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

Building a Multi-Agent Containerization System at Bunnyshell

At Bunnyshell, we’re building the environment layer for modern software delivery. One of the hardest problems our users face is converting arbitrary codebases into production-ready environments, especially when dealing with monoliths, microservices, ML workloads, and non-standard frameworks. To solve this, we built MACS: a multi-agent system that automates containerization and deployment from any Git repo.
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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.

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.

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.

Cloud Log Management: A Developer's Guide to Scalable Observability

As systems move to microservices, serverless, and multi-cloud setups, debugging gets harder. You’re no longer dealing with a single log file; you’re looking at logs from dozens of services, running across different environments. Traditional debugging methods like SSH-ing into servers or adding print statements don’t scale in these environments. Cloud log management tools help by collecting logs from all your services into one place.

What is Log Loss and Cross-Entropy

You're building a classification model, and your framework throws around terms like "log loss" and "cross-entropy loss." Are they the same thing? When should you use binary cross-entropy versus categorical cross-entropy? What about focal loss? This blog breaks down these loss functions with practical examples and real-world implementations.