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CloudZero in 3 Minutes: See What Your AI and Cloud Spend Actually Buys

Cloud and AI spend in one place, tied to the teams, products, and customers behind it. Your bill tells you what you spent. CloudZero tells you why, and whether it paid off. This quick tour shows how CloudZero pulls cost from AWS, Azure, GCP, Kubernetes, Snowflake, OpenAI, Anthropic, and more, then allocates it to what your business actually cares about. No perfect tagging required.

Evaluate AI models for observability: o11y-bench joins Fireworks' Specialized Intelligence Index and reaches v1.1

When evaluating AI models for observability, generating a plausible query or explanation isn't enough. Teams need to know whether an agent's conclusions are supported by telemetry, whether its changes to dashboards actually work, and whether it can deliver reliable results consistently. This is why we built o11y-bench, Grafana Labs' open benchmark for evaluating AI models on observability and SRE tasks. And today, we're excited to share two updates.

Can AI Do Your Software Testing? #softwaretesting #aitools #codingtips #devlife #tech #aiagents

Learn how AI handles different types of testing, from simple unit tests to complex end-to-end setups. Discover where AI succeeds in writing test code and where human strategy is still needed. Learn more: speedscale.com.

How AI Is Changing Cybersecurity and Exposing Decades of Bad Code

Was this the worst week in cybersecurity? AI-discovered PHP2Shell vulnerabilities, the LiteLLM supply chain attack, and reports of unauthorized AI agents accessing bank accounts are exposing serious weaknesses in software security. But is this really a cybersecurity problem, or has AI exposed decades of poor software engineering decisions? In this episode of ShipTalk, Martin Reynolds and Adam Arellano break down a remarkable week in AI security and debate whether engineering or security teams should be responsible for fixing what comes next.

Why AI-Generated code still needs tests

AI has made life easier for us developers. Dozens of AI tools are getting more advanced every day, shortening and speeding up the development cycle. Converting an idea into a concrete MVP is faster than ever before. Intelligent models turn your ideas into a working project with just a few prompts. End users or product owners can see the application, use the beta version, and give feedback on the spot.

Measuring What Matters in AI Applications

AI has become incredibly sophisticated and changed the way we build and power applications. But it has also introduced volatile complexity that challenges how we ensure reliability and protect user experiences. Join our panel of experts as they discuss the realities of building AI-powered applications. They’ll talk about how testing has evolved, share insights into how they do evals and experiments, and offer advice on how to get better visibility into how your AI is performing.

39% of Coralogix sessions are now headless

We set out to understand how customers were using Coralogix from outside the web app. Olly answers questions about a system in plain language. Coding agents query logs, examine traces, read metrics and configure the platform through the Coralogix CLI and MCP server. We wanted to see how much of this was becoming part of everyday work. In the week of 20–26 October, 39% of Coralogix sessions included Olly, the CLI or MCP.

AI Signals Demo: See Exactly What Your AI Spend Is Buying | CloudZero

See who’s spending on AI, on which models, for what work, and what you got for it. Most companies can tell you what they spent on AI last month. Very few can tell you what it bought. In this demo, we show how AI Signals captures AI usage as it happens and ties every dollar to the person, team, and work behind it, across every model and provider.

Five things you can do with Sentry's Agent Tracing

AI-powered apps are hard to debug without observability. From one run to another, AI produces different results, goes down different paths, and you’re none the wiser as to why it’s happening. Agent Tracing bridges the gap between prompt, tool calls and final output, all within the context of your app. If things break (it always does at some point, we know from experience), you get everything in one complete trace.