rust.godbolt.org

No. Compiler Explorer (rust.godbolt.org) is a browser-based interactive compiler that you can open and use without paying, creating an account, or installing a compiler locally. The page evidence shows no pricing, subscription, or login gate — instead it offers optional support channels (Become a Patron, Sponsor on GitHub, Donate via PayPal, Compiler Explorer Shop). One real constraint: the regular site requires JavaScript; a noscript version is provided for environments without it.

What you actually need

• A modern browser with JavaScript enabled for the full interface.
• Nothing else. No local toolchain, no package installation, no account.
• If JavaScript is unavailable, use the linked noscript version — the page states the regular website is not functional without JavaScript.

What is free vs. what is optional

The presence of donation and shop links does not imply that core features are paywalled; the page simply lists them alongside other site links (Wiki, Report an issue, Contact the author, Statistics, Changelog).

Practical limits to expect

• JavaScript dependency. Without it, the main UI won't work; the noscript fallback is the documented alternative.
• Session and sharing. The UI includes Save/Load, History, Share Short Link, Full Link, and Embed in iframe, so you can persist and share snippets without an account.
• Execution. There is an "Execute the code" option and a Compiler Execution / Execution Only mode, meaning you can run code remotely rather than locally.

If you want to get started

1. Open rust.godbolt.org in a JavaScript-enabled browser.
2. Pick Rust as the language and choose a compiler from the compiler selector.
3. Paste or type code in the Source Editor.
4. Read the assembly in the Output pane, or switch tools (Rust MIR, Rust HIR, Rust Macro Expansion, LLVM IR, etc.) from the "Add tool" menu.
5. Use Share Short Link or Full Link to keep or send the example.

If any step fails, the most common cause is a disabled or blocked JavaScript environment — switch to the noscript version or enable JavaScript and reload.

A postal code API returns structured location data for a given ZIP Code or Canadian postal code. A typical response includes the code itself, city, state or province, county, latitude/longitude, and — where available — ZIP+4 detail. More complete services add time zone, area codes, boundary geometry, and demographic fields. You send a code (or an address), and the API sends back a machine-readable record you can store, validate, or display.

This article explains what those responses contain, how requests are usually shaped, and where postal code data fits into real applications.

First, clear up the word "code"

The keyword "code" is overloaded, and that causes real confusion for developers:

• Postal code — the ZIP Code (U.S.) or postal code (Canada) that identifies a delivery area.
• API key — the credential you use to authenticate your requests. It is not postal data.
• Source code — the program you write to call the API.

When someone searches for "postal code API code," they usually want example request/response code for a postal code service. The rest of this article treats it that way.

What a postal code API actually returns

Response fields vary by provider and endpoint, but the core set is fairly consistent. A single-code lookup commonly returns:

Richer datasets add 90+ fields: boundaries, population, income, elevation, and more. You rarely need all of them — request only what your application uses.

A representative JSON response

{
  "postal_code": "90210",
  "city": "Beverly Hills",
  "state": "CA",
  "county": "Los Angeles",
  "latitude": 34.0901,
  "longitude": -118.4065,
  "timezone": "America/Los_Angeles",
  "area_codes": ["310", "424"]
}

XML responses carry the same information in tag form. Choose based on what your stack parses most easily; JSON is the common default.

Common request patterns

Most postal code APIs support four patterns. Knowing which one you need prevents wasted calls.

1. Lookup by code

You have a code and want its details. This is the simplest and fastest call.

GET /lookup?code=90210

2. Reverse lookup by address

You have a street address and want to confirm or complete the code. This is the pattern behind checkout address validation.

GET /validate?street=...&city=...&state=...

3. Radius search

You have a center point and want all codes within a distance. Useful for store locators and delivery zones.

GET /radius?code=90210&miles=10

4. Batch validation

You have a file of addresses and want them cleaned in bulk. Batch endpoints trade latency for throughput and usually have their own limits.

Handling missing and ambiguous matches

Real data is messy. Plan for these cases:

• No match — the code doesn't exist or the address is malformed. Return a clear error rather than a silent empty object.
• Multiple matches — a city name may map to several codes, or a code may span several acceptable city names. Decide whether to pick the primary or return a list.
• Partial match — the street is valid but the ZIP+4 isn't. Fall back to the 5-digit code.
• Stale data — codes are added, retired, and reassigned. Refresh your dataset on a regular schedule.

A practical rule: validate at the point of entry, store the normalized result, and never re-derive it later from raw user input.

Practical use cases

• Checkout address validation — catch typos before shipping, reduce failed deliveries.
• Shipping zone lookup — map a code to a zone, carrier route, or rate table.
• Data enrichment — append county, coordinates, or demographics to existing records.
• Store and service locators — radius search to find nearby branches or coverage areas.
• Territory and tax logic — county and boundary data drive jurisdiction rules.

Licensing and data-source considerations

Postal code data originates with national authorities — USPS in the United States and Canada Post in Canada. Providers license and repackage it, which is why accuracy, update frequency, and field coverage differ between services. Before committing:

• Confirm the data source and how often it refreshes.
• Check whether ZIP+4 and boundary data are included or sold separately.
• Review usage limits and whether batch processing is allowed.
• Read the license terms for redistribution and storage.

Pricing and plan details change, so check the provider's current documentation rather than relying on secondhand figures.

Getting started

1. Decide which request pattern you need (lookup, reverse, radius, or batch).
2. Pick the fields you'll actually store.
3. Write a small test call and inspect the raw response.
4. Add error handling for no-match and ambiguous cases.
5. Cache results where the same codes repeat.

A postal code API is ultimately a translation layer: you give it a code or an address, and it gives back structured location facts. Understand the fields, match them to your use case, and handle the messy edges — that's most of the work.

Yes. Compiler Explorer (rust.godbolt.org) is built around exactly that use case: you can add multiple compilers or language versions in the same session and view their outputs side by side. The comparison works best when you keep the source code identical and change only one variable at a time — the compiler, the version, the optimization level, or the target platform.

How the comparison model works

The interface is organized as a set of panes. On the left is a source editor; on the right is one or more output panes, each tied to a specific compiler. When you add another compiler, you get another output pane, and both compile the same source. This is the core mechanism that makes side-by-side comparison possible.

The site describes itself as "an interactive online compiler which shows the assembly output of compiled C++, Rust, Go (and many more) code." The same source-to-output pipeline applies regardless of which language you select.

Adding compilers and language versions

Use the Add new... control to create additional compiler entries. Each entry has its own compiler selection, so you can:

• Pick two different compilers for the same language (for example, different Rust toolchain versions, or Rust versus another language).
• Pick the same compiler with different settings.
• Mix languages entirely, since the site supports C++, Rust, Go, and many others.

Once added, each compiler gets its own output pane, and all panes recompile when you edit the source.

What you can compare

The output selector is worth noting for Rust work specifically: the page evidence lists Rust MIR, Rust HIR, and Rust Macro Expansion as available outputs, alongside the standard assembly and LLVM IR views. That means you can compare not just final assembly but intermediate representations across compilers or versions.

A practical comparison workflow

1. Write or paste your source into the editor.
2. Add a second compiler with Add new....
3. Set each pane's compiler and version to the two you want to compare.
4. Set the same optimization flags on both so the only difference is the compiler.
5. Read the two output panes side by side.

Expected result: two independent outputs for identical input, so any difference you see comes from the compiler, version, or flags — not from the code.

Common sticking points

• Different flags on each pane. If you change optimization on one side only, you are comparing flags, not compilers. Keep flags matched unless flags are the variable under test.
• Output type mismatch. Comparing one pane's assembly against another pane's LLVM IR is not a like-for-like comparison. Match the output type first.
• Target differences. A different target architecture changes instruction selection, so pin the target when the compiler is the variable.
• JavaScript requirement. The page states that "without Javascript the regular website is not functional" and points to a separate noscript version. The interactive multi-pane comparison depends on the regular site.

When this is the right tool

Compiler Explorer fits when you want to see what the compiler produced — assembly, IR, or macro expansion — for a small, self-contained piece of code, and you want that view for more than one compiler or version at once. It is a codegen inspection and comparison tool, not a benchmark harness: the page evidence shows execution and runtime tooling options, but the primary value for comparison is the static output panes.

Compiler Explorer (rust.godbolt.org) is an interactive online compiler that shows the assembly output of compiled code, and it also exposes a much wider set of compiler outputs and intermediate representations. For Rust specifically, you can inspect Rust HIR, Rust MIR, and Rust macro expansion in addition to the final assembly. For C, C++, and other LLVM-based languages, the tool offers LLVM IR, ClangIR, preprocessor output, and AST views. GCC-based compilers add GIMPLE, Tree/RTL, and optimization pass dumps. The exact set of available views depends on the selected language and compiler, and some tools are disabled when the current language or compiler does not support them.

Core output views

These are the outputs most users start with, and they are available across many language/compiler combinations.

The output pane also supports options such as demangling identifiers, filtering unused labels, library functions, directives, and comments, and toggling Intel asm syntax. These filters matter because raw compiler output is noisy; hiding library functions and directives usually makes the relevant codegen easier to read.

Rust-specific intermediate representations

Rust has its own pipeline stages that sit between source and machine code, and Compiler Explorer exposes them directly.

• Rust HIR (High-level IR): A desugared but still fairly source-like representation. Useful for seeing how syntactic constructs are lowered before type checking and borrow checking.
• Rust MIR (Mid-level IR): The representation used for borrow checking and many optimizations. This is the most informative view for understanding why the borrow checker accepts or rejects code, and how control flow is modeled.
• Rust macro expansion: Shows the result of expanding macros, which is essential when a macro-generated error or unexpected codegen appears.

A practical example: if a match or ? operator produces surprising assembly, checking MIR first shows how the control flow was lowered, and macro expansion shows whether a macro introduced the extra branches. That sequence — macro expansion → HIR → MIR → assembly — lets you localize a problem to a specific compiler stage instead of guessing from the final output.

LLVM and Clang views

For compilers backed by LLVM, additional views are available:

• LLVM IR: The target-independent intermediate representation. Useful for seeing optimizations that happen before machine code generation.
• ClangIR: A newer Clang IR, available where supported.
• Opt Pipeline / optimization remarks: Shows which optimization passes ran and what they did, including missed and passed optimization remarks. This is the view to use when you expect an optimization (inlining, vectorization, constant folding) but it did not happen.

LLVM IR options include demangling symbols, -fno-discard-value-names for readable value names, and filters for debug info and instruction metadata. Enabling value names makes IR far easier to follow.

GCC views

When a GCC-based compiler is selected, the output options include GCC-specific dumps:

• GCC Tree/RTL: Tree and register-transfer language dumps.
• GIMPLE: GCC's mid-level representation, with options for raw or slim dumps, addresses, alias info, basic blocks, line numbers, pass details, pass stats, unique IDs, and virtual operands.
• Passes / Passes Tree / RTL Pass / IPA Pass: Views of individual optimization passes.

These are the GCC equivalents of LLVM's opt pipeline and are the right place to look when GCC performs (or fails to perform) an optimization.

Other representations and tools

The interface also lists views such as GHC Core, GHC STG, GHC Cmm (Haskell), Lean C output, Clojure macro expansion, Yul (Solidity IR), GNAT debug trees (Ada), and a control flow graph. Availability is compiler-dependent, and the UI explicitly notes when the current language or compiler does not support a given tool.

How to choose a view

1. Start with assembly to confirm the behavior you care about.
2. If the assembly is surprising, move one stage up: MIR for Rust, LLVM IR for LLVM compilers, GIMPLE for GCC.
3. If the IR looks wrong at the source level, check macro expansion (Rust) or the preprocessor (C/C++).
4. If an optimization is missing, open the optimization remarks / pass view to see whether the pass ran and why it was skipped.

The main constraint to keep in mind is that not every view exists for every compiler, so the available list changes with your language and compiler selection.

Open rust.godbolt.org in a browser with JavaScript enabled, type Rust code into the left-hand source editor, and read the generated assembly in the right-hand output panel. The site compiles as you type, so no build step or local toolchain is required. This works for inspecting assembly, LLVM IR, Rust MIR/HIR, macro expansion, and optimization remarks.

Before you start

• JavaScript is required. The regular site is not functional without it; a noscript version exists but is limited.
• No login is needed to compile and view output. Save/Load and short-link sharing are available from the UI.
• The default language is Rust on this host, so the compiler dropdown already points at a Rust compiler.

Step-by-step: compile Rust and read the assembly

1. Enter your code. Click into the Source Editor on the left and type or paste a Rust function. For example:

   pub fn add(a: u32, b: u32) -> u32 {
       a + b
   }
   

2. Confirm the compiler. Use the compiler selector above the output pane to pick the Rust compiler and version you want. The output refreshes automatically after each change.

3. Read the assembly. The default output pane shows the compiled assembly for your code. Each source line is mapped to the instructions it produced, so you can trace which Rust statement generated which instructions.

4. Change the optimization level. Add flags in the compiler options field, for example -O or -C opt-level=3, then watch the assembly change. This is the fastest way to see how inlining and loop optimizations alter the output.

5. Switch output views. Use the output dropdown to select a different representation instead of assembly:

   View	What it shows
   Assembly	Final machine instructions
   LLVM IR	Intermediate representation after Rust lowers to LLVM
   Rust MIR	Mid-level IR, useful for borrow-check and optimization questions
   Rust HIR	High-level IR, closer to your source
   Rust Macro Expansion	What macros expand into
   Preprocessor / AST	Earlier stages of the pipeline

6. Add tools when needed. The "Add tool…" menu attaches extra panes such as Control Flow Graph, Stack Usage, or optimization remarks. Some tools report "The current language or compiler does not support this tool" — that message means the selected compiler cannot produce that output, so switch compilers or drop the tool.

7. Share or save. Use Share for a short or full link, or Embed in iframe to place the example in a page. Save/Load keeps named snippets.

Common snags

• Empty or stale output. Confirm the compiler finished; a compile error replaces the assembly with diagnostics.
• Assembly looks unoptimized. The default build is typically debug-style. Add -O or -C opt-level=3 to see optimized code.
• Symbols are hard to read. Enable Demangle identifiers in the output options so Rust-mangled names become readable.
• Too much noise. Use the output filters to hide directives, comments, library functions, or unused labels.
• Wrong tool for the compiler. If a pane says the tool is unsupported, pick a different Rust compiler version from the dropdown.

What you can decide from the output

Use the assembly view to check whether a function was inlined, whether bounds checks remain, or how a data structure is laid out. Use Rust MIR when the question is about borrow checking or a specific optimization pass rather than final codegen. Use LLVM IR when you want to see what the optimizer received before it produced machine code.