The Moment of Discovery: Finding the Right API Endpoint in a Distributed Proving System

In the course of debugging a complex distributed system for Filecoin proving, a single message can represent the culmination of a chain of reasoning—the moment when a persistent error finally yields to understanding. Message 1040 in this opencode session is exactly such a moment. The assistant writes:

There's no /api/instances endpoint — it's /api/dashboard. Let me check the current state through the dashboard endpoint.

This brief statement, followed by a successful API call, represents the resolution of a frustrating debugging loop that had consumed several preceding messages. The assistant had been repeatedly querying a non-existent endpoint, receiving 404 page not found responses, and now—after checking the actual source code—it has discovered the correct route. The message is a small but pivotal turning point in a larger narrative about building and debugging a distributed GPU proving infrastructure for the Filecoin network.

The Context: Debugging a Distributed Proving System

To understand why this message matters, we must first understand the system under construction. The assistant is building a "vast-manager"—a management service that orchestrates GPU instances rented from Vast.ai, a marketplace for cloud GPU compute. These instances run a custom proving engine called "cuzk" that generates proofs for Filecoin's storage verification protocol. The system has been plagued by Out of Memory (OOM) crashes, particularly on lower-RAM instances, and the assistant has been iterating on fixes while monitoring live instances in Canada (BC, with 2x RTX 3090) and Norway (1x RTX 4090).

In the messages immediately preceding message 1040, the assistant has been trying to check the status of these instances through the manager's API. It attempted curl -s http://127.0.0.1:1235/api/instances and received 404 page not found ([msg 1035]). It tried port 1236 instead, with the same result ([msg 1038]). It even tried /instances on the root path, also getting a 404 ([msg 1036]). Each failed attempt deepened the mystery: the vast-manager service was running, the ports were listening, but the expected API route didn't exist.

The Reasoning Process: Tracing the 404

The assistant's thinking process in this sequence is a textbook example of systematic debugging. When the first curl to /api/instances returned 404, the assistant didn't just retry blindly—it checked whether the service was actually running (systemctl is-active vast-manager), confirmed the ports were listening (ss -tlnp), and tried different host bindings (127.0.0.1 vs. external IP). Each negative result narrowed the possibilities.

The critical insight came in message 1039, when the assistant used grep to search the source code for registered API routes:

grep HandleFunc.*api
Found 5 matches
/tmp/czk/cmd/vast-manager/main.go:
  Line 1290: mux.HandleFunc("/api/log-push", s.handleLogPush)
  Line 1293: mux.HandleFunc("/api/dashboard", s.handleDashboard)
  Line 1294: mux.HandleFunc("/api/manager-logs", s.handleManagerLogs)
  Line 1295: mux.HandleFunc("/api/instance-logs/", s.handleInstanceLogs)
  Line 1296: mux.HandleFunc("/api/kill", s.handleKill)

This grep output is the key that unlocks message 1040. The assistant had been assuming the endpoint was /api/instances—a reasonable assumption given that the manager manages instances, and many REST APIs use plural nouns for collection endpoints. But the actual code had /api/dashboard instead. The assistant's assumption was wrong, but it was a natural inference from the system's purpose.

Assumptions Made and Corrected

The assistant made several assumptions that turned out to be incorrect:

  1. The endpoint name assumption: The assistant assumed the API would follow a conventional REST pattern with /api/instances. This is a common pattern in web APIs, but the developer of this particular service chose /api/dashboard instead—perhaps because the endpoint returns a curated view of instance state rather than raw instance data.
  2. The port binding assumption: The assistant initially tried the external IP (10.1.2.104) and later switched to 127.0.0.1, correctly recognizing that the service might only bind on localhost. This assumption was correct—the service was listening on localhost.
  3. The route structure assumption: The assistant tried both port 1235 and port 1236, suggesting it assumed the API might be on a separate port from the UI. In reality, the same port (1235) served both the HTML UI (at /) and the JSON API (at /api/dashboard). The most significant incorrect assumption was the endpoint name. This kind of mistake is nearly inevitable in any evolving codebase where API routes are defined ad-hoc rather than following a strict convention. The assistant's response to the 404 errors was methodical: rather than guessing more endpoint names, it went to the source code to discover the actual routes.

Input Knowledge Required

To understand and produce this message, the assistant needed:

Output Knowledge Created

The message produces several valuable pieces of knowledge:

  1. The correct API endpoint: /api/dashboard on port 1235, not /api/instances. This is immediately actionable—the assistant can now reliably query instance state.
  2. The state of the Norway instance (ID 32711934): It is in params_done state, meaning it has finished downloading Filecoin proof parameters and is ready to begin the benchmark. The bench_rate is ? (unknown), indicating the benchmark hasn't started or hasn't reported results yet.
  3. The state of the BC Canada instance (ID 32711932): It is in killed state with bench_rate: 0. This confirms the OOM crash that was suspected earlier—the instance was destroyed after failing to complete its benchmark. The killed state is a lifecycle state set by the manager when it detects a failure and destroys the instance via vastai destroy.
  4. The dashboard response structure: The assistant learns the JSON shape of the dashboard response—an object with an instances array, and fields like total, running, and bad_hosts. This knowledge enables future queries.

The Broader Significance

This message is more than just a successful API call. It represents a shift from blind probing to informed investigation. Before this message, the assistant was firing curl commands at guessed endpoints and getting 404s. After this message, the assistant has a reliable way to observe the system's state. The very next message (1041) leverages this new knowledge to get the "full picture" and plan the OOM fix.

The message also reveals an important design principle: when building distributed systems, the ability to introspect the system's state is as important as the system itself. The vast-manager's dashboard API is the window into the fleet of GPU instances, and finding that window was a prerequisite for any further debugging.

In the larger narrative of this session, message 1040 is the turning point where confusion gives way to clarity. The assistant stops guessing and starts reading code. The 404 errors stop. The dashboard opens up. And from that vantage point, the assistant can see both the success (Norway benchmarking) and the failure (BC Canada OOM) that will drive the next phase of work: fixing the OOM crash by reducing partition workers during warmup, rebuilding the Docker image, and redeploying.

Conclusion

Message 1040 exemplifies a fundamental debugging skill: when your assumptions about an API lead to repeated failures, stop guessing and read the source code. The assistant's methodical progression—from failed curl commands, to service checks, to port binding tests, to source code grep, and finally to the correct endpoint—is a model of systematic troubleshooting. The message itself is brief, but it encapsulates the reasoning, the corrected assumptions, and the new knowledge that together move the project forward. In the complex world of distributed GPU proving, finding the right API endpoint is the first step toward finding the right fix.