Can I bias toward a region or culture?

The current pool is mostly English-language Western names. For other locales, generate names and post-process or use a locale-specific source.

Where does generation happen?

Entirely in your browser tab. Nothing is sent to a server.

fake names · first · last · bulk · local

Generate fake person names

Q: Are these real people?

No. First and last names are mixed at random from generic Western name pools — they look plausible but are not tied to actual individuals.

Hundreds of realistic person names for QA fixtures, demos, and design comps. Pick full names, just firsts, or just lasts — all picked from curated pools with crypto randomness.

Fake Name Generator

Names

50 requested

Mode

full

shape

Pool

50×50

first×last

Output

0 B

txt

input · controls

Pick the mode, slide the count, then click Generate. The pool is curated — names are common but not tied to real people.

For full names, first and last are picked independently — combinations may repeat but feel natural.

output · names (0)

Click Generate to produce fake names

Quick Start

How to fake real-looking names

Two curated word lists, picked uniformly at random. Use full mode for “Alice Cooper” style outputs, or single mode for separate first/last pools.

01
Pick a mode
full = first + last, first = first names only, last = last names only.
Inputs are treated as UTF-8 throughout the pipeline and validated client-side as you type — nothing dispatches until the value is well-formed.
modeinpututf-8client-sidevalidated
mode · textfull · set
```
Alice Cooper
```
loaded · parsedclient-side
02
Choose the count
1 to 1000 names per batch. The slider lets you find the right amount fast.
Each toggle re-computes through a memoised hook, so flipping options never resets your input and the UI stays snappy on large inputs.
countoptionslivememoisedtoggles
count · memoised50 · live
```
50 names
```
options set · memoisedlive
03
Generate
Each name is an independent pick — collisions possible but rare.
Randomness — where used — comes from crypto.getRandomValues, never Math.random. Heavy work sits in useMemo.
mintcomputeinstantlocaldeterministic
mint · instantrand · computed
```
Bob · Carol · David
```
computed · instantinstant
04
Copy or download
Drop into a spreadsheet, fixture, or demo dataset.
Copy writes through navigator.clipboard and downloads use a Blob — zero network traffic. Same input plus same options always yield the same bytes.
exportcopydownloadzero-networkreproducible
ok · copiedready · copied
```
Copied
```
shipped · ready to shipsuccess

Pro tips

Pro tips for everyday utilities

Reach for crypto.getRandomValues

Cryptographically random for IDs, tokens, salts. Math.random is for animation and games, not security.

Sort IDs lexicographically

UUID v4 has no time ordering. Use ULID or UUID v7 if you need sorted IDs.

Mock data with seeds

Faker-style libraries can be seeded so tests are reproducible. Embed the seed in the fixture file.

Reference cards beat memory

Cheatsheets for Vim, Git, Docker, Linux signals are faster than man pages when you’re mid-flow.

Browser APIs vary

Touch events, gamepads, and clipboard differ across vendors. Feature-detect before use.

Persist in the right place

localStorage for user preferences, sessionStorage for transient state, IndexedDB for structured data.

Use Cases

When to use fake names

Demos, fixtures, design comps, screenshots.

Design comps

Realistic placeholder names beat Lorem ipsum in screenshots. Random values come from crypto.getRandomValues — cryptographically secure for tokens, salts, and session IDs. Math.random is deterministic enough for animations but predictable enough to be a security bug.

CSPRNG-backed

QA fixtures

Seed test users without ever touching real PII. Pair with hashids, ULID, or UUID v7 when you need both random and sortable — useful for primary keys, public-facing IDs, and event log ordering without a sequence column.

reproducible

Demo videos

Show live data without exposing your team's names. Output is deterministic given the same seed, so fixtures and tests stay reproducible across CI runs and contributor machines. Embed the seed in the fixture file for full replay.

snappy

Database seeding

Fill staging with realistic-looking users. Heavy work runs through useMemo so the UI stays snappy on large inputs. Same input + same options yields identical bytes — the kind of stability snapshot tests rely on.

feature-detected

Conference talks

Slide-safe sample names that don't dox anyone. Storage tools persist to the right backend: localStorage for user preferences, sessionStorage for transient state, IndexedDB for structured data. Pick the right shelf and quota worries disappear.

deterministic

Customer service training

Practice scripts with believable customer names. Browser feature detection: clipboard, fullscreen, gamepad, and Web Bluetooth APIs gracefully fall back when unavailable. No surprise white screens on Safari or older Firefox.

production-ready

FAQ

Frequently asked questions

Are these real people?

No — the pools are common first and last names. Coincidental matches with real individuals are possible but unintended.

Why so many duplicates?

Independent uniform picks naturally repeat (birthday paradox). For unique-only lists, dedupe in your spreadsheet.

Can I add my own?

Open a PR — the FAKE_FIRST_NAMES and FAKE_LAST_NAMES arrays live in _utility-helpers.ts.

Are these culturally diverse?

The pools lean Anglophone. Open a PR to expand or to add regional variants.

Does it run locally?

Yes — picks happen in your browser with crypto randomness. Nothing is sent to a server.

Suggest a tool

Something missing?

Tell us what you need. We ship the top requests every two weeks.

47 in queue·8 shipped this month

Request queued. Thanks!