dupeguru.voltaicideas.net

dupeGuru ships with interface translations for 11 languages: English, French, German, Chinese (Simplified), Czech, Italian, Armenian, Russian, Ukrainian, Brazilian Portuguese, and Vietnamese. If your system language is one of these, you can use dupeGuru without working in English. The list comes from the official dupeGuru site (dupeguru.voltaicideas.net), which states these as the supported languages.

Full list of supported languages

What this means in practice

• Coverage is broad but not exhaustive. Major European and several Asian languages are included, but there is no Spanish, Japanese, Korean, Polish, Dutch, or Arabic on the official list. If your language is not listed, you will likely fall back to English.
• "Brazilian Portuguese" is specified, not generic Portuguese. If you expect European Portuguese terminology, the translation may read differently from what you are used to.
• "Chinese (Simplified)" is specified. Users who prefer Traditional Chinese are not covered by the official list.
• The language applies to the interface, not your files. Changing the UI language does not affect how dupeGuru scans filenames, tags, or pictures — those depend on the scan mode and settings, not the display language.

How to check or change the language

The official page lists supported languages but does not document a specific menu path for switching them. In practice, desktop applications like dupeGuru typically follow one of these patterns:

1. Follow the system locale automatically — the app opens in your OS language if it is supported.
2. Offer a language selector in Preferences/Settings — you pick from a dropdown.

To verify on your machine:

1. Install dupeGuru for your platform (Windows x64/x32, Ubuntu x32/x64, macOS 10.12+, or from source).
2. Open the app and look at the Preferences or Settings area for a language option.
3. If no selector exists, change your operating system's display language and restart dupeGuru — it should pick up the supported language automatically.

Expected result: the menus, buttons, and result-window labels appear in your chosen language. If your language is not among the 11 listed, expect English.

Common sticking points

• Assuming your language is supported because the app is cross-platform. Cross-platform (Linux, OS X, Windows) refers to where it runs, not which languages it speaks. The two lists are separate.
• Confusing scan language with UI language. dupeGuru's Music mode scans tags and its Picture mode scans images fuzzily — these work regardless of the interface language.
• Expecting a partial translation. The official list presents these as supported languages; it does not describe partial or community-in-progress translations, so treat the list as the reliable reference.

If your language is on the list, you can use dupeGuru in it. If it is not, the tool still works — you will just navigate an English interface.

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.

dupeGuru reduces the risk of deleting the wrong file through two built-in safeguards: a reference directory system and a grouping system. According to the project's own description, its scanning engine was "especially designed with safety in mind," and these two mechanisms exist specifically to "prevent you from deleting files you didn't mean to delete." If your main worry is losing an original while clearing out duplicates, these features are the reason to look at dupeGuru rather than a raw file-listing script.

The reference directory system

A duplicate scan only makes sense once you decide which copy is the "original" and which copies are redundant. dupeGuru's reference directory concept addresses exactly this:

• You point the scan at the locations you want to clean up.
• You designate a reference location whose files are treated as the ones to keep.
• Matches found elsewhere can then be treated as the duplicates to act on, rather than the file you care about.

The practical effect is that the tool's default framing of "which file is the duplicate" is anchored to a directory you chose, not to whichever file the scan happened to encounter first. That is the core protection: the decision about what counts as expendable is made by you before you start deleting, not by scan order.

The grouping system

dupeGuru presents results as groups of matching files rather than as a flat list of individual hits. Each group represents one set of files the engine considers duplicates of each other.

This matters for safety because:

• You see the full set of copies together, so you can judge which one to keep in context.
• Actions apply to a group, so you are less likely to act on a single file without realizing other copies exist.
• It makes false positives visible — if a group contains files that clearly aren't the same, you can skip the whole group instead of deleting one member by mistake.

Filtering and sorting to weed out false duplicates

Low-threshold scans (especially fuzzy filename or picture matching) intentionally cast a wide net, which means some results will not be true duplicates. dupeGuru provides multiple ways to filter and sort results so you can "easily weed out false duplicates" before taking any action.

A sensible workflow for a cautious cleanup:

1. Run the scan with a conservative (stricter) threshold first.
2. Sort or filter the results to review the weakest matches.
3. Remove obvious false positives from consideration.
4. Only then apply actions to the remaining groups.

You are not forced to delete

Deletion is one option, not the only one. dupeGuru lets you delete, move, or copy the duplicates it finds. If you are not yet confident in a result set, moving duplicates to a separate folder is a reversible alternative to deleting them outright — you can verify nothing important was moved before clearing that folder yourself.

What dupeGuru does not do for you

The safety features reduce risk; they do not eliminate the need for judgment. Specifically:

• The reference directory and grouping systems protect against acting on the wrong file within the tool's model, but you still choose the reference directory and confirm the actions.
• Fuzzy matching (filenames, and pictures in Picture mode) is designed to find similar items, so a match is not proof of redundancy.
• The project description does not claim any automatic backup or undo of deleted files, so treat deletion as final unless you use move or copy instead.

Quick decision guide

dupeGuru runs on Linux, macOS, and Windows, and is written mostly in Python 3 with Qt5 on Linux/Windows and Cocoa on macOS. The safety mechanisms above are part of the core engine, so they apply regardless of which platform build you use.

dupeGuru finds duplicates by scanning either filenames or file contents, depending on the mode you choose. Filename scans use a fuzzy matching algorithm, so they can surface files whose names are similar rather than identical. For music and pictures, dedicated modes scan tags or compare images fuzzily to catch files that aren't exact copies. You can then adjust the matching engine in Preferences to control how strict or loose those matches are.

The two core scan types

dupeGuru's engine works on one of two inputs, and the choice determines what counts as a "duplicate."

A filename scan is fast and useful when your duplicates share naming patterns. A content scan is the reliable way to catch the same file saved under different names, but it reads file data, so it takes longer on large drives.

How fuzzy filename matching works

The filename scan doesn't require an exact string match. Its fuzzy matching algorithm compares names and scores how similar they are, so vacation_photo_2019.jpg and vacation-photo-2019.jpg can be grouped as likely duplicates even though they differ in punctuation.

Because fuzzy matching produces scores rather than yes/no answers, low-threshold scans can return false positives. dupeGuru addresses this with filtering and sorting options in the results window, letting you weed out matches that aren't real duplicates before acting on them.

Specialized modes for music and pictures

Two modes change what the engine looks at:

• Music mode scans audio tags and displays music-specific information in the results window. This lets you match tracks by their embedded metadata rather than filename alone.
• Picture mode scans pictures fuzzily, so it can find images that are similar but not byte-for-byte identical — useful for resized, re-encoded, or lightly edited copies.

Both modes still feed into the same core Python engine; only the comparison logic and the results display change.

Tuning strictness in Preferences

The matching engine is customizable. The Preference page of the help file lists the scanning engine settings you can change, which is how you dial the scan toward "only exact duplicates" or toward "anything roughly similar." If a scan returns too many near-misses, tighten the relevant setting; if it misses files you expected to match, loosen it.

Safety when acting on results

dupeGuru is designed so you don't delete the wrong file. Its reference directory system and grouping system work together to prevent accidental deletion. Once results are shown, you can delete duplicates, or move or copy them elsewhere instead — so "removing" a duplicate doesn't have to mean destroying it.

Choosing a scan for your situation

• Same files, different names → content scan.
• Messy naming, same folder structure → filename scan with fuzzy matching.
• Music library cleanup → Music mode.
• Photo library with resized or edited copies → Picture mode.
• Too many false positives → tighten the matching engine in Preferences and use result filtering.

dupeGuru runs on Linux, macOS, and Windows, with downloads for Windows (x64 and x32), Ubuntu (x32, x64), macOS (10.12+), and source archives. The interface is available in English, French, German, Chinese (Simplified), Czech, Italian, Armenian, Russian, Ukrainian, Brazilian Portuguese, and Vietnamese.

dupeGuru is a cross-platform GUI tool for finding duplicate files on your computer. It scans either filenames or file contents, runs on Linux, macOS, and Windows, and adds specialized Music and Picture modes for tag-based and fuzzy image matching. It fits you if you want a visual, configurable way to locate duplicates before deciding what to delete, move, or copy — and you're comfortable installing a desktop application rather than using a command-line tool.

What dupeGuru actually does

The core job is simple: point it at folders, run a scan, and get a list of files it considers duplicates. Two scan approaches are available:

• Filename scan — matches names, and uses a fuzzy matching algorithm so it can flag filenames that are similar but not identical.
• Content scan — compares file contents rather than names.

That fuzzy filename matching is the feature that separates it from a strict "same name, same size" checker. It's designed to surface near-matches, which is useful when files have been renamed, copied, or exported with slight variations.

Modes for specific file types

Beyond the general scan, dupeGuru ships with two purpose-built modes:

Picture mode is the one to reach for when you suspect you have resized, re-compressed, or lightly edited copies of the same image — cases where a byte-for-byte comparison would miss the relationship.

Platform support

dupeGuru is cross-platform and runs on Linux, macOS, and Windows. The downloads page lists builds for Windows (x64 and x32), Ubuntu (x32, x64), macOS (10.12+), plus source archives in zip and tar.gz form.

Under the hood it's mostly Python 3, with a notable design choice: the same core Python code is paired with different GUI toolkits per platform. On macOS the UI layer is Objective-C using Cocoa; on Linux and Windows it's Python using Qt5. For you as a user, that mostly means the app looks and behaves natively on each system rather than being a single cross-platform skin.

The interface is available in English, French, German, Chinese (Simplified), Czech, Italian, Armenian, Russian, Ukrainian, Brazilian Portuguese, and Vietnamese.

Safety and result handling

Deleting the wrong file is the main risk with any duplicate finder, so it's worth knowing how dupeGuru guards against it. Two mechanisms are built in:

• A reference directory system — you designate which location is the reference, so the tool knows which copies are the ones to keep.
• A grouping system — duplicates are presented in groups, which makes it harder to act on a file you didn't mean to touch.

Once you've reviewed results, you're not limited to deletion. You can delete duplicates, or move or copy them elsewhere. Results can also be filtered and sorted in multiple ways, which matters most on low-threshold scans where false positives are more likely — sorting and filtering is how you weed those out before acting.

Is it the right tool for you?

dupeGuru is a reasonable fit if:

• You want a GUI rather than scripting your own duplicate search.
• You need cross-platform coverage across Linux, macOS, and Windows.
• You care about similar filenames or similar images, not just exact matches.
• You want to tune the matching engine — the help file's Preference page documents the scanning engine settings you can adjust to target exactly the kind of duplicates you want.

It's a weaker fit if you only ever need exact byte-for-byte matches on a single folder, where a simpler or built-in tool may be enough.

Getting started

1. Go to the downloads section and pick the build for your platform — Windows (x64 or x32), Ubuntu (x32 or x64), or macOS (10.12+). Source archives are available if you'd rather build it.
2. Install and launch the app.
3. Choose a scan mode: standard (filename or content), Music, or Picture.
4. Add the folders you want scanned and, where relevant, set your reference directory.
5. Run the scan. Results appear grouped, and you can filter and sort them.
6. Review the groups, then delete, move, or copy the duplicates you've confirmed.

If a scan returns too many near-matches, adjust the matching engine settings in Preferences and re-run rather than acting on a noisy first pass.