/* global React */ const { useRef: useR1 } = React; // ── Cohort Dashboard ─────────────────────────────────────────────────────── function CohortDashboard({ cohort, totals, weekly, onOpenStudent }) { const rootRef = useR1(null); useReveal(rootRef); const fmt$ = n => n >= 1e6 ? `$${(n/1e6).toFixed(2)}M` : `$${(n/1000).toFixed(0)}K`; return (
{/* ── Hero — true black, photographic gallery feel ─────────────────── */}
{/* Subtle ambient light, not gradient chrome */}
FDE Cohort · Five-Week Report · Spring 2026

Thirty-three students.
Five weeks. {totals.positive} live conversations.

What the cohort built between {totals.window.start} and {totals.window.end} — measured honestly, in conversations opened with US decision-makers who responded with intent.

{/* Hero numerals row */}
} sub="across LinkedIn outreach"/> } sub={`${(totals.accepted/totals.requestsSent*100).toFixed(1)}% acceptance`}/> } accent sub="positive replies — interest or meeting-intent"/> } sub={`of ${totals.students} — actively dialoguing with US leads`} last/>
{/* Pipeline pull-quote */}
Opportunity created
{fmt$(totals.pipelineLow)} — {fmt$(totals.pipelineHigh)}
{totals.positive} live conversations × $3K–$6K per US engagement. A floor, not a ceiling — every figure counts only what was captured inside Antern.
{/* ── Cadence chart — parchment ─────────────────────────────────────── */}
Activity

Weekly cadence, all {totals.students} students.

Inbound replies climbed steadily as the cohort tuned messaging in week three and four.

{/* ── Conversation directory — pure white ────────────────────────── */} {/* ── Funnel — parchment ─────────────────────────────────────────── */}
The funnel

{totals.requestsSent.toLocaleString()} in. {totals.positive} conversations open.

Every step retains a meaningful share. The narrowing happens at intent, not delivery.

{/* ── Footer ─────────────────────────────────────────────────────── */}
); } // ── Hero stat ─────────────────────────────────────────────────────────────── function HeroStat({ eyebrow, value, sub, accent, last }) { return (
{eyebrow}
{value}
{sub}
); } // ── Cohort cadence (line chart, animated stroke) ─────────────────────────── function CohortCadence({ weekly }) { const ref = useR1(null); const W = 1180, H = 320, PL = 60, PR = 30, PT = 30, PB = 50; const innerW = W - PL - PR, innerH = H - PT - PB; const maxR = Math.max(...weekly.map(w => w.replies)); const maxA = Math.max(...weekly.map(w => w.ai)); const x = i => PL + (i / (weekly.length - 1)) * innerW; const yR = v => PT + innerH - (v / maxR) * innerH; const yA = v => PT + innerH - (v / maxA) * innerH; const repliesPath = weekly.map((w, i) => `${i === 0 ? "M" : "L"}${x(i)},${yR(w.replies)}`).join(" "); const aiPath = weekly.map((w, i) => `${i === 0 ? "M" : "L"}${x(i)},${yA(w.ai)}`).join(" "); // animate stroke draw on intersect useEffect(() => { const svg = ref.current; if (!svg) return; const io = new IntersectionObserver((entries) => { entries.forEach(e => { if (e.isIntersecting) { svg.querySelectorAll(".draw").forEach(p => { const len = p.getTotalLength(); p.style.strokeDasharray = len; p.style.strokeDashoffset = len; p.getBoundingClientRect(); p.style.transition = "stroke-dashoffset 1.6s cubic-bezier(.2,.7,.2,1)"; p.style.strokeDashoffset = 0; }); io.disconnect(); } }); }, { threshold: 0.3 }); io.observe(svg); return () => io.disconnect(); }, []); return (
a+b.ai,0).toLocaleString()} total`}/> a+b.replies,0).toLocaleString()} total`}/>
{/* gridlines */} {[0,0.25,0.5,0.75,1].map(t => ( ))} {/* x labels */} {weekly.map((w, i) => ( {w.week} ))} {/* AI faint line */} {weekly.map((w, i) => )} {/* Replies blue line */} {weekly.map((w, i) => ( {w.replies} ))}
); } function Legend({ swatch, label }) { return
{label}
; } // ── Conversation directory ───────────────────────────────────────────────── function ConversationDirectory({ cohort, totals, onOpenStudent }) { // Cohort is pre-sorted by build-data.js: in-conversation by positive desc, then lean-fixes. const inConv = cohort.filter(s => s.bucket === "in_conversation"); const fixes = cohort.filter(s => s.bucket === "lean_fixes"); return (
The cohort, in conversation

{inConv.length} students, in active dialogue.

Each card below is a student already in conversation with US decision-makers — qualified replies that signal real intent. The number on each card is the count of active conversations near closing.

{inConv.map(s => )}
{fixes.length > 0 && (
Lean fixes recommended

Coaching session in progress.

These students are tightening their LinkedIn presence and messaging with the team. Reports will land after the coaching pass is complete.

{fixes.map(s => )}
)}
); } function ConversationCard({ s, onOpen }) { return ( ); } function LeanFixCard({ s }) { return (
{s.name}
Lean fixes in progress
Report holding for coaching pass on LinkedIn presence and messaging.
); } // ── Cohort funnel ────────────────────────────────────────────────────────── function CohortFunnel({ totals }) { const stages = [ { label: "Requests sent", value: totals.requestsSent }, { label: "Connections accepted", value: totals.accepted }, { label: "Conversations opened", value: totals.positive }, ]; const max = stages[0].value; return (
{stages.map((s, i) => { const w = (s.value / max) * 100; return (
{s.label} {i > 0 && {((s.value / stages[i-1].value) * 100).toFixed(1)}% of prior}
{s.value.toLocaleString()}
); })}
); } window.CohortDashboard = CohortDashboard;