Don't worry, the Trump administration won't let a little acid rain get in the way of more data centers.

Explore the changes we've shipped across npm and GitHub Actions over the past few months to disrupt supply chain attack techniques and limit their impact. The post Disrupting supply chain attacks on npm and GitHub Actions appeared first on The GitHub Blog .

Warner Bros. Discovery is bringing a vertical video feed to HBO Max to help you find something new to watch. Streaming platforms like Netflix, Disney Plus, and Peacock already offer TikTok-like vertical video feeds to surface content that might be interesting to users, and now HBO Max will have a similar feed of its own. […]

An Xbox executive said yesterday's outage was due to "a licensing service that sits outside of Xbox."

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 […]

Github URL: https://github.com/SID-Technologies/Pilum In December 2025, I open-sourced Pilum , a multi-cloud deployment CLI that deploys to Cloud Run, Lambda, Azure, Cloudflare Pages, npm, Homebrew, and Docker Hub from a single pilum.yaml . The announcement post covered the architecture: recipes, ingredients, handlers, wave-based execution. That was the "it compiles and the tests pass" version. Three months and 40+ pull requests later, Pilum deploys all of SID Technologies , platform-core, Torch, Statio, every website, every npm package, and itself. This post is about everything that broke between "it works" and "it ships production software." ( Update: * there's now a seven-months-in addendum at the bottom. It's mostly about what stopped breaking.)* The Timeline Dec 2, 2025 : First commit. Baseline CLI with recipe system. (#1) Dec 4 : Homebrew release workflow. Pilum dogfoods its own deployment. (#2-#4) Dec 31 : Service graph, --only-changed , file embedding support. The "I need this for real" features. (#22-#24) Jan 3-12 : Documentation and bug fixes. The quiet "oh, this doesn't actually work" phase. (#25-#29) Feb 6-7 : The big feature sprint — 8 features in 48 hours. Wave deployments, npm recipe, Cloudflare Pages, Azure Container Apps, Cloud Run Jobs, environment variables, JSON output, history command. (#30-#38) Feb 8-12 : The big fix sprint — YAML parsing, package manager issues, build failures, error swallowing, GCP secrets, Cloudflare execution. Everything from the feature sprint broke something. (#42-#49) Feb 24 : Security hardening and npm publishing fixes. (#51-#53) Mar-Apr : Memory-based worker allocation, wave ordering bug, orchestrator rewrite. The "I thought this was done" phase. (#58-#62) The pattern is clear: features ship fast, fixes ship faster, and the real bugs show up a month later. Wave-Based Deployments Were Broken Wave-based deployment was the headline feature in #31. Services declare dependencies, Pilum builds a dependency graph, topological

The largest grid operator in the U.S. says it will cut power to large data centers to prevent blackouts starting next year.

Introduction Transitioning from GitLab’s centralized CI/CD pipeline structure to GitHub Actions presents a unique challenge for developers accustomed to GitLab’s modular approach. In GitLab, a central 'pipelines' repository acts as a single source of truth, referenced by individual projects via the include keyword. This mechanism eliminates duplication of CI/CD configurations, ensuring consistency and reducing maintenance overhead. However, GitHub Actions operates under a different paradigm, where workflows are typically defined within the .github/workflows directory of each repository. This disparity forces users to rethink how to achieve centralization without GitLab’s native include functionality. The core issue lies in GitHub’s scoping rules for reusable workflows. While GitHub supports uses to reference workflows from a central repository, these workflows must reside in a publicly accessible repository or the same repository. This constraint introduces versioning challenges , as changes to the central workflow can inadvertently break dependent projects if not managed carefully. For instance, updating a reusable workflow without tagging a stable version can lead to inconsistent behavior across projects, as GitHub defaults to using the latest commit. Another friction point is the lack of direct equivalence between GitLab’s include and GitHub’s uses . GitLab’s include allows for seamless integration of CI configurations, treating the included file as part of the local context. In contrast, GitHub’s uses references an external workflow, which operates in its own scope . This means inputs and outputs must be explicitly defined, increasing the complexity of migration. For example, a GitLab CI job that references a shared script might fail in GitHub Actions if the script relies on environment variables not passed through the uses interface. To address these challenges, developers must adopt a hybrid approach . Composite actions , which bundle multiple steps into a sin

If you have ever tried to find a new physician through a search box, you already know the frustration: outdated phone numbers, doctors who left the practice two years ago, and "accepting new patients" labels that turn out to be fiction. Anyone who has read the candid breakdown in Online Doctor Directories: A User's Guide to a Very Imperfect Tool will recognize the pattern immediately, because the core problem is not laziness on anyone's part — it is a data engineering problem hiding inside a healthcare product. And for those of us who build software for a living, it is a fascinating case study in what happens when stale data meets high-stakes decisions. The Root Cause Is a Data Pipeline, Not a Design Flaw Most doctor directories aggregate information from insurance networks, state licensing boards, hospital affiliations, and self-reported provider profiles. Each of these sources updates on its own schedule, uses its own identifiers, and defines fields differently. One system records a physician under her maiden name; another lists the clinic's billing address instead of the practice location; a third still shows a specialty she stopped practicing in 2019. The result is a classic entity-resolution nightmare. Without a reliable primary key shared across sources, merge logic has to guess whether "J. Martinez, Internal Medicine, Suite 400" and "Julia Martinez-Reyes, IM" are the same human. Get it wrong in either direction and the user suffers: duplicates erode trust, while over-aggressive merging attaches one doctor's malpractice history to a stranger with a similar name. If you have ever built a CRM deduplication service or wrestled with customer identity graphs, you have fought this exact battle — just with lower stakes. Staleness compounds the problem. Physicians change practices constantly. A directory that syncs quarterly is, by definition, wrong about a meaningful slice of its records at any given moment. Harvard Health has pointed out that an ongoing physician sh

Stop Waiting. Start Building. For a long time, I believed I needed to be "ready." Ready with a stronger portfolio. Ready with more skills. Ready with a clearer career plan. So I waited. Eventually I realized something uncomfortable: Confidence isn't built before you start. It's built because you start. I'm an AI & Data Science graduate figuring things out in public. Right now I'm: Learning AI beyond what college taught me. Building projects that solve real problems. Applying for internships and jobs. Documenting everything I learn. I don't have all the answers. But I do have curiosity. This blog is my commitment to consistency over perfection. I'll be sharing projects, mistakes, things I learn, career experiments, and ideas that hopefully help someone else who's also trying to figure things out. If you're also building before you feel ready Welcome. 🚀 Let's learn together. What was the project that finally pushed you to stop waiting?

Hands-On tests with 'UserHarbor' and IBM Bob: A Modular Approach to Python User Authentication and Permissions Introduction When evaluating open-source libraries for core application infrastructure - such as authentication, session management, and fine-grained Role-Based Access Control (RBAC) - getting hands-on with a complete reference application is invaluable. This is especially true for libraries aiming to be framework-agnostic, promising flexibility but requiring more explicit wiring. Recently, I wanted to explore UserHarbor ( github.com/userharbor/userharbor ), a lightweight Python user-management library designed without direct coupling to any web framework or database toolkit. Rather than manually bootstrapping a new project, setting up the SQLite database, and writing boilerplate code to explore every edge of the library, I used IBM Bob , to scaffold and implement an end-to-end reference demonstration integrated with FastAPI , SQLAlchemy for persistence, and SMTP (or a local console fallback) for transactional emails. The goal was to rapidly test UserHarbor's entire feature lifecycle - registration, email verification, session tokens, optional authentication, RBAC guards, password resets, and account deletion, which I personally find really useful. These capacities could be implemented in many applications and ease the phase of user registration, email validation etc… This post details the architecture built, highlights the key implementation logic, and illustrates how easily a decoupled core can be integrated into a modern web stack. UserHabor (from official GitHub repostory) Image from official project's repository Project status: UserHarbor is currently in an early stage of development. The API may change frequently. The library is not ready for production use yet . UserHarbor is a framework-agnostic Python library for user account management. Its goal is to provide a simple, stable, and framework-independent interface for common user-related operations: