GitHub Star History API: From 2,500 Requests to 24
GitHub's star history API cuts react/react's full curve from about 2,511 requests to 24 with no stargazer identities. How filtering by time first made it cheap.
Datadog CI/CD Optimization: The Case for Treating Your Pipeline as a Production Service
Datadog's CI/CD Optimization combines pipeline telemetry, flaky test detection, and test impact analysis. Betterment cut builds from 40 minutes to under 10.
Scale Your Platform Without Becoming the Bottleneck: The JFrog Projects Framework
Growing engineering orgs hit a wall when centralized platform admins become the constraint. JFrog Projects introduces delegated administration, project-scoped RBAC, and isolation by default — with a model that scales to 120,000+ repos.
AGENTS.md vs. Skills: How to Actually Steer a Coding Agent
CircleCI's breakdown of when to use AGENTS.md versus modular skills for coding agents — and why the real problem isn't the format, it's measuring whether steering works at all.
Datadog JFR: Why CPU-Time Profiling Beats Wall-Clock in JDK 25
JDK 25 adds unbiased CPU-time profiling to Java Flight Recorder, fixing wall-clock sampling bias that misleads Java performance work.
The Velocity Gap Is Getting Worse: What CircleCI's Q2 2026 Data Shows
Elite engineering teams now run 9x more main-branch workflows than median teams — up from 8x in Q1. CircleCI's Q2 Pulse report introduces a new metric, MER, that explains why the gap compounds.
CircleCI Q2 2026: The 9x Velocity Gap Between Elite Teams and Everyone Else
CircleCI analyzed 20 million workflows and found the gap between top 5% engineering teams and median teams widened to 9x in Q2 2026. The differentiator isn't AI — it's what happens to code before it reaches the AI pipeline.
Datadog AI Impact: Connecting AI Coding Tool Usage to Deployment Performance
Datadog AI Impact maps AI-assisted commits to lead time, change failure rate, and deployment frequency — measuring AI tool ROI against actual delivery performance.
Datadog's 4th DevEx Dimension: Why AI Changes What You Have to Measure
Datadog added a 4th dimension to the DevEx framework for the AI era: AI adoption and impact. With 80% AI-assisted PRs, what you measure has to change.
Platform Engineering Metrics: What to Measure, What to Ignore
Datadog maps the three-tier metrics hierarchy every platform team needs — outcomes, drivers, diagnostics — and explains why optimizing CI duration in isolation is a trap. Here's the framework.
Datadog Experiments: Measuring the Business Cost of Every Product Change
Datadog Experiments connects A/B tests to APM, warehouse metrics, and Session Replay — measuring business impact without a separate analytics platform.