Learn algorithms from engineers who solve them daily
When Lena joined our graph algorithms course, she was stuck debugging a pathfinding issue for three days. Twenty minutes into the first session, the instructor spotted her problem—she was rebuilding the adjacency list on every query. One small change cut her processing time from 4 seconds to 80 milliseconds.
What makes this different
We built this platform because most algorithm courses teach theory but skip the messy parts—the debugging sessions, the performance trade-offs, the decisions engineers make when specs change mid-project.
Watch actual debugging
See instructors walk through their thought process when code doesn't work. You learn where to look first and what questions to ask.
Real performance data
Every optimization shows before and after benchmarks. You understand which changes matter and which ones waste time.
Multiple approaches shown
Instructors implement the same problem three different ways, then explain which one they'd actually use and why.
Ask during sessions
Pause the video, write your question, get a response within 6 hours from instructors who remember the specific line you're asking about.
Go at your pace
Materials stay accessible. Some people finish in three weeks, others take four months. Both groups learn the same concepts.
Complexity controls
Start with basic implementations, then toggle complexity settings to see how the same algorithm handles edge cases and larger datasets.
Stuck on interview prep patterns
You recognize the problem type but can't remember which variation of binary search to use. Our sessions show you how to identify patterns quickly—not by memorizing solutions but by understanding the underlying structure.
Code works but runs too slowly
Your solution passes small test cases then times out on larger inputs. We break down exactly where the bottleneck is and show practical ways to optimize without completely rewriting everything.
Theory doesn't translate to code
You understand Big O notation in principle but struggle to apply it when writing actual implementations. Our instructors code alongside you, explaining decisions in real time.
Debugging takes forever
You spend hours tracking down a bug in your tree traversal logic. We demonstrate systematic debugging approaches—how to isolate the issue, what to check first, which tools actually help.
How the sessions actually work
Each masterclass follows a specific structure. The instructor codes a solution, hits problems, fixes them, then optimizes the working version. You see the entire process, not just the final clean implementation.
Problem setup and constraints
First five minutes establish what you're building and what the performance target is. The instructor explains why this matters and where you'd encounter it in real projects.
Initial implementation
Live coding with actual mistakes included. When the instructor makes a typo or logic error, they don't edit it out—they debug it on screen and explain their thought process.
Testing and edge cases
Run the code against different inputs. Find where it breaks. Figure out why. This section usually reveals assumptions that seemed obvious but weren't actually valid.
Performance analysis
Measure actual runtime with profiling tools. Identify the slow parts. Try different optimizations and show which ones work. Numbers matter more than theory here.
Alternative approaches
Implement the same solution two other ways. Compare trade-offs. Explain when you'd choose each version based on constraints like memory limits or data characteristics.
Platform by the numbers
Started in 2021 with six instructors and one course on sorting algorithms. These numbers reflect where we are now.
Active courses
Enrolled learners
Video content
Teaching engineers
The dynamic programming course changed how I approach optimization problems. Before, I'd try to memorize patterns. Now I actually understand when to apply memoization and when it's overkill. Helped me pass two technical rounds that would've gone badly otherwise.
I needed to optimize a search feature that was taking 12 seconds on large datasets. The graph traversal masterclass walked through three different approaches with actual performance metrics. Ended up using a modified BFS that cut the time to under 400 milliseconds. Code's been running in production for eight months.
