying an AI system with grounded organizational context. The 2026-07-28 release candidate matters because it addresses several requirements that become more important as AI workflo

Being a Java Developer, I always thought about the programming language i'm working in, if it's the right one for all along the career ahead. I went through some web-based studies and, completely satisfied with the information I got to know. So, the short answer to the prime question is: Yes, Java is absolutely relevant and, here's why:- Still a Top Language Java has been in the top 3 programming languages worldwide for 2+ decades. Historical Dominance: The Backbone of Enterprise Systems: Since its inception, Java’s mantra of "Write Once, Run Anywhere" (WORA) revolutionized software development. It quickly became the foundation for global financial systems, insurance platforms, healthcare infrastructure, and e-commerce giants. Unrivaled Stability: Indexes like TIOBE and GitHub Octoverse have consistently ranked Java among the top most used languages for over 20 years. Companies do not shift their backend infrastructure on a whim; billions of dollars of existing, mission-critical infrastructure rely on the Java Virtual Machine (JVM). Enterprise Backbone Banks, insurance, e-commerce, and global-scale companies still rely heavily on Java. 95% of enterprise systems use it in some form. Banking and Financial Services (FinTech): Transactional Integrity: Mega-banks require high concurrency and absolute compliance with ACID (Atomicity, Consistency, Isolation, Durability) properties. Java's robust memory management and strict type safety prevent multi-threading errors that could result in catastrophic financial discrepancies. Legacy Settlement Layers: Systems managing global wire transfers, electronic clearing houses (ACH), and high-frequency trading platforms were built on the Java Virtual Machine (JVM) over the last 30 years. Rewriting these multibillion-dollar codebases carries massive operational risk with zero business incentive. Insurance Platforms: Complex Risk Modeling: Insurance giants process enormous volumes of historical actuarial tables and continuous risk data.

We had a checkout test at my last job that everyone called "the coin flip." Green for a week, red twice on a Tuesday, green again. Someone eventually wrapped it in a retry and it sat like that for eight months before anyone looked at it again. Turned out the real bug was a webhook that occasionally fired before the order record finished writing to the DB - a two-hundred-millisecond gap that only showed up under load. The test wasn't broken. It was the only thing in the entire pipeline that noticed. That's usually the story. Someone blames the test - bad selector, missing wait, a sleep(2) some intern left in there three years ago, and half the time they're right. But when a test flakes repeatedly and nobody can explain why, the test is rarely the actual problem. It's just the part of the system rude enough to say something. A few places I keep finding the real cause hiding. Tests that quietly depend on each other Test A writes a row, Test B reads it and never knew it needed to. Run B by itself, it passes. Run the suite in a different order, or in parallel, and B fails for no reason anyone can point to. I've lost a full afternoon to this exact thing more than once - a cache value from Test 12 leaking into Test 47. The actual fix is annoying and unglamorous: every test gets its own fixtures, its own scoped data, no assumptions about what ran before it. If your suite only goes green in one specific order, you don't have a flaky test. You have an undocumented dependency graph, and it's going to bite someone eventually. The app is racing, not the test Click a button, immediately assert on the result - that's a bet that the UI update lands the instant the click handler returns. It usually does, on your machine, on a good day. Add a debounce, a background job, or just enough network latency and that bet stops paying off. This one's frustrating because the test isn't being paranoid. The app genuinely has a race condition. The test just runs the interaction often enough, acro

Multi-agent examples often jump straight to models, tools, and production claims. That makes it difficult to see what the protocol is doing. Before adding an LLM, it is useful to watch a small system discover specialists, delegate a task, and return a result that you can inspect. This tutorial uses A2A Orchestration Lab , an open-source Python project by Fernando Paladini. It starts three local agents: an orchestrator, a researcher, and a writer. The researcher and writer are deterministic stubs, so the example isolates the Agent2Agent (A2A) communication flow from model behavior. The result is a runnable research-to-write pipeline that helps explain where A2A fits next to the Model Context Protocol (MCP). TL;DR Install the lab with uv , run its demo command, and inspect the three local Agent Cards and the delegated result. The project is a learning lab, not a production runtime. That is a feature for this tutorial because every moving part remains visible. Prerequisites You need: Python 3.12 or newer. uv for environment and dependency management. A terminal with network access for the initial dependency download. The repository declares version 0.1.0 , requires Python >=3.12 , and depends on the A2A Python SDK, httpx , and uvicorn . It is licensed under MIT. Create and run the lab Clone the public repository and let uv create the environment from the locked dependencies: git clone https://github.com/paladini/a2a-orchestration-lab.git cd a2a-orchestration-lab uv sync Run the bundled end-to-end demo: uv run a2a-lab demo "Explain A2A and how it relates to MCP" The CLI starts the three agents as subprocesses, waits for their Agent Cards, sends a message to the orchestrator, prints the response, and terminates the child processes. The default prompt is the same explanation used by the repository README, but using your own prompt makes the delegation easier to recognize. On a successful run, the output contains sections similar to these: [demo] asking orchestrator: 'Expl

submitted by /u/goto-con [link] [留言]

Netflix will hold the exclusive rights to an extended look at Grand Theft Auto VI before it hits YouTube six hours later.

The launch of these new features reflects Google’s ambitions to transform Google Maps from a navigation tool into an assistant that's capable of helping users complete real-world tasks.