Why Spark Felt Different From Every Other AI Support Tool
Brandon Woods, Keysight Technologies, on the Difference Between a DEX-Powered IT Agent and a Generic AI Assistant “Spark’s real power is the Nexthink data behind it.
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How we cut Spark compute costs by 44% with agentic AI and Datadog Jobs Monitoring
Spark jobs only get more expensive and harder to debug as they scale. It’s a problem we’ve run into ourselves. Our Referential Data Platform team builds and maintains the knowledge graph that maps relationships between customers’ observability entities. ServiceQueryEdge is at the center of that graph, mapping service entities to their associated metric and log queries.
From Cleanup to Animation in One Workspace: Redefining the Editing Loop
For the past several months, I have been watching a pattern emerge in how people actually use AI image tools. The pattern is not about any single feature. It is about how often a task that starts as a simple cleanup request evolves into something entirely different. A user uploads a product shot to remove a stray reflection. Then they wonder what the same image would look like with a different background. Then they think about turning it into a short social video. Each step is logical, but traditional workflows treat each step as a separate job requiring a separate tool.
$500 Million in One Month: What Happens When AI Has No Guardrails
A company burned $500M in Claude credits because nobody set a limit. Uber exhausted its 2026 AI budget. Microsoft canceled Claude Code licenses. The problem isn't AI - it's the total absence of governance around how people use it.
AI in SRE: Where and how Google is deploying agentic AI to improve operations
With SRE AI, Google plans to fully adopt AI and agentic technologies, leveraging AI as a force multiplier while also maintaining control.
Anthropic's Mythos, Glasswing, and how the industry must move forward | Harness Blog
When Anthropic broke the news of Mythos and Project Glasswing, the security community did what it always does. It published a flurry of papers asking "What does this mean for security?" It's a reasonable instinct, but it's the wrong question. The real question is who actually owns the problem?
Biggest MCP update yet? Breaking changes - explained in 60 seconds
The new MCP (Model Context Protocol) release makes it stateless, here's what you need to know if you are building AI agents and remote MCP servers.
Monitor LLM routing with the Kubernetes Inference Extension
If you serve LLMs on Kubernetes without inference-aware routing, your load balancer is likely wasting inference capacity. Generic HTTP traffic management blindly routes requests, assuming the backends in your cluster are interchangeable. But your model-serving backends are stateful and unevenly prepared to handle any given request. As a result, requests are often routed to the backend that’s not the one best suited to respond.
Uber blew its annual AI budget in 4 months
Uber burned through its entire annual AI budget in under 4 months. Here's what went wrong — and what every engineering org should be doing instead. The data: 80% more code is getting pushed with AI… but only 18% of AI-written code actually ships to production. That's not a productivity story. That's a spend problem. If you're scaling AI tooling without real-time monitoring and guardrails, you're Uber.
Konstruct product updates: Global resources, MCP support, and smarter permissions
May has been one of our busiest months yet for Konstruct. Across three releases, 0.5, 0.5.1, and 0.5.2, we've shipped some of the most requested platform-level changes since we launched: a unified model for sharing resources across organizations, native support for AI-driven workflows via MCP, a completely redesigned API keys experience, and a cleanup to how permissions actually work in multi-org environments. Let's walk through what shipped and why it matters.