Slopsquatting: the AI supply chain attack you haven't patched
Eighty percent of new developers on GitHub start using Copilot within their first week. How many really know what it's pulling in?
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SharePoint Audit Log Noise: Platform Activity That Looks Like an Attack
Much of what the Microsoft 365 audit log records as user activity is the platform itself. Twelve patterns that look like exfiltration, password spray or privilege escalation, and what each one really is.
MITRE ATT&CK Techniques in the Microsoft 365 Audit Log
A reference mapping MITRE ATT&CK techniques to the SharePoint, OneDrive and Entra ID audit events that actually evidence them, with the detection signal for each.
Launching our rebrand on socials with a short film
We are so back. Launch automated infrastructure on demand, globally with Megaport. In a cool way.
Introducing MCP Connections: Netdata AI Now Reads From the Tools You Already Run
Netdata AI can now connect outward to the tools your team already runs, like GitHub, PagerDuty, Atlassian, or any custom MCP server, and read from them during an investigation. We call this MCP Connections. It’s the missing piece in the middle of every root-cause investigation: the alert tells you what changed, but the why is usually somewhere else entirely.
Netdata Network Topology: Live SNMP Discovery & Container Connection Maps
Netdata now builds live network topology maps directly in the agent: no scheduled scans, no stale picture the next morning. In this walkthrough, we cover both sides of the new Topology view: Network device discovery (SNMP): Container & process network connections.
Netdata NetFlow: Top Talkers, Traffic Sankeys & Global Flow Maps
A look at Netdata's new NetFlow analyzer: see exactly what's flowing through your network, with no packet capture and no separate appliance.
Orchestrating Agents: From Proof of Concept to Kepler
Running Claude in one terminal and Cursor in another isn't a workflow. It's a juggling act. We talked to the engineer who got tired of it and built Kepler instead: one place to run agents, review PRs, and stay in control.
Shipped: Put every AI task on the cheapest model that can actually do it
If your team builds with AI, someone is defaulting to the biggest model available (say, Fable) because it feels like the safe pick, and the safe pick is almost always the most expensive one. One over-powered choice looks harmless on its own, but multiplied across every prompt, agent, and workflow, and you get a big number on the P&L. All that, yet nobody chose which model on purpose. As we like to say, using a default is not a decision.
AI cost reduction: tactics that preserve performance
AI cost reduction means lowering what you spend to run AI (tokens, inference, and compute) without sacrificing quality. The highest-leverage tactics, prompt caching, batching, and routing easy work to smaller models, cut spend 50 to 90% by removing waste, not capability. Somewhere right now, a finance leader is opening an AI bill that has quietly tripled, with no new product to show for it. Nobody approved it. No single decision caused it.