API errors in the browser remain a daily friction point for front-end engineers, QA, and on-call developers: teams routinely encounter opaque HTTP responses, cryptic JSON error payloads, or 500s that force manual cross-referencing to OpenAPI specs, logs, and recent deploys to diagnose. With roughly 25 million professional developers and an estimated $12.0B addressable market for dev tools (about $480/yr average spend), even modest reductions in time-to-fix scale to meaningful productivity and cost savings for engineering organizations. You could build a browser-based debugging assistant — a DevTools panel or extension that intercepts HTTP responses, parses structured error payloads and headers, correlates them with OpenAPI schemas, recent CI changes, and observability traces, and uses an LLM to generate concise, actionable remediation steps, code snippets, tests, and suggested PRs. A hybrid architecture (on-device inference for sensitive payloads and cloud reasoning for complex diagnostics) plus integrations with Sentry, Datadog, and GitHub would balance privacy, latency, and accuracy; go-to-market could start as a per-seat SaaS with enterprise options for on-prem inference and custom rule packs. This is an attractive moment: model quality now enables reliable parsing of structured errors into context-aware fixes, API-first adoption increases error surface area, and the shift-left movement favors developer-facing tools that prevent lengthy incidents—hence the strong market and revenue indicators. To stand out you must prioritize precision and trust—schema-aware reasoning, provenance and confidence indicators, tight company-integrations, and privacy-first deployment—while confronting real challenges around model cost, fragmented API ecosystems, and the risk of occasional misleading recommendations.