标签：#go

Google Home is rolling out a new Gemini-powered automation feature that can trigger smart home routines based on what your security cameras can see. This is one of several updates announced yesterday for Gemini for Home, including enhanced voice command support and general stability improvements, following its early access launch in October. "We are introducing […]

Tabla de Contenidos Objetivo Restricciones de los Errores Basados en Texto Errores Personalizados en Go Conceptos Clave Implementar e Inspeccionar Errores Casos de Uso Comunes Para no morir en el intento y consejos que no pediste Conclusiones del Tema Objetivo Aprender a diseñar tipos de errores personalizados con metadatos de negocio en Go y a propagarlos correctamente a través del flujo de la aplicación sin perder su tipo ni su información de origen. Restricciones de los Errores Basados en Texto En programas sencillos, el uso de errors.New es perfecto. Sin embargo, en aplicaciones empresariales los errores necesitan transportar datos estructurados. Por ejemplo, si una validación de entrada falla, el frontend no solo quiere saber que "hubo un error", necesita saber qué campo exacto falló (ej. email ) y qué regla se violó (ej. formato inválido ). Si solo devolvemos una cadena de texto, la capa superior de nuestra aplicación tendría que parsear el texto del error con expresiones regulares para extraer los metadatos. Esto es extremadamente ineficiente, propenso a errores de formato y acopla la lógica interna a los mensajes de texto visibles al usuario. Tipos de Errores Comunes en Go Antes de analizar cómo propagar y envolver errores, es fundamental comprender los dos patrones principales que se utilizan en Go: Sentinel Errors : Son variables globales predefinidas que representan un estado de error estático y específico. Se definen a nivel de paquete y se comparan directamente por valor. Ejemplos estándar: io.EOF , sql.ErrNoRows . Creación: Generalmente declarados con errors.New (como var ErrNotFound = errors.New("not found") ). Custom Structs (Errores estructurados personalizados): Son estructuras que implementan la interfaz error y contienen campos adicionales para transportar metadatos dinámicos del fallo en tiempo de ejecución (como códigos de estado o parámetros de entrada). Creación: Un struct personalizado que implementa el método Error() (ej. type ValidationErr

As a backend engineer, I have worked on many services where the hard part was not only writing the code. The hard part was keeping the project clean, reproducible, easy to build, and safe to maintain as the team and codebase grew. In Go, a big part of that discipline comes from understanding Go Modules . At first, Go Modules may look simple: a go.mod file, a go.sum file, and a few commands like go mod init and go mod tidy . But in real projects, these small tools decide how your service builds in CI, how your dependencies are verified, how private repositories are handled, and how other developers can use your package. In this article, I want to explain Go Modules in a practical way, from the mindset of someone building production backend systems. We will cover: What Go Modules are and why Go does not work like NPM or Pip How go mod init , go mod tidy , and go mod vendor actually help How I think about Go project structure without over-engineering How to publish your own Go package A few production tips that matter in real teams What Are Go Modules? A Go module is a versioned collection of Go packages. In simple words, it is the boundary of your project. It tells Go: what your project is called which Go version it targets which dependencies it needs which versions of those dependencies should be used A Go module is defined by the go.mod file. Before Go Modules, Go projects were commonly managed inside GOPATH . That worked, but it created friction around dependency versions and project location. Go Modules solved that by making dependency management explicit and project-based. Today, when I start a serious Go project, one of the first things I do is initialize a module. Why Go Packages Feel Different From NPM or Pip If you come from JavaScript or Python, Go package management may feel a little strange at first. In Node.js, packages are usually published to NPM . In Python, packages are usually published to PyPI . Go is different. Go uses the module path as an import

Most trading ideas start as vague thoughts. "Buy when RSI is oversold and price bounces from support." It sounds reasonable. But the moment you try to test or automate it, the ambiguity becomes obvious. What exactly counts as oversold? How is support defined? What qualifies as a bounce? When do you exit? Without precise answers, the idea cannot be tested, measured, or executed consistently. This gap between intuition and execution is exactly what no-code strategy builders are designed to close. Why vague trading ideas fail Most traders think in concepts rather than rules. "Buy the dip." "Trade strong momentum." "Enter when the trend looks healthy." These ideas feel intuitive, but they are unusable in practice unless translated into explicit logic. Without clear definitions, you cannot backtest a strategy, cannot repeat decisions consistently, and cannot diagnose why results change over time. Ambiguity leads to second-guessing. Second-guessing leads to inconsistent execution. Inconsistent execution makes performance impossible to evaluate. What a no-code strategy builder actually does A no-code strategy builder is a visual system that forces clarity. Instead of writing code, you select indicators, define conditions, combine logic using AND/OR rules, specify entries and exits, and then test the strategy on historical data. Conceptually, it works like assembling building blocks. Each block represents a condition such as "RSI below 30" or "price above moving average." When combined, those blocks form a complete, testable trading system. The key benefit is precision. From idea to testable strategy The transformation follows a predictable workflow. You begin with a loose idea, such as buying when a stock is oversold and starting to recover. You then break that idea into components. What defines oversold? What signals recovery? How do you enter? How do you exit? How much do you risk? Once those questions are answered, the idea becomes a set of explicit rules. For example,

According to the complaint, a Google engineer risked over $2.7 million on wagers related to Google's 2025 Year in Search campaign.

Google is embarrassing itself, again.

Federal prosecutors charged a Google employee with fraud after he allegedly made $1.2 million on Polymarket bets related to Search-related trends in 2025, as reported earlier by ABC News. In their now-unsealed complaint, prosecutors allege that Michele Spagnuolo "knew the outcome of these wagers before the trading public did because he had accessed Google's confidential, […]

According to federal prosecutors, Michele Spagnuolo made more than $1 million on the prediction market platform using confidential information about Google Search traffic.

AI videos that are animated, unrealistic, or only have a little AI may still hide their origins, though.

PR exec says Govee "did not meet the standard required."

Motorola says that recently discovered behavior, which saw some of its phones sending users to an affiliate tracking website before opening the Amazon app, was "unintended" and has been "promptly corrected." The company didn't explain how the error was introduced in the first place. "Recently, Motorola acted quickly to resolve an issue that was identified, […]

“Republicans are just going to have to suck it up and get it done,” says one Trump aide about the funding melee. The votes, though, may simply not be there.

ElevenLabs' new model will let users regenerate a section of a song without affecting the rest of the track.

The 2025 Internet Crime Report was published a few weeks ago, but I only just saw it. Lots of interesting statistics. Press release . News articles .

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

China's AI boom is producing world-class talent, and Beijing is increasingly reluctant to let them go elsewhere.

The office was created a year ago and seemingly named for a far right European plan to expel minorities and immigrants from Western nations. It now works, a source says, with little to no oversight.

The company is mapping Alexandria and, soon, Arlington—right across from the power center of Washington, DC.

The email instructs workers to report behavior that predates Donald Trump’s second inauguration. One employee tells WIRED it felt like a “reminder to narc on your coworkers.”

Not identifying people based on their use of Wi-Fi routers, but identifying people using Wi-Fi signals . This is accomplished through what is known as WiFi sensing , or the use of WiFi signals to infer information about a physical environment. When radio signals like WiFi travel through a space, they interact with the objects and people around them. Those signals can be reflected, scattered, or absorbed. By analyzing how the signal is expected to behave compared with how it is actually received, researchers can infer details about the surrounding environment...