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AI got cheap enough that Duolingo’s most expensive plan may not survive it. I read the earnings call transcript and opened the app to see what is actually changing for learners.
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See how topology-aware GPU scheduling and cluster recovery can improve the efficiency of large-scale LLM training and inference.
TiDB combines transactions, analytics, and vector computations in a MySQL-compatible distributed database to simplify application stacks.
A 1990s-style game puts participants in breach scenarios to make data security decisions that strengthen infrastructure.
Learn to test Crossplane Compositions in isolation and manage upgrades from local commits through controlled releases to customer environments.
Shows how containerd’s Sandbox API supports runtimes spanning VMM, user-mode kernel, WebAssembly, and runc sandboxes.
How CubeFS addresses the scale, performance, stability, and cost demands of storage for AI model training.
See how WebAssembly and WebGPU bring machine learning models into browsers, reducing reliance on cloud GPU clusters and constant internet access.
A Rust and WebAssembly runtime makes LLM applications portable across devices, from simple sentence completion to RAG agents running on heterogeneous Kubernetes clusters.
Introduces a model service mesh for scalable AI model delivery, including dynamic routing, multi-model endpoints, cache-aware scheduling, and lifecycle management.
A Chinese retail bank’s three-year cloud-native shift shows how a next-generation internal developer platform reshaped engineering for thousands of developers.
Use Ollama Operator to bundle, distribute, deploy, and scale large language models across Kubernetes environments.
How Xiaohongshu uses Karmada to distribute applications across Kubernetes clusters, migrate them securely, and improve elasticity in hybrid cloud infrastructure.
NanoVisor reduces FaaS cold starts with lightweight container setup, read-only filesystems, and sandbox forking to launch secure containers in milliseconds.
Learn how to prepare Kyverno for production, from policy creation and enforcement to scaling, troubleshooting, and integration with existing workflows.
See how eBPF profiles memory, network, and GPU bottlenecks in LLM training and inference without disrupting workloads.
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