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Boba Paradox

It's 2PM on a Thursday. Your engineering team is knee-deep in bugs from a recent release. But what's the Slack channel buzzing about? Not flaky tests. Not integration coverage. Not mocking services. It's whether to order brown sugar boba or taro with oat milk. Let's be honest: for many companies, it's easier to justify $8 on boba than $800 on testing tools. And we're not here to judge-we're here to understand why.

From Guesswork to Guarantees: How Traffic Replay Improves Release Confidence

In modern software development, the pressure to move fast is matched only by the need to get it right. Teams working within the software development lifecycle (SDLC) must constantly balance velocity and quality, ensuring releases are stable, secure, and performant. Traditional software development models often relied on manual verification and human intuition to validate releases; however, as systems have grown in complexity, guesswork is no longer sufficient to meet these rising needs.

4 Chaos Engineering recommendations from Gartner

Gartner recently published their annual Hype Cycle reports, including the Hype Cycle for Infrastructure Platforms. Designed to help heads of infrastructure and IT operations make informed decisions about infrastructure platforms, it includes over thirty different topics covering everything from platform engineering to distributed cloud to policy as code—including Chaos Engineering and Site Reliability Engineering.

Elasticsearch with Python: A Detailed Guide to Search and Analytics

If you’re using Python for search, log aggregation, or analytics, you’ve probably worked with Elasticsearch. It’s fast, scalable, and fairly complex once you go beyond the basics. The official Python client gives you raw access to Elasticsearch’s REST API. But getting it to work the way you want, especially under load, can be tricky. This blog walks through practical ways to index, query, and monitor Elasticsearch from Python code, without getting lost in the docs.

Deploying secure AI: Canonical + SpectroCloud for federal missions

As mission requirements evolve, federal agencies and defense teams need infrastructure supporting AI/ML workloads anywhere, from secure cloud environments to disconnected edge locations. In this fireside chat, Mark Lewis (VP, Application Services at Canonical) and William Crum (Senior Defense Success Engineer at SpectroCloud) discuss how their organizations are helping federal customers deploy secure, scalable, and consistent Kubernetes and AI infrastructure across hybrid and edge environments.

Streamline API testing with Proxy Mock! Capture, mock, and replay API calls locally

Alan Mon introduces Proxy Mock, a powerful tool for capturing and replaying API calls. Learn how to effortlessly record inbound and outbound API requests and responses. The demonstration highlights how Proxy Mock operates entirely on your local machine, eliminating the need for cloud services or internet connectivity for testing. See how to set up Proxy Mock, inspect captured API calls (including request/response headers, body, and unique signatures), and leverage it to mock API responses for seamless local testing, ultimately boosting productivity and reducing the need for costly non-production environments.

From painted doors to real prototypes - a mindset shift

The economics of building software are changing everything. For years, entrepreneurs used "painted doors" - fake features to test demand - because building was too expensive. But when AI drops development costs, you can create real prototypes and gather genuine user data instead of pretending. This mindset revolution treats experiments like cheap option contracts - the lower the cost, the more you can explore. Ready to abandon painted doors for unlimited experimentation?