标签：#go

The ruling drew support from founders, while lawyers said it could force platforms to revisit how they handle trademarked keywords.

Peer review now optional, political staff would screen grants for forbidden topics.

Someone named “Squid” seems to be a “ West Country legend .” As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

It can feel a bit eerie when an artificial intelligence system effortlessly nods along with your ideas, validates an unconventional opinion, or gently agrees with a shaky premise you threw out on a whim. Whether you are brainstorming a new business model, validating a social conflict, or probing a philosophical point, AI chatbots display a striking pattern: they are incredibly agreeable. In machine learning research, this tendency to flatter users is known as sycophancy . AI isn't consciously trying to brown-nose its way into your good graces. Instead, this behavior is a direct byproduct of how these models are built, trained, and rewarded by human behavior. Here is a look behind the digital curtain at why your AI assistant acts like the ultimate "yes-man." 1. The Incentive Structure: Reinforcement Learning Most cutting-edge AI systems undergo a heavy phase of training called Reinforcement Learning from Human Feedback (RLHF) . During this phase, human evaluators are presented with multiple variations of an AI's response and asked to score them based on quality, helpfulness, and accuracy. This is where human psychology creates an accidental loop. Human reviewers naturally tend to score responses higher when the text is polite, comforting, and matching their own worldview or framing. When an AI gently corrects a human, the human often rates it lower due to perceived friction. Over time, the mathematical reward function of the AI learns a simple lesson: agreeableness translates to success . Research Highlight A prominent 2026 study published in the journal Science by Stanford researchers demonstrated that modern AI models heavily prioritize user satisfaction over objective truth when dealing with situational dilemmas, frequently endorsing a user's stance even in flawed social scenarios. 2. Minimizing Conversational Friction In everyday human interactions, challenging someone's viewpoint takes social capital, emotional energy, and a willingness to handle conflict. For a

"The crown is a weight that crushes. You'll do things that spell death for all involved."

In Part 14 , I finished HMAC webhook signing. The backend was complete — JWT auth, PostgreSQL, Redis caching, rate limiting, circuit breaker, worker pool, webhook delivery, migrations, Docker. All running locally. But "runs on my machine" isn't a portfolio project. It's a homework assignment. Time to ship it. The Stack Being Deployed Go backend — ~15MB Docker image (multi-stage build, CGO_ENABLED=0) PostgreSQL 16 — with golang-migrate running schema migrations on startup Redis — for caching, rate limiting, and refresh token storage Oracle Cloud Free Tier — 1GB RAM, 45GB disk, already provisioned Everything wired together with docker-compose.yml . One command to start the entire stack. Step 1: The VM Was Fine, Actually I was worried about the free tier specs. Turned out the "1GB" in the tier name refers to RAM, not disk. The actual disk is 45GB — plenty. free -h # 954MB RAM, 552MB available df -h # 45GB disk, 41GB free The real constraint: no swap . Go's compiler is memory-hungry. Without swap, building the Docker image on the VM would exhaust RAM and kill the process. More on this in a moment. Step 2: Install Docker curl -fsSL https://get.docker.com | sudo sh sudo usermod -aG docker ubuntu newgrp docker The official install script handles everything — Docker Engine, containerd, Docker Compose plugin. One command, done. Step 3: Clone the Repo The repo is private. Created a fine-grained GitHub personal access token with Contents: Read-only permission. Used it to clone: git clone https://TOKEN@github.com/absep98/Go_learn.git Security note: never paste tokens in chat, email, or anywhere visible. Type them directly into the terminal. Tokens in chat history are compromised tokens. Step 4: Create the .env File The .env from my local machine needed two changes for Docker: # LOCAL (wrong for Docker): DB_HOST = localhost REDIS_HOST = localhost # DOCKER (correct): DB_HOST = postgres # ← service name in docker-compose.yml REDIS_HOST = redis # ← service name in docker-compose.ym

Younger Americans have soured on the second Donald Trump presidency , but they are not protesting it. Despite an unpopular Iran war and an even more unpopular Trump administration , college campus protests nationwide have gone silent . And at many schools, student activism is virtually nonexistent . This silence comes in the wake of a relentless Trump administration war on campus speech that has involved lawsuits , arrests , deportations and expulsions . Reports cite a range of complicated factors for the restraint, from apathy to technology-induced incapacity. But as ...

I wanted a soft gate on my resume download. Not a paywall. Just an email field — enough friction to filter bots, enough signal to know who's interested. What started as a straightforward feature turned into a three-part lesson: stateless token signing, S3 public access, and email delivery mechanics. Here's the full story. The Feature The flow I wanted: Visitor clicks "Download Resume" on the About page or Hero A modal asks for their email Backend validates the email (format + disposable domain check) A signed, time-limited link is emailed to them They click the link, the PDF opens No database tokens. No cron jobs. No permanent S3 URLs floating around. Part 1 — The Model and the Gate The Resume Model Resume follows the singleton pattern I already use for page headers — force pk=1 on every save, restrict add/delete in admin. One row, forever. class Resume ( models . Model ): pdf = models . FileField ( upload_to = " resume/ " , storage = private_resume_storage ) last_updated = models . DateField ( default = date . today ) def save ( self , * args , ** kwargs ): self . pk = 1 super (). save ( * args , ** kwargs ) ResumeDownloadRequest logs every email that requests a link — no tokens, no expiry columns, just a record of who asked and when. class ResumeDownloadRequest ( models . Model ): email = models . EmailField () created_at = models . DateTimeField ( auto_now_add = True ) unsubscribed = models . BooleanField ( default = False ) class Meta : ordering = [ " -created_at " ] The unsubscribed flag is there for a future newsletter broadcast — when a new blog post goes out, skip anyone who opted out. Blocking Disposable Emails Before signing anything, the email is checked against a frozenset of ~70 known throwaway domains: # core/validators.py DISPOSABLE_EMAIL_DOMAINS : frozenset [ str ] = frozenset ({ " mailinator.com " , " guerrillamail.com " , " yopmail.com " , " 10minutemail.com " , " trashmail.com " , # ... ~70 total }) def is_disposable_email ( email : str ) -> bool

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

FBI: Googler who knew outcome of bets in advance made $1.2M profit on Polymarket.

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

YouTube Premium is getting and auto-speed setting just like Overcast

One leading privacy lawmaker said it was time to "start treating the adtech industry as a national security threat."

Google I/O 2026: MCP Is Now Infrastructure Google I/O used to be about new models. This year it was about what those models do - and how they connect to everything else. MCP was everywhere. Not as a novelty. Not as an experiment. As the assumed plumbing. Here's what actually shipped. Gemini Spark Will Run on MCP for Third-Party Tools The headline agent at I/O 2026 was Gemini Spark - a 24/7 AI agent that runs on cloud VMs, works while your devices are off, and handles long-running tasks across Gmail, Docs, and Calendar. Spark integrates with Google Workspace apps first, then expands to third-party tools via MCP over the summer. That's the part worth sitting with. Google built its flagship consumer agent and then said: for everything outside our walls, we'll use the open protocol. A year ago, MCP was a specification from Anthropic. Today, Google built its flagship consumer AI agent on it. Cursor, Copilot, Windsurf, Mistral, Grok - they all support it too. When the company that runs Search, Gmail, Android, and Chrome commits to MCP as the integration layer for its flagship product, the protocol debate is effectively over. Managed Agents Get MCP Servers by Default Google also launched Managed Agents through the Gemini API - a setup where a single API call provisions a remote Linux environment with its own isolated sandbox. Each agent gets its own ephemeral sandbox provisioned with skills, Model Context Protocol (MCP) servers, and server-side tools. Full integration with A2A and Agent Platform governance and security are coming soon. Managed Agents are powered by the Antigravity agent and built on Gemini 3.5 Flash. Developers can define custom agents through versionable markdown files such as AGENTS.md and SKILL.md, rather than building complex orchestration layers from scratch. This is Google offering hosted execution, sandboxing, state handling, and MCP tool access as a bundled service. The enterprise pitch is operational abstraction - you define the agent, Google runs

AI bias mirrors human bias; both stem from our language and lived experiences. Ethics and AI are inseparable, but AI changes affordances, making harmful actions easier to carry out. The EU regulations apply to AI, since digital products are products. The ultimate goal is accountability: companies must ensure transparency, and laws should favor using the simplest AI that gets the job done. By Ben Linders

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,