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Clean invisible characters. Free online tool, no login, no sign-up.

LocalPersonal data security

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Usage Guide & Tech FAQ

Usage Guide

  1. Import a HAR file (or exported capture data).
  2. Inspect request list and timeline metrics.
  3. Focus on status codes, latency and retries.
  4. Copy the key findings for performance/network debugging.
  5. Redact Authorization, cookies, URL tokens, and PII in response bodies before sharing.
  6. Use waterfall views to spot blocking CSS/JS, font blocking, and long third-party tails.
  7. Compare HARs before/after optimizations on the same browser/network to reduce noise.
  8. Break down `stalled`/`DNS`/`SSL`/`TTFB` to see whether the bottleneck is network or app.
  9. Service worker cache hits may differ from direct network captures—interpret accordingly.
  10. Huge HARs are slow to render—filter by domain or path first.
  11. Read the title and description first to confirm this utility matches your task (avoid using the wrong tool and misinterpreting output).
  12. Paste or type input in the editor; if a sample/template is provided, load it first to learn the expected output shape.

Related Tech Knowledge

  • HAR is a JSON record of browser network interactions.
  • Analysis usually centers on timing, status codes and resource size.
  • If HAR is incomplete, some metrics may be missing.
  • Filter irrelevant domains to improve readability.
  • HAR 1.2 fields vary by exporter; truncated JSON imports will fail.
  • Redirect chains appear as multiple entries—trace the full sequence.
  • CORS preflights are separate entries from the actual request.
  • Compressed responses may differ between `content.size` and `bodySize`—don’t conflate them.
  • Parsing runs locally by default with safeguards for personal data.
  • HAR is highly sensitive—handle storage/transit per your data classification policy.
  • Core parsing and computation run in your browser; by default your raw business payload is not persisted to this site’s servers (see on-page privacy notes).
  • The pipeline is typically: read input → parse (lexical/syntactic/structured) → transform → render; failures aim to be diagnosable.