Operations | Monitoring | ITSM | DevOps | Cloud

Teaching AI How to Refinery

At the beginning of February, we released v3.1 of Refinery, our advanced, tail-based sampling solution. The new version comes with more performance enhancements, bug fixes, and a few new pieces of telemetry. In tandem with the 3.1 release, we also released a new tool for our MCP server which helps your AIs understand Refinery, and how Honeycomb handles sampling.

Why Your AI Code is Breaking (And How to Fix It) #speedscale #aicoding #aiagents #code #devops

New data from CodeRabbit shows AI makes 70% more errors than humans—mostly in logic. Stop shipping "AI Vibes" to production. Use the new Testing Pyramid: Deterministic (Validation) Record & Replay (Mocking) Probabilistic (Vibes) Don't let your agents break prod.

AI SRE in Practice: Diagnosing AWS CNI IP Exhaustion Before Widespread Outage

IP address exhaustion in Kubernetes doesn’t announce itself with clear error messages. Pods fail to schedule, services degrade unpredictably, and the symptoms look like a dozen different problems before anyone realizes the cluster has run out of available IP addresses. By the time the root cause becomes clear, multiple services are affected and recovery requires coordination across infrastructure layers.

16 new integrations - powered by AIready Low Code Plugins

Today marks a big milestone in our mission to bring more data, more context, and more visibility into a single, unified view. We’re excited to announce 16 brand‑new integrations, extending the range of data sources you can connect with just a few clicks. But the integrations themselves are only half the story.

AI for nuclear safety: Predicting component remaining useful life

As industrial systems become more complex in 2026, the reliability of critical infrastructure depends on shifting from reactive to predictive strategies. In this session from Civo Navigate India, Muthukumar Ganesan, a scientist at the Indira Gandhi Centre for Atomic Research (IGCAR), explores the application of AI and machine learning in securing the future of nuclear energy.

How to Remove Watermark from AI Generated Images (Midjourney, DALL·E & Canva)

AI image tools have made it ridiculously easy to create artwork, mockups, social media visuals, and even product photography. But many platforms add watermarks - especially on free plans or previews. If you've downloaded an image and realized there's a logo, text overlay, or semi-transparent branding across it, you're not alone. Let's go through what actually works when it comes to removing watermarks from AI-generated images - and what usually makes things worse.

Boosting IT Productivity with AI-Driven Spreadsheet Automation

Modern IT teams operate under constant pressure. They are expected to deliver faster, reduce errors, maintain uptime, and extract meaningful insights from ever-growing volumes of operational data. Spreadsheets remain one of the most widely used tools in IT operations, even in organizations that rely heavily on cloud platforms, monitoring systems, and DevOps pipelines. However, manual spreadsheet work often becomes a productivity bottleneck.

AI-driven caching strategies and instrumentation

The things that separate a minimum viable product (MVP) from a production-ready app are polish, final touches, and the Pareto 'last 20%' of work. Most bugs, edge cases, and performance issues won't show up until after launch, when real users start hammering your application. If you're reading this, you're probably at the 80% mark, ready to tackle the rest.

How To Design AI-Native SaaS Architecture That Scales Without Killing Your Margins

AI-native SaaS products aren’t failing because the models are bad. They’re failing because the architecture can’t keep up with how AI actually behaves in production. What looks affordable in staging can erode your margins once real customers, workflows, and automation come into play. Designing AI-native SaaS architecture is now as much a margin decision as it is a technical one.