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Why AdGuard Home Overtook Pi-hole Last month, while attempting to add ad filtering to the internal network of a production ERP system, the Pi-hole configuration escalated to 85% CPU usage within an hour, causing DNS responses to lag. AdGuard Home resolved the same scenario with 3% CPU and an average latency of 15 ms , which is why it has dethroned Pi-hole. In the following sections, I detail the architecture, performance, security features, and my real-world deployment experience with both products. While they may seem similar at first glance, the fundamental differences directly impact network stability and management overhead. How AdGuard Home Works AdGuard Home is designed as a fully modular DNS forwarder that supports DNS‑over‑HTTPS (DoH) and DNS‑over‑TLS (DoT). Clients first send queries over 53 UDP/TCP or 443 DoH; AdGuard caches the query, checks it against local blacklists, and then forwards it to an upstream DNS service based on preference. # /etc/AdGuardHome.yaml (partial) bind_host : 0.0.0.0 bind_port : 53 upstream_dns : - https://1.1.1.1/dns-query - https://9.9.9.9/dns-query blocking_mode : default blocked_response_ttl : 300 $ dig @127.0.0.1 example.com +short 93.184.216.34 $ curl -s -H "Accept: application/dns-json" "https://adguard.example/dns-query?name=ads.google.com&type=A" | jq . { "Status" : 0, "Answer" : [] , "Question" : [ { "name" : "ads.google.com." , "type" : 1 } ] } Why is it so fast? Cache-first strategy : The initial query goes to an upstream DNS, but subsequent identical domains are returned directly from RAM cache. Parallel upstreams : Since multiple DoH endpoints are tried concurrently, the primary response time drops to an average of 15 ms. Advanced blocklist engine : Thanks to a combination of regex-based filtering and Bloom filters, thousands of ad domains are eliminated in a single query. This architecture, when running as a systemd-based service, shows only 12 ms CPU consumption in systemd-analyze blame output; Pi-hole showed 150 ms

Sean Klein discusses why "human error" is a dangerous myth in complex systems. Sharing the inside story of Azure’s 2023 global WAN outage, he explains how modern incident analysis looks past the "Five Whys" to uncover systemic issues. Learn how engineering leaders can move away from blame, improve Standard Operating Procedures, and design resilient systems that actively protect their engineers. By Sean Klein

I was convinced these devices were just clutter. I was proven wrong.

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening in real time. By the time the "X gained 3 million followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve as it happens. Here's how it works. Why the official APIs were a dead end My first instinct was to do this "properly" with official APIs. That died fast: Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's research API is academics-only and takes weeks of applications. X's API now starts at $100/month. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using SociaVault , which wraps the public profile data from each platform behind one API and one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? dat

Amflow, the e-bike brand spun out of DJI, just announced its TL series, a do-it-all "eSUV" suitable for both bikepacking adventures and dropping the kid at daycare on your cycle to work. The all-terrain TL series is built around Amflow's incredibly compact yet powerful Avinox M2 mid-drive motor. The Amflow TL Carbon offers 125Nm of […]

“Coffee” made with functional mushrooms like lion’s mane and chaga is all the rage. We tried the most popular brands to find which were the most palatable.

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening. By the time the "X gained 3M followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve in near real-time. Here's how it works. The problem with doing this "properly" My first instinct was the official APIs. That died fast. Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's Research API is academics-only and takes weeks of applications. X's API now starts at $100/month and climbs steeply from there. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using the SociaVault API , which wraps public profile data from each platform behind one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? data . data ?? data

Most organizations know exactly how many employees they have. Far fewer know how many non-human identities currently have access to their cloud environment. That blind spot is becoming one of the fastest-growing attack surfaces in modern security. For years, enterprise security focused primarily on protecting human identities. We deployed Single Sign-On (SSO), enforced Multi-Factor Authentication (MFA), and implemented Conditional Access policies. And it worked — human identities have become significantly harder to compromise. Meanwhile, another class of identities has quietly exploded across cloud environments: service principals, workload identities, OAuth applications, CI/CD runners, and AI service roles. Today, these Non-Human Identities (NHIs) often outnumber human users by a factor of 10 to 50. As organizations accelerate cloud adoption and integrate AI into daily operations, this imbalance continues to grow. Defining the Non-Human Identity Landscape Unlike human users, machine identities rarely appear in HR systems or organizational charts. Yet they frequently hold some of the most privileged access in the environment. Common high-risk categories include: OAuth Applications and Third-Party Integrations — Apps granted broad access to Microsoft 365, Salesforce, Google Workspace, or Slack via delegated permissions. Service Principals and Managed Identities — AWS IAM roles, Azure Managed Identities, and GCP service accounts used by Lambda functions, EC2 instances, or Bedrock agents. Workload Identities — Kubernetes Service Accounts (e.g., Amazon EKS) and GitHub Actions OIDC roles. CI/CD Pipeline Identities — Tokens used by automation platforms to deploy infrastructure. AI Service Roles — Dedicated identities for Amazon Bedrock agents, model invocation, vector stores, and retrieval pipelines. Every new AI workflow creates additional machine identities. Why Attackers Are Targeting NHIs Attackers follow the path of least resistance. While human accounts are now heav

This is a submission for the June Solstice Game Jam I've been in online queer communities for a long time, and one thing that's always stood out is the endearing obsession with pop culture. The artists, the music, the fashion, the references. Every form of art gets appreciated, deeply analyzed, and celebrated. Diva Academy is an attempt to reflect that energy and honor Pride month and the pop culture that comes with it. What I Built Diva Academy is a pop culture trivia adventure. You play as a fresh face entering a campus where the currency is knowledge. The questions cover everything from ballroom culture and drag history to Beyoncé's discography and the origins of the Pride flag. The game runs in sessions: NPCs challenge you to timed trivia battles. Reach your REP(utation) goal to win, or hit zero and you're out. Earned REP converts to permanent currency between sessions, making it a rogue-lite-lite-lite experience where you gradually get stronger even when you lose. The game is built with vanilla HTML5 Canvas, CSS, and JavaScript. It features: 6 explorable rooms 4 NPC tiers - Starlet , Diva , DJ , and Mother - each with distinct personalities and increasing difficulty A rival system where a recurring NPC named Vex Vivienne spawns across the map and hunts you down A permanent perk system where REP earned in each run converts to permanent currency for buying perks like Grace (forgive one wrong answer), Clutch (survive at 0 REP once), and Haste (extra time on the timer) A Spotlight mechanic - defeat a Diva-tier or higher NPC and you earn a one-time 1.5x REP buff for your next face-off Two minigames - Hangman (guess the pop star name) and Pop Connect (link two artists through a mathematically perfect, AI-grounded collaboration graph with look-ahead validation) The Turing Challenge - Archivist Alan tests your ability to distinguish real pop culture quotes from AI-generated fabrications A customization system that unlocks new dress and hair colors as you defeat NPCs An

Network-attached storage (NAS) provides accessible shared space on your home network. After testing, these are my favorite NAS devices.

I did the research and taste-testing to find the best greens powders worth your money. Bloom Nutrition’s Superfood Greens Powder is my tried-and-true pick.

AWS recently introduced Amazon Cognito multi-region replication, which automatically replicates user identities and user pool configurations from a primary region to a secondary one. This enables applications to continue authenticating users from a replica region during outages, without requiring custom replication and failover mechanisms. By Renato Losio

Two monitoring approaches answer two different questions. Synthetic monitoring answers "would the checkout flow work right now if someone tried it?" Real user monitoring answers "what did the checkout flow actually do for the 4,000 people who tried it today?" The first is a robot testing a path on a schedule; the second is instrumentation recording reality as it happens. Teams reach for one when they need the other, then conclude monitoring "doesn't work." The fix is understanding what each is structurally good at — and where each is blind. Synthetic monitoring: proactive, scripted, continuous Synthetic monitoring runs scripted checks against your application from the outside, on a fixed schedule. An HTTP check hits an endpoint and asserts on the response; a browser check drives a headless Chromium through a journey — log in, add to cart, pay — and asserts on what the user would see. The defining property is that it does not need real traffic. The check runs every 30 seconds whether or not anyone is using the app, from datacenters you choose, testing exactly the journeys you scripted. When a deploy breaks checkout at 3 AM, a synthetic check catches it at 3 AM — not at 9 AM when the first customer wakes up. Real user monitoring: passive, real, traffic-dependent RUM instruments your actual frontend with a JavaScript snippet that reports back what real visitors experience: page load times, Core Web Vitals (LCP, INP, CLS), JavaScript errors, the device and network and geography of every session. It is a recording of reality with perfect fidelity — these are real people, real conditions, real outcomes. The cost of that fidelity is that RUM is entirely traffic-dependent and entirely retrospective. It can only report on paths real users took, after they took them. A page nobody visited generates no RUM data. A broken deploy at 3 AM is invisible to RUM until a real user hits it and the error is recorded. The core difference, side by side Dimension Synthetic monitoring Real

Most synthetic monitoring setups fail in one of a few predictable ways. They monitor everything and alert on nothing useful. They assert on status code 200 and miss the empty response body. They run flaky browser checks that page someone at 2 AM for a problem that fixed itself by 2:01. Or they go stale — the checkout flow changed three months ago and the check has been failing-then-being-ignored ever since. These are not exotic failures. They are the default outcome of setting up synthetic monitoring without a discipline. Here is the discipline. 1. Monitor the journeys that cost money, not everything Every browser check costs compute and, more importantly, costs maintenance. A check on a path that does not matter is worse than no check — it generates noise that trains your team to ignore alerts. Rank your journeys by cost of silent failure and monitor the top of the list: Authentication — login, signup. The gate to everything else. The revenue path — checkout, upgrade, add payment method. The core product action — the one thing your product exists to do. Critical third-party handoffs — OAuth redirects, payment iframes, SSO. Leave static pages, read-only endpoints, and admin screens to cheaper uptime and API checks . A good rule: if a path breaking would not generate a support ticket or lose revenue, it does not need a browser check. 2. Assert on what the user sees, not just the status code The entire point of synthetic monitoring is catching the failure that a 200 OK hides. So your assertions have to go past the status code. // Weak: passes even when the page renders an error await page . goto ( " https://shop.example.com/checkout " ); expect ( page . url ()). toContain ( " /checkout " ); // Strong: asserts the user can actually complete the action await page . getByRole ( " button " , { name : " Pay now " }). click (); await expect ( page . getByText ( " Order confirmed " )). toBeVisible ({ timeout : 10000 , }); await expect ( page . getByTestId ( " order-number "

You already have Playwright tests. They run in CI on every pull request, they assert that login works and checkout completes, and then they stop — because CI only runs them against a branch, at merge time. The moment the code is in production, those tests go silent. A third-party script breaks checkout at 3 AM and your perfectly good test suite says nothing, because nothing triggered it. Playwright monitoring closes that gap: you take the same browser tests and run them on a schedule against production, turning your end-to-end suite into a synthetic monitoring system that watches real user journeys continuously. Prerequisites Node.js 18+ and an existing project ( npm install -D @playwright/test , then npx playwright install chromium ). A deployed production (or staging) URL to run checks against. A dedicated synthetic test account — never a real customer's credentials. A secret store for that account's credentials (GitHub Actions secrets, or your platform's equivalent). Never hard-code them. Step 1 — Write a check that asserts on what the user sees A monitor-grade check is not "did the page load." It is "could a user complete the thing they came to do." Assert on the outcome, with a generous timeout for real-world latency: import { test , expect } from " @playwright/test " ; test ( " checkout reaches confirmation " , async ({ page }) => { await page . goto ( " https://shop.example.com " ); await page . getByRole ( " button " , { name : " Add to cart " }). click (); await page . getByRole ( " link " , { name : " Checkout " }). click (); await page . getByLabel ( " Email " ). fill ( process . env . SYNTHETIC_EMAIL ! ); await page . getByLabel ( " Card number " ). fill ( " 4242424242424242 " ); await page . getByRole ( " button " , { name : " Pay now " }). click (); // The assertion a 200 OK can never make for you: await expect ( page . getByText ( " Order confirmed " )). toBeVisible ({ timeout : 15000 , }); }); Credentials come from process.env , not the source. The t

Synthetic monitoring tools all promise the same thing — catch the broken checkout before your users do — and then bill you in seven different ways for it. The hard part of choosing one is not the feature checklist; it is predicting what you will actually pay when a single browser check running every 30 seconds from three regions turns into 259,200 runs a month. We compared seven synthetic monitoring tools on what separates them in practice: browser engine and fidelity, how you author checks (code, recorder, or AI), location coverage, alerting and on-call, failure forensics, and — the one that surprises teams — the pricing model. Every price below was verified against official pricing pages in June 2026. For the concepts behind these tools, start with what synthetic monitoring is . TL;DR comparison Tool Best for Browser engine Authoring Pricing model Browser price Checkly Code-first teams running Playwright suites Chromium (+ suite) Code (TypeScript) Per-run, 3 separate bills ~$4–6.50 / 1k Datadog Enterprises that want APM correlation Chrome/FF/Edge Recorder + code Per-run × freq × locations ~$12–18 / 1k Grafana Cloud / k6 OSS-leaning teams, best free tier Chromium (k6) Code (k6) + convert Per-execution ~$50 / 10k Better Stack Bundled monitoring + on-call Chromium Code + codegen paste Per-minute + per-seat ~$1 / 100 PW-min New Relic Broad type matrix + compliance Selenium (Chrome/FF) No-code step + code Per-check + seats + ingest ~$50 / 10k Sematext Predictable per-monitor pricing Chromium Code Per-monitor / month ~$7 / browser monitor Site24x7 No-code recorder + many locations Chrome/FF Recorder Pooled "advanced checks" ~$10 / 10k runs How we evaluated Real synthetic monitoring is more than a scheduled ping, so we scored each tool on six dimensions. Browser fidelity : does it run a modern engine (Playwright/Chromium) or older Selenium, and how faithfully does it reproduce a real user? Authoring mode : can you write checks as code, record them point-and-click, or gen

You don’t have a naming problem. You have a 40‑minute decision problem. Here’s a practical, timer-based method to name your SaaS in 2026, without spiraling into a 3‑week Notion rabbit hole. Ground rules for 2026 A few constraints you can’t ignore: .com is crowded. There are around 157 million .com domains registered globally as of 2026, so the obvious one-word .com you want is almost certainly taken or expensive. What is .com domain Domains cost real, recurring money. Typical 2026 guides put standard TLDs at about $10–18/year to register and $14–20/year to renew for .com , and $12–18 / $14–20 for .net/.org . Domain name statistics How much does a domain name cost? Good .coms are often not $10. Clean, short, brandable .com resales routinely land in three to five figures , which is why many early SaaS founders default to modified names or non-.com extensions. How much does a domain name cost? AI-era TLDs are legit now. Investors report 69% positive sentiment toward .ai and 64% toward .io , so those are no longer “hacky” domains; they read like normal startup brands. A look at who invests in domain names .ai is basically a global startup extension. It’s widely described as a “global AI branding extension” and used by SaaS far beyond Anguilla now. .ai TLD explainer The domain space is huge. Roughly 386.9 million domains were registered worldwide by end of 2025, up ~6.2% YoY. Most popular TLDs Your first idea is probably used somewhere. Prices are drifting up, not down. ICANN raised its per-domain fee from $0.18 to $0.20 in mid‑2025, and that cost is now baked into 2026 retail pricing. Domain name market trends So: stop hunting for a perfect single-word .com at $12. Optimize for speed and defensibility , not romance. Set a timer for 40 minutes. Follow this. Minute 0–5: Positioning, not poetry Open a blank doc. In 5 minutes, write three bullets : Who you’re for (ICP in one line). What painful outcome you fix. What “shape” of product you are (API, analytics tool, ops dashb

Block, Inc. describes migrating ~450 JVM repositories into a monorepo across Cash App and Square engineering to reduce dependency drift and coordination overhead. The system supports ~8,800 weekly builds with ~10 min p90 CI time. The approach improves cross-service changes, build visibility, and developer experience through dependency graph–based builds, selective CI, and custom IDE tooling. By Leela Kumili

As a solo developer I wanted a fast, mobile-friendly interactive map for Palworld that didn't bury me in ads. The result is Pindrop , and here are a few of the technical decisions behind it. Rendering 1000+ markers without jank The interactive map uses Leaflet with a custom marker-clustering layer. Markers are served as static JSON from the edge and hydrated client-side, so the first paint is server-rendered and the heavy marker work happens after. A breeding calculator as a pure function Palworld's breeding combos are deterministic, so the breeding calculator is just a lookup over a precomputed table rather than a backend call. That keeps it instant and fully cacheable. Stack Next.js (App Router) for SSR + static generation Leaflet for the map layer Supabase for the small amount of dynamic data Vercel for hosting and edge caching If you play Palworld, the guides section collects the breeding, location and boss notes I kept losing track of. Feedback from other devs welcome — especially on the clustering approach.

We break down the current iPad lineup to help you figure out which of Apple’s tablets is best for you.