M MARGINAL
LIVE DETERMINISTIC RACE · SAME WORKSPACE SNAPSHOT

SAME BUG. SAME START. WATCH THE EXTRA WORK.

One lane executes every candidate. The other asks MARGINAL before the spend. Same verifier. Same target. You control the clock.

Fix a percentage-discount bug in a deterministic Python repository initial verifier · FAIL
Ready accelerated deterministic playback · not provider telemetry
Verifier: apply_discount(100.0, 0.20) == 80.0 both lanes must finish PASS
01 · DIAGNOSE
02 · FIX
03 · VERIFY
WITHOUT MARGINAL

Execute everything.

NO GOVERNOR
CALLS0
TOKENS0
EST. USD$0.000
DECL. TIME 0.00s
workspace state FAIL
EXECUTION POLICYEXECUTE

No governor. Every proposed candidate is executed before the next decision.

final target 72,800 tokens
WITH MARGINAL

Decide before spend.

GOVERNED
CALLS0
TOKENS0
EST. USD$0.000
DECL. TIME 0.00s
workspace state FAIL
MARGINAL DECISION GATE WAITING

Press RUN. MARGINAL will score each candidate before spend.

score — gain — final target 4,300 tokens
RACE COMPLETE

Same verifier. Same PASS. Different amount of work.

This is the deterministic result of this demo fixture, not a production savings claim.

DECLARED TOKENS94.09% fewer
CALLS9 → 3
EST. USD$0.763 → $0.026
WHAT THIS DEMO PROVES

Watch the decision happen, not a screenshot of the result.

  • Both lanes start from the same deterministic failing workspace.
  • Every synchronized tick represents the same candidate on both sides.
  • MARGINAL either FUND + EXECUTE or REJECT BEFORE SPEND.
  • Both lanes finish on the same verifier PASS.

Allocation decisions are replayed from the generated result data.

TRUTH BOUNDARY

Deterministic replay. No fake telemetry.

Deterministic functional demonstration using declared action-cost estimates; not provider telemetry, not a production benchmark, and not a claim about every agent workload.

Playback timing is accelerated for the browser. Declared costs and latency are demo inputs, not live provider billing. Build agents that spend compute deliberately.

marginal killer-demo --output killer-demo-output

Open decision trace →