In the cloud-native ecosystem, velocity is everything. We built Kubernetes, microservices, and CI/CD pipelines to ship faster and more reliably. Now, AI coding assistants and autonomous agents are pushing that accelerator to the floor. What started as simple code completion has evolved into tools that draft requirements, generate Helm charts, scaffold microservices, and optimize CI/CD pipelines. For those who care deeply about security hygiene, and especially dependency management, this acceleration requires a hard look at how we manage risk. When an AI agent can scaffold a microservice in seconds, it also makes dozens of architectural and dependency decisions in the…
Author: drweb
Guest Contributor Mar 13, 2026 Achieving Test Reliability for Native E2E Testing: Beyond Fixing Broken Tests Stop chasing flaky native E2E tests. Learn how to stabilize environments, define ownership, improve alerting, and scale runs with Dockerized emulators. Read now
A global survey of 700 software engineering practices published this week finds that thanks to increased reliance on artificial intelligence (AI) coding tools, well over a third (35%) are either achieving daily or more frequent product deployments, with 36% deploying software multiple times per week. However, more than half (51%) also noted AI-generated code leads to deployment problems at least half the time. Conducted by the market research firm Coleman Parkes on behalf of Harness, the survey also finds more than three quarters (78%) admit they have fragmented delivery toolchains, with 70% of respondents also conceding their pipelines are plagued…
End-to-end (E2E) tests are particularly important for native applications that run on various platforms (Android/iOS), screen sizes, and OS versions. E2E testing picks up differences in behavior across this fragmented ecosystem. But keeping E2E tests reliable is often more challenging than writing them in the first place. The fragmented device ecosystem, gaps in test frameworks, network inconsistencies, unstable test environments, and constantly changing UI all contribute to test flakiness. Teams easily get trapped in a cycle of constantly fixing failing tests due to UI changes or environment instability rather than improving the overall reliability of their test infrastructure. They end…
For years, most low-code platforms have focused on one primary challenge: efficiency. The goal was to help teams build applications faster and with less effort, reducing manual coding, speeding up iterations, empowering non-developers, and enabling apps to be created in just a few clicks. That focus delivered real value, but it’s no longer enough. Today, the low-code conversation is shifting. While automation and speed still matter, they are no longer what sets platforms apart. The next phase of low-code is about fit—how well a platform supports the real-world needs of specific industries. This new frontier moves beyond simply closing productivity…
Docker Captain Mar 11, 2026 Building AI Teams: How Docker Sandboxes and Docker Agent Transform Development Learn how Docker Agent + Docker Sandboxes let AI agent teams build, test, and iterate safely in isolated microVM workspaces. Esteban Maya Cadavid and Marco Franzon Read now
Tech LeadBuild the future of Manufacturing📍 Hackney, London (3 days in-office / 2 remote)💰 Highly Competitive + EquityThe MissionGeomiq is the global OS for manufacturing. We bridge the gap between complex engineering and global production through AI-driven automation and high-performance logic. We are looking for a hands-on leader to own our backend and data engineering as we scale.The Role: Lead from the FrontThis is a technical-first leadership role. You won’t just manage; you will architect, build, and ship.Write Production Code: You’ll be in the codebase (Laravel/Python) daily.Architect Systems: Design the data pipelines and logic powering global supply chains.Lead Senior Talent:…
Leaders are heavily investing in helping their teams become more productive. Yet very few can explain what’s actually slowing them down. The quest for improved enterprise productivity typically includes purchasing productivity tools, updating operating models, hiring consultants and, of course, AI. Despite the investment, the problem remains unsolved and it’s felt from the boardroom to the water cooler. Meanwhile, one part of the organization has figured out how to deliver higher-quality work faster. Software teams are some of the most efficient teams in the world. Not because they’re smarter or more technical, but because they’ve learned to design the way…
As AI coding tools accelerate software delivery, they are also intensifying a problem DevOps and SRE teams have been dealing with for years: the unchecked growth of observability data. In this conversation, the founders of Sawmills argue that telemetry volume is no longer just a cost issue. It is becoming a data quality problem that affects how effectively teams can monitor systems, troubleshoot incidents and make sense of production behavior. Ronit Belson and Erez Rusovsky describe how the rise of AI-generated code is making observability harder to manage. Instrumentation is often treated as an afterthought, which means more logs, metrics…
As a DevOps practitioner, I’ve always focused on performance, scalability, and automation. But as cloud adoption has matured, I’ve come to realise that building and running great systems isn’t enough. Cloud efficiency isn’t just technical—it’s financial. That’s where FinOps comes in. It’s not just another buzzword; it’s an essential discipline for anyone working in the cloud.FinOps represents a shift in mindset, one where we move beyond the traditional silos of IT and finance and start thinking holistically about cloud usage. Cloud platforms have given us incredible flexibility and speed, but that comes with complexity—especially when it comes to costs. What…