We started SparkEvolve because we remember how confusing algorithms felt when textbooks didn't match real code. Now we teach what actually helped us break through.
Back in 2019, I was stuck debugging a graph traversal that should've been straightforward. The theory made sense on paper, but my implementation kept hitting edge cases I hadn't anticipated. That's when it clicked—most algorithm resources teach the concept but skip the messy parts where real code breaks.
My co-founder Elke had a similar moment with dynamic programming. She'd spent weeks trying to understand memoization from academic sources before a colleague showed her a simple real-world example that made everything obvious. We kept talking about this gap between theoretical knowledge and practical skill.
So we built something different. Not another course promising to make you a genius overnight. Just honest teaching from people who remember what confusion feels like and know which explanations actually help.
We're not celebrity programmers with viral channels. Just two engineers who spent years building systems and figured out how to explain the hard parts clearly.
Founder & Lead Educator
Spent eight years as a backend engineer optimizing search algorithms before switching to teaching. Still writes production code occasionally because debugging keeps teaching skills sharp. Believes most algorithm books are written backwards—starting with theory when you need the intuition first.
Co-Founder & Curriculum Director
Former systems architect who specialized in database optimization and distributed algorithms. Designs our curriculum based on patterns she noticed while mentoring junior developers—what actually trips people up versus what textbooks think is hard. Obsessed with finding the shortest path to understanding.
We show you implementation first, then explain why it works. Most people learn better by seeing the pattern in action before diving into mathematical proofs.
Some concepts take time to sink in. We don't pretend everything is easy or promise instant mastery. Just honest progression at a pace that lets understanding stick.
You'll learn algorithms that solve real problems, not obscure edge cases that only appear in academic papers. Our examples come from actual systems we've built.
The hardest part isn't implementing a known algorithm—it's recognizing which approach fits your problem. We spend time building that intuition through varied examples.
