Every developer has written the loop. Read a list of files, process each one, write the result. It works for 200 items. At 50,000 it falls apart: one failure kills the run, there's no record of what finished, and restarting means starting over. The usual next step is a jump to Kubernetes jobs, Airflow or a cloud batch service. That's a lot of infrastructure for what is still, … [Read more...] about BatchComputing.com Names the Gap Between a Shell Loop and Kubernetes
AI inference
AIInferences.com Names the Observability Layer for AI Model Calls
Every AI feature in production is a stream of inference calls. Each one has a model, a prompt size, an output size, a latency, a cost, a set of parameters, a cache hit or miss, and an outcome. Most companies record almost none of it in a structured way. They find out what happened when the monthly bill arrives. AIInferences.com names the system of record for that … [Read more...] about AIInferences.com Names the Observability Layer for AI Model Calls