Meta launched a stand accessory that lets you display the company's smart glasses on your desk or nightstand while they're being recharged. The new Charging Stand is listed at $59 on Meta's website - $30 less than the charging case for Meta's non-Ray-Ban glasses - and resembles a metal tube with a groove you can […]

I've been working as Software Developer mainly focussed on Java and builts many application using Eclipse RCP framework or VS Code Application. Almost all the time I had to deal with multiple large files (either read/generate/validate) them which seemed very difficult and some of them almost impossible as most of them would be dependant on each other and would be referencing each other (just like how java files work together). Now assume client1 requires the same content in multiple Json files and client2 needs it in xml files. We couldn't go on writing a different application or go on adding if conditions and blah blah blah !!!! Wouldn't it be easier if as soon as I execute the application it generates the content in whatever format I choose and also taking care of dependencies/ references (like adding import statements). Additionally integrate with features of IDE and provide proposals, perform validations on the fly. Rela World Examples : Try googling Arxml once (Trust me I've dealing with these files for almost 7 years and it's always a nightmare to debug these) Solution: Xtext framework In this tutorial, I will show you how to use Eclipse Xtext and Xtend to build a simple, readable DSL that automatically generates Java boilerplate for you. Fair Warning: There will be no running executions screenshots or anything. You are gonna have to run it yourself and check the results and of course questions are always welcome in the comments section. But if for some reason you are unable to replicate this then let me know I'll try to explain further. I believe the best way to learn is by doing it yourself. The Goal: What are we building? Instead of writing 100 lines of Java with private fields, getters, and setters, we want our developers to write 5 lines of code in our own custom language (basically you can create your own programming language with your own custom syntax), like this: entity User { var name : String var age : Integer } When this file (assume file extension

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A few weeks ago I published corridor-lab — a Docker lab that proved a triage mismatch: a service that stores nothing sensitive can become high-priority because of where it sits in the path to a sensitive downstream system. The lab proved the premise. The next question was whether a tool could identify those nodes automatically — without manual path declaration, without value labels, from graph position alone. So I built corridor-id. You point it at a Docker Compose file. It discovers the topology, computes depth from exposed surfaces, and identifies which nodes expand forward reach into deeper parts of the environment. No asset-value labels. No sensitivity ratings. No human classification. Reach and graph position only. Then I pointed it at four architecturally different Docker environments. Two had corridor nodes. Two had none. Both answers were useful. But the zero-corridor results taught me more than the positive ones. What corridor-id does The tool reads a Docker Compose file and builds a reachability graph from service definitions, network memberships, and port mappings. It then orients that graph from exposed surfaces using BFS and identifies nodes that provide forward reach — access to strictly deeper nodes that the exposed surface cannot reach directly. The output is a ranked list with two metrics: exposure distance (how close to the surface) and forward reach gain (how many deeper nodes become reachable through this node). One command: python corridor-id.py docker-compose.yml No manual path declaration. No value labels. No configuration. From graph position alone. The four tests corridor-lab — segmented, depth 3 My own lab, five services across five segmented networks. The tool independently identified status-api as a corridor node — the same finding the lab was built to prove. Corridor nodes found: 3 → status-api Exposure distance: 1 Forward reach gain: 1 → log-monitor Exposure distance: 1 Forward reach gain: 1 → internal-admin-api Exposure distance: 2 For

Originally published on satyamrastogi.com Three malicious npm packages masquerading as PostCSS tools delivered Windows RAT payloads. Analysis of supply chain attack mechanics, payload delivery chains, and detection gaps in dependency management. Malicious npm Packages Pose as PostCSS Tools to Deliver Windows RAT Executive Summary This is a textbook supply chain attack leveraging npm's trust model. Three packages published in June 2026 - aes-decode-runner-pro , postcss-minify-selector , and postcss-minify-selector-parser - delivered Windows RAT payloads to developers. The attack demonstrates why automated dependency management without behavioral validation is a critical vulnerability. What makes this particularly effective: PostCSS is a legitimate, widely-used build tool. Developers hunting for PostCSS plugins via search or copy-pasting dependency names from tutorials become easy prey. The attacker didn't need zero-days, social engineering sophistication, or exploit kits. Just npm account registration and package uploads. This follows the exact pattern we've seen in credential theft campaigns prioritizing convenience over complexity . Low barrier to entry, high payoff. Attack Vector Analysis MITRE ATT&CK Framework Mapping This attack chains multiple techniques: T1195.001: Compromise Third-Party Software Supply Chain - Malicious package publication on npm registry T1566.002: Phishing - Spearphishing Link - Package discovery and recommendation (implicit trust) T1059.003: Command and Scripting Interpreter - Windows Command Shell - RAT payload execution T1105: Ingress Tool Transfer - Initial RAT download mechanism T1571: Non-Standard Port - C2 communication channels (typical) Kill Chain Breakdown Stage 1: Reconnaissance & Naming Attacker identifies PostCSS as high-value target (builds present in thousands of projects) Creates names that blend legitimacy with search results: postcss-minify-selector exploits incomplete package searches The aes-decode-runner-pro variant sug

Some days the work spreads across a few projects instead of landing as one big feature. Today was that — three distinct threads, each with a lesson worth keeping. I'll keep things generic and teach the pattern rather than the project, but the through-line is the same: move things that were hardcoded or ephemeral into something you can configure, repeat, and trust. Thread 1 — Make scheduled tasks configurable instead of code-only If you've run a Laravel app for any length of time, you know the scheduler lives in code: routes/console.php or the kernel, a wall of ->daily() , ->everyFiveMinutes() , ->cron(...) . That's fine until the day an operator — not a developer — needs to change when something runs. Then you're shipping a deploy just to nudge a cron expression. Silly. Today's work pulled scheduler configuration into a settings-backed UI. The pattern is worth stealing: instead of the schedule being a literal in code, the code reads its cadence from a settings store, and there's an admin screen to edit it. // Instead of a hardcoded cadence... $schedule -> command ( 'subscriptions:reconcile' ) -> daily (); // ...read it from settings, with a sane default baked in. $schedule -> command ( 'subscriptions:reconcile' ) -> cron ( $this -> schedulerSettings -> reconcileCron ?? '0 2 * * *' ); Two things made this clean. First, a SchedulerSettings object (Spatie's settings pattern) so the values are typed, cached, and migratable — not loose rows you Setting::get('...') by string key. Second, grouping the more user-facing schedules behind their own modal rather than dumping every cron in one giant form. A subscription-related schedule belongs next to subscriptions; a platform schedule belongs in admin. Same data, but organized by who needs to touch it . The edge case to watch: a UI-editable cron is a foot-gun if you let people type nonsense. Validate the expression on save, and always keep a default so a blank setting can never silently disable a job. Thread 2 — A load-testing

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

The Steam Machine is here, with a base price of $1,049. Yes, that's nearly twice the price of a PS5, but what you're buying here isn't a console but a full-on PC - and a tiny one at that. Valve says it's selling the Steam Machine basically at cost. So we asked ourselves: What would […]

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Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!

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