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Most remote job listings are ghosts — already filled, never opened, or posted just to farm résumés. As a developer, that annoyed me enough to solve it with code instead of complaining about it on X . So I built Remoty.work , which grades every listing A–F on how likely it is to be real . Here's how the detection actually works under the hood. The problem, from an engineering angle Job boards are an endless scrape loop. Board A scrapes Board B, which scraped Board C. The same dead listing propagates across a dozen sites, and none of them verify anything. There's no signal for "is this real," so the noise compounds until every board looks identical. I didn't want to build board number thirteen in that loop. I wanted a layer on top that answers one question every listing should have to: is anyone actually going to read my application? The architecture Everything runs on a single VPS — Postgres, scrapers, and the scoring jobs, supervised on a schedule. Deliberately boring. High level: Ingestion: scheduled scrapers pull from source boards and company ATS feeds into Postgres. Each raw listing is deduplicated by a fingerprint (title + company + normalized URL) so the same job reposted across five boards collapses into one row with a repost_count . Scoring engine: every listing gets a ghost-risk score from a handful of signals, then mapped to an A–F grade so it's human-readable, not a black-box number. The "rant" signal: I cross-reference what people say about companies in places like r/recruitinghell and hiring threads. That's where the truth about a company's hiring leaks out, and it turns out to be a strong predictor. Agents: I use DeepSeek to classify and summarize the messy text job descriptions, company chatter. DeepSeek because running this over thousands of listings every night on GPT-4-class models would have killed the unit economics before I had a single user. One infra detail I didn't expect to spend a weekend on: the frontend is on Cloudflare's edge, but the ed

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

Metron was the Greek word for a measure: the standard you judge a thing against. It gives us metric, and metronome. Pick the wrong metron and everything you count against it comes out wrong, no matter how carefully you count. Ask most teams how they know AI adoption is working, and the answer is some version of: we’re shipping more. More PRs, more tickets closed, more velocity on the board. None of those numbers were ever measuring what people thought they were measuring. They were proxies, and bad ones, for whether the team was creating value. AI didn’t break that. It just made the proxies lie louder. An agent can produce PRs faster than any team can review them. It can close tickets all day. None of that tells you whether the thing built was worth building, or whether it fixed the actual problem, or whether anyone downstream is better off. Burning tokens isn’t the same thing as creating value. It just looks the same on a dashboard built to reward the first thing. The same DORA numbers I pointed at earlier are blunt about it: individual output climbs while delivery throughput and stability drop. Same bottleneck, counted at the wrong end . This is Theory of Constraints in one sentence: speeding up a step that wasn’t the bottleneck doesn’t speed up the system. It just piles more work in front of whatever the real bottleneck is. Implementation used to be the constraint. Now it isn’t. Review is. Coordination is. Making sure everyone agrees on what “done” even means is. Agents made the fast part faster and left the slow part exactly where it was, except now it’s buried under more output arriving to be reviewed by fewer people who understand any of it. Measuring individual speed after that shift is measuring the wrong clock. The team can be faster and worse off in the same quarter. The instinct, when this gets noticed, is to add another skill to the agent. Automate the review step too. Automate the coordination. Whatever’s slow, throw a capability at it. That doesn’t fix

Roblox's new "Build" feature lets users generate basic games using a single text prompt.

Most "I built a home SOC" posts show you the pretty dashboard and stop there. This one is about what happened after the dashboard loaded — because that's where the real learning was. 🖥️ The setup Three Mac Minis, one control machine, all Ubuntu. Mac Mini 1 runs the Wazuh manager, indexer, and dashboard — the security monitoring core. Mac Mini 2 handles AWS IAM permission scanning (Cloudsplaining, ScoutSuite). Mac Mini 3 is a documentation hub plus a custom vulnerability-intelligence pipeline pulling from the NVD API and NIST's CPRT compliance framework data. A fourth machine, my daily driver, orchestrates all three over SSH. Today's goal: get every machine reporting into Wazuh, so I'd have real, fleet-wide visibility — not just a single monitored box. 🐛 Bug #1: the silent feed stall After installing the Wazuh agent on Mac Mini 3, vulnerability scanning just... sat there. The log showed Initiating update feed process and then nothing. No error, no timeout, no progress. I ruled things out methodically: DNS resolved fine, curl could reach Wazuh's CTI feed endpoint and complete a full TLS handshake, disk space was nowhere close to full, and there were no stale lock files sitting around. Every individual piece of the pipeline worked in isolation — but the feed download itself never completed. The actual fix: upgrade Wazuh from 4.14.5 to 4.14.6. A newer point release fixed whatever was silently stalling the content-updater module. Once upgraded, the feed downloaded, decompressed, and the scanner came alive within minutes. Lesson: when every individual network/system check passes but a specific module still won't progress, check for a version-specific bug before going deeper down the debugging rabbit hole. 🔒 Bug #2: locking myself out with fail2ban Mid-troubleshooting, my control machine's SSH connection to Mac Mini 3 started getting flatly refused — no prompt, no error, just Connection refused. Ping worked fine. Every other port responded. Just port 22, silently dead. fai

If the large, rocky planet is losing helium, then we can infer what is left behind.

Sometimes it feels like keeping your floors clean is one of those never-ending chores, which is why it's nice to have a versatile robot vacuum take it off your hands. The Ecovacs Deebot X11 robovac / mop hybrid is designed to do just that, and it's now $699 ($400 off) at Amazon and directly from […]

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Sonos has added some handy quality-of-life updates to its mobile app.