Qt: A Powerful Framework for Cross-Platform UI Development

Windows 11 25H2 is a refined update focused on stability, performance, and incremental AI improvements. It’s ideal for users who want a smoother experience without changing hardware. Windows 12, if released as rumored, will be a major leap in AI, UI design, and system intelligence—but it will likely require newer hardware and may introduce a subscription model for premium AI features

Android Emulator vs. iOS Simulator — They’re NOT the Same! 🚀 A lot of developers still use these terms interchangeably, but they work very differently under the hood. Here’s a simple breakdown: 🔸 Android Emulator Think of it as a virtual Android phone. It boots up an entire Android operating system and tries to behave like a real device. Loads a full Android system image (powered by QEMU + virtualization) Can emulate ARM CPUs with hardware acceleration Simulates core device features: Google Play, phone services, Play Services, APK installs, etc. More complex, but closer to real-device behavior 🔸 iOS Simulator This one is very different. It doesn’t emulate iPhone hardware at all. Instead, it runs your app natively on your Mac with simulator-specific frameworks. Does not emulate the iOS kernel or any iPhone hardware Executes binaries compiled for macOS architecture Uses simulator runtime frameworks Extremely fast, but can’t fully mimic real device features (camera, sensors, performance, etc.) In short: 👉 Android Emulator = full system emulation (slower, but realistic) 👉 iOS Simulator = runtime simulation (faster, but not fully accurate)

Evergine 2025 Major Release is out! We’re pleased to announce a new major version of Evergine — our most complete release so far. Evergine continues to evolve as a 3D engine focused on industrial visualization, digital twins, and real-time 3D experiences, now surpassing 4.5 million downloads on NuGet. Highlights of this release: PointCloud Add-on – load and visualize large point clouds (LAS/LAZ, E57, PCD) with GPU acceleration and progressive loading. Native runtimes for IFC, USD, CAD, OBJ, and more — integrate your industrial data directly without complex conversions. WebGPU support – bring high-performance 3D visualization to the web. Plus, KTX2 texture compression, Mesh Shaders, ARM64 support across Windows, Linux, macOS, and Android SDK 35 updates. With this release, we continue to stay aligned with our customers’ evolving needs — improving performance, simplifying workflows, and expanding platform reach. Read the full post and see what’s new: https://lnkd.in/dhJcg3yA #Evergine #3D #DigitalTwin #IndustrialAI #WebGPU #PlainConcepts #XR #Innovation

Android Emulator vs iOS Simulator — they are not the same thing! Many people confuse the terms. Why? Well, here’s a practical breakdown: Android EMULATOR – runs a full Android OS, basically a virtual phone. This makes it more complex but closer to real-device behavior. • Runs a full Android system image (uses QEMU + virtualization). • Can emulate ARM CPU's using hardware acceleration. • Simulates many device components (Google Play, Phone, Play Services, APK installation, etc). The iOS SIMULATOR, on the other hand, doesn’t emulate hardware or iOS itself. It runs your app’s code natively on your Mac using simulator frameworks, which is faster but can’t fully reproduce device-specific features. • Does not emulate the iOS kernel or iPhone hardware. • Runs binaries compiled for the host architecture (Mac) linked with frameworks built for the simulator runtime.

🧩 Building local AI isn’t just about shrinking models — it’s about rethinking architecture. Over the last few weeks, I’ve been experimenting with running models on Android, iPhone, Windows, and macOS. Here’s what I’ve learned 👇 1️⃣ Quantization matters. GGUF is still the most efficient way to get performance without losing coherence. 2️⃣ Hardware diversity is real. Even “similar” devices perform differently — optimizing for each is part art, part science. 3️⃣ Latency teaches empathy. When every millisecond counts, UX suddenly becomes personal. The Empisto framework now adapts across devices seamlessly — one step closer to truly local intelligence. 💬 What’s your biggest challenge when running models locally? #Empisto #EdgeAI #SoftwareDevelopment #AIEngineering #LocalAI #PrivacyByDesign #BuildInPublic

🧩 Building local AI isn’t just about shrinking models — it’s about rethinking architecture. Over the last few weeks, I’ve been experimenting with running models on Android, iPhone, Windows, and macOS. Here’s what I’ve learned 👇 1️⃣ Quantization matters. GGUF is still the most efficient way to get performance without losing coherence. 2️⃣ Hardware diversity is real. Even “similar” devices perform differently — optimizing for each is part art, part science. 3️⃣ Latency teaches empathy. When every millisecond counts, UX suddenly becomes personal. The Empisto framework now adapts across devices seamlessly — one step closer to truly local intelligence. 💬 What’s your biggest challenge when running models locally? #Empisto #EdgeAI #SoftwareDevelopment #AIEngineering #LocalAI #PrivacyByDesign #BuildInPublic

What Makes Sparksee Ideal for Embedded and Mobile Devices? Most databases were born in the cloud. Sparksee was born for devices. 🧩 Supported platforms: Windows, Linux, macOS, Android, iOS — plus custom POSIX builds for specific architectures. ⚙️ Architectures: x86, AMD64, ARM v6/v7/v8, ARM 64 Soft Float, and more. 💾 Small footprint: easily trimmed down to customer needs, with tailor-made indexes. 🔒 Page-level encryption and transaction logging ensure safety and recoverability. Sparksee’s adaptability means it can run inside chipsets, industrial controllers, or smartphones, providing local graph reasoning without cloud dependency. And when paired with Graph Convolutional Neural Networks (GCNNs), Sparksee enables a new class of efficient AI at the edge: 🔹 Higher accuracy than conventional CNNs by learning from relationships. 🔹 More compact models, saving memory and energy. 🔹 Real-time inference maintained — precision without added latency. That’s why Sparksee powers systems from automotive OEMs to mobile AI platforms — delivering performance, flexibility, and independence in one compact engine. #EmbeddedAI #GraphDatabase #Sparksee #MobileAI #EdgeAI #Sparsity #DeepTech 🚗 What kind of device would you empower if your database could run anywhere?

💡 𝐖𝐡𝐲 𝐌𝐨𝐬𝐭 𝐘𝐨𝐮𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐒𝐭𝐨𝐩 𝐚𝐭 𝐀𝐫𝐝𝐮𝐢𝐧𝐨 — 𝐚𝐧𝐝 𝐇𝐨𝐰 𝐭𝐨 𝐆𝐨 𝐁𝐞𝐲𝐨𝐧𝐝 When most of us start with embedded systems, we begin with Arduino — and that’s okay. It’s simple, visual, and gives quick results. But the problem starts when we stop there. Many never go beyond the “LED blink” stage — they never touch: - ⚙️ Low-level microcontroller architecture - 🧩 Registers, memory maps, and interrupts - 📡 Protocols like SPI, I2C, UART - 🧠 Real-time OS concepts or peripheral drivers That’s why I really appreciate this repository: 👉 https://lnkd.in/gDgM37WR It’s not just another tutorial list — it’s a complete guide on: - Where to start - What to learn next - How to progress from blinking LEDs to building real products It shows the 𝐟𝐮𝐥𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 — from understanding how compilers work, through microcontroller internals, to RTOS, communication protocols, and embedded Linux. 🔧 Whether you’re a student, a hobbyist, or a professional aiming to get serious with embedded systems, this roadmap will help you break the “Arduino ceiling” and truly understand the hardware you’re programming. #EmbeddedSystems #FirmwareDevelopment #Microcontrollers #EmbeddedC #RTOS #IoTDevelopment #EngineeringLearning #ElectronicsEngineering #MakersCommunity #LearningPath

🚀 Accelerate RFID Development with CSL Open Source 💻 Why start from scratch when you can build on proven code? CSL RFID has released 35+ open-source repositories on GitHub — designed to help developers integrate RFID technology faster than ever. From C# and Java to Python, C, and Swift, our SDKs and sample apps cover multiple platforms and reader models — making RFID integration simple, fast, and flexible. 💡 With CSL's open-source tools, you can: ✅ Jumpstart development in your favorite programming language ✅ Work seamlessly across Windows, macOS, Linux, iOS, and Android ✅ Cut integration time and speed up your go-to-market Ready to build smarter and faster? 🔗 Learn more about our open-source approach: 👉 https://lnkd.in/gbMNAcRv 🌐 Explore our GitHub repos and start creating today: 👉 https://github.com/cslrfid #OpenSource #Developers #RFIDTechnology #IoT #Innovation #Automation #CSLRFID #RFIDIntegration #SoftwareDevelopment #TechCommunity

💡 No-Std and Embedded Rust In most Rust programs, developers rely on the standard library (std), which provides powerful features like heap allocation, file I/O, threads, and networking. However, these features depend on an operating system they require system calls, schedulers, and memory management provided by Linux, Windows, or macOS. But what if there’s no OS at all like in a microcontroller, bootloader, or custom kernel? That’s where #![no_std] Rust comes in. By declaring #![no_std] at the top of your program, you tell the compiler not to link the standard library and instead use only the core library (core), which provides essential low-level components such as primitive types, traits (Copy, Clone, Drop), and mathematical operations. In a no_std environment, there’s no heap allocation, no threads, and no OS so developers must handle memory manually and interact directly with hardware registers. This is perfect for bare-metal programming, where Rust code can directly control LEDs, sensors, or CPUs, often using memory-mapped I/O. For example, embedded developers write to specific memory addresses to turn on a GPIO pin or read sensor data. Rust provides crates like embedded-hal, cortex-m, and rtic that help write high-level abstractions for these low-level operations while maintaining memory safety and zero-cost efficiency. no_std Rust is also how developers build operating systems, kernels, and firmware environments where there’s no runtime support. You even implement your own main() entry point and define a custom panic handler since the default one depends on std. This approach gives developers complete control over the hardware with the safety guarantees that Rust is famous for. In short, no_std Rust transforms Rust from a systems programming language into a bare-metal powerhouse, combining C-level control with modern compile-time safety the perfect tool for embedded systems, IoT devices, and OS development.