Operations | Monitoring | ITSM | DevOps | Cloud

Claude Code Observability at Scale: How We Did It With Bindplane

At Bindplane, we iterate fast. One of the most important tools we've adopted across our organization is Claude Code. It helps every team here build solutions to complex problems with both speed and precision. But speed without visibility is a liability. We needed a reliable way to monitor and audit how Claude Code was being used across our team. Luckily, we build the best platform on the market for data in motion.

GitHub Copilot Price Hike Developers Outraged! V2

What used to be $50 a month is now $3,000 — overnight. Microsoft just moved GitHub Copilot to token-based billing, and devs are split between calling it a "rug pull" and admitting someone always had to pay the bill. Here's the part that should worry every engineering leader: most can't tell you what percentage of their AI-generated code actually ships, or where the tokens went. When the meter is running on every prompt, "it feels productive" isn't good enough — you need to know that bug cost you $2,700 in tokens to fix.

Automating Device and OS Compliance in Air-Gapped Networks with Agentic AI

For network operations and security teams, maintaining compliance across device hardware and operating systems is a complex and time-consuming task. At any given moment, your network contains thousands of devices from dozens of different vendors. To keep this infrastructure secure, you must constantly know which devices are approaching end-of-life (EOL) milestones, and which platforms are vulnerable to active common vulnerabilities and exposures (CVEs).

iFrame Expands AI Infrastructure Offering With Hosted Inference Service for Open-Weight Models

Organizations looking to reduce AI operating costs while maintaining performance are increasingly turning to open-weight models. This trend accelerated throughout 2024 as businesses sought alternatives to expensive proprietary systems and greater control over their AI infrastructure.

Why AI Evaluation Is Becoming a Business Priority, Not Just a Technical Task

Artificial intelligence products are evolving at a pace that challenges traditional quality assurance and validation processes. As organizations race to release new AI-powered features, many product teams face the same question: how do they know a system is ready for real-world use? As reported by AI Journal, conversations with product leaders across different sectors reveal a growing focus on AI evaluation as a critical part of product development. Their experiences highlight the challenges of balancing innovation, risk management, customer expectations, and future regulatory requirements.

AI Dev Tools: What 100K Engineers at Google Really Taught Us

AI developer productivity, agentic workflows, and the lessons learned running engineering tools for 100,000+ software engineers at Google. John Montgomery, CCO at GitKraken, sits down with Asim Hussain, co-founder of Alterion AI and former Google VP of Engineering Productivity, to get real about what AI actually changes for engineering teams in 2025.

Autonomous Error Remediation in Cursor with Lightrun MCP

Lightrun's Gidi Freud demonstrates how your AI coding agent can now investigate and fix production errors, autonomously. Watch how Cursor, guided by Lightrun's Error Remediation skill, picks up a Sentry error, instruments the live service with a runtime snapshot, captures real evidence, and opens a validated PR for approval.

21 AI concepts every beginner should know before their first interview

If you’re prepping for your first AI or MLOps interview, the hardest part usually isn’t always the hands-on element. For me, it’s the vocabulary. Interviewers sometimes lob single-word concepts at you (“what’s quantization?”) and watch how far you can carry the thread. The questions sound clear-cut, but each one is really a doorway into a bigger topic, and the interviewer is judging how cleanly you walk through it.

CloudZero AI Hub: The nexus of autonomous AI cost control

CloudZero originated as a way to make sense of your cloud costs. Costs spread across bills with billions of line items belonging to resources that might or might not have been tagged (or taggable), spun up by engineers working across teams, on different microservices, features, and products, that served a wide range of customers. Kubernetes. Multi-cloud. Check, check, check.

AI ROI: How to measure and provide the return on AI investments in 2026

Every quarter, the same scene plays out in boardrooms across the Fortune 500. The CEO asks: “What is the return on everything the company is spending on AI?” The CTO talks about productivity gains and developer velocity. The CFO points at a cloud bill that doubled but cannot isolate which line items are AI. The board nods politely and tables the discussion until next quarter, when the same question will produce the same non-answer. (If this sounds familiar, you are not alone. Keep reading.)