Each chapter walks one job end to end. Recordings are still in production, so every chapter below runs as an animated walkthrough instead — the same ground, in the same order.
A tour of the routing problem: why a single frontier model for every coding task overspends on the easy ones and underperforms on the hard ones, and how Arc Router weighs five independent opinions before it picks a backend.
Cloning the repository, restoring the .NET solution, building the GUI, and getting to a first launch on Windows. Covers the prerequisites and the usual first-build snags end to end.
Running the benchmark sync three ways (Governance in the GUI, the sync_benchmark_data MCP tool, and the command line), then verifying the row counts that land in the local SQLite file.
Pointing an existing coding agent at Arc Router through the system proxy, watching requests arrive in the console, and reading the three response headers that report what actually happened.
The dashboard, cost analytics, and token accounting: where the numbers come from, how reported usage is reconciled per provider, and how to read a routing decision's full vote breakdown.
CandidateScores — the full vote breakdown
Voter
Pick
Weight
dim_best
backend-3
0.90
memory_kNN
backend-3
0.57
cluster_best
backend-1
0.50
logreg
backend-7
0.43
llm_router
abstained
—
argmax
backend-3
1.47
Every non-abstaining vote is logged per request, keyed by voter and model.
Illustration for Reading cost and token telemetry. The written documentation linked below covers the same ground.
Turning on the MCP endpoint, registering the router's tools with an MCP client, and driving a benchmark sync and a routed completion without touching the GUI.
Governance → Providers
openai6 modelshealthy
anthropic4 modelshealthy
together9 modelsdegraded
local-ollama3 modelsstopped
Provider and model state is operator-controlled; a stopped model is never routed to.
Illustration for Exposing the MCP endpoint. The written documentation linked below covers the same ground.