We’ve all been there.
It’s a Saturday morning. The sun is shining, the coffee is hot, and you wake up with a jolt of inspiration: The Billion-Dollar Idea.
You open your laptop with that fresh weekend energy. By 10:00 AM, you’re flying. You’ve defined your core Python classes, mapped out the data flow, and sketched the logic. With your favorite AI coding assistant riding shotgun, you’ve even spun up a basic frontend. The first artifacts of your creation are alive.
What could possibly go wrong?
The 2:00 PM Wall: The Persistence Problem
The complexity starts to creep in. You need a place to keep your users’ data. At first glance, a document database looks promising — flexible, modern, easy. But then you realize it may not scale the way you need. Or maybe you need real transactions. So you switch to Postgres.
“Just use Postgres,” they say.
Great. Now you realize your hierarchical, deeply nested data doesn’t really fit the relational model all that well. Fine — let’s do this properly. Sound technical modeling. ERD diagrams. Strong types. Your software will be as solid as a medieval fortress.
But it takes time.
And before you notice, it’s already 5:00 PM.
The Midnight Spiral: The ORM Trap
Ha — but we’re only halfway through.
Now we need to connect the database model with our Python classes. Piece of cake, right? Just let SQLAlchemy do the magic.
It finally works… after another two hours of sweating over Claude.
But oh dear — you forgot to add a critical feature to your app.
So you revisit everything:
- the data model
- the ERD
- indexes
- SQLAlchemy mappings
- objects
- API contracts
It works again.
Gosh — it’s well past midnight now.
And then it hits you: this is still just a tiny prototype. Any real user load will require caching (Redis? Something custom?). What about data consistency? Race conditions? And of course, you’ll need to process events.
Kafka? Maybe microservices?
But how will it scale? Do you really know how to shard the data? Or is it enough to just upgrade your $150/month Postgres instance on AWS to $300/month?
Overwhelmed by questions with expensive answers, you close the laptop. "I'll look at it tomorrow," you say. But you won't. The spark is gone. The idea is buried under infrastructure.
When Infrastructure Kills Momentum
This is an imagined story — but does it sound familiar?
We often start with great ideas, only to watch them get buried under nuances, edge cases, and the drudgery of real-world constraints. AI agents help — for a while. They give us confidence and speed early on, but notoriously give up at the worst possible moment, leaving us alone with critical bugs, mounting technical debt, and architectural mistakes that are expensive to undo.
This story highlights a deeper problem: our foundation technologies have become overwhelmingly complex and creatively limiting. It feels almost impossible to start a real project today without a PhD in computer science — or at least ten years of corporate battle scars.
Enter dbzero: Radical Simplification of the Stack
We’ve felt this pain ourselves — many times — and decided to do something about it. That’s how dbzero was born.
Dbzero is an open-source Python package you can install right away:
pip install dbzero
It promises radical simplification. We believe it can replace large parts of your current stack — including, in many cases, the database itself.
The idea behind dbzero is simple — and it’s more about forgetting than learning new things.
Just forget about the database.
From today onward, let’s agree: no such thing exists.
A database is just software sitting on top of your filesystem, doing something useful — but at a high cost. It limits creativity, hurts performance, and floods developers with an endless stream of very serious-sounding terms:
atomicity, consistency, isolation levels, row-level locks…
The “Infinite Memory” Philosophy
For most application logic, let’s be honest — these details rarely matter.
How often do you think about electrons flowing through CPU circuits? Or photons traveling through fiber-optic cables between your servers?
As Harry Percival and Bob Gregory once put it:
“If we had infinite memory in our laptops, we’d have no need for clumsy databases. We could just use our objects however we liked.”
At dbzero, we believe this idea still holds.
Of course, infinite memory alone isn’t enough. We still need important guarantees: atomicity, isolation, transactions, concurrent process safety. And yes — we need to run across not just one laptop, but whole clusters of containers.
But here’s the difference:
We don’t make these concepts first-class citizens.
They are implementation details. Sometimes they matter — but most of the time, for most developers, they don’t. Dbzero lets you use your Python objects however you like, without constantly bending your design around storage constraints.
Build First. Optimize Later.
Dbzero goes beyond objects.
We extended the language with tagging. We added memory partitioning (dbzero prefixes). We built powerful querying capabilities — and more.
But all of this can come later.
These tools are there when you need them — not before. You can start building what feels natural. What makes sense. What will make users fall in love with your software.
Forget the data-juggling acrobatics. Forget the persistence boilerplate. Forget the architectural gymnastics you’re forced to perform before you’ve even validated your idea.
That problem is no longer yours.
It’s ours.
So maybe it’s time to open that drawer of unfinished projects and give one of them another chance — this time on dbzero, a simpler yet stronger foundation.
Top comments (2)
It's great news that dbzreo is now available in open source. I've been using it in commercial projects for a year now and it's a really great piece of software.
Yes, dbzero is not just a proof-of-concept. It's battlefield tested - already fueling few real projects from e-commerce and healthcare. And it scales beautifully - the performance of one of e-commerce apps originally built on Java + MongoDB + Postgres, when reimplemented on Python with dbzero, increased 250x (!!!). In the coming posts we'll describe how we did it.