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.
AI ROI is an allocation problem
AI spend is going parabolic, and the labels on the bill (OpenAI, Anthropic, Gemini) are about all a CXO gets to work with. The hard part of tying that spend to outcomes is structural. A major portion of AI spend isn’t COGS. It’s the spend on coding agents producing the software, the spend on building marketing content, the spend on custom sales tooling, the spend on Intercom agents and Sybill analysis.
Shifting Streams and AI Surges: What Our Data Reveals About the OTT Landscape
OTT data from early 2026 shows streaming hierarchies holding steady while AI platforms reshuffled rapidly. Claude has substantially increased traffic since January, overtaking Gemini, and is on pace to challenge ChatGPT by fall. Doug Madory digs into the data in this new analysis.
Inside the Grafana AI Team Weekly: AI Observability for the OTel demo and LLMSpec (May 12, 2026)
This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann demos how he integrated AI Observability into the OTel demo, complete with the guards feature he introduced last week, and Principal Software Engineer Yas Ekinci gives a rare glimpse of LLMSpec, the internal counterpart of the o11ybench benchmark that we use to evaluate Assistant.
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.