The Ignore Button: Adding Manual Blacklisting to a GPU Instance Management Dashboard

"In search add a 'ignore' button to mark the host as 'definitely not good enough'"

This single sentence, uttered by the user at message index 1365 in an opencode coding session, represents a pivotal moment in the evolution of a GPU instance management dashboard. It is a deceptively simple feature request that reveals deep assumptions about operational workflow, trust in automated systems, and the human role in machine selection. To understand why this message was written, we must reconstruct the context in which it appeared: a sprawling, multi-session effort to build a system called vast-manager — a web-based control plane for provisioning, benchmarking, and managing GPU compute instances on the Vast.ai marketplace, used for Filecoin proof generation.

The Context: A System Taking Shape

In the moments immediately preceding this message, the assistant had been implementing a series of UI and backend features for the vast-manager. The system already had an Offers panel — a searchable, filterable table of available GPU instances from Vast.ai, each row showing hardware specifications (GPU model, CPU architecture, RAM, PCIe bandwidth, network speed) with color-coded quality indicators. A Deploy dialog had been added that auto-calculated a minimum proofs-per-hour rate from the instance's dollar-per-hour cost, enforcing a cost-efficiency floor of $0.008 per proof. A "Loading" state had been introduced to show instances that existed in the Vast cache but hadn't yet contacted the manager's registration endpoint. The dashboard showed summary cards for Running, Benchmarking, Fetching, and Killed instances.

But there was a gap. The system could automatically mark a host as bad — if a deployed instance failed its benchmark by falling below the minimum rate, the handleBenchDone endpoint would add it to a bad_hosts table. But there was no way for a human operator to pre-emptively blacklist a host they knew, from experience or intuition, would be problematic. The user wanted that capability.

Why This Message Was Written

The motivation is rooted in operational pragmatism. The user had been iterating on this system for days, deploying instances, watching them fail, investigating root causes. They had developed a mental model of which hosts were reliable and which were not. Perhaps they had seen a particular GPU model underperform repeatedly. Perhaps a certain geographic region consistently had network issues. Perhaps a specific Vast.ai operator's machines were known to have thermal throttling problems. The automated system could only learn from benchmarks it had actually run — but the user had knowledge that predated and transcended the system's empirical data.

The phrase "definitely not good enough" is telling. It signals certainty born from experience. The user isn't asking for a probabilistic filter or a scoring system. They want a hard, manual veto. A host that is "definitely not good enough" should never be deployed to, should never appear in filtered search results, should be struck from consideration entirely. This is a classic pattern in operational tooling: the human operator needs a way to inject their judgment into an otherwise automated pipeline, creating a feedback loop that combines machine data with human intuition.

Assumptions Embedded in the Request

The user's request carries several implicit assumptions. First, that the search results panel is the right place for this control — the user wants to act on offers at the point of evaluation, not after deployment. This assumes the user is spending significant time browsing offers and making decisions, which was indeed the workflow the system was designed to support.

Second, the request assumes that "badness" is a binary, permanent property of a host. The user doesn't ask for a temporary mute or a soft filter — they want to mark the host as bad, period. This is a strong assumption that would later be challenged: in message 1371, the user asks "Is the 'known perf' 'bad' marker from db? if so make clicking the marker undo the mark," revealing that they recognized the need for reversibility. The initial request, however, treated blacklisting as final.

Third, the request assumes that the system has a concept of "host" that persists across sessions and deployments. This is correct — the bad_hosts table already existed in the SQLite database, keyed on host_id (the Vast.ai operator account identifier). The Ignore button would simply add a row to this table with a reason string.

How the Assistant Interpreted and Implemented the Request

The assistant's response reveals its understanding of the system architecture. It immediately located the relevant code — the offer row rendering in ui.html — and identified where to insert the button. The implementation was straightforward:

  1. Add a red "Ignore" button to each offer row in the search results panel.
  2. On click, confirm with the host ID, GPU name, and location.
  3. POST to /bad-hosts to add the host to the bad_hosts table with reason "Not good enough (GPU, location)."
  4. Immediately update the UI — if "Hide bad hosts" is checked, the row disappears; otherwise it appears struck-through.
  5. Refresh the bad hosts panel to keep the sidebar in sync. The assistant's thinking process is visible in its tool calls: it read the UI file to find the offer rendering code, identified the right insertion point, made the edit, added the JavaScript function, rebuilt the Go binary, deployed it, and verified. The entire cycle took about 30 seconds of wall-clock time across messages 1366-1370.

What the Assistant Got Right

The assistant correctly identified that the bad_hosts table already existed and that the backend handler for adding bad hosts was already implemented. It correctly placed the button in the offer row, near the existing Deploy button. It correctly handled the UI update — filtering or striking through the row immediately, rather than requiring a page refresh. It correctly included a confirmation dialog to prevent accidental clicks.

What the Assistant Got Wrong

The assistant assumed the Ignore button should be a one-way operation — mark as bad, done. It did not anticipate that the user would want to undo the mark. This became apparent in message 1371, where the user immediately asked about making the BAD badge clickable to remove the mark. The assistant had to add a second function (unignoreHost) and wire up the BAD badge as a clickable element that calls DELETE /bad-host/{id}.

This is a subtle but important lesson in feature design: any operation that removes an option from the user's view should be reversible. The assistant treated "ignore" as a permanent action, but the user treated it as a toggle. The user's mental model was that the BAD badge was a piece of state that could be flipped on and off, not a permanent record.

Input Knowledge Required to Understand This Message

To fully grasp this message, one needs to understand:

Output Knowledge Created by This Message

The message produced:

The Broader Significance

This message exemplifies a recurring pattern in the development of operational tooling: the tension between automation and human judgment. The vast-manager system was becoming increasingly automated — it could search for instances, calculate cost efficiency, deploy workers, run benchmarks, and kill underperforming instances automatically. But the user kept finding edge cases where their own judgment was necessary. The Ignore button was a valve for that judgment, a way to inject human expertise into an algorithmic pipeline.

The message also reveals the iterative nature of the development process. The user didn't specify the full design — they didn't ask for undo, they didn't specify where the button should go, they didn't define the confirmation dialog. They trusted the assistant to fill in the details. And the assistant did, correctly in most respects, but missing the reversibility requirement that the user immediately identified. This back-and-forth, where each implementation reveals new requirements, is the engine of the entire session.

In the end, the Ignore button was a small feature with large implications. It acknowledged that the system's automated evaluations — benchmark rates, cost-per-proof calculations, hardware quality indicators — were not sufficient. The human operator had knowledge that the system could not capture, and the system needed a way to absorb that knowledge. The Ignore button was that mechanism: a simple, binary, human-powered signal that said "this host is definitely not good enough," fed directly into the machine's decision-making process.