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On September 4, Rapid7 Labs published research on a Linux espionage toolkit found at two organizations in South Korea. The centerpiece is a backdoor the researchers call "Ted," which was hidden inside a modified HAProxy build running on the victims' load balancers. Rapid7 attributes the campaign to North Korean state-sponsored actors with medium confidence. The same toolkit tampered with multiple tools across the victims' systems, including an SSH keylogger.

On August 27, Google sent us an email. We did not. We barely recognized the hostname. None of us had thought about dev-docs.trackjs.com in about ten years.

Perplexity pricing runs $0 for Free, $20 a month for Pro, and $200 a month for Max, with enterprise seats listed from $40 per user. Pro fits most people who search for work daily. Max exists for heavy automation. The prices are verified against Perplexity's live plans page as of September 2026. When Perplexity published a customer quote on its own pricing page, it chose an unusual one.

Build when the problem is narrow (one provider, one team, simple attribution) and the tooling is strategically yours to own. Buy when AI spend spans providers, arrives untagged, and needs unit costs finance will trust, because that build is a multi-quarter platform project with a permanent maintenance tail. Price both paths in engineer-years before deciding. CloudZero sells the “buy” side.

The PostgreSQL database went from a platform developers respected to the one they reach for first. Today, it’s used by over 55.6% of all developers and 58.2% of professionals, powering everything from SaaS startups to AI platforms. For many new projects it’s now the default choice. But what pushed PostgreSQL ahead of so many alternatives? This guide answers that question by explaining what PostgreSQL is, how it works, and what makes it different from other databases. Table of contents.

A shared Kubernetes cluster rarely belongs to one team. Payments runs checkout in one namespace, search runs search-api in another, and a risk team runs a scorer somewhere else. One Speedscale forwarder captures API traffic for all of them. Redacting that traffic before it leaves the cluster is what makes it safe to use for testing (the background is in The PII Testing Dilemma). Until now, that forwarder ran exactly one DLP rule. Every team that needed a field redacted had to edit the same JSON document.

What caught my attention in the recent OpenAI and Hugging Face incident wasn’t any one exploit. It was the way the models could keep progressing across systems, combining techniques and acting with a level of speed and persistence that changes how security teams need to operate. The incident emerged during internal cybersecurity evaluations in July 2026.