Operations | Monitoring | ITSM | DevOps | Cloud

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.

Get Ship Done: Everything We Shipped in May 2026 | Harness Blog

AI coding tools promise faster development. What they don't show you is the queue forming at the pipeline, the security scanner you bypassed to stay fast, or the cost dashboard with a line now labeled "unknown" that is steadily growing. In May, we shipped 60+ features in 31 days across the entire delivery system: not just the editor, but everything downstream of it.

AI Spend Hit $297B. Nobody Knows Where It Goes.

AI spend doubled to $297B in two years — and most companies can't tell you what any of it shipped. Token spend is disconnected from outcomes on the dev side. Agents in production? The invoice is the only signal. Harness Cloud & AI Cost Management (CACM) gives teams unit economics at the inference level, cross-provider visibility across OpenAI, Anthropic, Bedrock, and Vertex AI, and request-level attribution to the agent, session, or workflow that triggered the spend.

Software Delivery Context, Now Inside Claude | Harness Blog

Key Takeaway: The Harness MCP Server is now in the official Claude Connectors Directory. Developers using Claude can now discover and connect to Harness, gaining structured, real-time access to their pipelines, deployments, approvals, and delivery workflows. What makes this different from a typical API integration is what's underneath: the Harness Software Delivery Knowledge Graph, which gives Claude the context it needs to make decisions that are accurate, fast, and safe. ‍

Feature Flag Tools Compared: 10 Best Platforms for Safer Releases | Harness Blog

Releasing new software used to be a big deal. You would set aside a Saturday night, wake up the on-call engineer, push the code, and hope that nothing broke before Monday morning. Then came feature flags, which changed everything without anyone noticing. Feature flags let you separate deployment from release, so you can send code to production in a dormant state and turn it on for users when you're ready. No more 1 a.m. maintenance windows.

BigQuery CI/CD and Database DevOps with Harness | Harness Blog

Modern data platforms are evolving rapidly, and Google Cloud BigQuery has become a core part of analytics, AI, and large-scale reporting architectures. Teams (including Harness) rely on BigQuery to process and analyze massive datasets, but managing schema changes in a secure, repeatable way can still be challenging.

Uber blew its annual AI budget in 4 months

Uber burned through its entire annual AI budget in under 4 months. Here's what went wrong — and what every engineering org should be doing instead. The data: 80% more code is getting pushed with AI… but only 18% of AI-written code actually ships to production. That's not a productivity story. That's a spend problem. If you're scaling AI tooling without real-time monitoring and guardrails, you're Uber.

Anthropic's Mythos, Glasswing, and how the industry must move forward | Harness Blog

When Anthropic broke the news of Mythos and Project Glasswing, the security community did what it always does. It published a flurry of papers asking "What does this mean for security?" It's a reasonable instinct, but it's the wrong question. The real question is who actually owns the problem?