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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

ACP: The Protocol Powering Kepler

The secret sauce that powers Agentic Development Environments (ADEs) like Kepler is a little thing called the Agent Client Protocol (ACP). In this context, Kepler is the Client and harnesses like Claude Code and the Codex CLI are the Agents. We’re going to go over some of the details about how it works, how we use it at GitKraken, and how the protocol may be changing for the better.

GitLens 19: Your Entire Development Workflow in the Commit Graph

GitLens 19 is here, with a reimagined Commit Graph built to be your workbench for modern parallel development. See what’s happening across branches, worktrees, and supported coding agent sessions, then move the work forward without constantly jumping between views and tools.

Anthropic's Mythos 5 Created Fake GitHub Identities & the U.S. Government's New AI Review

Anthropic's Mythos 5 created fake GitHub identities to get malicious code approved. Cybersecurity advisor and author Nicole Dove joins ShipTalk to explain what this means for AI agent security, device code phishing, open-source software, and secure software delivery.

Faster SQL Server Development Without the Downstream Risk | The Tony and Tonie Show Ep 48

Faster SQL coding only releases engineering capacity if it doesn’t create more late-stage rework and deployment risk. Tony and Tonie discuss how SQL Toolbelt Essentials helps teams keep speed and control aligned. Learn how SQL Toolbelt Essentials helps SQL Server teams write better code faster, catch issues while the code is still being developed, and apply shared standards before changes reach review, test, or deployment.

Shipped: Cost anomalies and savings recommendations, delivered into ServiceNow

If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.

How to build the business case for AI

A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.
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Flamegraphs Find It. Replay Proves It.

I made an API endpoint 13 times faster. Then I realized my first verification only checked the status, headers, and response schema. I had not checked the totals. I had made the bug faster. That is the problem with giving an AI coding agent one kind of evidence. A CPU profile can show where the application is slow, but not whether an optimization preserves behavior. A traffic replay can prove that behavior stayed stable, but not explain why the code burns CPU. This walkthrough gives the agent two independent witnesses: Together, they turn AI code verification into an experiment with two independent checks.

The Pod Was Cheaper. The Service Wasn't.

A smaller Kubernetes pod can lower allocation cost while completing less work. Green status codes and matching schemas can hide it. This walkthrough combines OpenCost allocation data with proxymock behavior and performance evidence. A candidate passes only when behavior and throughput hold while unit cost falls.

Platform engineering is not just a developer trend, but a practice ITOps should be paying attention to

Riya has managed IT operations at a mid-sized FinTech company for six years. She knows the infrastructure inside out: Every server, monitoring alert, and compliance requirement is owned by her team. So when Riya heard the engineering lead mention their new internal developer platform in a quarterly review, she assumed her team would be looped in eventually. This did not happen. Three months later, Riya's team was called in to investigate an outage.

Diagnose Serial N+1 API Calls With Tempo + proxymock

One API request took 302 milliseconds. Nothing failed. CPU was mostly idle. The response was correct. The trace made the problem obvious: eight inventory calls, each waiting for the previous one. But the trace could not tell me why the application made eight calls, or whether changing their execution would preserve the response. It showed the shape of the wait, not the input that created it.