The right-wing think tank is actively pushing “civil terrorism”—increasing penalties for minor crimes committed while people engage in constitutionally protected free speech.

Introduction Managed Kubernetes platforms such as Amazon EKS, Google Kubernetes Engine (GKE), and Azure Kubernetes Service (AKS) abstract away much of the operational complexity involved in running Kubernetes clusters. While this significantly improves developer productivity, it also hides many of the internal systems responsible for cluster orchestration, networking, node registration, and workload scheduling. As a result, many engineers interact with Kubernetes daily without fully understanding the components that keep a cluster operational behind the scenes. To better understand Kubernetes from an operational perspective, I set out to build and operate a self-managed Kubernetes platform on AWS using kubeadm. Unlike lightweight local environments such as Minikube or kind, kubeadm bootstraps Kubernetes in a way that closely resembles how real-world self-managed clusters are provisioned and operated. The objective of this project was not simply to install Kubernetes, but to explore: How the control plane components interact. How worker nodes register with the cluster. How Kubernetes networking behaves. How cloud integrations work. How traffic reaches workloads running inside the cluster. How operational failures surface during deployment and runtime. How production-style systems behave beneath managed abstractions. This article documents the architecture, implementation process, engineering decisions, operational lessons, and troubleshooting insights encountered during the effort to bring the platform to a healthy operational state. Project Objectives The primary objectives of this project were to: Provision infrastructure on AWS using Terraform. Bootstrap a self-managed Kubernetes cluster using kubeadm. Configure Kubernetes networking using Calico. Integrate Gateway API with AWS Load Balancer Controller. Expose workloads externally using AWS Application Load Balancers. Validate cluster functionality through application deployment. Understand the operational mechani

This is a submission for the GitHub Finish-Up-A-Thon Challenge The Project That Got Away Three months ago, I started building something I was genuinely excited about: devto-mcp — a Model Context Protocol (MCP) server that would let AI agents interact with Dev.to's API natively. No more cobbling together curl commands. No more writing custom wrapper scripts for every AI tool. Just a clean, standards-compliant MCP server that any AI agent could plug into. I had a vision: an AI agent that could autonomously research trending topics, draft articles, publish them, track engagement, and iterate — all through a single protocol. The kind of thing that sounds simple until you actually sit down to build it. I got about 40% of the way through. Then life happened. A client project deadline. A cross-country move. A laptop that decided to corrupt its SSD at the worst possible time. The repo sat there on GitHub, collecting digital dust, with half-implemented tool functions and a README that promised way more than the code delivered. Sound familiar? If you've been a developer for more than a year, you have at least one of these ghost repos. That ambitious side project you were so sure you'd finish "next weekend." The one with the clever name and the detailed architecture doc but barely functional code. Two weeks ago, I saw the GitHub Finish-Up-A-Thon announcement. I looked at my list of abandoned repos. And I thought: it's time. What I Built: devto-mcp devto-mcp is a Model Context Protocol server that exposes Dev.to's entire API as MCP-compatible tools. If you're not familiar with MCP, it's the protocol that lets AI assistants like Claude, Cursor, and other coding agents interact with external tools in a standardized way. Think of it as a universal adapter between AI models and the services developers actually use. Here's the problem it solves: Every time you want an AI agent to interact with Dev.to — whether it's searching for articles, publishing a post, checking analytics, or ma

There is a version of organizational life that feels, from inside, like maturity. Meetings reach conclusions. Decisions move forward without extended debate. The leadership team operates with visible coherence. Escalations are rare. When concerns are raised, they are quickly absorbed into the existing framework and resolved without disruption. Everything functions exactly as designed. That is what makes it difficult to notice that something essential has stopped occurring. The more stable the system appears, the more completely it has eliminated the conditions under which instability would be visible. The Epistemic Function of Conflict Conflict in organizations is not primarily a social problem. It is an epistemic mechanism. When a decision is challenged, something precise occurs: the decision's internal logic is forced into the open. Its assumptions are made explicit. Its evidence is tested against contrary interpretation. The challenge does not guarantee a better outcome — but it generates information the unchallenged decision never produces. Conflict is not disruption of the system. It is how the system verifies itself against reality. Remove the disagreement, and the system continues deciding. It simply stops testing whether its decisions are sound. The absence of challenge feels like confidence. It is blindness — a blindness that is, from inside, indistinguishable from clarity. What Fills the Vacuum When legitimate conflict disappears, the space does not remain empty. It fills with the performance of conflict. Meetings still contain discussion. Questions are still asked. Concerns are occasionally raised. But the texture has changed in ways that experienced practitioners feel before they can articulate. Questions are asked to signal engagement rather than to probe assumptions. Concerns are framed to demonstrate awareness rather than to force resolution. Debate occurs within the boundaries of what the system has already decided is acceptable to debate. The ritual

Server-side tracking on Shopify Plus is no longer optional in 2026. Browser-side analytics tags now miss around 30-40% of conversion events on Safari, Firefox, and ad-blocked sessions when ITP, consent rejection, and ad-blockers combine, and the server-side fix — a GTM server container or an equivalent gateway — is the difference between a usable Meta CAPI feed and a reporting hole that quietly tanks your paid-media ROAS. Why browser-side pixels broke first The structural decay started years ago and accelerated through 2025. Safari's Intelligent Tracking Prevention caps JavaScript-set first-party cookies (anything set via document.cookie ) at 7 days, and 24 hours when the URL carries a tracking parameter like fbclid or gclid . Server-set first-party cookies sent via the HTTPS Set-Cookie header can still persist up to 400 days, unless the cookie's host resolves through a CNAME to a third-party — then ITP collapses that lifetime back to 7 days. Combine that with Firefox Enhanced Tracking Protection (around 5-8% of UK desktop traffic), ad-blockers (around 30-35% adoption on desktop), and consent-management platform rejection (typically 20-40% of EU sessions), and a typical Shopify Plus storefront ships measurable signal for only 60-70% of real purchase events. We have audited stores where a server-side migration recovered around 28% of attributed purchases inside the first 7 days of switchover — not because the conversions stopped happening, but because the browser layer stopped reliably reporting them. What a server-side gateway actually does A server-side tracking gateway intercepts the event between the storefront and the destination platform (Meta, Google Ads, TikTok, etc.) and re-emits it from your domain. The browser still fires a lightweight web-side ping, but the heavy payload — order ID, customer hash, line items, value — travels server-to-server. Cookies stay first-party because the request originates from your own subdomain. The destination platform sees a c

The problem Every time I started a session with Claude Code I had to re-explain my entire project. What framework I use. How my folders are structured. What naming conventions I follow. What decisions I have already made. Every. Single. Session. It was slowing me down and I knew there had to be a better way. What I built I built stackbrief. One command scans your repo and opens a local visual dashboard showing your full codebase intelligence. npx stackbrief scan It opens a dashboard at localhost:3000 showing: Interactive code map of your architecture Dependency version comparison against npm Convention detection (naming, async patterns, error handling) Context health score MCP server so Claude Code pulls context automatically How it works stackbrief reads every file in your project and builds a structured understanding of it. It detects your framework, architecture pattern, modules, dependencies, and coding conventions. It then writes a CLAUDE.md file to your project and starts an MCP server on port 3001. Claude Code picks this up automatically before every session. No more explaining your project from scratch. AI chat that actually knows your code The dashboard has an Ask your codebase section. Unlike generic AI chat, this assistant has read every file in your project. Ask it about your own architecture and get answers specific to your code. Works with Ollama (free, fully local), Claude, OpenAI, or any OpenAI-compatible provider including Groq, Mistral, and local runners like LM Studio and AnythingLLM. Zero config, fully local No cloud. No telemetry. No account required. Everything runs on your machine. npx stackbrief scan That is it. The dashboard opens automatically. Try it GitHub: https://github.com/ragavtech/stackbrief Built with Node.js and TypeScript. Open source, MIT license. Would love to hear what you think.

Stanford Just Published Rules for AI Coding Agents — What Devs Should Know Stanford dropped a document last week that every developer using AI coding tools should read. It's called CLAUDE.md , it's part of CS336 (Language Modeling from Scratch), and it's a brutally honest set of rules for how AI agents should — and shouldn't — help students write code. The document hit #1 on Hacker News for good reason. It doesn't just apply to students. If you use Claude Code, Cursor, Copilot, or any AI coding assistant, these rules expose the uncomfortable gap between what these tools can do and what they should do. GitHub just rolled out token-based billing for Copilot, and developers are furious. The tension is the same: when does AI assistance stop helping and start hurting? The Core Principle: Teaching Assistant, Not Solution Generator Stanford's position is unambiguous: "AI agents should function as teaching aids that help students learn through explanation, guidance, and feedback — not by completing assignments for them." This isn't academic hand-wringing. It's a design constraint that maps directly to professional development. The same agent that writes your PR in 30 seconds is also the one that leaves you unable to debug it when it breaks at 2 AM. The AI agent role framework from Stanford's CS336 guidelines: teaching assistant vs solution generator The document draws a hard line: What agents SHOULD do: Explain concepts by guiding toward understanding Review your code and point out areas for improvement Ask guiding questions instead of giving fixes Reference documentation, lectures, and debugging tools Suggest sanity checks, assertions, and profiler investigations What agents SHOULD NOT do: Write any Python or pseudocode Complete TODO sections in assignments Give solutions to problems Edit code in the student repo Convert requirements directly into working code Point to third-party implementations If you're a professional developer, the "SHOULD NOT" list probably looks extr

Original post: GitHub Copilot for Engineers: Getting Better Results GitHub Copilot moved to usage-based billing in June 2026, dropping the flat subscription model that made monthly costs predictable. For teams using it heavily across multiple projects, that shift puts a premium on being deliberate: reaching for the right model, keeping prompts focused, and building a configuration that produces good results without a lot of back-and-forth iteration. Many of us install the extension, start with the defaults, and only tune settings later. The defaults are a reasonable starting point, but they are not a full configuration. A small investment in setup changes how much you get out of every request on an ordinary working day, and that matters more now that each request has a cost attached. This guide covers the full path: getting the tooling in place, choosing models with cost in mind, layering global and project-level rules, and building out instructions, agents, and skills that make Copilot predictable across different kinds of work. Architecture overview Diagram fallback for Dev.to. View the canonical article for the full version: https://sourcier.uk/blog/github-copilot-for-engineers Before you start Subscription and VS Code extension You need an active GitHub Copilot subscription. Plans are available at individual, business, and enterprise tiers at github.com/features/copilot . Once active, all tools use your GitHub account credentials. The GitHub Copilot extension for VS Code is the primary day-to-day interface. Install it from the Extensions panel or via the CLI: code --install-extension GitHub.copilot The extension provides inline completions as you type, Copilot Chat in the sidebar, inline chat on any selection via Cmd+I / Ctrl+I , agent mode for multi-step tasks, and multi-file edits with a single review step. Defaults keep improving, so avoid cargo-culting old setting lists. Focus on non-default tweaks that improve signal quality and control usage: Setting Value

File manager for Linux homelab and NAS-style servers Discussion | Link

I learned this the hard way: when an LLM says “it matches the docs”, it can still be wrong for a boring reason—it didn’t read the part that matters. I’m building a small SaaS (checklists as a service). No users yet. Plenty of documentation already. And at some point my docs stopped being an asset and started turning into a liability. This is the story of how I rebuilt my documentation so that an LLM could actually read it end-to-end —and how that restructure helped me. The moment I got scared: “silent misses” The docset grew. I kept asking the LLM to verify tasks against it. And then I noticed a pattern that felt worse than hallucinations. Not “the model invented stuff”, but “the model confidently said it matches ”—while quietly missing exceptions, prohibitions, and thresholds. Keyword scanning instead of reading. I called it silent drift : code slowly moves away from conventions, while the invariants remain only in my head. In a project with roles, audit, and CI/CD security gates, that kind of drift isn’t “just messy docs”. It’s how you lose the ability to implement and review changes consistently. I couldn’t do it manually (and I couldn’t delegate it fully) I knew I had to redo the documentation. But I also knew I couldn’t realistically do it all by hand. At the same time, I couldn’t just tell an LLM: “Rewrite everything according to approach X.” Not enough context, too easy to lose control. So I went with a third option: build a reliable process out of unreliable components— me + an LLM . Step 1: I separated my docs into domains (and forced the model to actually read) First, I extracted domain areas from the old documentation—the vocabulary I was using to describe the project and its parts. I tried to keep domains mutually independent (so the overall framework stays holdable in my head). Then I ran the same loop for each domain: I asked the LLM to read all old docs carefully and extract requirements for that domain. I moved those requirements into a dedicated fil

AI applications are generating more data than ever before. From model telemetry and user interactions to observability events and real-time analytics, modern systems need infrastructure that can ingest, process, and query massive datasets with low latency. That's exactly the problem ClickHouse is targeting with its latest release. The update introduces improvements across query performance, memory management, Kafka integration, lakehouse support, and developer tooling. While many of these changes appear incremental on the surface, together they highlight a much larger shift happening across the industry. One of the most notable additions is improved memory management for large joins. ClickHouse can now automatically spill hash joins to disk when memory usage exceeds configured thresholds. Instead of failing due to memory pressure, queries can continue running using more efficient execution strategies. For teams working with large feature tables, event enrichment, AI telemetry, or observability data, this can significantly improve reliability. The release also expands ClickHouse's Kafka capabilities with Schema Registry integration, AvroConfluent write support, metadata mapping, and zone-aware communication. These improvements make it easier to integrate ClickHouse into real-time event pipelines while reducing latency and unnecessary cross-zone traffic in cloud environments. Another major focus is support for modern lakehouse architectures. Improvements for Apache Iceberg and Apache Paimon strengthen ClickHouse's ability to query data stored in open table formats while maintaining high analytical performance. As more organizations separate storage and compute, ClickHouse is increasingly positioning itself as a high-speed query layer on top of cloud-native data lakes. Performance optimization remains a major theme throughout the release. Improvements include faster JOIN execution, better ORDER BY LIMIT performance, enhanced JSON processing, smarter index pruning, redu

A linked list is an ordered linear data structure where elements are not stored in sequential memory locations, instead they are stored in nodes that are linked together by a pointers. Linked list are used in data intensive application because linked list offer specific benefits for high frequency data manipulation, this benefits include: Efficient insertion and deletion Adding and removing elements from a linked list is highly efficient, unlike arrays which requires shifting all subsequent elements to maintain indexing. A linked list only requires updating the pointer. Dynamic sizing: linked list can grow or shrink during runtime without needing to pre-allocate memory. Memory management: Nodes in a linked list are only allocated when needed which prevents memory wastage. Flexible Traversal: Doubly and circular list allow you to move forward or backward, which makes them helpful for complex navigation The first node in a linked list is called the head which signifies the start of the list, while the last node is called the tail and has a pointer of null except in a circular linked list. Each node in a linked list has two things which are: the actual data the pointer or reference There are three main types of linked list: Singly linked list Doubly linked list Circular linked list Singly Linked List: Singly linked list are lists where each node has a next pointer that points to the next node. Doubly Linked List: Doubly linked list are list where each node has a next and previous pointer that points to the previous and next node. Circular Linked List: Circular linked list are list where the last node points back to the first node, forming a circle. Table of Contents create node class create linked list class isEmpty and getSize Methods prepend and append Method removeHead and removeTail Methods insert and search Methods getIndex and removeIndex Methods clear and print Methods create node class First let's open our code editor and create a new file called singlyLinkedLi

Keeping up with trending technical topics and new tools on developer forums can be time-consuming. To save time, I wanted to automate the process of finding popular articles, reading the comments to understand community sentiment, and drafting a summary. While I could write a standard Python script to scrape the dev.to API, simple scripts tend to be brittle. If an article doesn't have comments yet, a basic script will likely crash unless you write extensive error-handling logic. Instead of a rigid script, I built an Agent —a program that can dynamically reason about errors and adjust its approach. If one task fails, it can figure out the next best step. In this tutorial, I'll show you how to build a Trend-Spotting Agent using Python, the Google Agent Development Kit (ADK) , and Gemini 2.5 Flash. What We're Building We are going to write a Python application that acts as an autonomous agent. We'll give it three abilities: Search the dev.to API for rising technical articles based on specific tags. Dynamically fetch the top comments of those articles to read real community sentiment. Automatically draft a newsletter-style article on your DEV.to account summarizing its findings. Prerequisites Python 3.9+ installed on your machine. Google ADK . (Check out the Google ADK Docs if you need help installing). A DEV API Key . Grab this from your DEV.to account settings under "Extensions" and throw it in a .env file. Step 1: Giving the Agent its "Hands" (API Tools) Large Language Models (LLMs) are incredibly smart, but out of the box, they can't actually do anything on your computer. The coolest part about Google ADK is that we can write standard Python functions, hand them to the LLM as "tools", and let the AI decide how and when to use them. Let's write our API functions. Tool 1: Finding Rising Articles Here is our function to fetch rising articles. Pay close attention to the docstring ( """Fetches the top...""" ). We aren't writing this for other developers; the ADK actually

Stop Shipping 20 Locale Files in React Native: On-Device Translation for Dynamic Language Packs Internationalization in mobile apps usually starts clean and then gets expensive. At first, you keep a couple of JSON files: en.json es.json fr.json That works when your product is small and the set of languages is stable. It breaks down when: you want to support many languages the product team keeps changing copy translated files drift out of sync some languages are only partially used you do not want to run every string through a server-side translation pipeline This is the problem @tcbs/react-native-language-translator is trying to solve. It lets a React Native app keep a source language, translate missing keys on device, and cache the generated language pack locally. Package: @tcbs/react-native-language-translator The problem Many React Native apps treat localization as a static asset problem: keep one JSON file per language ship all of them in the app update all of them whenever English changes That model has real costs. 1. Translation files become operational debt Every new feature adds more keys. Every copy change forces translators to update multiple locale files. Over time, the translation layer becomes a maintenance queue. The result is predictable: missing keys stale translations untranslated fallback strings inconsistent release quality across languages 2. Shipping many locales is wasteful Most users only need one target language. But many apps ship every locale anyway. That increases bundle size and creates a lot of dead weight for users who will never use most of those files. 3. Dynamic product copy is hard to localize well If your app changes quickly, static translation files lag behind. Teams either accept stale translations or build a backend workflow to keep everything synchronized. That is often more infrastructure than the app actually needs. 4. Server-side translation is not always the right tradeoff Calling a translation API at runtime introduces: la

Teach your AI to build document templates Discussion | Link

presenting a poster there, and have registration covered. but they are placing me on waitlist for travel funds. As my travel depends on whether I get the travel grant, I need to get this off of my mind, either invite me or just say no. I'm waiting forever for this, more wait again? should i ask for a decision, or what to do. submitted by /u/Active-Tip3130 [link] [留言]