The Request That Defined a Service: Creating the CuZK Run Script

In the middle of an intense Docker build iteration for Filecoin's CuZK proving engine, a single user message arrives that crystallizes the entire deployment philosophy:

"Create a run script which starts cuzk; will later also start curio but for now just cuzk"

This is message [msg 693] in the conversation, and on its surface it is deceptively simple — a straightforward request to produce a startup script. But this message is far more than a feature request. It is a architectural decision point that reveals the user's mental model of the system, encodes assumptions about the production environment, and sets the trajectory for how the entire proving stack will be deployed on remote GPU infrastructure. To understand why this message matters, we must unpack the context that produced it, the reasoning it triggered in the assistant, and the knowledge it both consumed and created.

The Context: From Benchmarking to Production

The conversation leading up to this message had been a marathon of Docker image construction. The assistant had built a multi-stage Dockerfile for curio-cuzk, pushed it to Docker Hub, and then engaged in a rapid iteration cycle driven by real-world testing on vast.ai — a marketplace for renting GPU instances. The user had been SSH'ing into remote instances and running benchmark.sh, discovering bugs in real time: the -n flag didn't exist in cuzk-bench batch (it needed --count), the daemon's default param_cache path (/data/zk/params) didn't match where params actually lived (/var/tmp/filecoin-proof-parameters), and the on-start script ran in the background on vast.ai, meaning param fetches could still be in progress when the user SSH'd in.

Each of these discoveries had been patched. The benchmark script had grown from a simple test harness into a sophisticated tool that waited for param fetches, auto-generated daemon configs, and exported environment variables. But benchmark.sh was still fundamentally a benchmarking tool — it ran a fixed number of proofs, reported timing, and then stopped. It was not a production service launcher.

This is the crucial context for understanding [msg 693]. The user had been iterating on a benchmarking workflow, but their real goal was always production proving. The benchmark script was a stepping stone. Now they were ready for the next step: a script that would actually run the proving service in production, starting with the CuZK daemon and later expanding to include Curio itself.

The Architecture of a Simple Request

The message contains three distinct layers of intent, each more significant than the last.

Layer 1: The explicit request. "Create a run script which starts cuzk." This is the literal ask. The user needs an executable that launches the cuzk-daemon process. This is the minimum viable product.

Layer 2: The architectural constraint. "Will later also start curio but for now just cuzk." This is a critical design signal. The user is not asking for a one-off script to start the daemon manually. They are asking for a composable entry point that can be extended to orchestrate multiple services. The script must be designed with future integration in mind — it should use patterns (foreground execution, signal handling, configuration generation) that will work when Curio is added alongside CuZK.

Layer 3: The unspoken deployment model. The user is building for vast.ai and similar GPU rental platforms. On these platforms, instances are provisioned with an "on-start" script that runs in the background. The run script must therefore be robust to asynchronous initialization (param fetches may still be running), must handle process lifecycle correctly (no orphaned daemons), and must work as a foreground entrypoint for Docker containers. The user doesn't say any of this explicitly — it's encoded in the history of fixes they've already requested.

The Assistant's Reasoning Process

The assistant's response to this message reveals a careful, context-aware reasoning process. Rather than immediately writing a script from scratch, the assistant first reads the existing benchmark.sh ([msg 694]). This is a deliberate decision: the benchmark script already contains battle-tested patterns for daemon startup, param directory detection, and config generation. By reading it, the assistant can extract the reusable infrastructure and compose a new script that is consistent with the existing codebase.

The assistant then writes run.sh ([msg 695]), makes it executable ([msg 696]), and adds it to the Dockerfile ([msg 697]). The subsequent summary ([msg 698]) reveals the design decisions:

  1. Foreground execution with exec: The script uses exec to replace the shell process with the daemon. This is critical for Docker containers, where PID 1 must handle signals properly. Without exec, a shell wrapper would intercept SIGTERM/SIGINT and the daemon might not shut down cleanly.
  2. Auto-generated config: The script generates a minimal TOML config file that sets param_cache to the correct directory. This solves the problem discovered during benchmarking — the daemon's hardcoded default of /data/zk/params doesn't match where curio fetch-params actually places the files (/var/tmp/filecoin-proof-parameters or wherever FIL_PROOFS_PARAMETER_CACHE points).
  3. Param fetch wait: The script waits for any in-progress curio fetch-params process before starting. This is a direct response to the vast.ai on-start behavior — the on-start script runs in the background, so param fetches might still be downloading when the run script executes.
  4. Extensibility: The script is structured to allow adding Curio later. By using exec for the daemon, the script establishes a pattern where each service can be started in sequence, potentially with a supervisor process or a more complex orchestration layer.

Assumptions Embedded in the Request

The user's message makes several assumptions that are worth examining:

Assumption 1: The CuZK daemon is the right starting point. The user assumes that the proving workflow begins with the CuZK daemon — a GPU-accelerated proving service that accepts proof requests over RPC. This is correct for the architecture they've built, but it's a non-trivial design choice. The daemon could have been embedded in Curio itself, or the proving could have been done via direct CLI invocation. The decision to run a persistent daemon implies a service-oriented architecture where proving is decoupled from the main Curio process.

Assumption 2: The run script will be part of the Docker image. By asking for a "run script" in the context of Docker build iterations, the user assumes this script will be baked into the image at /usr/local/bin/run.sh (or similar), available as both an entrypoint and a manual invocation tool. This is confirmed when the assistant adds it to the Dockerfile.

Assumption 3: The script should be simple now, complex later. The user explicitly defers Curio integration. This assumes that a phased rollout is feasible — that CuZK can operate independently of Curio for testing and validation, and that the integration point is well-defined enough to be added later without restructuring the script.

Assumption 4: The environment is hostile to naive startup. The user's prior requests — waiting for param fetches, handling background on-start scripts, fixing param directory mismatches — all point to an assumption that the runtime environment is unpredictable. The run script must be defensive.

Input Knowledge Required

To fully understand this message, one needs:

Output Knowledge Created

This message and its response produced several forms of knowledge:

The run.sh script itself: A concrete artifact that starts the CuZK daemon with correct configuration, signal handling, and defensive startup checks. This becomes part of the Docker image and the deployment workflow.

A pattern for service composition: The script establishes how multiple services (CuZK now, Curio later) can be orchestrated. The use of exec for the final process means that future Curio integration might require a different approach — perhaps a supervisor process or a more complex init system.

Validation of the deployment model: By creating a dedicated run script, the conversation implicitly validates that the CuZK daemon is ready for production deployment. The benchmarking phase is complete; the service phase has begun.

Documentation of assumptions: The script encodes the team's understanding of the runtime environment — where params live, how vast.ai works, what signal handling is needed. This knowledge is now preserved in executable form.

The Significance of Deferral

Perhaps the most interesting aspect of this message is what it doesn't ask for. The user explicitly says "will later also start curio but for now just cuzk." This deferral is a conscious architectural choice. It acknowledges that:

  1. CuZK can be validated independently before the full Curio integration is tested.
  2. The proving service (CuZK) is the performance-critical path that needs the most tuning.
  3. The Curio integration may require additional configuration (storage paths, task queues, API endpoints) that isn't needed for CuZK alone.
  4. A phased rollout reduces risk — if the run script has bugs, they're isolated to CuZK and don't affect the broader Curio system. This deferral also creates a natural boundary in the codebase. The run script for CuZK can be simple and focused. When Curio is added, it may need to become more complex — perhaps using wait and background processes, or a proper process manager. By deferring, the user avoids over-engineering the solution before the requirements are fully understood.

Conclusion

Message [msg 693] is a turning point in the conversation. It marks the transition from benchmarking to production, from testing to deployment, from ad-hoc debugging to structured service management. The user's simple request — "create a run script which starts cuzk" — carries the weight of all the context that came before it: the param directory bugs, the vast.ai on-start quirks, the Docker signal handling requirements, and the long-term vision of a full Curio+CuZK proving stack.

The assistant's response demonstrates the value of context-aware engineering. By reading the existing benchmark script, understanding the deployment environment, and anticipating future integration, the assistant produces a script that is not just correct but resilient — it handles the edge cases discovered through real-world testing and sets the stage for the next phase of development. In doing so, it transforms a simple request into a foundational piece of the deployment architecture.