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Smartphones are welcoming the agentic AI overlords.

Operation by two Russia-state groups has been ongoing since at least March.

The three-layer model works. Part 1 of this series is the invoice incident that proved it. Part 2 is...

trpc has a scheduled workflow called "Lock Issues & PRs." Its own scorecard shows it failing on almost every run. It is still scheduled, still running, still red. trpc ships excellent software, which is exactly the point: if a project this careful has a workflow that has been red for ages, the rest of us almost certainly do too. It is not a one-off. drizzle-orm has one ("Unpublish release"). cal.com has one ("PR Update"). I scanned 35 popular open-source repos and the same thing kept turning up: a scheduled workflow that fails on nearly every run, quietly, for a long time. Why nobody notices GitHub does email you when a scheduled workflow fails. So how do these survive? Two reasons. First, those emails are routine. You get them for flaky reruns and transient blips too, so you filter them out. Second, a workflow that is always red stops reading as a signal. It is just how that row looks now. I did exactly this on my own project. GitHub emailed me that a workflow had failed. The next day it emailed again. I saw it, told myself I would fix it tomorrow, and promptly forgot. It was my nightly database backup, quietly broken the whole time, and I only caught it when a failure-rate number crept up where I would notice. An always-red workflow is not free It burns minutes every run to produce nothing but a red X. Worse, it trains you to ignore the failure that actually matters: the day a real one lands in the same inbox you have learned to skim past. How to find yours Open your Actions tab and look at the scheduled workflows, the cron-triggered ones nobody watches. If the last several runs are all red, you found one. From the CLI: gh run list --workflow = "Lock Issues & PRs" --status = failure What to do about it Two honest options: fix it, or if the workflow is genuinely abandoned, turn it off. Do not leave it scheduled and red. gh workflow disable "Lock Issues & PRs" Or drop the schedule trigger from the workflow file if it should not run on a timer at all. A disabled work

Two days ago GitHub emailed me to say one of my workflows had failed. The next day it emailed me again. I saw it, told myself I would fix it tomorrow, and promptly forgot. It was my nightly database backup, quietly broken the whole time, and I only caught it because a failure-rate number nudged up. A failed run at least gets you an email. A slow run gets you nothing. GitHub never pings you when CI quietly takes twice as long, runs the whole suite twice per PR, or rebuilds dependencies from scratch every time. That waste compounds where no one looks. Here are the usual culprits, each with the exact fix. When I scanned 35 popular open-source repos, not one had a fully clean config. 32 of 35 had no concurrency control, 33 of 35 had no job timeouts, and 22 of 35 ran the full suite twice on every PR. If projects this polished leave minutes on the table, the rest of us definitely do. Your suite runs twice on every PR Trigger a workflow on both push and pull_request and, for a branch in the same repo, opening a PR fires both. You just paid for two identical runs. This one is pure waste and it can roughly halve your PR-related minutes. Trigger on pull_request , and keep push for your default branch: on : push : branches : [ main ] pull_request : Old runs don't cancel when you push again Push a fix 30 seconds after the first push and, with no concurrency group, both runs go to completion. The first is dead weight, and it is holding a slot in your queue while it finishes. This hides even when you do have a group: astro has a concurrency group on one workflow but left off cancel-in-progress , which our scan estimates leaves roughly 1,850 minutes a month on the table. Add a group keyed on the branch, with cancel-in-progress , so a new push supersedes the old run: concurrency : group : ${{ github.workflow }}-${{ github.ref }} cancel-in-progress : true Every run reinstalls dependencies from scratch No cache means every run re-downloads and rebuilds your dependencies. On a typical

Hundreds of contractors working on a project for Meta pretended to be kids—and then prompted rival chatbots like Gemini and ChatGPT to discuss high-risk subjects.

Starting in IT I started in IT at a local school in my small town in December 2024. It was my first job out of college after earning my B.S. in Cybersecurity. None of the infrastructure was updated. Everything was failing, and luckily, I had one other IT person there: my director. I honestly think he knew less than I did, and he would get frustrated at almost every ticket. Even then, I knew I wanted a role where I could code. Fast forward to more recently, he had a freak out and quit. Now we have a two-person team, and everything is finally up to date and functioning. Even with things improving, I still knew I wanted to move toward a SWE-type role. Learning to Code I first decided to learn C# for Unity. I was really into game dev while I was in college, so it felt like a natural place to start. I began with the Microsoft/freeCodeCamp C# certification, and I surprisingly really enjoyed it. I made a few small games on itch.io that no one cared about, but I had fun building them. After that, I went on a bit of a language-hopping spree. I jumped from C# to C++, then into full-stack web development. I actually stuck with web dev for a while and really enjoyed it. But this cycle went on for awhile of just constant swapping. Wannabe Founder Then something switched overnight. I went from writing maybe 0-5% AI-generated code to using AI for nearly everything. I started spam-building startup ideas that did not really go anywhere. I may have made around $2-3k from them, but most of the time I was just chasing money and building whatever I thought had the quickest path to making some. I got seriously addicted to vibe coding. I tried Codex, Cursor, Claude, and basically anything with AI in it. I did like Codex the most, though. Eventually, I realized I had almost completely stopped coding by hand. I was not passionate about the startup ideas I was building. I loved coding, and I knew I had to step back. Back to Coding Now I am back to coding without AI assistance. I will eventua

Modern Property Management Systems depend on continuous data exchange between internal modules and external services. Bookings, calendar updates, guest communication, cleaning tasks, and maintenance triggers all generate operational events that must be processed quickly and reliably. Free PMS platforms such as PMS.Rent rely on robust queueing and message‑broker layers to ensure that these events never get lost and are always processed in the correct order. At the core of this architecture is the concept of distributed message‑broker orchestration, which enables the PMS to scale horizontally, maintain predictable performance, and avoid bottlenecks during peak operational periods. Why Message Brokers Matter A PMS handles thousands of small but critical operations every day. Without a message broker, these operations would compete for system resources, causing delays, blocking workflows, and creating inconsistent states. A broker solves this by: receiving events, storing them durably, routing them to the correct processors, retrying failed operations, ensuring ordered execution when required. This creates a stable foundation for automation and real‑time synchronization. Queue Types Inside a PMS A modern PMS typically uses several queue types: Operational queues for bookings, calendar updates, and guest messages Automation queues for cleaning tasks, reminders, and workflow triggers Synchronization queues for channel managers and external APIs Fallback queues for events that require manual review Each queue isolates a specific category of tasks, preventing unrelated operations from interfering with each other. Distributed Workers Workers are lightweight processes that consume events from queues. They operate in parallel, allowing the PMS to scale dynamically. If the system detects increased load — for example, during high‑season booking spikes — it simply launches more workers. Workers typically perform tasks such as: updating property calendars, generating guest notific

This blog post is created for the purposes of entering the Hack the Zero Stack with Vercel v0 and AWS Databases hackathon. #H0Hackathon Building Innward: A B2B Hospitality Operating System with Vercel and Amazon Aurora The hospitality industry is notorious for relying on "legacy" software—clunky, slow, and disconnected. For the Hack the Zero Stack hackathon, I set out to build Innward , a modern, AI-ready Property Management System (PMS) that proves you can build enterprise-grade B2B tools in record time using Vercel v0 and AWS Databases. The Vision: Moving Beyond the Spreadsheet Hotel managers don't just need a place to store "Room 101: Occupied." They need to solve the "Hidden Math" of revenue management. This means: Relational Complexity: Linking dates, room groups, and individual stays. Dynamic Pricing: Deriving rates based on occupancy and logic-based rules. Market Intelligence: Real-time benchmarking against competitors. The "Zero Stack": Vercel + Amazon Aurora To handle this complexity, I chose Amazon Aurora PostgreSQL (Serverless v2) . Why Aurora for B2B? In a B2B SaaS environment, data isolation and relational integrity are non-negotiable. Aurora provided the robust relational power needed to join complex pricing tables while scaling automatically as more hotels (tenants) join the platform. The Zero-Secret Architecture One of the most rewarding parts of this build was implementing the AWS RDS Signer . Following the "Zero Stack" philosophy, I moved away from static database passwords. Innward uses IAM-based authentication to communicate between Vercel and AWS. By utilizing the @aws-sdk/rds-signer , the application generates short-lived tokens on the fly. This means even if an environment variable were leaked, the database remains locked tight. // lib/db.ts snippet const signer = new Signer ({ credentials : awsCredentialsProvider ({ roleArn : process . env . AWS_ROLE_ARN ! , clientConfig : { region : process . env . AWS_REGION }, }), region : process . env .