Why real-time AI at scale is so hard
Real-time AI at scale is harder than it looks. Pipelines that hum along in development routinely hit problems in production. It’s always easy to blame the model for all your…
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Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent
When you run Kubernetes at the scale we do on Amazon EKS, nodes break constantly. GPUs fall off the PCIe bus. Container runtimes wedge. Network interfaces disappear. Across tens of…
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Move code review before the code
The pull request as we know it is roughly 20 years old, younger than the careers of many people now defending it as non-negotiable. Code review, too, feels permanent. It…
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Platform engineering’s new job: serving environments at agent speed
Platform engineering has won the argument. Some 90% of organizations have adopted at least one internal platform; golden paths are orthodoxy, and environment requests that once took days now close…
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AI didn’t replace our security team — it multiplied it.
For years, the assumption in security has been straightforward: mature detection and response programs require a Security Operations Center (SOC). A team of analysts looking at a wall of monitors…
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APIs aren’t dead. Here’s where MCP fits alongside them.
The allure of emerging technology is undeniable, but adopting it rarely means completely ripping out what already works. Instead, new capabilities must find their place alongside existing infrastructure, complementing the…
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85% say code review is the new bottleneck. Here’s what the AI coding narrative leaves out.
A merge is a contract. The moment a change lands on main, every other team in the organization starts building on the assumption that it works. They branch from it,…
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The impressive AI demo is dead. Here’s what actually reaches production
Most engineering teams I talk to can ship an AI demo. The prototype works, stakeholders are impressed, and everyone agrees the use case has potential. Then the project hits a…
