Showcase: ensemble approach for algorithm design (A*, pathfinding, optimization) #31

Summary

One of thinktank's most compelling use cases: algorithm design problems where design choices significantly impact performance. A task like "write an A* algorithm" can produce wildly different results depending on heuristic choice, data structure selection, and optimization decisions.

The insight

Traditional single-agent coding produces ONE implementation with ONE set of design choices. Ensemble mode produces N implementations with N DIFFERENT design choices, letting you:

1. Compare performance — which heuristic is actually fastest?
2. Combine best ideas — take agent 1's data structure with agent 3's heuristic
3. Discover approaches you wouldn't have considered

Proposed showcase

Create a benchmark suite in examples/ with algorithm challenges:

Example 1: A* Pathfinding

• Grid-based pathfinding with obstacles
• Test harness: correct shortest path + performance benchmark
• Run ensemble: thinktank run "implement A* pathfinding for the grid in src/grid.ts" -n 5 -t "npm test"
• Compare: Manhattan vs Euclidean vs diagonal heuristics, binary heap vs fibonacci heap

Example 2: Sorting for specific data distributions

• Nearly-sorted arrays, many-duplicates arrays, etc.
• Test harness: correctness + performance on each distribution
• Ensemble reveals: agents pick different algorithms per distribution

Example 3: Cache eviction strategy

• Implement a cache with eviction policy
• Test harness: hit rate on a realistic access pattern
• Ensemble reveals: LRU vs LFU vs ARC vs Clock approaches

Value

This becomes a key part of the README and marketing: "thinktank doesn't just fix bugs — it explores design spaces." The A* example is particularly visual and compelling for demos.

Acceptance criteria

• At least one algorithm example in examples/ with test harness
• Run thinktank on it and document the divergence in approaches
• Performance comparison across agents
• Write-up in README or blog post showing the ensemble advantage