Claude Code Powers K8s CronJobs and Z80/ZX Spectrum Emulation in Production

Everyrow’s engineering team discovered that Claude Code can be run as a true “server‑less” workload inside Kubernetes, but only after wrestling with a handful of undocumented runtime quirks. In their blog post “Claude Code as a K8s CronJob – how we do it and what we learned running it in production,” the authors detail how the default container image for Claude Code lacks a proper entrypoint for non‑interactive execution, forcing them to wrap the binary in a custom shell script that supplies the required `--no‑interactive` flag and redirects stdout to a structured JSON logger (Everyrow.io). The Helm chart they released also includes a sidecar that streams logs to a centralized Loki instance, enabling real‑time observability of the model’s token usage and latency. The team notes that the CronJob’s back‑off policy had to be tuned manually because Claude Code retries on transient network errors in a way that conflicts with Kubernetes’ exponential back‑off defaults.

The production pipeline built on top of this CronJob is essentially a data‑centric ETL engine that invokes Claude Code to “forecast, score, classify, or research every row of a dataset,” as Everyrow describes on its website. By scheduling the model to run on a nightly cadence, the service can ingest fresh CSV uploads, generate embeddings for each record, and write the results back to a Postgres store without human oversight. The authors point out that the model’s token limits required them to batch rows into groups of roughly 500 records, a constraint that is not mentioned in Anthropic’s official documentation but emerged during load testing. They also implemented a lightweight retry wrapper that catches Claude Code’s `429 Too Many Requests` responses and re‑queues the batch after a configurable pause, preventing the CronJob from being throttled into a failure loop.

Beyond data processing, the same Claude Code binary was repurposed to drive a Z80/ZX Spectrum emulator, a surprising use case that demonstrates the model’s versatility in handling low‑level instruction sets. In a separate write‑up titled “Implementing a Z80 / ZX Spectrum emulator with Claude Code,” the author describes how the emulator’s core loop feeds raw Z80 opcodes into Claude Code, which then returns the next CPU state as a JSON payload. The model, trained on a mixture of code and documentation, is able to infer the correct register updates and memory accesses for most instructions, effectively acting as a just‑in‑time interpreter. The article notes that the emulator runs entirely in production on Everyrow’s Kubernetes cluster, leveraging the same Helm chart and logging infrastructure used for the CronJob, thereby unifying the deployment pipeline for both data‑science and retro‑computing workloads.

Performance metrics collected from Loki reveal that the emulator incurs an average latency of 45 ms per instruction, a figure that is acceptable for interactive emulation of classic games but still an order of magnitude slower than native Z80 cores. The authors attribute this overhead to the round‑trip time between the CronJob container and Anthropic’s API endpoint, as well as the model’s tokenization step for each opcode. They also observed that the emulator’s accuracy degrades when presented with undocumented hardware quirks—such as the ZX Spectrum’s contention model—because Claude Code’s training data does not cover those edge cases. Nonetheless, the proof‑of‑concept validates that large‑language models can serve as flexible back‑ends for niche compute tasks that traditionally require hand‑written interpreters.

The open‑source repository released by Everyrow (linked in both blog posts) includes the Dockerfile, Helm chart, and a set of example CronJob manifests that can be deployed with a single `helm install` command. The documentation explicitly calls out the need to set the `ANTHROPIC_API_KEY` secret and to configure resource limits that match the model’s memory footprint (approximately 2 GiB of RAM). By publishing these artifacts, Everyrow invites the community to experiment with Claude Code in other batch‑processing scenarios, from nightly report generation to automated code review pipelines. As the team notes, the real value lies not just in the model’s capabilities but in the operational patterns they have codified—patterns that could become a de‑facto standard for running LLM‑backed services at scale on Kubernetes.