
Claude and Obsidian: How I Built a Personal Knowledge Base Run by an AI
I keep my knowledge base in Obsidian and put a Claude Code agent on top: it lives in the notes folder, knows my rules, and handles the routine for me.

A while ago, while testing another payment integration, I ran into the same annoying problem that every developer eventually faces: finding decent test credit card data.
A while ago, while testing another payment integration, I ran into the same annoying problem that every developer eventually faces: finding decent test credit card data.
Not real card data, obviously — just realistic-looking numbers for testing forms, payment flows, validation logic, APIs, and staging environments.
And honestly? Most tools out there were terrible.
Some looked like they hadn’t been updated since 2009. Others were packed with aggressive ads, fake “hack” vibes, suspicious redirects, or weird promises about “working cards with balance” — which immediately tells you the site is either sketchy or targeting the wrong audience entirely.
I just wanted a clean, fast tool for legitimate development work.
So I built one.
The tool generates fake but structurally valid credit card numbers for testing purposes.
That means the generated numbers:
But they are not connected to real bank accounts and cannot magically be used to buy things online.
This is important to understand because there’s a lot of misinformation around tools like this.
A proper credit card generator is basically a developer utility — not some “free money” machine from TikTok or shady Telegram channels.
If you build anything involving payments, subscriptions, billing, SaaS, marketplaces, or e-commerce, you constantly need test data.
For example:
Using real customer card information for this is a terrible idea for both security and compliance reasons.
So developers use generated test data instead.
Honestly, most of the time you don’t even need a “working” payment method — you just need something that looks real enough for your system to behave correctly.
A lot of people don’t realize how many systems only validate the structure of a card number.
For example, many forms simply check:
That checksum is usually based on something called the Luhn algorithm.
So if a generated number passes the algorithm, many systems will initially accept it as “valid format,” even though it’s completely fictional and unusable for real payments.
That's exactly what makes these generators useful for testing.
Most existing tools felt built either for SEO spam or for people trying to do questionable things.
I wanted something simpler:
Just: open page → generate test data → copy → continue working.
That's it.
So the version on Ravy.pro is intentionally minimal.
I want to be very clear about this part.
This tool is intended for legal testing and development only.
Some completely normal use cases include:
Generating realistic datasets for automated testing pipelines.
Testing integrations with Stripe, Adyen, Braintree, PayPal, and other providers.
Designing checkout forms, wallet interfaces, and billing pages with realistic-looking data.
Teaching fintech, web development, cybersecurity, or QA engineering concepts safely.
Testing validation logic, anti-fraud systems, rate limiting, and payment edge cases.
Not everything that is technically possible is acceptable.
This tool should not be used for:
If you're trying to use generated card data against real payment systems, you're missing the entire point of the tool.
One thing I've realized recently is that small utility tools are surprisingly satisfying to build.
They solve narrow, real-world problems.
No giant startup pitch. No “AI-powered revolution.” No bloated roadmap.
Just a useful thing that saves developers time.
And honestly, the internet probably needs more software like that.
If you want to try the tool yourself, you can find it here:

I keep my knowledge base in Obsidian and put a Claude Code agent on top: it lives in the notes folder, knows my rules, and handles the routine for me.

How a line from my market-niche notes became a live micro-SaaS: what Steam's AI content disclosure is, why getting it wrong can delay or delist a game, how the tool is built, the honest legal standing of the protocol it generates, and a pre-upload checklist.
#devBefore I was an engineer, I was a Counter-Strike player.