Two movements meet: AI is getting production responsibilities, and production data is flowing through AI. Anthropic’s September report, malicious routers, training data provenance and model drift show what to verify before an AI reaches production.
AI products moved from the answer arrives to the answer flows. What streaming inference changes in capacity planning: TTFT, token throughput, concurrent streams.
Switching tile formats on a LiDAR ingestion pipeline cut output object count by roughly 25x at the same data volume, independent of compression. Here is why object count is its own cost and performance axis on object storage, and what to check before you scale a pipeline that writes many small files.
I used whisper.cpp on an RTX 5070 to transcribe about 100 hours of podcasts in roughly three hours, then turned the Markdown output into a searchable prompt knowledge base.
MinIO has no NameNode. It puts the namespace on XFS as real directories and xl.meta files. On Lots of Small Files that means you can exhaust inodes while df -h still looks fine, and a flat leaf can stall PUT, LIST, scanner, and ILM together. Here is the on-disk model, the inode math, and a prefix recipe that keeps XFS inside a regime you can operate.
Spark sets executor CPU requests equal to limits, so a Kubernetes cluster reserves twice the CPU it uses and refuses to schedule pending pods. Kubernetes has no native overcommit. Here is the mutating-webhook operator I use to fix it.
Bring the IOPS dude ! As a solutions architect working with MinIO storage solutions, I’ve seen firsthand the challenges that come with on-premises deployments using hard disk drives (HDDs). While HDDs may seem like a cost-effective option initially, they can introduce significant performance bottlenecks that impact the overall efficiency and Read more