69. Lucy density — synthetic organism

Part: V · Systems
Package: github.com/openfluke/welvet/lucy
Status: ok — ✅ BuildLPD

When

Adaptation, measurement, or evolution (lucy).

Where

import "github.com/openfluke/welvet/lucy"

cd 69-lucy-density && source ../env.sh && go run .

Why

A new host (char LM, MNIST, a layer sprint) should not copy tide's goldilocks math. The question is always the same: can the net run and train at the same time in a small box, then how far that live-fit condenses without becoming a trap. That ruler belongs in the engine.

What

lucy.BuildLPD ranks Samples for consciousness (Acc / Throughput / Availability keep vs learner peaks) then Lucy density (Q × shrink vs Acc-champ RAM). Gold / near / keep / trap bands, consciousness radar, and memory-density radar are computed here. Tide dash and Lucy PDF only draw the board.

Sample output (captured)

lead=int8 band=gold LPD=5.11 trap=bin

Live capture from go run ./cmd/runall on local ../../welvet (exit 0).

Source

Copied from the Welvet feature book examples (openfluke.github.io/welvet/examples/69-lucy-density).

main.go

Download source ↓

package main

import (
	"fmt"

	"github.com/openfluke/welvet/lucy"
)

func main() {
	board := lucy.BuildLPD([]lucy.Sample{
		{ID: "f32", Mode: "sgd", Acc: 90, Thru: 200, Avail: 40, Score: 100, RAMKiB: 1000},
		{ID: "int8", Mode: "sgd", Acc: 82, Thru: 180, Avail: 38, Score: 85, RAMKiB: 180},
		{ID: "bin", Mode: "sgd", Acc: 12, Thru: 400, Avail: 50, Score: 40, RAMKiB: 40},
	})
	top := board.Top[0]
	fmt.Printf("lead=%s band=%s LPD=%.2f trap=%s\n",
		top.ID, top.Band, top.LPD, board.Trap[0].ID)
}