For roblox cube infrastructure and platform teams
Roblox Cube: model startup or accelerated inference path?
Connect the first useful exception with the relevant runtime evidence. Was optional fast inference enabled on a supported backend?
Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.
Replay a resolved Roblox Cube workflow incident
Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.
Before uploading, review cloud data handling and local options.
Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.
Try Denpex for 30 days. FREE.
No credit card. No automatic subscription. See whether Denpex helps with an incident you already understand.
Start your free 30-day Team evaluation
Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.
What a useful evaluation should establish
Compare one incident your team already resolved with the first failure, cited evidence and remaining questions. Was optional fast inference enabled on a supported backend? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.
Was optional fast inference enabled on a supported backend?
Evidence to compare: Cube revision, fast-inference setting and first backend initialization error.
The optional execution path has platform and memory constraints. Compare the selected backend before treating an unsupported fast path as faulty hardware.
Did model initialization finish before image generation?
Evidence to compare: Weight-loading completion, environment identity and the first worker exception.
An unavailable model artifact or incompatible environment can prevent execution before image generation starts. Do not infer device exhaustion without allocation evidence.
Did execution fail or did output miss its quality target?
Evidence to compare: Process exit status, model artifact identity and separate task-evaluation result.
A completed model run can still produce an unsuitable result. Keep infrastructure reliability separate from scientific validity, task accuracy or creative quality.
Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.
Inside Denpex
See the evidence, not just the claim
Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.
Follow the failure through the cascade

Prepare the hardware escalation

Ask for the evidence that is missing

Keep the investigation in the editor
