AMD Developer Hackathon · Track 1

Route locally.
Spend only when needed.

Watch a learned router make a zero-token decision in this browser, compare it with a paid prompt-based decision, and inspect provider-reported Fireworks usage in live mode.

18/19previous accuracy
97.14%v11 projected accuracy
9,685previous API tokens
1,824v11 expected API tokens
0v11 routing tokens
130/130tests passing
Live routing lab

See the decision cost.

Each run reveals the local route first, then performs the paid baseline classification, and finally returns an answer with exact provider usage when live.

Browser router loadedhashed-logistic-v1 · compact-80a9ce84c395 · 167 training labels · held-out 77.5% acc / 100.0% escalation recall
ZERO FIREWORKS TOKENS
Walkthrough console math
01

Learned compact router

Waiting

P(escalate) latency
0 billed routing tokens
02

Prompt-based baseline

Waiting

The paid comparison starts after the local decision.

prompt N/Acompletion N/Atotal N/A
Waiting for baseline
03

Answer stage

WAITING

The answer appears after both routing decisions are visible.
prompt N/Acompletion N/Atotal N/A
Answer model
Per-query policy comparison

Run a query to compare

Both bars share the same answer usage, isolating the cost of paying an LLM to route.

N/Afewer policy tokens
Learned policy · shared answerN/A
routing N/A · answer N/A
Prompt-routed policyN/A
routing N/A · answer N/A
Actual walkthrough API spendN/A tokens across 0 calls
Session totals
0queries
0learned policy
0prompt baseline
0tokens avoided
QueryLocalBaselineSaved
Your narrated walkthrough log will build here.
Submission architecture

Learn once. Route free.

The router is trained on empirical local-model outcomes. At judge time it spends zero Fireworks tokens and keeps deterministic verification as a second quality gate.

01

Score locally

The learned classifier estimates whether the local pipeline will succeed.

02

Generate locally

A quantized Qwen model handles the zero-token path inside the container.

03

Verify

Arithmetic, code, logic, and output constraints are checked deterministically.

only if needed
04

Escalate

An allowed Fireworks model repairs the small subset that needs more capability.

Evidence before resubmission

Measured under the real limits.

The new image is accepted only after a disjoint accuracy check, 2-vCPU timing rehearsal, router class metrics, and provider-reported token comparison against the previous 9,685-token run.

Inspect the benchmark code →
linux/amd64Judge-compatible image
< 10 GBCompressed image gate
4 GB / 2 CPUMatched label + rehearsal limits
< 30 sPer-task call deadline