I built Pebble as a student trying to get into a big-tech company. Most of my prep time wasn't spent solving — it was spent deciding what to solve, tracking what I'd forgotten, and rebuilding a plan every week. That's the part a machine should do.
I know what it's like to prepare for a big-tech interview as a student: you open a problem list, pick something more or less at random, and afterwards you can't tell whether you actually learned anything. Days go by where the honest score is "I moved through problems" rather than "I understand more than I did on Monday."
And most of the effort wasn't even the hard part. It was finding the right problems, remembering which ones I'd botched, deciding when to revisit them, keeping a spreadsheet honest. Logistics, not problem-solving — and all of it runs on the same focus the problems need.
So I wanted a platform that simply does that for you. You give it time and discipline; it spends every minute of that on coding problems, not on planning. The work still has to be put in — nobody escapes that — but the tooling around it can make it far less wasteful.
People say learning data structures and algorithms is pointless now. I think solving these problems is deeply underrated — and more valuable in this era, not less. When a model produces three plausible solutions, the person who can tell which one holds, why it holds, and where it breaks is the one making the decision. Everyone else is copying with confidence.
Being able to juggle between solutions and read them profoundly is the edge — against a lot of code written on a very surface-level understanding.
Walking into your first job out of college and feeling like a fraud is normal. What I found is that genuinely understanding the structures, the algorithms and the language underneath you does more for that feeling than any amount of reassurance.
That confidence shows up everywhere: in interviews, in team meetings, in whether you speak up or stay quiet, in whether you take the opportunity or talk yourself out of it. Building it is a legitimate goal for a practice tool — maybe the real one.
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