Operations | Monitoring | ITSM | DevOps | Cloud

GitHub Copilot Price Hike Developers Outraged! V2

What used to be $50 a month is now $3,000 — overnight. Microsoft just moved GitHub Copilot to token-based billing, and devs are split between calling it a "rug pull" and admitting someone always had to pay the bill. Here's the part that should worry every engineering leader: most can't tell you what percentage of their AI-generated code actually ships, or where the tokens went. When the meter is running on every prompt, "it feels productive" isn't good enough — you need to know that bug cost you $2,700 in tokens to fix.

Automating Device and OS Compliance in Air-Gapped Networks with Agentic AI

For network operations and security teams, maintaining compliance across device hardware and operating systems is a complex and time-consuming task. At any given moment, your network contains thousands of devices from dozens of different vendors. To keep this infrastructure secure, you must constantly know which devices are approaching end-of-life (EOL) milestones, and which platforms are vulnerable to active common vulnerabilities and exposures (CVEs).

Speed with Confidence: Managing Delivery Risk in an AI-driven Development World

In the modern development landscape, we are seeing a shift in how work is managed. The rise of AI-assisted development and highly distributed teams means that work is moving faster than ever before. However, this increased velocity often comes with a hidden tax: complexity. We are seeing more parallel work streams, more intricate dependencies, and a constant stream of shifting priorities. In this environment, simply moving fast is not enough to guarantee success.

AI Governance: Closing the Policy Gap feat. Brooke Johnson, Ivanti

AI governance isn't optional — it's the difference between scaling AI confidently and exposing your organization to serious risk. Watch Brooke Johnson, Ivanti's Chief Legal Counsel, SVP HR and Security, break down why AI policy alone isn't enough and what it actually takes to close the governance gap.

How Fragmented Data Breaks AI Strategy feat. Sterling Parker, Ivanti

Your AI is only as good as the data it sits on — and fragmented IT data isn't just inefficient; it's dangerous. Watch Ivanti's Sterling Parker, SVP of Global Solutions and Services at Ivanti, explain why a unified IT platform and a clean system of record are the true foundation of secure, scalable AI.

AI inference vs. training: What they are and how they differ

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily. According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.

Autonomous IT Is Here. Are You Prepared?

Enterprise IT was built for a more predictable workplace, where support began when an employee reported a problem and IT worked backward from the details they could provide. That model made sense when devices, applications, and ways of working were easier to control. Today, the digital workplace moves too quickly for IT to rely on reported issues alone. By the time a ticket appears, employees may have already lost time, worked around the problem, abandoned the tool, or turned to an unmanaged alternative.

iFrame Expands AI Infrastructure Offering With Hosted Inference Service for Open-Weight Models

Organizations looking to reduce AI operating costs while maintaining performance are increasingly turning to open-weight models. This trend accelerated throughout 2024 as businesses sought alternatives to expensive proprietary systems and greater control over their AI infrastructure.