Operations | Monitoring | ITSM | DevOps | Cloud

honeycomb

Measuring Claude Code ROI and Adoption in Honeycomb

Jan 22, 2026 By Mae Capozzi In Honeycomb
At Honeycomb, we’ve been using Claude Code across our engineering team for a while. Anecdotally, I had a sense of who the power users were, and I had seen some examples of complex usage. But I wanted to be able to confidently answer questions, like: Claude Code supports OpenTelemetry out of the box, which means sending telemetry to Honeycomb takes just a few minutes of configuration.
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grafana

ChatOps that actually works: Grafana Cloud, Slack, and AI-powered observability

Jan 22, 2026 By Ksenia Yadav In Grafana
Context switching isn’t just inefficient—under pressure, it’s exhausting. It slows decision-making, increases the risk of mistakes, and makes even experienced engineers feel like they’re always a step behind the system they’re responsible for. At Grafana Labs, we want to build tools that meet you where you are. That's why we embedded Grafana Assistant, our context-aware AI assistant, directly in Grafana Cloud.
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How to Troubleshoot BGP Faster with Kentik AI Advisor

Jan 22, 2026 By Kentik In Kentik
A BGP session goes down because a transit provider exceeded the maximum prefix limit. How do you find the root cause — fast? In this 10-minute demo, we walk through two approaches using Kentik AI Advisor. First, we troubleshoot step by step using natural language: asking AI Advisor to identify the affected interface, check for interface flapping, and review syslog messages until we find the maximum-prefix violation. Then we show how custom network context and natural language runbooks let AI Advisor do the entire investigation autonomously — following the same four steps a senior engineer would.
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MCP: Why AI Needs Git Intelligence

Jan 22, 2026 By GitKraken In GitKraken
GitKraken CTO Eric Amodio breaks down the Model Context Protocol (MCP) and explains why Git intelligence is critical for AI agents at GitKon 2025. In this session, Eric covers: What MCP is and why every major AI company adopted it Why AI needs Git history, not just file system access How GitKraken MCP removes Git pain safely The future of agentic developer workflows How Commit Composer uses AI to organize commits without losing data.
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GitKraken Insights | Engineering Intelligence in Minutes

Jan 22, 2026 By GitKraken In GitKraken
Most software intelligence tools take months to implement, cost a fortune, and end up collecting dust. GitKraken Insights is different. It helps engineering leaders measure what matters: AI impact, code quality, delivery performance, and developer experience, all in one place. It’s the latest evolution of the GitKraken DevEx platform, trusted by over 40 million developers. Insights connects data from across your GitKraken tools to give you a complete picture of engineering health and value. We're talking DORA metrics, pull request metrics, and AI impact.
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devart

How to Use PostgreSQL AI for Query Writing and Optimization

Jan 21, 2026 By Rosemary Asufi In Devart
PostgreSQL AI is gaining attention as SQL complexity increases in production environments. It addresses a common problem: extended queries that accumulate joins, nested logic, and edge cases. Without AI assistance, these queries are often harder to write and review, driving 20–40% of developer time into debugging. In practice, these challenges affect PostgreSQL users in different ways.
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gitkraken

How GitKraken's AI-Powered Commit Composer Eliminates Git Cleanup Headaches

Jan 21, 2026 By Iva Dobreva In GitKraken
As developers, we’ve all been there: a frantic coding session, a few hasty commits, and suddenly our Git history looks like a patchwork quilt of “fix,” “oops,” and “stuff.” While git rebase -i is a powerful tool for cleaning up, it’s also a source of anxiety for many, often leading to more headaches than it solves. What if you could achieve a pristine, meaningful commit history without the fear of breaking things or hours spent squashing and rewriting?
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logicmonitor

Why AI Automation for ITOps Needs Context Graphs

Jan 21, 2026 By Margo Poda In LogicMonitor
AI automation in ITOps fails because execution loses decision context, and context graphs turn incident history into durable execution memory that systems can actually reuse. AI automation for ITOps fails because it remembers what it did, but not why. Fixing an issue depends on what was tried last time, what failed, what worked, which exceptions were approved, and under what conditions. That information rarely lives in the system.
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speedscale

Digital Twins Gone Wild: My Unexpected AI Doppelgänger

Jan 20, 2026 By Alan Mon In Speedscale
I recently tried using AI to create a digital twin of myself. I uploaded a photo, expecting a futuristic, slightly improved version of me... and what did I get in return? A picture of Kim Jong Un. Clearly, AI has a sense of humor-or a very different definition of "twin." Forget Arnold Schwarzenegger and Danny DeVito. Digital Twins 2-Now Starring My AI Doppelgänger From Speedscale's perspective, a digital twin is built from real production traffic, continuously updated, and executable in your test and CI/CD environments.
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