Vibe-blogging: Why I Prefer Bottom-Up Programming

I’ve been wanting to get back into technical writing, and something has been bouncing around in my head lately: the perennial discussion of top-down versus bottom-up programming approaches.

Let me define my terms, at least for this discussion. I see the top-down approach as emphasizing upfront planning, designing systems based on anticipated user needs and abstract structures. Conversely, I view the bottom-up approach as more discovery-driven – figuring out the requirements and the best way to implement them during the process, often starting with concrete pieces and building upwards.

I want to be clear: I firmly believe both approaches have their place and are often needed in tandem for large, successful projects. However, personally? I find myself consistently drawn to, and simply enjoying, the bottom-up process much more.

My journey with this preference started early. When I began coding, the landscape felt dominated by a choice: dive into compiled languages or embrace dynamic ones like Python (or JavaScript, pre-TypeScript). Apps were taking off, but jobs often fell into distinct “backend” or “frontend” buckets. This meant choices like Java/C#/C++ on one side, and PHP or JavaScript on the other. Python felt like something else entirely back then – often described as “glue code,” useful for scripting, but not quite a heavyweight contender in either the pure backend or frontend space.

Frankly, I disliked Java intensely. To me, it felt like the epitome of the top-down philosophy. While the approach itself isn’t inherently flawed, my perception was that the Java ecosystem often went overboard with abstractions, boilerplate, and scaffolding. It felt heavy and prescriptive. Python, for me, always represented the opposite end of that spectrum – lean, flexible, and immediate.

Looking back, what kept pulling me towards Python? Time and again, it was its power in specific, practical contexts.

My first real Python “aha!” moment came during mathematical research involving computer simulations. I initially started with C++, chasing raw performance – a common choice when speed is paramount and you think you know exactly what needs to be computed. But I wasn’t experienced enough to make C++ truly sing quickly. Slowly, I discovered NumPy. The revelation wasn’t just about avoiding C++ compilation times; it was about the speed of experimentation. NumPy allowed me to rapidly test different hypotheses, tweak algorithms, and visualize results. The feedback loop was drastically shorter. Eventually, most of the simulation logic migrated to Python/NumPy, and I rarely needed to touch the C++ code again. This was bottom-up discovery in action – finding the path through iteration.

The second Python “trap” I happily fell into was Pandas. It’s worth appreciating Pandas’ origin: Wes McKinney, working in finance, needed better tools for data analysis than Excel or existing libraries offered. He wasn’t necessarily setting out to build a grand, perfectly designed system; he was solving a real, pressing problem for himself. And boy, did that library take off. For me, Pandas is a quintessential example of bottom-up success. It became, and largely remains, the lingua franca of data analysis.

Now, the “top-down” perspective often criticizes Pandas for its sometimes inconsistent API or its historical quirks (like the way it handled missing values via np.nan, a legacy of being built on top of NumPy). These criticisms have merit. Reading complex, heavily overloaded Pandas code can be challenging. And newer libraries like Polars have emerged, learning from Pandas’ history to offer a cleaner, potentially more consistent API designed from the ground up. I understand the appeal, though habit keeps me reaching for Pandas often.

But this slight digression highlights the core point. Pandas exploded in popularity because it solved immediate, tangible problems effectively, even if its initial form wasn’t perfectly architected. Its “messiness” is arguably a side effect of its organic, bottom-up growth. The succinctness, despite the quirks, was a massive advantage when you were still discovering what analysis needed to be done.

This leads me to think about programming more broadly. For me, programming is fundamentally an engineering discipline. A programmer is a tool-wielder. If the best way to solve the problem is writing clean, well-structured code following a grand design, great. If it involves wrestling with a library’s API or finding clever workarounds to get the job done now, that’s also part of the game. The focus is on solving the problem.

Think about other foundational pieces of software. Linus Torvalds wrote Git initially to solve his specific, pressing problem of managing the Linux kernel’s source code. Satoshi Nakamoto created Bitcoin to address a specific set of ideas about decentralization and currency (whatever the exact motivation, it wasn’t purely an exercise in abstract system design).

These weren’t born from committees planning abstract interfaces; they were born from individuals or small groups tackling concrete challenges. A programmer takes a problem and solves it using the tools available, including writing code. The bottom-up approach, the journey of discovery, feels incredibly aligned with this pragmatic, problem-solving core of engineering. While top-down planning provides necessary structure, especially at scale, the initial spark often comes from that bottom-up need.