Stack-trace investigation
Start with the failure you can reproduce. HawkTrace is a Windows AI coding workspace for following a stack trace through project files and reviewing a focused correction without losing the original exception.
try {
await reproduceWithMinimalInput();
} catch (error) {
reportOriginalError(error);
}Connection
Base URL, credentials and a reachable server.
Project
Reproduce with a minimal input and retain the original error when cleanup also fails.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
try {
await reproduceWithMinimalInput();
} catch (error) {
reportOriginalError(error);
}Keep the useful evidence while investigating a failure
HawkTrace supports a debugging conversation grounded in the files you maintain. In the Windows workspace, bring a reproducible failure and the relevant project code, then ask the connected model to distinguish the observed exception from possible explanations. That distinction is valuable when asynchronous cleanup produces another error and distracts from the first failure.
The project's rejected-promise example offers one starting point. Identify the first application frame, reduce the input needed to trigger it and examine what happens when cleanup fails too. Ask for a proposed correction that preserves useful evidence instead of replacing the original error with a less informative message. This is a task you can perform on your application; the example does not imply a bundled exception-monitoring service.
After reading the suggested edits, reproduce the initial failure and the cleanup failure separately. Compare the resulting messages and run the relevant regression checks before keeping the repair. HawkTrace connects to supported OpenAI-compatible or Anthropic endpoints, with credentials when required. An unfiltered model may be selected where offered, but its server controls tools and context limits and its provider sets applicable restrictions. The value of the session comes from connecting the proposed explanation to a failure you can actually reproduce.
Keeping a debugging session grounded
- What if the stack trace mostly contains library frames?
Locate the first frame belonging to your application and provide the surrounding call path. Include the input and runtime circumstances that reproduce the issue, so the investigation does not rest on an isolated library error message.
- Should cleanup errors replace the original exception?
Decide how your application should report both failures while keeping the original cause available. Review the error-handling code and test the double-failure path; a new message should not silently erase the evidence needed for diagnosis.
- How small should the reproduction become?
Remove unrelated input and setup while retaining the same observed failure. Keep the relevant dependency and runtime conditions explicit. If reducing the case changes the error, record that difference before asking the assistant to explain it.
- Does HawkTrace collect production errors automatically?
The described product is a project coding workspace. Bring appropriate sanitized evidence from your own process; do not assume an installed monitoring agent, production access or automatic exception collection from the application's name or example.
HawkTrace · Updated 2026-10-25
