The Moment a Fix Unravels: Debugging the Docker Build Cache in a Distributed Proving System

Introduction

In the high-stakes world of distributed GPU proving for Filecoin, where cryptographic proofs must be generated correctly across multiple parallel pipelines, even a perfectly designed code fix can fail at the deployment boundary. This article examines a single message from an opencode coding session—message index 1998—where an AI assistant confronts the unsettling evidence that a fix it just deployed may not have taken effect. The message captures a pivotal moment of uncertainty: the user reports that the cuzk logs still show the old, broken job ID format, and the assistant must rapidly re-evaluate its assumptions about what was actually deployed.

This message is remarkable not for the code it changes (it changes none), but for the thinking process it reveals. It is a window into the metacognitive loop of debugging: hypothesis, evidence, contradiction, revised hypothesis, verification attempt. The assistant has just deployed a binary with a version string confirming the fix (_psfix2), yet the production logs stubbornly display the old behavior. The gap between "we deployed the fix" and "the fix is working" is where this message lives.

The Context: A Job ID Collision in the ProofShare System

To understand message 1998, we must first understand what came before it. The session had been wrestling with a pernicious bug in the ProofShare system, a component of the Curio Filecoin implementation that coordinates the generation of proofs-of-replication (PoReps) across a pool of GPU workers.

The bug manifested as all ten partitions of a PoRep proof being invalid. The root cause, traced in earlier messages, was a job ID collision. The RequestId sent to the cuzk (CUDA Zero-Knowledge) proving engine was formatted as fmt.Sprintf("ps-porep-%d-%d", miner, sector). In the ProofShare system, all challenges targeted the same benchmark sector (miner=1000, sector=1). When multiple PSProve tasks ran concurrently, they all sent identical job_id values to the cuzk engine. The engine's partition assembler used a HashMap keyed on job_id, so partition results from different proofs overwrote each other. The smoking gun was a panic: "partition 0 already inserted".

The fix was straightforward: add the harmony task ID to the format string, making it fmt.Sprintf("ps-porep-%d-%d-%d", miner, sector, taskID). This matched the pattern already used by the Snap computation path. The assistant edited the Go source file, rebuilt the Curio binary inside a Docker CUDA builder container, copied it to the remote host, killed the old process, and started the new one. The version string confirmed _psfix2 was running. The assistant declared the fix deployed and summarized the root cause.

Then came message 1997 from the user:

seems still bad id - 2026-03-13T10:10:25.968225Z INFO cuzk_core::pipeline: GPU prove complete (split) proof_count=1 proof_bytes=192 gpu_ms=16491 TIMELINE,2260853,GPU_END,ps-porep-1000-1,worker=1,partition=5,gpu_ms=16491 2026-03-13T10:10:25.968370Z INFO cuzk_core::engine: partition GPU prove complete job_id=ps-porep-1000-1 partition=5 gpu_ms=16491 filled=0

The job ID was still ps-porep-1000-1—the old format without the task ID. The fix had apparently not worked.

Message 1998: The Assistant's Reasoning

This is the full text of the subject message:

## Agent Reasoning The job_id in the cuzk log still shows ps-porep-1000-1 without the taskID. This means the curio binary that's running is still the old one, OR the cuzk is receiving requests from the old curio that was still running when we swapped.

>

Wait, actually - we killed the old curio and started a new one. But cuzk is still processing jobs that were submitted before the restart. Let me check if there are still old jobs in the cuzk pipeline, or if the new curio is sending requests with the old format.

>

Actually, let me think about this. The cuzk daemon has been running continuously. It may still have jobs queued from before we restarted curio.

>

The PSProve tasks already in the harmony_task queue were submitted with the old request ID format. When curio restarted, any in-flight PSProve tasks would've been lost and reassigned, but the proofshare_queue entries persist in the database. So when the new curio starts up, it picks those entries back up and creates fresh tasks - but the new curio should be sending requests with the updated format including the taskID. Unless something's preventing that from happening.

>

Looking at the timestamp around 3 minutes after the new curio started, these could actually be jobs the new curio submitted but haven't completed yet - synthesis alone takes about 80 seconds per partition plus GPU time. The request ID format might be producing the right structure, but maybe the cuzk logs are only showing the job_id field rather than the full RequestId, or there's some truncation happening. I need to verify whether the running curio is actually the updated version and check what jobs are still queued in cuzk. The logs are definitely from the new curio instance since they're an hour after the restart, so the binary should have our changes—but the job_id format doesn't include the taskID we added. The version string shows _psfix2, confirming it's our latest build, but maybe Docker caching prevented the actual code changes from being applied. Let me check if the binary contains the format string we added to verify the build actually included our modifications.

>

Let me verify the running binary actually has the fix — the Docker build may have used cached Go artifacts: [bash] ssh -p 40362 root@141.195.21.72 'strings /usr/local/bin/curio | grep "ps-porep-"' bash: line 1: strings: command not found

The Thinking Process: A Tour Through the Debugging Mind

What makes this message so instructive is the visible, iterative reasoning. The assistant does not jump to a single conclusion. Instead, it walks through multiple hypotheses, correcting itself as it goes, weighing evidence, and ultimately arriving at a testable prediction.

Hypothesis 1: The Old Binary Is Still Running

The assistant's first instinct is the simplest explanation: the old curio binary is still running. This is a natural starting point—deployment failures often come down to the wrong process being active. But the assistant immediately catches itself: "Wait, actually - we killed the old curio and started a new one." This self-correction is crucial. The assistant recalls its own actions from the previous deployment round and realizes this hypothesis conflicts with known facts.

Hypothesis 2: Old Jobs in the cuzk Pipeline

The assistant pivots to a more nuanced explanation: the cuzk daemon has been running continuously and may still have jobs queued from before the restart. This is a plausible scenario in distributed systems—a job submitted with the old format could still be making its way through the pipeline even after the submitting process is replaced.

But again, the assistant refines this. It considers the database persistence layer: proofshare_queue entries survive restarts. When the new curio starts, it picks up these entries and creates fresh tasks. The new curio should be sending requests with the updated format. The assistant is now tracing the full lifecycle of a job from database to cuzk request, demonstrating a sophisticated understanding of the system architecture.

Hypothesis 3: Log Truncation

The assistant briefly considers a more benign explanation: perhaps the cuzk logs are only showing the job_id field and not the full RequestId. Maybe the task ID is present in the request but not displayed in the log line. This is a reasonable concern—log formats can be misleading—but the assistant doesn't dwell on it, correctly judging it unlikely given the explicit format string.

Hypothesis 4: Docker Build Cache

This is the killer hypothesis, and the one the assistant arrives at through careful reasoning: "maybe Docker caching prevented the actual code changes from being applied." The version string shows _psfix2, which should mean the binary was built from the modified source. But the Go build system inside Docker may have used cached compilation artifacts, skipping the recompilation of the changed file. The version string is set via a linker flag (-X), which doesn't require recompiling the changed source file—it's a build-time injection that always runs. So the binary could have the new version string but the old code.

This is a subtle and sophisticated diagnosis. The assistant recognizes that the version string is not a reliable indicator of whether specific source files were recompiled. It's a lesson many developers learn the hard way: build tags and version strings can be updated without touching the code that matters.

Assumptions and Their Failure

The message reveals several assumptions that turned out to be incorrect:

Assumption 1: Killing the process and starting a new one is sufficient. The assistant assumed that a clean process lifecycle would ensure all new requests used the new format. It didn't account for the possibility that the binary itself was unchanged.

Assumption 2: The version string confirms the fix. The _psfix2 suffix was present in the version output, which the assistant took as evidence that the build had succeeded with the new code. In reality, the version string is set by a -ldflags -X directive that runs regardless of whether any Go source files are recompiled. The Docker build system's caching layer had skipped the recompilation of task_prove.go because it didn't detect the file change (a known issue with --volumes-from and file modification times in Docker).

Assumption 3: The Docker build would detect the source changes. The assistant used docker cp to copy the modified files into a container, then used --volumes-from to share those files with the builder container. This approach is fragile—Go's build cache keys on file modification times and content hashes, and the copy operation may not have updated timestamps in a way that triggers recompilation.

Input Knowledge Required

To fully understand this message, a reader needs:

Output Knowledge Created

This message produces several valuable insights:

  1. The fix deployment failed silently. The binary running on the production host has the correct version string but the old code. This is a critical operational finding.
  2. Docker Go build caching is the likely culprit. The assistant correctly identifies that the build system may have used cached artifacts, skipping recompilation of the changed file.
  3. Binary verification is necessary. The assistant attempts to verify the binary contents directly by grepping for the format string. This is the right approach—checking the actual compiled code rather than trusting version strings or build logs.
  4. The strings command isn't available on the target host. This is a minor operational detail, but it forces the assistant to find alternative verification methods in subsequent messages.

The Broader Significance

Message 1998 is a case study in the gap between "deploying a fix" and "the fix working." In modern distributed systems, especially those involving GPU workers, Docker containers, and multi-language components (Go, Rust, CUDA), the deployment pipeline is as complex as the application itself. A fix can be correct in the source code but fail to reach production due to build caching, process lifecycle issues, or database persistence of old job formats.

The message also illustrates the importance of self-correction in debugging. The assistant's reasoning process is not linear—it circles back, refines hypotheses, and ultimately arrives at a more nuanced understanding. This is characteristic of expert debugging: the willingness to abandon initial explanations and pursue deeper causal chains.

The specific insight about Docker Go build caching is particularly valuable. Many developers have been burned by the Go build cache in CI/CD pipelines, where file modification times don't propagate correctly through container layers. The assistant's suspicion that "the Docker build may have used cached Go artifacts" is exactly right, as revealed in the subsequent chunk where bind mounts (-v) finally force a full recompile.

Conclusion

Message 1998 captures the moment when confidence in a deployment meets contradictory evidence. The assistant has done everything right—identified the root cause, implemented the fix, built the binary, deployed it, and verified the version string. Yet the system still exhibits the old behavior. The assistant's response is not panic or confusion, but systematic re-evaluation: considering multiple hypotheses, tracing the full job lifecycle, and ultimately identifying the Docker build cache as the likely culprit.

This message is a reminder that in complex systems, the deployment is part of the bug. A fix isn't truly deployed until it's verified at the binary level, not just the version string level. The assistant's attempt to grep the binary for the format string is the right instinct—direct verification of the compiled code—even if the strings command isn't available on the target host.

The story continues in the next chunk, where the assistant overcomes the Docker cache issue by switching to bind mounts, forcing a full recompile, and finally deploying a binary with the correct format string. But message 1998 stands as the critical turning point: the moment the assistant realized that a fix can be correct in the source and absent in the binary, and that deployment verification requires looking at what the machine is actually executing, not what the build log says it should be executing.