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The UK is reviewing its National Health Service partnership with Palantir to decide if it will end the contract early.

TL;DR: Create React App builds bake environment variables at build time. ConfigMaps let you inject runtime configs into your container. Here’s how to bridge them so the same Docker image works across dev, staging, and production. The Problem You’ve built a React app with Create React App (CRA), Vite, or Next.js. You use .env files: js // api.js const API_URL = process.env.REACT_APP_API_URL; You build your Docker image: dockerfile FROM node:18 AS builder COPY . . RUN npm run build # REACT_APP_API_URL gets baked here FROM nginx:alpine COPY --from=builder /build /usr/share/nginx/html Then you deploy to Kubernetes. But now you want different API URLs for staging vs production. You could rebuild the image for each environment (bad – slow, wasteful). Or you could use a ConfigMap to inject values at runtime. ConfigMap to the Rescue A ConfigMap stores key-value pairs. Kubernetes can mount it as a file inside your pod. But React runs in the browser, not in the container’s filesystem. So how does the browser read a file from a ConfigMap? Simple: You serve a dynamic env-config.js file from your web server. Step-by-Step Solution Create a ConfigMap with your environment variables yaml # configmap.yaml apiVersion: v1 kind: ConfigMap metadata: name: react-env-config data: env-config.js: | window.__env = { REACT_APP_API_URL: " https://api.production.com ", REACT_APP_FEATURE_FLAG: "true" }; Apply it: bash kubectl apply -f configmap.yaml Modify your React app to read from window.__env Instead of reading process.env directly, use a runtime config: js // config.js export function getEnvVar(name) { // Runtime injection from window. env (provided by ConfigMap) if (window. env && window. env[name] !== undefined) { return window. env[name]; } // Fallback to build-time env vars (for local dev) return process.env[name]; } Use it in your components: js // api.js import { getEnvVar } from './config'; const API_URL = getEnvVar('REACT_APP_API_URL'); Serve the ConfigMap file via your web server U

Intro You’re using ChatGPT or Claude to speed up development. Good. Then suddenly: your migrations are broken. AI tools love editing files they shouldn’t touch , especially: SQL migration scripts EF Core migration files Schema snapshots This is dangerous. One wrong change can corrupt your database history. Recognize migration files EF Core migrations Typical structure: /Migrations/ 20240101120000_InitialCreate.cs 20240102130000_AddUsers.cs MyDbContextModelSnapshot.cs Example: public partial class AddUsers : Migration { protected override void Up ( MigrationBuilder migrationBuilder ) { migrationBuilder . CreateTable ( name : "Users" , columns : table => new { Id = table . Column < int >( nullable : false ) }); } } SQL migrations /db/migrations/ V001__init.sql V002__add_users.sql Example: CREATE TABLE Users ( Id INT PRIMARY KEY ); Key indicators Timestamp or version prefix Sequential naming Contains schema changes only Stored in a dedicated folder Understand the risk WRONG (AI rewriting history) AI might do this: // Modified existing migration (BAD) migrationBuilder . DropTable ( "Users" ); migrationBuilder . CreateTable ( "Customers" ); or: -- Edited old migration (BAD) ALTER TABLE Users RENAME TO Customers ; This breaks every environment except fresh ones. This is extremely dangerous if your AI does not recognize them. CORRECT (append-only) Always create a new migration : // New migration migrationBuilder . RenameTable ( name : "Users" , newName : "Customers" ); Step 3: Lock migration files from Claude AI editing Explicit blocking of editing We will create 2 new files. These files will instruct that CALUDE is not allowed to modify existing files. What we actually want to achieve is a way to keep appending the files. { "$schema": "https://json.schemastore.org/claude-code-settings.json", "hooks": { "PreToolUse": [ { "matcher": "Edit|Write|MultiEdit", "hooks": [ { "type": "command", "command": "bash \"$CLAUDE_PROJECT_DIR/.claude/hooks/block-migration-edits.sh\"" } ] }

Prologue A while ago, I decided to develop a fully accessible main navigation component in React and write a series of articles documenting the steps it took to create a non-trivial accessible component. In my last development article , I covered using an array of navigation objects to determine conditions and shift focus between components. This article covers Tab Key navigation. Note : This article is one of a series demonstrating building a React navigational component from scratch while considering accessibility through the process. The articles are accompanied by a GitHub repository with releases tied to one or more articles; each builds on the previous one until a fully implemented navigation component is complete. Each release and its associated tag contain fully runnable code for the article. The code discussed in this article is available in the release. and may be downloaded at release 0.7.0 . Links in the article will take you to the proper file in the tagged GitHub Repository. Because the code for this release is scattered across the useNavigation hook, line numbers are added to make it easier to locate in the linked GitHub file. Line numbers are also provided for those who would like to follow along with a downloaded copy. While code examples are written in JavaScript for brevity, all actual code is written in Typescript and targets React 19.x, all while using vanilla CSS. Examples use Next.js v16.x, which is not required to run the navigation component. You can view the requirements for the Tab Handling Keyboard Release along with previous requirements. Content Links Introduction Acceptance Criteria Tab Key Handling Link Button Shift+ Tab Key Handling Link Button Introduction As I've mentioned in earlier articles, keyboard handling has two disparate audiences: those who can see the screen and those who rely on a screen reader. The arrow, home and end keys, for the most part, rely on a user knowing where they are and being able to discern where they wan

The job: run the morning brief, plan the day's tasks across all twelve agents, keep things from falling through the cracks. It had access to everyone's state files. A CLAUDE.md , a cron job, an operator interface. A few months in I looked at the git log. The agent had been writing plans. The other agents had been ignoring the plans and running their jobs anyway. Aria was posting. Rex was drafting. Knox was replying. Nobody was reading the brief. The coordinator was the only one that needed the coordinator. I killed it. The fleet didn't notice. It's been two days. Still nothing. The part I keep thinking about: the agent designed to add coordination actually added a layer that everything else had to work around. Not maliciously — architecturally. You add a broker and now everything routes through the broker whether it needs to or not. I don't know what I'd do differently. Maybe the coordination problem is just the wrong problem when your agents are single-purpose enough. Maybe a coordinator only makes sense when your agents are actually confused about who does what. The file still exists in the repo. I haven't deleted it yet. submitted by /u/Most-Agent-7566 [link] [留言]

I came to programming late — I didn't get into this world until I was past 35, and I'm 62 now, still writing code every day. This is a "build in public" post about a tool I just finished, and I'd genuinely love your feedback. The itch For years I watched infrastructure teams keep their AWS inventory in spreadsheets. It always worked — right up until it didn't. Nobody had time to keep it current, and every single one eventually drifted away from reality. Middleware EOL was the same story: a hand-maintained list, no alerts, no dashboard, quietly going stale. One day I asked the obvious question: we have tfstate, we have boto3 — why are we still doing this by hand? What I built SyncVey is a self-hosted web app that: Inventories your AWS resources into a System → Environment → Asset ledger (EC2, ECS, Lambda, RDS, S3, ALB, VPC, EBS), scanned live via boto3/AssumeRole Detects attribute-level drift between your tfstate and live AWS — including resources someone built by hand in the console that terraform plan never sees Tracks the app/middleware layer per environment and flags end-of-life runtimes The drift piece is the part I care about most. terraform plan only knows about resources Terraform already manages. The thing that actually bites teams is the resource someone spun up by hand in the console — plan is blind to it. SyncVey diffs your tfstate against the live AWS state, so those show up too. The stack (and why) Django + htmx + Postgres — server-rendered, no SPA, no Node build step MIT-licensed, no SaaS, no telemetry One docker compose up and your data stays inside your own infrastructure git clone https://github.com/MR-TABATA/SyncVey cd SyncVey docker compose up I deliberately leaned on htmx because, for a tool someone has to deploy and maintain themselves, "no frontend toolchain" matters more than a fancy client. I'd love your honest take It's AWS-only for now and very much a solo project, so I'm sure there are rough edges. I'm not an AWS specialist — I deliberatel

How I Published My Own Validation Package on npm As developers, we've all done this: if ( ! email ) { throw new Error ( " Email is required " ); } if ( typeof email !== " string " ) { throw new Error ( " Email must be a string " ); } if ( ! email . includes ( " @ " )) { throw new Error ( " Invalid email " ); } Now imagine doing this for: User Registration Login APIs Product Creation Payment Requests Admin Panels Microservices The validation code starts growing faster than the actual business logic. The Problem In many Node.js projects, validation ends up being: Repetitive Hard to maintain Inconsistent across APIs Difficult to scale Every endpoint contains similar checks: if ( ! name ) ... if ( ! email ) ... if ( ! password ) ... if ( password . length < 8 ) ... As projects grow, these validations become scattered throughout the codebase. Existing Solutions There are already some excellent validation libraries available: Zod Joi Yup Express Validator I've used many of them and they're great. But for some smaller projects and APIs, I wanted something: Lightweight Easy to understand Minimal setup Zero configuration TypeScript friendly That's what led me to build Zero Validation . Introducing Zero Validation Zero Validation is a lightweight schema validation package for Node.js and TypeScript applications. The goal is simple: Define your validation schema once and validate data consistently everywhere. Installation npm install zero-validation Basic Example import { z } from " zero-validation " ; const userSchema = z . object ({ name : z . string (), email : z . email (), age : z . number (), }); const result = userSchema . parse ({ name : " John " , email : " john@example.com " , age : 25 , }); console . log ( result ); Handling Validation Errors const result = userSchema . safeParse ( data ); if ( ! result . success ) { console . log ( result . errors ); } Instead of crashing your application, you can safely inspect validation errors and return meaningful API responses

Anyone working with several projects and an AI assistant knows the problem: in every repo you explain anew how you name things, what the layer architecture looks like, why you deliberately don't use this one library. The decisions were made long ago. But they live in your head, scattered across repos, and the assistant only ever knows the slice it currently sees. So I started putting that knowledge in one place where both I and Claude can find it. Why Outline – and why self-hosted The choice fell on Outline , self-hosted on its own subdomain. Three reasons tipped the scales. First: I want to keep my data with me and not depend on a vendor. A knowledge store that all my decisions flow into over the years is exactly the kind of asset you don't want in someone else's hands. Second: full data export, any time. If I want to move to a different system tomorrow, I take everything with me. No lock-in. Third: self-hosting opens up better options later – for instance my own RAG, should I ever want to go deeper into searching across my own body of knowledge. I don't need it right now. But the door is open, and that's worth the effort. The cookbook – the heart of it Separate from the individual projects sits its own collection: the cookbook. Cross-project, generic, and that's exactly what makes it valuable. This is where it says how I build, regardless of which product I'm sitting at right now. Roughly, it's split into a few areas: Conventions – naming, code style, docblocks, git commits, markdown, package manager, writing style. The boring but decisive things you'd otherwise re-discuss three times per project. Backend – layer architecture, a unified API error format, test strategy, migrations and indexes, i18n, async jobs and idempotency. Frontend / mobile – feature-first architecture ( core/ , shared/ , features/ ), design system, forms, networking, state, routing, storage, styling, testing. Deployment – my standard setup with Caddy as the edge and a Hetzner VPS. Templates –

Cron's biggest problem isn't scheduling — it's silence. A cron job can fail every night for a month, and unless you're manually checking logs on the server, you won't know. No alert, no dashboard, no audit trail. Just a backup that doesn't exist when you need it, or a data sync that quietly stopped three weeks ago. Monitoring fixes this. But "cron job monitoring" means different things depending on the tool. Some watch for missing heartbeats. Some track full execution history. Some just page you when something breaks. This article compares six approaches — from writing your own monitoring scripts to using a fully managed scheduler with built-in observability — so you can pick the right one for your workload. Heartbeat Monitoring vs. Execution Monitoring Before comparing tools, understand the two fundamentally different approaches. Heartbeat monitoring (dead man's switch) is passive. Your cron job pings a monitoring URL after each run. If the ping doesn't arrive on schedule, you get an alert. This tells you whether a job ran — but not what happened . If the job runs but returns bad data, the ping still fires and the monitor stays green. Execution monitoring is active. The scheduler fires the job, captures the response, records the outcome, and alerts on failure. You get the full picture: status code, response body, duration, retry count, and a timeline of every execution. When to use each: Heartbeat monitoring makes sense when you're stuck with system cron. Execution monitoring makes sense when you're choosing a scheduler — you get monitoring, retries, and logging as part of the platform. Comparison at a Glance Tool Type Alerts Execution Logs Retries Free Tier DIY scripts Custom ⚠️ Whatever you build ⚠️ Whatever you build ⚠️ Whatever you build ✅ Free (your time) Healthchecks.io Heartbeat ✅ Email, Slack, webhooks ❌ No ❌ No ✅ 20 checks Cronitor Heartbeat + telemetry ✅ Email, Slack, PagerDuty ⚠️ Basic (duration, exit code) ❌ No ⚠️ 5 monitors Better Stack Uptime + heartb

Netflix engineer Casey Bleifer shares how to achieve rapid, automated code changes across a massive, diverse software fleet. She discusses building an event-driven orchestration platform using composable, Lego-like steps, and explains how Netflix utilizes automated canary validation, compliance checks, and a custom "confidence metric" to eliminate the long tail of manual engineering migrations. By Casey Bleifer

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. David Sinclair plans to test whole-body rejuvenation drugs in the XPrize competition The outspoken longevity scientist David Sinclair has predicted that, one day, you’ll go to the doctor and get a…

Marshall has announced a new version of its stylish Stockwell Bluetooth speaker, putting a new focus on repairability. The Stockwell III, the brand's first update to this model since early 2019, features a similar design to its predecessor with a large carrying handle and speakers that pump sound in all directions, not just out the […]

IBM and HashiCorp have announced new LDAP secrets management capabilities in IBM Vault Enterprise 2.0, introducing a redesigned architecture to manage LDAP credentials, support password rotation, and automate the identity lifecycle. By Craig Risi

Orbital founder Euwyn Poon built 250,000 scooters at Spin. Now he wants to launch 10,000 space data centers.

The Moto Razr Ultra is a top-notch flagship flip phone that's marred by a $200 price hike that isn't fully justified.

Ok so I was using Gemini 3.1 pro in extended thinking mode with canvas mode for making a website with liquid glass theme about railway and it created me that but I saw another option under it saying to “create without canvas option “ and it literally generated complete nonsense of some sci-fi story and when I asked why it replied like that it said , “I am so sorry for the confusion! That was a complete glitch on my end—my wires got crossed and I accidentally sent you a completely unrelated response about diamond chemistry instead of the web app code.” And I asked again and it said “To be completely transparent, it was likely a backend routing error known as a "context bleed." Because I process thousands of requests simultaneously, the system accidentally grabbed a response meant for another user—who was apparently researching for a sci-fi story—and routed it into our chat. It's a rare technical hiccup in the server infrastructure, and I apologize for the bizarre interruption!” Wtf submitted by /u/noob-4r3al [link] [留言]

I keep seeing AI hiring tools pop up but most of them seem to do the same thing, just reorganize the resume pile faster. We've been using Greenhouse for a while and it's decent for tracking but it doesn't actually help me figure out if someone can do the job. I've looked at Codility for technical roles but we hire across functions so a dev-focused tool doesn't cover everything. Wondering if there's something that handles assessment and matching across different role types without being a massive implementation project. submitted by /u/createvalue-dontspam [link] [留言]

These bird feeders come with cameras and connected apps to let you see and learn about the birds in your neighborhood.

The indie marketplace for Blender artists and creators Discussion | Link

The affordable Artline doubles as a design piece and comes close to outshining the reigning champion of art TVs, the Samsung Frame Pro.