The Moment of Discovery: Tracing a Lifecycle Bug Through Code

The Message

[assistant] [read] /tmp/czk/cmd/vast-manager/main.go
<path>/tmp/czk/cmd/vast-manager/main.go</path>
<type>file</type>
<content>498: 	}
499: 
500: 	passed := req.Rate >= minRate
501: 
502: 	if passed {
503: 		_, err = s.db.Exec(
504: 			`UPDATE instances SET state = 'bench_done', bench_done_at = CURRENT_TIMESTAMP, bench_rate = ? WHERE uuid = ?`,
505: 			req.Rate, req.UUID,
506: 		)
507: 	} else {
508: 		_, err = s.db.Exec(
509: 			`UPDATE instances SET state = 'killed', bench_done_at = CURRENT_TIMESTAMP, bench_rate = ?, killed_at = CURRENT_TIMESTAMP...

This is not a message that does something. It is a message that sees something. It is a read tool call—a deliberate act of investigation. The assistant, deep in a debugging session spanning dozens of messages, pauses the flow of action to examine source code. What it finds in those few lines will expose a subtle lifecycle bug that threatens to waste money and undermine the entire automated deployment pipeline.

Context: The Norway Instance and the 50 Proofs/Hour Threshold

To understand why this message was written, one must understand the situation that preceded it. The assistant had been building a sophisticated system for renting GPU instances on Vast.ai, running PoRep (Proof of Replication) benchmarks using the CuZK proving engine, and automatically managing their lifecycle. A benchmark had just completed on a Norway-based instance (a single RTX 4090) with a throughput of 41.32 proofs per hour ([msg 1076]). This fell below the configured minimum of 50 proofs per hour.

The assistant checked the manager's dashboard and saw the Norway instance listed with state killed and a bench_rate of 41.32 ([msg 1077]). It then checked the actual Vast.ai instances and discovered a troubling discrepancy: the Norway instance was still running on Vast.ai, despite being marked as killed in the manager's database ([msg 1078]). The manager had not issued a vastai destroy command. This meant the instance was still accruing hourly charges while producing no useful work—a direct drain on resources.

The assistant's immediate reaction was suspicion: "The Norway instance is still running on vast — the manager marked it as killed in the DB but may not have destroyed it yet" ([msg 1079]). This prompted a search for the monitor logic that handles failed benchmarks.

The Investigation: Tracing the Lifecycle Chain

The assistant's thinking process in the messages leading up to this one reveals a methodical debugging approach. First, it grepped for relevant patterns (bench.*fail|failed.*bench|destroy.*bench) and found the monitor's "Step 4: Kill failed benchmarks" section ([msg 1079]). It then read that section and saw the SQL query: SELECT uuid, label, bench_rate, min_rate FROM instances WHERE state = &#39;bench_done&#39; AND bench_rate &lt; min_rate ([msg 1080]).

This was the critical clue. The monitor checks for instances in state bench_done that have a rate below the minimum. But the Norway instance was in state killed, not bench_done. The assistant realized the disconnect: "the monitor checks for state = &#39;bench_done&#39; with bench_rate &lt; min_rate. But let me check what state the entrypoint sets when the benchmark fails" ([msg 1081]). It then grepped for handleBenchDone to find the handler that processes benchmark completion reports from the entrypoint script running on each instance.

This brings us to the target message ([msg 1082]): the assistant reads the handleBenchDone handler to see exactly what state transition occurs when a benchmark fails.

The Discovery: A State Mismatch

The code snippet reveals the handler's logic with stark clarity. The variable passed is set to req.Rate &gt;= minRate. If the benchmark passed, the instance state is set to bench_done. If it failed, the state is set to killed.

This is the root cause of the lifecycle bug. The monitor's Step 4 queries for instances with state = &#39;bench_done&#39; that have a rate below the minimum—but the handler never sets bench_done for failed benchmarks. It sets killed directly. The monitor's query will never match these instances, so it never issues the vastai destroy command. The instance remains running on Vast.ai, burning money, until it is manually destroyed or the contract expires.

The assistant's assumption, visible in the earlier messages, was that the lifecycle worked as a two-stage process: the entrypoint reports the benchmark result, the handler sets the state, and then the monitor's periodic check catches failures and destroys them. But the handler short-circuits this by immediately setting killed—a state that the monitor's failure-detection query does not check. The monitor only looks for bench_done instances with low rates, not killed instances.

Input Knowledge Required

To fully understand this message, one needs several pieces of context:

  1. The architecture of the vast-manager system: There is a central manager service that tracks instances in a SQLite database, a monitor loop that periodically checks instance states and performs actions, and entrypoint scripts running on each rented GPU instance that report benchmark results via HTTP API calls.
  2. The state machine for instance lifecycle: Instances transition through states like registered (just created), params_done (parameters fetched), bench_started (benchmark running), bench_done (benchmark completed successfully), and killed (terminated). Each state transition is an SQL UPDATE.
  3. The monitor's role: The monitor runs periodic checks and performs cleanup actions—killing timed-out instances, destroying failed benchmarks, and maintaining the mapping between Vast.ai instance IDs and internal UUIDs.
  4. The benchmark threshold: The minimum acceptable throughput is 50 proofs per hour. This is configurable and stored in the min_rate column of the instances table.
  5. The specific flow being debugged: The Norway instance completed its benchmark at 41.32 proofs/hour, reported this via the /api/bench-done endpoint, and was expected to be destroyed automatically.

Output Knowledge Created

This message produces a precise understanding of a specific code path. Before reading this handler, the assistant knew that the monitor checked for bench_done instances with low rates. After reading it, the assistant knows that the handler sets killed directly for failures, meaning the monitor's check will never trigger. This is new knowledge that changes the assistant's mental model of the system.

The message also reveals the exact SQL statements used for the state transitions, which is essential for crafting a fix. The handler updates bench_done_at and bench_rate even on failure (storing the rate for later analysis), and also sets killed_at to the current timestamp. This means the failure data is preserved in the database even though the state is killed rather than bench_done.

Assumptions and Their Consequences

The assistant operated under several assumptions that this message helps validate or refute:

Assumption 1: The monitor's failure-detection logic would catch all instances with below-threshold benchmark rates. This was incorrect—the monitor only checks instances in bench_done state, and the handler never sets that state for failures.

Assumption 2: The lifecycle was a clean pipeline where each stage handed off to the next. In reality, there was a fork: successful benchmarks went to bench_done (which the monitor could act on), while failed benchmarks went to killed (which the monitor ignored for destruction purposes).

Assumption 3: The killed state was the terminal state that triggered destruction. While the monitor does have destruction logic for killed instances in other contexts (e.g., instances that were manually destroyed), the specific "kill failed benchmarks" step only queries bench_done instances. This means failed benchmarks fall through a gap in the state machine.

The Broader Significance

This message is a turning point in the debugging session. It transforms the assistant's understanding from "the Norway instance wasn't destroyed" (a symptom) to "the state machine has a missing transition" (a root cause). The fix will require either changing the handler to set bench_done even on failure (and letting the monitor handle the destruction) or adding a separate monitor step that checks for killed instances that need destruction.

The message also demonstrates a fundamental debugging principle: when a system does not behave as expected, trace the actual code paths rather than relying on mental models of how it should work. The assistant could have assumed the monitor was broken or that the entrypoint failed to report. Instead, it methodically traced the chain: entrypoint → HTTP handler → SQL state transition → monitor query. The bug was found at the junction between the handler and the monitor, where their state expectations diverged.

In the broader arc of the session, this discovery leads to a fix in the handleBenchDone handler that ensures failed benchmarks are properly destroyed ([chunk 8.1]). The assistant will go on to add a "post-restart warmup" proof and refine the partition worker logic, but this moment—the reading of a few lines of Go code—is where the critical insight emerges.