From QBasic Gorillas to High‑Performance Simulation: Philipp Aumayr, Senior Software Developer Simulation at TGW Logistics Group

A developer who started early—and never stopped iterating

Listening to “Philipp Aumayr, Senior Software Developer Simulation at TGW Logistics Group,” we at DevJobs.at were reminded how engineering careers are shaped by curiosity, hands‑on tinkering, and deliberate iteration. In a handful of vivid scenes, Philipp outlines a learning arc many of us recognize: from poking around on a home PC and taking on ambitious, unfinished projects to building simulations where performance has no ceiling.

His story kicks off at around age “11, 12,” on a DOS 386 machine. A QBasic game about gorillas throwing bananas becomes the gateway to the code beneath—along with a simple question that changed everything: what do these cells of code actually do? Curiosity turns into practice: type, run, see. Those first miles of programming evolve into a mindset Philipp still carries today: build first, understand through results—and build again.

DOS, QBasic, and the moment curiosity becomes a method

Philipp describes how that gorilla game sparked a habit of peeking under the hood. QBasic made it easy to edit and execute code—almost frictions free. The internet “wasn’t really a thing” yet; progress came from trial, error, and lucky finds.

More than the game itself, what shaped him was the realization that every output is backed by logic you can touch and change. Pragmatism arrived early with a typical teenage task: homework. He automated his math assignments. The outcome was both ironic and instructive:

There’s a clear lesson here: programming can sharpen processes and structure without automatically transferring manual skills. Philipp learns early that formal correctness and by‑hand arithmetic are different disciplines. Automation delivers results—but not every missing piece. And yet, that moment reveals the potency of software when it transforms routine work into executable logic.

“Learn C”—a piece of advice that opens doors

A single recommendation becomes a turning point: if he’s serious about programming, “learn C.” For anyone who’s moved from a friendly beginner environment to a systems language, the leap is clear: memory, types, compilation, build chains. Philipp doesn’t flinch; it’s the next logical step.

That step leads to a classic experience for many developers: online friendships and a collective project that becomes a learning engine—even if it never ships. In Philipp’s case: reprogram Quake. “Shoot high once you’ve got ‘Hello World’ down.”

They never shipped a full game. But Philipp takes no loss from it. Quite the opposite. He emphasizes how much he learned: networking, computer graphics, and more. As we see it, this is the essence of a practical learning philosophy: stretch yourself with ambitious goals, start small, and grow through hands‑on experiments.

Why a “failed” game can still be a success

Philipp puts it plainly: “We didn’t build a real game … but I learned a tremendous amount.” That stance shifts the focus from product to learning. For early‑career developers, it’s liberating: a project may fail, if it pulls you deep into technologies you would otherwise never touch.

What matters is real code, genuine collaboration, and visible intermediate outcomes. The ambitious target sets direction, but the value accrues in the doing: once you move data across a network, put together even a rudimentary rendering path, or chase input latency, you learn not just APIs but also how to think in dependencies, dataflows, and performance.

From playful curiosity to industrial simulation: a sturdy bridge

In his role in simulation, Philipp connects those acquired muscles. He sums up what simulation means at TGW:

There’s nothing academic about that—it’s a customer promise backed by virtual models. And here’s where game‑dev experience meets industrial reality:

• Simulations and virtual environments require precise modeling of states, rules, and constraints.
• Computer graphics can make complex flows and bottlenecks visible and explainable.
• Networking and real‑time thinking help orchestrate distributed processes.
• Performance as a core principle means waiting is not an option.

Philipp is explicit: “It should be as fast as possible; there’s basically no limit to how fast it could be.” That’s the voice of a developer who treats performance as both a feature and a culture.

Performance: when “fast” is still not enough

“No upper bound.” It’s rare to hear performance goals framed this directly, and it’s precisely aligned with simulation reality. Simulation lives by two beats: accuracy and speed. Too slow, and answers arrive too late. Too imprecise, and answers are useless. Philipp doesn’t force a trade‑off; he highlights that performance is a non‑negotiable part of the equation.

For day‑to‑day engineering, that implies:

• Profiling early and repeatedly, not just at the end.
• Choosing algorithms as a feasibility decision, not merely an aesthetic one.
• Recognizing that scale is merciless. The O(n²) corner you ignore in a toy problem becomes a roadblock when systems grow.

He states the reason clearly: “At the size of the systems we’re building, algorithmic choices matter.” Simulation pushes you into conscious complexity management. If you think this way, you build systems that can grow without collapsing under their own weight.

Models that convince—and why visualization is a thinking tool

Philipp talks about “virtual models” that demonstrate the promised performance of processes. There are two intertwined aims:

• Internally, the models must approximate real behavior closely enough to make throughput, utilization, and delays meaningful.
• Externally, stakeholders—especially customers—need to understand what they’re seeing and why it matters.

Visualization sits in the middle. For Philipp, computer graphics aren’t a gimmick. They’re part of how insight is produced. If you’ve ever watched a visual debug view suddenly reveal where a queue backs up, you know why it matters.

The working method: build first, understand through results—then build again

The talk condenses into a practical mantra. Philipp says:

He warns against a common trap:

We read three steps from this:

1. Reduce the distance from idea to running code.
2. Pick a goal that stretches you beyond your comfort zone.
3. Learn through impact: observable results first, refined understanding next.

This isn’t anti‑theory. It’s anti‑paralysis. Philipp pushes against the illusion that you can fully grasp complexity before you touch it. Build to see; seeing becomes understanding.

Our editorial takeaways

We distilled Philipp’s remarks into five principles you can apply immediately:

• Start small, think big: “Hello World” is a launchpad, not a destination. Then pick a goal that challenges you.
• Learn from outcomes: build, measure, interpret, improve. Without intermediate results, learning stays abstract.
• Keep performance visible: measure early, measure often. Acceleration is part of the mainline of work, not a side quest.
• Treat visualization as a reasoning tool: what you can see, you can explain—and improve.
• Embrace unfinished work: projects may fail to ship, but succeed brilliantly at teaching you.

A practical path for newcomers

Philipp’s closing advice is crisp: “Get started!” Translating that into concrete steps:

• Set up a tiny loop: editor, compiler or interpreter, a working “Hello World.”
• Choose an oversized goal: a small game, a network tool, a simple simulation—whatever excites you.
• Break it into visible milestones: input, output, a primary loop, a basic visualization.
• Learn just‑in‑time: research only what unblocks the next milestone. No more, no less.
• Work iteratively: results first, understanding follows; then more results.
• Watch performance: track run time and responsiveness. Notice what changes move the needle.

This plan is intentionally simple. It’s designed to get you into practice faster than into perfection.

For experienced engineers: algorithmic thinking at system scale

Philipp’s point about scale—“algorithms matter at the size of the systems we’re building”—is a nudge to those advancing in their careers. Complexity isn’t a theoretical debate; it’s a production constraint. Whether your domain is simulation, backends, data pipelines, or rendering, the rules echo:

• Know the complexity of your hot paths. The “acceptable” approach in a toy setup may collapse at scale.
• Design for observability. Without metrics, logs, and visualizations, performance work is blind.

These practices flow naturally from Philipp’s performance focus and generalize well across technical domains.

Why simulation attracts developers with game‑dev DNA

Philipp names exactly what fascinates him: simulations, virtual environments, computer graphics, performance. If you grew up around games, these are familiar coordinates—with a twist. The target isn’t entertainment; it’s proof. The question isn’t “is this fun?” but “does it deliver the promised performance?” That shift—from play space to evidence—can be deeply motivating: the skills you learned for passion become tools to validate real‑world systems.

Learning without internet comfort—and what remains

One revealing detail from Philipp’s beginnings: “The internet wasn’t really a thing back then.” Missing resources made trial and error a necessity, not just a method. Today, the firehose of knowledge can be its own trap—the one he calls out explicitly: “It’s easy to get stuck … trying to understand everything first.”

His antidote still works, perhaps more than ever: read just enough, build a bit more.

A line that sticks

Condensed to its essence, his message is a disciplined call to action:

It sounds simple, but it’s a daily practice: shrink the gap between idea and code.

Conclusion: Ambition as fuel, iteration as method

“Philipp Aumayr, Senior Software Developer Simulation at TGW Logistics Group” is a concise, powerful invitation to anyone learning or deepening software engineering. The story moves from a QBasic gorilla game, through C and an oversized Quake goal, into a world where simulation makes business promises verifiable—and where performance is never “good enough.”

We’re leaving with a handful of durable lessons:

• Big goals aren’t risky if you slice them into visible, incremental wins.
• Failing to ship can still be a win if the learning is maximal.
• Performance isn’t cosmetic; it’s culture.
• Simulation unites playfulness with rigor—a fitting home for those who like to work on algorithms, graphics, and models.

The first step is the same as on that old DOS 386: open the editor, write a line, run it. Then the second, then the third. Get started.

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