Code Snippet Explainer
AI-assisted code explanation. 5 free explanations per day.
AI-assisted explanations are usually right but not guaranteed. For load-bearing code (security, financial, anything in production), have a human engineer verify.
What this tool does
Reads a code snippet and produces a plain-English walkthrough: what the snippet does overall, what each block or line contributes, and a short notes section if anything in the code is unusual, likely buggy, or relies on subtle runtime behavior. Works for any common language; a language hint helps the explanation be more accurate but is not required. The snippet goes to the service for processing and is not stored after the request completes.
How to use it
Paste the snippet into the input. Optionally type the language name in the hint field. Press Explain. The free tier allows 5 explanations per IP per day. Heavier use is on the roadmap.
Common use cases
- Reading code in a language you do not normally use (Rust, Haskell, OCaml, etc.).
- Understanding a regex-heavy or bit-twiddling utility someone else wrote.
- Documenting an unfamiliar codebase area before refactoring.
- Checking that a Stack Overflow answer's code does what the answer claims.
- Onboarding to a project where the code style is dense and undocumented.
Common pitfalls
- Out-of-context snippets. A short snippet pulled from a larger file may use names defined elsewhere. The explanation works from what is in the snippet; it cannot infer module-level imports or class hierarchies it cannot see.
- Vendor extensions. Database SQL dialects, framework macros, and custom DSLs may be misread as standard syntax. Provide the language hint and consider including a short comment explaining the dialect.
- Bugs vs intent. The explainer describes what the code does, not what you wanted it to do. If the explanation is correct but surprising, the bug is yours; if it disagrees with what the code obviously does, flag the explanation.
Frequently asked questions
- Where does my code go?
- The snippet is sent to the glunty server which forwards it to Anthropic Claude for processing. glunty does not log or store your code; Anthropic API receives it for the duration of the request, governed by Anthropic data-usage policy (no training on API inputs by default). Do not paste proprietary or secret-containing code; strip credentials, API keys, and confidential business logic before submitting.
- Why 5 per day instead of more?
- Code explanations consume more tokens than short pattern explanations because the input is longer. The cap keeps the tool free for typical human use without enabling bulk processing. If you need higher volume (hundreds per day), the Claude API directly is the right path; the per-call cost is small but adds up over thousands.
- How accurate is the explanation for unusual languages?
- The model handles mainstream languages (Python, JavaScript, Java, C++, Rust, Go, Ruby, PHP, Swift, Kotlin, C#) reliably. Less common languages (Forth, Haskell, OCaml, Erlang, COBOL) are handled less consistently. The language-hint field improves accuracy for any language; for very obscure dialects, expect the explanation to be a starting point that you cross-check against the language documentation.
- Why does the explanation sometimes split into "what" and "notes"?
- The model is instructed to first describe the code purpose and flow, then call out anything unusual: a likely bug, an idiomatic shortcut not everyone knows, a performance concern, or a subtle invariant. The notes section is where surprises land. If you skim, read the notes first.
- How does this compare to GitHub Copilot or Cursor?
- Same family of capability (LLM reading code), different surface. Copilot and Cursor are integrated with your editor and have access to the surrounding files; this tool is standalone and works on the snippet alone. For one-off explanations from the web or chat, this is faster than booting an editor; for ongoing development inside a codebase, an integrated tool wins.
- Will it explain malicious code?
- It will explain what most code does, including code with surprising or insecure behavior. The explanation often surfaces issues you would not have noticed (a buffer overflow, an SQL injection, an unsanitized template render). It does not deliberately help write malicious code; that is a separate guardrail handled at the model level.
Cite this tool
For academic, journalistic, or technical references. Pick a format:
Citations use 2026 as the publication year. Access date is left as a fillable placeholder where the citation style expects one.