Brandon Schneider
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159ba50059
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feat: Tailscale graceful degradation — chain never fails
RouteCost.lean:
- latencyClass 4 = 'offline' (Tailscale down/unreachable)
- networkLatencyCost returns qOne for offline (maximum cost)
- Computation continues with local-only fallback
scale_space_solver.py:
- detect_tailscale(): returns available=False if not installed/running
- get_latency_class(): returns 4 (offline) when Tailscale unavailable
- latency_to_voltage/sigma(): map any class to FPGA parameters
- Chain never raises — offline is just another latency class
Verified:
- Tailscale up: 4 peers detected, latency classes assigned
- Tailscale down: returns class 4 (offline), computation continues
- Unknown IP: returns class 4 (offline), no crash
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2026-05-28 19:19:14 -05:00 |
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Brandon Schneider
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f5bc4ab941
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feat: network latency as coursing agent in RouteCost
9th dimension: networkLatencyCost (12% weight)
- latencyClass: 0=local, 1=near, 2=far, 3=derp
- DERP relay (129ms) → qHalf cost → σ=1.0 (coarse BRAM)
- Local (<1ms) → qZero cost → σ=0.0 (exact BRAM)
Latency maps to FPGA voltage mode:
local(0) → 1.2V σ₀ (exact) BRAM Bank 0
near(1) → 1.0V σ₁ (normal) BRAM Bank 1
far(2) → 0.8V σ₂ (approx) BRAM Bank 2
derp(3) → 0.6V σ₃ (coarse) BRAM Bank 3
Consistent latency is computable — not noise, but a fixed phase offset.
The latency IS the computation: it determines which precision to use.
Weights rebalanced: kernel 20→18, street 14→12, topology 16→14,
substrate 12→10, proof 14→12, risk 14→12, latency +12.
lake build: 2 jobs, 0 errors
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2026-05-28 19:16:00 -05:00 |
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