KMKashif Mohammadinblog.lucrolearning.com·3d ago · 11 min readInside the Pipeline: Clustering, Summarizing, and Mapping Themes at ScaleIn Part 7 we followed a batch of raw text responses through fetching, pruning, deduplication, preprocessing, and embedding, and ended up with one clean dataset: every response paired with its vector, 11X
KMKashif Mohammadinblog.lucrolearning.com·4d ago · 11 min readInside the Pipeline: Fetching, Cleaning, and Embedding Survey Data at ScaleIn Part 6 we closed the loop on the execution platform: a worker process boots in isolation, reports progress through a callback, listens for SIGTERM, and cleans up after itself no matter how it exits00
KMKashif Mohammadinblog.lucrolearning.com·5d ago · 5 min readMaking Workers Production ReadyThe Worker Has Been Loaded… Now What? In the previous article we followed a job from the Queue Manager into a newly created worker process. By this point our worker has: Started a new Python process 00
KMKashif Mohammadinblog.lucrolearning.com·Jul 18 · 6 min read"Inside the Worker: Building a Resilient AI Execution Platform" This is Part 5 of the Beyond FastAPI series In the previous article, The Listener Loop: Turning Jobs into Work, we built the scheduling layer responsible for monitoring the queue, enforcing concurrenc00
KMKashif Mohammadinblog.lucrolearning.com·Jul 17 · 6 min readThe Listener Loop: Turning Jobs into WorkSo far, we've built an execution engine that starts alongside our FastAPI application and runs independently of incoming HTTP requests. We have also seen the role of a queue manager that schedules and00
