How Kentik Catches Bad AI Answers
Hear how Kentik uses AI to validate AI and catch 90%+ of bad networking answers before users ever see them.
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Testing AI Code is a Security Nightmare? #Speedscale #DevOps #Kubernetes #AICoding #SoftwareTesting
AI can write a feature in seconds, but where are you testing it? Sending production traffic, API payloads, and auth headers to a third-party SaaS is a massive security risk. In this video, we break down why the Bring Your Own Cloud (BYOC) model is the ultimate fix for DevSecOps. Learn how to safely test AI-generated code against real production traffic entirely within your own VPC or Kubernetes cluster. No data leaks, no massive DLP pipelines, and no endless masking rules.
Claude Code Observability at Scale: How We Did It With Bindplane
At Bindplane, we iterate fast. One of the most important tools we've adopted across our organization is Claude Code. It helps every team here build solutions to complex problems with both speed and precision. But speed without visibility is a liability. We needed a reliable way to monitor and audit how Claude Code was being used across our team. Luckily, we build the best platform on the market for data in motion.
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.
The skill gap AI can't close, with Hywel Carver of Skiller Whale
If 90% of code is now AI-generated, what happens to the engineers who never had to write it themselves? The ability to review code well, to catch what the LLM missed, depends on mental models that only come from practice. That tension sits at the center of this conversation.