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Instant Java Client SDK, no spec required!

Learn how to generate a client SDK for a production service when you have no documentation, no OpenAPI spec, and no remaining team knowledge of the original Ruby code. This demo shows you how to capture real production data from a running app and transform it into a functional Java client library in minutes. Visit proxymock.io OR speedscale.com to learn more.

AI Might Break Open Source Differently Than You Think

AI coding agents may not replace open source libraries overnight. But Adam Arellano, Field CTO at Harness, thinks models like Mythos could expose a bigger problem: finding bugs, vulnerabilities, and edge cases faster than maintainers can keep up. That might be the real threat to tools and libraries.

Building a Defensible AI Compliance Framework

Organizations have moved past theoretical conversations about AI adoption. Models, agents, and autonomous workflows are entering production environments. Business leaders are optimistic about potential gains in efficiency, decision support, and operational scale. Yet beneath this momentum, compliance and risk teams feel a different pressure.

Run your first microbuild in 5 minutes

AI coding agents produce code faster than most teams can validate it. Without a validation step between the agent and CI, every problem gets caught after the push, and feedback arrives long after the agent has lost context. Agents need consistent feedback while they’re working so that small failures get fixed locally and CI stays focused on moving code into production.

Measure the real impact of AI coding tools on software delivery with Datadog AI Impact

Engineering teams have rapidly adopted AI coding tools, but organizations still struggle to understand their impact. Existing dashboards focus on activity, such as daily active users, acceptance rates, or lines of generated code, but these metrics don’t answer a more important question: Are teams actually shipping more, faster, and with fewer issues?

Your agent can't fix what it can't see

Agents are getting better and better at fixing bugs. They’re even getting better at testing their work, thanks to headless browsers, sandboxes, simulators, etc. But what about the bugs that only show up once you bring in different browsers, languages, extensions, internet speeds, and all the other variables that get mixed in the second you ship to prod? Or all the bugs that only show up when you account for… well, humans being humans and doing weird stuff you didn’t expect them to do?

Preview launch: the Agent Impact Leaderboard and the Business Impact & ROI Dashboard

The Agent Impact Leaderboard and the Business Impact & ROI Dashboard are live in preview inside GitKraken Insights today. We built them because the questions engineering leaders are getting asked about AI shifted faster than the tools to answer them. Here’s what shipped and how to get access.

Your developers are using AI agents, your data exposure just multiplied

Your developers are already using AI agents. GitHub Copilot, Cursor, Claude Code. Not just for autocomplete, but to generate features, run test suites, and iterate across branches. Each agent needs a database to work against. And in most organizations, nobody has checked what's actually in that database, or whether it should be there.

The SaaS SEO Playbook That Prioritizes Conversions Over Clicks

For years, SaaS companies treated SEO like a traffic competition. The goal was simple: publish more content, rank for more keywords, and drive as many visitors as possible to the website. Marketing dashboards looked impressive. Teams celebrated traffic milestones. Investors saw upward graphs and assumed momentum was building. But somewhere along the way, a lot of SaaS founders started noticing a problem nobody wanted to talk about openly.

6 Real Benefits of Outsourcing Financial Operations in the Restaurant Industry

Running a restaurant doesn't slow down. Between managing a rotating staff, watching food costs creep up, and keeping guests satisfied enough to come back, the financial side of operations often becomes an afterthought until something goes wrong. And for most operators, something eventually does. Margins get tighter. Compliance gets messier. The spreadsheet that "works for now" suddenly doesn't. There's a better approach, and a growing number of restaurant groups across the country are already using it.