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Beyond the code: Shipping faster with AI with Leo P.

We’re running a short mini-series on The Debrief podcast called Beyond the code, where we interview our engineers about what it’s really like to build at incident.io. In this episode, we chat with Product Engineer Leo about how we’re using AI tools like Claude Code to ship more product, more quickly.

How to Use an AI Assistant with Your Monitoring System - VictoriaMetrics MCP Server

Alex Marshalov explores the new VictoriaMetrics MCP Server. He moves beyond the hype to show what's truly possible today. The presentation offers a builder's perspective on integrating AI with time-series data, featuring a demo that showcases both the potential and the current realities (yes, there are some). See how we're thinking about solving complex monitoring challenges with AI. Resources for Further Learning.

Can AI/ML Guide Observability? Tech Talk #6

This talk will examine the application of Artificial Intelligence and Machine Learning in observability. It will cover how AI/ML is being used to monitor systems, detect anomalies, and extract insights from telemetry data. The session will provide information on integrating AI/ML into observability pipelines, improving analytical capabilities, and system performance.

Engineering Excellence in the Age of AI: It's Not Dead, It's Maturing

On a recent episode of The Product Manager podcast, Cortex CEO Anish Dhar joined host Hannah Clark to challenge a growing narrative: that software engineering is obsolete in the age of AI. His take? Engineering isn’t disappearing, it’s maturing. At Cortex, we work with some of the most forward-thinking engineering organizations at companies like Canva and Fanatics.

Introducing AI Agent Monitoring in Sentry

Monitoring agents and LLM applications is... different. Managing everything from tool calls, to model configurations, token usage, and AI systems do their best to solve problems on their own - so errors aren't always clear. Sentry's agent monitoring focuses on making it easy to dive into your AI applications and understand whats breaking, where, so you can fix it faster.

Introducing AI Agent Monitoring

AI is changing how we build software — but debugging code still comes down to having context. One minute the model’s performance is cruising. The next, you’re hit with a KeyError from a tool you forgot existed, triggered by a model that silently timed out, and a retrieval call that returns... nothing, or 11 “Let me try this a different way" messages before failure. You’re stitching together LLM calls, agents, vector stores, and custom logic. Then hoping it holds up in prod.

Why Every Digital Marketer Should Use a Proxy Browser in 2025

Arguably in the year 2025, digital marketing encompasses far more than simply keywords, an ad budget, and creative optimization of promotional materials. Rather, it is structured around visibility of competition; this includes tracking competitors as well as platforms, campaigns and audiences. However, such visibility is increasingly being restricted by advanced user fingerprinting, bot detection systems, and even regional filters. As these obstacles continue to rise, traditional browsers such as Chrome and Firefox will further fail to keep up with the operational burden marketers face on a daily basis.

How One MSP Used AI to Cut Noise by 78% and Reclaim Engineering Time

An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside. The scale of the problem was overwhelming their capacity to respond effectively.