Design
Turn ambition into a testable mechanism, program, strategy, or learning experiment.
FTC Team 36009 // Becoming Frontier
Build Boldly.
Challenge Everything.
OtterBots, FTC Team 36009 at Lewis Middle School, is becoming Frontier: expanding what students believe they can build by combining human ambition with AI, evidence, and disciplined iteration.
Human ambition in the era of AI
AI can accelerate the loop.
Students choose the frontier.
We use AI to search farther, compare more alternatives, preserve evidence, and challenge our assumptions. Students still define the problem, test the machine, judge the evidence, accept responsibility, and decide what comes next.
Frontier is not a finish line.It is the habit of moving from uncertainty to understanding—then using that understanding to attempt something harder.
Our control loop
Design. Measure. Analyze. Improve. Control.
Every pass should make the next decision better. We preserve both successful and failed experiments so learning compounds instead of restarting.
Measure
Capture timing, accuracy, reliability, behavior, constraints, and unexpected outcomes.
Analyze
Compare evidence with the goal. Find the bottleneck, contradiction, or failed assumption.
Improve
Change one meaningful variable, test again, and retain only measured progress.
Control
Make the improvement repeatable through feedback, documentation, training, and safe limits.
Microsoft Scout
From question to coordinated action
Microsoft Scout helps us research official sources, inspect code, coordinate tools, test websites, and turn a student question into a verifiable next action. It expands reach; it does not replace ownership.
LLMWiki
Learning that survives the session
Our LLMWiki compiles sources, decisions, experiments, contradictions, and procedures into connected team memory. Instead of asking the same question again, we begin from the strongest evidence we already earned.
Reinforcement learning mindset
Act. Observe. Reward evidence. Repeat.
We borrow the logic of reinforcement learning for team development: attempt a bounded action, observe the result, reward reliable improvement, and update the next action. The reward is not praise or novelty—it is stronger evidence, greater capability, and more consistent performance.
01
Code
Open every block.
Interactive diagrams, full comments, downloadable Blocks files, JavaScript, and student-friendly math explainers.
Visit Code Lab →02Our Work
Follow the whole journey.
Engineering decisions, control systems, professional learning, community resources, sustainability, and student ownership.
See what we are building →03Roblox Simulation
Test the strategy.
Use our Roblox Simulation to explore scoring cycles, traffic, parking, and alliance decisions.
Launch Roblox Simulation →Our season loop
Design, measure, explain.
Every technical choice becomes stronger when students can show the alternatives, the test, the result, and what changed next. Microsoft Scout accelerates the work; our LLMWiki makes the learning durable.
student@otterbots:~$ season-loop
01 → Ask a precise question.
02 → Design and document alternatives.
03 → Test the Roblox Simulation and physical robot.
04 → Measure, learn, and iterate.
05 → Let every student explain the evidence.