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Resources

Testing Guides & Best Practices

Explore comprehensive guides, tutorials, and best practices for automated testing, debugging, and quality assurance. Stay updated with the latest testing tools and techniques.

When Tests Pass but Users Still Fail: Rethinking Debugging and Reliability in AI-Assisted Software Development

When Tests Pass but Users Still Fail: Rethinking Debugging and Reliability in AI-Assisted Software Development

Why green CI and passing tests still miss real failures, and how to build debugging and testing systems around user workflows, observability, and reliable end-to-end validation.

How to Use an AI Agent to Sort Emails

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How to Use an AI Agent to Sort Emails

Learn how to use an AI agent to sort emails automatically using n8n and GPT. This step-by-step guide shows how to build a simple workflow that reads incoming emails, categorizes them, and moves them to the right folders—no coding required. Perfect for anyone looking to organize their inbox with AI.

DebuggAI Team

11 min read

4/14/2026

Debugging JavaScript in Production: A Practical, Technical Guide for Engineers

Debugging JavaScript in Production: A Practical, Technical Guide for Engineers

A deep, hands-on guide to debugging JavaScript issues in production systems: logging, source maps, error tracking, observability, browser and Node.js techniques, tool comparisons, and best practices for reliable triage and fixes.

Diagnosing and Fixing “JavaScript Heap Out of Memory” in Node.js: A Practical Guide for Developers

Diagnosing and Fixing “JavaScript Heap Out of Memory” in Node.js: A Practical Guide for Developers

A deep, practical guide to understanding, reproducing, profiling, and fixing Node.js “JavaScript heap out of memory” errors using built-in diagnostics, heap snapshots, allocation profiling, and leak-hunting workflows—with code examples, tool comparisons, and production best practices.

Debugging JavaScript Like a Pro: Tools, Techniques, and Best Practices for Modern Web Apps

Debugging JavaScript Like a Pro: Tools, Techniques, and Best Practices for Modern Web Apps

A deep, practical guide for developers and engineers on debugging JavaScript in modern web applications, covering browser DevTools, Node.js debugging, source maps, logging, breakpoints, performance profiling, common failure modes, and best practices for reliable, reproducible fixes.

Debugging Distributed Systems: Practical Techniques, Tooling, and Best Practices for Engineers

Debugging Distributed Systems: Practical Techniques, Tooling, and Best Practices for Engineers

A practical, engineer-focused guide to debugging distributed systems using logs, metrics, and traces; common failure modes; concrete code examples; tool comparisons; and battle-tested best practices for observability and incident response.

Privacy-First Code Debugging AI: Redaction, On-Device Inference, and Audit Trails

Privacy-First Code Debugging AI: Redaction, On-Device Inference, and Audit Trails

A practical, opinionated blueprint for building a secure code-debugging AI pipeline: PII/secret redaction, minimal trace packs, on-device or VPC inference, encrypted prompts, and auditable data flows that satisfy compliance without starving models of context.

Debugging and Observability in Modern Software Systems: Tools, Techniques, and Best Practices

Debugging and Observability in Modern Software Systems: Tools, Techniques, and Best Practices

A deep, practical guide for engineers on debugging production issues using observability fundamentals (logs, metrics, traces), structured logging, distributed tracing, profiling, tool comparisons, incident workflows, and code-level best practices across microservices and cloud-native systems.

Your Stack Trace Is Leaking: How Code Debugging AI Pipelines Exfiltrate Secrets—and How to Stop It

Your Stack Trace Is Leaking: How Code Debugging AI Pipelines Exfiltrate Secrets—and How to Stop It

Stack traces and logs can leak keys and PII when routed to code debugging AI. We trace exfil paths across IDEs, CI, and APM, then show mitigations: redaction, taint tracking, on-device inference, RBAC, and enforceable audit/policy gates.

Stop Prompt-Glut: Tooling Up Debug AI with MCP to Run Real Debuggers

Stop Prompt-Glut: Tooling Up Debug AI with MCP to Run Real Debuggers

Why Model Context Protocol beats ever-longer prompts: wire a code debugging AI into gdb, static analyzers, and build systems via MCP to get reproducible traces, fewer hallucinations, and CI-safe fixes.

Teach Your Debug AI to Read Traces: OpenTelemetry Is the New Stack Trace

Teach Your Debug AI to Read Traces: OpenTelemetry Is the New Stack Trace

How to pipe OpenTelemetry traces, logs, and metrics into debug AI workflows to cut hallucinated fixes, prioritize root causes, and make AI-driven debugging reproducible across CI and prod.

Poisoned Logs: Prompt-Injection Attacks on Debug AI and How to Defend

Poisoned Logs: Prompt-Injection Attacks on Debug AI and How to Defend

As engineering teams pipe logs and traces into AI debuggers, attackers can smuggle prompt-injection payloads in error text. This technical guide maps the threat model and recommends concrete defenses: sandboxed tools, content filters, provenance checks, and signed data pipelines.

Trace-Aware Code Debugging AI: Wiring OpenTelemetry and eBPF into Automatic Root Cause Analysis

Trace-Aware Code Debugging AI: Wiring OpenTelemetry and eBPF into Automatic Root Cause Analysis

A practical architecture for feeding spans, logs, and kernel probes to a code debugging AI that pinpoints faulty commits, reproduces crashes, and suggests fixes without noisy heuristics. Includes data shaping, privacy, and performance trade-offs.

Taming Flaky Tests with Debug AI: Trace Replay Architectures that Actually Work

Taming Flaky Tests with Debug AI: Trace Replay Architectures that Actually Work

A practical, opinionated blueprint for building a deterministic trace capture and replay pipeline with time-travel debugging and event timelines to fix flaky tests reliably, even when LLMs struggle with non-determinism.

Proof-Carrying Patches: Making code debug ai Prove Its Fixes Before You Merge

Proof-Carrying Patches: Making code debug ai Prove Its Fixes Before You Merge

Build a pipeline where code debug ai must attach failing tests, deterministic replays, minimal diffs, and supply-chain attestations so only verifiably correct, auditable, and secure fixes get auto-merged.