Make visible
Show the decision.
A result is more useful when the path to it can be inspected. The demos expose strategies, state changes, and competing choices instead of presenting only the final answer.
About 0labs / A living laboratory
Build to understand.
0labs turns abstract ideas into things you can see, run, question, and change. It is a growing collection of experiments built by Hannah Lee, an aspiring engineer and data scientist.
01 / Why this exists
Ideas become honest when they have to move.
A heuristic can sound convincing on paper. An algorithm can look inevitable in a diagram. But put either one in motion and the tradeoffs surface: speed against accuracy, exploration against certainty, an elegant rule against a stubborn edge case.
That is why 0labs is built around interactive work. Pathfinding, game strategy, simulations, data structures, and creative systems become easier to understand when their decisions are made visible. The goal is not to hide complexity. It is to give curiosity something concrete to push against.
02 / The practice
Use AI. Challenge AI.
Work critically
Treat output as a hypothesis.
Artificial intelligence can propose code, strategies, explanations, and interfaces. Its first answer is a starting point—not authority. We inspect assumptions, test edge cases, and ask what the output missed.
Compare
Let alternatives compete.
BFS beside A*. Expectimax beside Monte Carlo search. Different heuristics on the same board. Comparison turns preferences into evidence and reveals where each approach bends or breaks.
Keep learning
Publish the experiment.
A working demo is not a declaration that the question is finished. It is an invitation to interact, notice, disagree, and imagine the next version.
03 / How we build
From question to observable system.
Start with tension
Choose a question where two reasonable strategies may behave differently.
Build the smallest world
Create enough rules, state, and feedback for the idea to reveal its behavior.
Instrument the choices
Make the algorithm's path, score, uncertainty, or tradeoff visible to the person watching.
Challenge the first answer
Change the inputs. Compare approaches. Look for assumptions, edge cases, and surprising failures.
Share what runs
Publish the experiment so understanding can come through interaction—not explanation alone.
04 / The person behind the lab
Hannah Lee
Aspiring engineer and data scientist
Hannah builds 0labs to connect theory with experience: to turn algorithms into behavior, data into questions, and AI-assisted ideas into work that can be inspected and challenged. The lab is a record of learning in public—one runnable question at a time.
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