Jack Lu

I spoke with Tom Wilkie, CTO of Grafana Labs about what it’s like scaling open source projects while building a commercial company around them. Discussed the challenges, lessons, and what goes on behind the scenes of a tool so many of us use: https://lnkd.in/gD92CXuD

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Kubernetes just got smarter about hardware — and that’s a big deal for AI. Dynamic Resource Allocation (DRA) that went GA in k8s 1.34 unlocks a new way to manage GPUs, FPGAs, and other specialized devices in Kubernetes. Instead of static allocation, DRA lets you define device classes and claims, so workloads get the exact resources they need — no more underutilization or rigid scheduling. Why it matters: 1. For GPU-intensive AI/ML workloads, DRA ensures fair sharing or dedicated allocation, improving performance and efficiency. 2. It simplifies scaling AI pipelines where multiple teams or models need controlled access to accelerators. 3. It future-proofs Kubernetes clusters for emerging workloads in generative AI, HPC, and data analytics. In our first two blog posts on the k8s DRA series, we break down: - Why DRA matters? - What DRA is and how it works - Roles of Cluster Admins and Workload Admins If you’re building or scaling AI workloads on Kubernetes, DRA is a must-know capability. 👉 https://lnkd.in/gEn5uwnS and https://lnkd.in/gVHKbjrx

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The Chainguard approach to reducing technical debt in an organization is: consistent nano updates. But, as I shared with Adrian Bridgwater, these updates only work when you’re starting with the latest and greatest software from the beginning. Once you do, you can eliminate vulnerabilities, reduce the risk of big-bang upgrades, and improve control and traceability. A win-win for developers who benefit from more frequent, reliable deployments. Read more, including my four principles of nano upgrades, in Forbes: https://lnkd.in/gvx5vAc5 

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Modern engineering teams have world-class observability for apps, infra, and data. Yet when it comes to voice, most teams are still operating in the dark (before they come to us at Hamming AI). Latency spikes, broken prompts, ASR drift, mispronunciations, etc, directly shape the customer experience, but without proper visibility, teams only discover problems after customers churn. That’s why I believe voice observability must emerge as its own discipline, complete with its own metrics, frameworks, and industry standards. Read more here: https://lnkd.in/eTTCUCQp

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Terraform's original sin is the state file. We built modern infrastructure on fragile JSON blobs in S3. Every project its own island, no global view, no history, just grep and jq when you need answers. Here's the reality: Terraform sold us "infrastructure as code," but stopped short of making it infrastructure as data. What we got was infrastructure as scattered text files with the relational depth of a Post-it note. State should be a living data source. Queryable. Auditable. Real-time. Exposed through APIs your tooling can actually use. #terraform

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