Cloud assistants, computer use and shared standards are changing how AI gets work done. Here is what matters before you give an agent access to your apps. Updated 5 November 2026 Agentic AI combines a language model with tools, access controls and a way to check results. Ask an AI to explain a spreadsheet formula and you get an answer. Give an agent a tightly defined assignment and it may open the spreadsheet, inspect the data, apply a change and check the result. That extra ability to act is useful, but it also changes what can go wrong. Recent announcements show where the technology is heading: agents that keep working in the cloud, operate software, and reuse tools and instructions across platforms. For businesses and everyday users, the practical question is how much responsibility to delegate and how to verify the work. What makes an AI system agentic An AI agent uses a model to choose steps and tools while pursuing a goal. A fixed automation follows a route its develope...
Finding every IP camera, NVR, and DVR on a local network can be surprisingly difficult. Devices may advertise themselves through ONVIF or mDNS, expose only an RTSP service, use a vendor-specific web endpoint, or respond on an unexpected port. CamScan brings these clues together in one Bash-based discovery tool designed for networks you own or are authorized to test. The open-source project is available on GitHub at error0327/CamScanner . It is designed and tested around Kali Linux, while also including fallback package mappings for other supported systems where possible. What makes CamScan useful? CamScan does more than look for a single port. It combines multiple discovery and fingerprinting signals, then scores each host as a possible, likely, or confirmed camera device. This helps reduce noise while still surfacing devices that may not identify themselves in an obvious way. Scans a subnet, IP range, or individual host. Uses ARP discovery when available, with an Nmap p...