标签：#pc

With protocol values and message framing complete, Stage 2 delivered the first end-to-end call: plaintext h2c unary RPC. This is already on main , and Stage 3 and Stage 4 subsequently completed all four RPC cardinalities on the same transport primitive. Previous: Building a Leak-Safe gRPC Frame Decoder on Reactor Netty Method Descriptor Is Where Protocol Meets Types A method requires a precise service name, method name, cardinality, and request/response marshallers: var echo = new GrpcMethod <>( "testing.EchoService" , "Echo" , GrpcMethod . Cardinality . UNARY , new ProtobufMarshaller <>( StringValue . parser ()), new ProtobufMarshaller <>( StringValue . parser ())); The generated path must be: /testing.EchoService/Echo The service registry matches by exact full path. An unknown path returns UNIMPLEMENTED ; registering the same path twice fails immediately when building the service definition. Server Validates Protocol Before Subscribing to Business Logic ReactorGrpcServer uses Reactor Netty h2c: DisposableServer bound = HttpServer . create () . host ( host ) . port ( port ) . protocol ( HttpProtocol . H2C ) . handle ( handler: : handle ) . bindNow ( Duration . ofSeconds ( 10 )); Incoming requests are validated in order: HTTP method must be POST; content-type must be application/grpc or application/grpc+... ; te must declare trailers; path must exist; currently only unary cardinality is allowed; metadata and message size must not exceed limits. Only after validation passes does it create a GrpcCallContext and subscribe to the request body, preventing invalid requests from entering the business handler. HTTP 200 Does Not Mean RPC Success The server writes a compatible content-type first; the final status comes from trailing headers: response . status ( 200 ) . header ( HttpHeaderNames . CONTENT_TYPE , "application/grpc+proto" ); response . trailerHeaders ( trailers -> { GrpcException error = terminal . get (); if ( error == null ) { writeStatus ( trailers , GrpcStatu

This article is an English translation of the original Japanese article. When I added an Expo app to an existing Next.js web service, I initially wanted to share as much code as possible. In practice, types and business rules share well, while UI and runtime dependencies are easier to manage separately. SquadNote uses pnpm workspace and Turborepo, composed of apps/web , apps/mobile , and packages/* . Current split apps/ web/ Next.js, Cloudflare Workers mobile/ Expo, React Native packages/ api/ tRPC and Zod shared parts db/ Drizzle schema design-tokens/ Root workspace configuration is simple: packages : - " apps/*" - " packages/*" turbo.json manages only build and typecheck dependencies. Rather than adding custom build steps for sharing, I started with a structure where each package exports TypeScript sources. What I share The biggest benefit came from tRPC types. Mobile type-imports the web AppRouter , using the same input and output. import type { AppRouter } from " ../../apps/web/src/server/api/root " ; export const api = createTRPCReact < AppRouter > (); I also separated colors, spacing, and font sizes into @squadnote/design-tokens . export { colors } from " ./colors " ; export { spacing } from " ./spacing " ; export { radius } from " ./radius " ; Business rules like waitlists become sharing candidates as pure functions with no dependency on React or DB. They are easy to test, and results do not diverge between web and mobile. What I do not share I do not share screen components. Next.js DOM and React Native View have different interactions, accessibility, and layout constraints, even when they look similar. Authentication storage also differs: Web: NextAuth cookie session Mobile: SecureStore Bearer JWT Both reach the same API, but sharing the login screen and token storage would require handling each environment's concerns with many branches. Routing also has separate implementations for Next.js App Router and Expo Router. What I share is the meaning of organiza

This is the second article in my grpc-reactor series. The first article explains why I chose to build the runtime directly on Reactor Netty and where its compatibility boundary sits. This article moves one layer down into the Stage 1 protocol implementation: the frame decoder that every RPC shape relies on. gRPC protobuf messages are not written directly as raw bytes into HTTP/2 DATA frames. Every message starts with a five-byte envelope: byte 0 bit 0 indicates compression; bits 1-7 must be zero bytes 1-4 unsigned big-endian payload length byte 5..n protobuf message, or its compressed representation Encoding this envelope is straightforward. The difficult part is decoding it without assuming that one input buffer contains one complete frame. HTTP/2, TCP, and Reactor Netty do not promise that buffer boundaries will line up with gRPC message boundaries. This post describes the Stage 1 protocol layer. The project has since progressed beyond it, but the ownership and bounded-decoding rules introduced here remain the foundation for the later transport stages. Encoding Must Define Ownership The contract of GrpcFrameCodec.encode is deliberately explicit: the returned frame and the input message have independent lifetimes. Encoding must not move the input reader index or release the input buffer. The implementation currently copies the readable bytes into a byte array before applying compression: public static ByteBuf encode ( ByteBufAllocator allocator , ByteBuf message , GrpcCompression . Codec compression ) { boolean compressed = ! compression . name (). equals ( "identity" ); byte [] payload = new byte [ message . readableBytes ()]; message . getBytes ( message . readerIndex (), payload ); if ( compressed ) { payload = compression . compress ( payload ); } return allocator . buffer ( GrpcFrameCodec . HEADER_SIZE + payload . length ) . writeByte ( compressed ? 1 : 0 ) . writeInt ( payload . length ) . writeBytes ( payload ); } This is not a zero-copy implementation, and

For decades, desktop computers followed a familiar formula: a large case, powerful components, dedicated graphics cards, and plenty of space for upgrades. But the way we use computers is changing. Today, many users are looking for something different: a computer that is powerful enough for their daily needs, consumes less energy, takes less space, and can adapt to modern workflows. This is where Mini PCs are becoming one of the most interesting trends in personal computing. What is a Mini PC? A Mini PC is a compact computer designed to provide desktop-like functionality in a much smaller form factor. Unlike traditional desktop towers, Mini PCs integrate most components into a small chassis while still offering modern performance. A typical Mini PC includes: Modern processors from AMD or Intel Integrated Radeon or Intel graphics RAM and SSD storage Multiple connectivity options Compact cooling solutions Companies such as Minisforum have helped accelerate this trend by creating small computers powered by modern Ryzen and Intel processors, showing that compact hardware can still deliver impressive performance. Why are Mini PCs becoming popular? Efficiency matters more than ever One of the biggest advantages of Mini PCs is their efficiency. Traditional desktop computers can require significant power depending on the hardware configuration. In comparison, many Mini PCs provide enough performance for everyday tasks while maintaining lower energy consumption. For many users, reducing power usage without sacrificing productivity is becoming increasingly important. Small computers, new possibilities A smaller computer changes how we think about desktop setups. Mini PCs can be used for: Software development environments Home servers Media centers Student workstations Office computers Compact gaming setups A powerful computer no longer needs to occupy a large space on or under your desk. Modern processors changed the game The biggest reason Mini PCs are becoming more capable i

“Myst” sold over 6 million copies in the ’90s. Its latest sequel couldn’t shore up any funding, pointing to an existential crisis among double-A games.

Can MS Paint run Doom? (Yes)

Memory and storage prices will be inflated for the foreseeable future, so we’re always happy to find a good deal on capable gaming hardware, like this discount on the Lenovo Legion Go S with a Z2 Go processor and SteamOS. Normally $1,137.41 (and selling for close to $1,000 at Best Buy), Lenovo has the handheld […]

There's a lot to like about the Yoga Slim 7i, but the current price is a bit too steep.

The next Xbox, Project Helix, could theoretically have the largest library of any home console. Not only will it play PC games, but we now know, courtesy of a leaked memo obtained by The Verge's Tom Warren, that it will run games from every generation of Xbox: the original 2001 Xbox, the 2005 Xbox 360, […]

When asked about product-market fit, Spiegel said he believes mass-market consumer adoption won't occur until the end of the decade.

Snapchat has adjusted its recommendation systems to ensure that only videos created by real people are eligible for Spotlight recommendations, taking a stance against AI slop.

If your Wi-Fi is a bit sluggish, there are some things you should check before upgrading your router.

Best Buy has raised the price of the Asus ROG Astral RTX 5080 OC to $2,099 - the first time a major US retailer is directly selling an RTX 5080 for more than the $1,999 MSRP of the RTX 5090, VideoCardz reports. The ROG Astral RTX 5080 OC now costs 40 percent more than its […]

Razer has insisted that optical keyboard switches are the best choice for competitive esports stars and the sweatiest of try-hards, but it's now relenting slightly by offering cheaper keyboards with magnetic switches and a similar array of gaming features. The $120 Razer Huntsman V3 HE Magnetic Mini and $140 Huntsman V3 HE Magnetic Tenkeyless, both […]

Over the last few years, CD Projekt Red put a ton of work into fixing Cyberpunk 2077, squashing bugs, polishing content, and even launching new DLC in 2023, Phantom Liberty. As a result, this game has gone from a source for funny, glitch-fueled videos to a world I’ve returned to a number of times to […]

The Framework 13 Pro is a class-leading Windows laptop that, right now, is just a little too pricey.

In June, Nintendo announced a new version of the Switch 2 that should let you easily replace its battery pack - but only for Europe. Logitech will do much the same. User-replaceable batteries will be the law in Europe in February 2027, but instead of bringing them to the rest of the world, Logitech plans […]

NVIDIA shipped a 30-billion-parameter model that can see, hear, and talk — and gave the weights away. The catch: the seeing and hearing parts didn’t run on a Mac. So I spent an afternoon writing the missing piece. Here is the whole thing in thirty-five seconds — the model reading a real cart off my own store, on the laptop, with nothing leaving it. Thirty-five seconds: the model reads a real cart off my own store, on the laptop, with nothing leaving it. The elapsed counter is the real measured latency. Every couple of weeks I go looking for whatever new open-weight model just dropped, pull it onto my laptop, and see what it can actually do. Most of the time it’s a coding model, I run it against the one I already use, and my current favorite wins again. That’s a fine result. It’s just not much of a story. This time I found something different. What Nemotron Omni actually is NVIDIA released Nemotron-3-Nano-Omni-30B-A3B — a “tri-modal” model, which is a fancy way of saying one brain with eyes and ears attached. You can hand it a picture, a sound file, or a video, and talk to it about what it saw or heard. It’s 30 billion parameters total, but only about 3 billion of them fire for any given word, which is why something this capable can run on a laptop at all. The weights are public. Someone had already done the hard, unglamorous work of shrinking it down to a 4-bit MLX version that fits on Apple Silicon — that’s yayr over at mlx-community, and this project doesn’t exist without that upload. About 19 GB on disk. Ready to go. Except for one line, buried in the model card: The text backbone loads with standard MLX nemotron_h tooling. The vision and audio towers require a multimodal runtime that implements the C-RADIO ViT-H and Parakeet Conformer forward passes (e.g. the Evorix on-device engine). Translated: the brain works on a Mac. The eyes and ears don’t. The weights for them are right there in the file — all the knowledge, sitting on your disk — but nothing I could find

The first modern GPU with 4GB of VRAM has appeared, as RAM prices and component shortages push tech prices higher. As Digital Foundry reports, a listing for an AMD Radeon RX 9050 with 4GB of RAM was spotted on ASRock's website by a user on X, the same day ASRock also launched an 8GB version […]

Logitech's new gaming boss, Robin Piispanen, tells me he likes the idea of gaming handhelds. "It's such a charming value proposition," he says, as we sip iced vanilla lattes at my local cafe. But he's not building one right now. He's not sure when or if Logitech should try again, after its underwhelming experiment with […]