chini-004-uber-dispatch

Ride Dispatch (Uber-style matching)

Match riders to drivers in real time. Stay alive when a region's matcher dies.

Source: Classic system-design interview corpus (Uber / Lyft dispatch)

Prompt

Design a real-time ride-dispatch system.

Functional:
- POST /ride accepts a rider's request (location).
- The system matches the rider to a nearby driver and returns the match.
- Driver location updates stream in continuously.

Non-functional:
- A spike in ride requests (rain in a city, end of a concert) at 5x must not collapse matching latency.
- If one regional matcher fails, requests in that region must reroute, not drop.
- Driver-location ingestion must not back-pressure the matching path.

Return a Chinilla CanvasState. You'll likely want separate paths for ingest vs match, a queue between them, and replication or routing for the matcher.

Constraints

Max components: 14
Required behaviors: queue, retry
Monthly budget: $1200

Stress scenarios

Baseline rides

baseline

Normal ride-request volume.

5x rush spike

spike

Ride volume jumps 5x (concert lets out).

Matcher outage

outage

A matching component fails. Requests must reroute.

Driver-location flood

cascade

Heavy location-update traffic with jitter. Must not back-pressure matches.

Pass criteria (overall)

Min stability score: 70
Max drop rate: 5.0%
Min delivery rate: 90.0%
Max errors: 5

Submit your run

Submissions go through the chini-bench CLI. It calls your model with your key, scores the result locally, and posts to the leaderboard. Nothing leaves your machine except the canvas it produces.

End-to-end:

pip install git+https://github.com/collapseindex/chini-bench-cli.git
export OPENROUTER_API_KEY=...

chini-bench run chini-004-uber-dispatch \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice --x alice --linkedin alice-builds

Or inspect the prompt first:

chini-bench prompt chini-004-uber-dispatch

Providers: openai · anthropic · google · openrouter · ollama

Leaderboard

Rank	Submitter	Model	Score	Stability	Delivery	Design	Pass	Links
#1	alex default	A anthropic/claude-sonnet-4.6	82	72.0	88.0	100.0	✗	X
#2	alex default	O openai/gpt-5.4	53	65.0	0.0	75.0	✗	X
#3	alex default	X x-ai/grok-4.20	33	26.0	0.0	75.0	✗	X
#4	alex default	G google/gemini-3.1-pro-preview	30	19.0	0.0	100.0	✗	X

Per-scenario breakdown of the top run

Scenario	Health	Drop rate	Delivered	Pass
baseline	68.0	0.6%	410	✗
rush-spike	70.0	0.8%	2030	✓
matcher-outage	75.0	0.0%	12	✗
ingest-pressure	76.0	0.7%	336	✓

How is this scored? →