Generate-Data.com

Realistic synthetic test data generation for developers, analysts, QA teams. Generate customer, product, transactional data, & more. No real data. No headaches.

[Generate-Data.com @Generate_Data]

@pasinduIshan3 FK consistency across related tables is usually the hard part with generators like this. Does it handle entity relationships, or is that left to the user?

[Generate-Data.com @Generate_Data]

@Nipsuli Smart pattern. Gets tricky when you need edge cases e2e workflows don't naturally produce — nulls, boundary values, FK violations across tables.

[Generate-Data.com @Generate_Data]

@supabase Live queries and optimistic mutations will surface bad data modeling fast. Seeding with realistic data before you demo saves a lot of debug time.

[Generate-Data.com @Generate_Data]

@mjovanovictech This holds for balanced distributions. Add heavy column skew and optimizer estimates start misfiring. That's where Medium advice really falls apart.

[Generate-Data.com @Generate_Data]

@natnaelkedir Async clicking for real when the data shows up and the UI responds. Only gotcha with random user APIs: great for happy-path testing, bad for edge cases (long names, nulls, unicode). Worth adding a few manual fixtures for the weird stuff as your app gets more complex.

[Generate-Data.com @Generate_Data]

@thebedigupta Solid move. Once test data lives in spec blocks, the next unlock is generating it for different states: empty, populated, edge cases. Decoupling is step one; schema-driven generation is step two.

[Generate-Data.com @Generate_Data]

@hridoyreh 26. A proper test data generator. Reads your schema and spits out FK-consistent seed rows. Nobody thinks about this until they're trying to seed a prod-scale schema at midnight with hardcoded [email protected].

[Generate-Data.com @Generate_Data]

@smitjoshi28 Synthetic wins for volume on demand, cross-border staging, and edge cases real data doesn't cover.

Where it fails: relational consistency. Generate users and orders independently and the foreign keys won't match. Schema-aware generation fixes that.

[Generate-Data.com @Generate_Data]

Your mock data lied to you.

Worked perfectly in tests. Null never appeared. Email was always valid.

Production disagreed.

The fix isn't better mocks — it's data that reflects how real users actually fill in forms.

[Generate-Data.com @Generate_Data]

@dharmvir_ One gap in this list: test data seeding. There's barely any OSS tooling for generating realistic, FK-consistent rows for your dev DB. Coolify handles infra beautifully but the test data problem is just unsolved.

[Generate-Data.com @Generate_Data]

Your test suite is green. Prod still breaks.

Most teams seed with one clean row 500x. Real users show up as O'Brien-Nunez, NULL phones, +44 numbers, -$50 refunds.

Clean fixtures test the happy path. Bugs live in the tails.

[Generate-Data.com @Generate_Data]

@TheWebTech The how-did-you-make-the-demo-data question never goes away. What most miss: demo data has to look like the prospect's own business, not generic. Coherent names, plausible deal sizes, relationships that hold when someone clicks in. That is what makes a demo land.

[Generate-Data.com @Generate_Data]

@crossiBuilds Realistic staging data is the task everyone postpones, because hand-writing it is miserable. Generating schema-aware fake data (real-looking names, valid relationships, no real PII) gets you a seeded staging env without the slog.

[Generate-Data.com @Generate_Data]

@mjovanovictech The data shape bites you too. In-memory DBs let clean rows pass, then real Postgres hits a unicode name, a NULL where you assumed NOT NULL, or a value that trips a CHECK constraint. Real PG plus realistic data catches both.

[Generate-Data.com @Generate_Data]

Built Generate-Data so you can spin up that messy, realistic dataset in seconds instead of hand-faking it. Plain-English schema in, valid data out, zero real PII. https://t.co/xoQdwkNlqL

[Generate-Data.com @Generate_Data]

Most teams seed their dev DB with one clean row 500x: Jane Smith, [email protected], $100.00.

Then prod sends O'Brien-Nunez, a NULL phone, a +44 number, a -$50 refund, and it all falls over.

Clean fixtures test the happy path. Bugs live in the tails.

[Generate-Data.com @Generate_Data]

New drop: AI schema creation on https://t.co/xoQdwkNlqL

Describe your dataset in plain English. We build the schema. You get the data.

No JSON. No config. Just tell it what you need.

→ https://t.co/xoQdwkNlqL

#devtools #syntheticdata #buildinpublic

[Generate-Data.com @Generate_Data]

If your training loop never sees fresh human signal, synthetic data just amplifies yesterday’s mistakes.

[Nazeer Mustapha @Nazeersmustaph]

Traditional AI training often suffers from synthetic data decay where AI models learn from other AI generated content. Perceptron solves this by injecting actual human activity into the data layer

@PerceptronNTWK

[Generate-Data.com @Generate_Data]

Code + synthetic data is a cheat code: you can verify every sample. The next step is bringing that rigor to app + pipeline test data too.

[Bojan Tunguz @tunguz]

@AlexanderKalian Synthetic data. It's already being done with code. And once we fully solve code - with autoformalization and automated code compilation proofs - it will open up a whole new level of intelligence for us.

[Generate-Data.com @Generate_Data]

Synthetic data fails when you treat it like truth. Used as targeted test data and never as labels, it can be a huge unlock.

[Dr Alexander D. Kalian @AlexanderKalian]

Synthetic data is practically a dead end, aside from limited and specific use-cases, as it amplifies errors and hallucinations downstream.

Code is perhaps a specific situation where it may work OK, due to local ability to rapidly verify, score etc. - but it becomes practically unfeasible for the natural sciences, social sciences, economic sciences etc. - where expensive and time-consuming real-world verification is critical.