[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_116":117,"_118":119},"routes/embed.x.$handle.$name.$",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":-5},"handle","pliplop","name","codex-observatory","path","frontend/components/App.tsx","project",{"_22":49,"_11":12,"_35":50,"_51":-5,"_52":44,"_53":54,"_55":54,"_56":57,"_58":59,"_60":61,"_62":63,"_64":34,"_65":-5,"_66":-5,"_67":68,"_69":70,"_71":72,"_73":74,"_75":76,"_77":-5,"_78":79,"_80":79,"_81":82,"_83":82,"_84":82,"_85":79,"_86":79,"_87":88,"_89":90,"_91":92,"_93":44},"file",{"_22":23,"_11":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":-5,"_13":46},"permalink","https://www.val.town/x/pliplop/codex-observatory/code/frontend/components/App.tsx","extension","id","8b52f64f-1f33-448b-9d3a-034695ed5477","App.tsx","fileId","01a00207-d0f6-73ab-b6be-cbf5a9ca8781","parentId","01a00207-d0f6-73ab-b6be-d1dd45bab156","branchId","540a14c0-5a62-449d-b096-c95d66aedbc1","branchVersion",2,"latest",true,"createdAt",["D",1786740647080],"version",1,"fileType","script","content","/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useMemo, useState } from \"https://esm.sh/react@18.2.0\";\nimport { DATA } from \"../data.ts\";\n\nconst COLORS:any = {\n \"gpt-5.6-luna\":\"#8cf6ff\",\n \"gpt-5.6-sol\":\"#b98cff\",\n \"gpt-5.6-terra\":\"#ffcf70\",\n \"gpt-5.5\":\"#ff8fc9\",\n \"codex-auto-review\":\"#8b96aa\",\n \"LifeOS\":\"#76f2c8\",\n \"spotipipe\":\"#b98cff\",\n \"miscellaneous\":\"#ffcf70\",\n};\n\nconst modelName = (m:string) => ({\n \"gpt-5.6-luna\":\"GPT-5.6 Luna\",\n \"gpt-5.6-sol\":\"GPT-5.6 Sol\",\n \"gpt-5.6-terra\":\"GPT-5.6 Terra\",\n \"gpt-5.5\":\"GPT-5.5\",\n \"codex-auto-review\":\"Auto-review\",\n}[m] || m);\n\nconst fmtTokens=(n:number, dec=1)=>{\n if(n>=1e9) return `${(n/1e9).toFixed(dec)}B`;\n if(n>=1e6) return `${(n/1e6).toFixed(dec)}M`;\n if(n>=1e3) return `${(n/1e3).toFixed(dec)}K`;\n return `${Math.round(n)}`;\n};\nconst money=(n:number,c=\"CHF\")=> c===\"CHF\" ? `CHF ${n.toLocaleString(\"en-CH\",{maximumFractionDigits:2,minimumFractionDigits:2})}` : `$${n.toLocaleString(\"en-US\",{maximumFractionDigits:2,minimumFractionDigits:2})}`;\nconst pct=(a:number,b:number)=>b?`${(a/b*100).toFixed(1)}%`:\"0%\";\nconst dateLabel=(s:string)=>new Date(s+\"T12:00:00\").toLocaleDateString(\"en-GB\",{day:\"numeric\",month:\"short\"});\nconst fullDate=(s:string)=>new Date(s+\"T12:00:00\").toLocaleDateString(\"en-GB\",{weekday:\"long\",day:\"numeric\",month:\"long\",year:\"numeric\"});\n\nfunction useView(project:string){\n return useMemo(()=>{\n const all=project===\"All\";\n const pDaily:any = {};\n DATA.dailyProjects.forEach((r:any)=>{\n if(all || r.project_label===project){\n pDaily[r.date] ||= {tokens:0,usd:0,chf:0};\n pDaily[r.date].tokens += r.total_tokens;\n pDaily[r.date].usd += r.cost_usd;\n pDaily[r.date].chf += r.cost_chf;\n }\n });\n const totalTokens=Object.values(pDaily).reduce((a:number,b:any)=>a+b.tokens,0);\n const totalUsd=Object.values(pDaily).reduce((a:number,b:any)=>a+b.usd,0);\n const totalChf=Object.values(pDaily).reduce((a:number,b:any)=>a+b.chf,0);\n return {pDaily,totalTokens,totalUsd,totalChf};\n },[project]);\n}\n\nfunction Heatmap({project}:{project:string}){\n const [metric,setMetric]=useState<\"tokens\"|\"chf\"|\"usd\">(\"tokens\");\n const {pDaily}=useView(project);\n const [hover,setHover]=useState(null);\n const start=new Date(\"2026-07-01T12:00:00\");\n const end=new Date(\"2026-08-31T12:00:00\");\n const days:any[]=[];\n for(let d=new Date(start);d<=end;d.setDate(d.getDate()+1)){\n const key=d.toISOString().slice(0,10);\n days.push({date:key, dow:d.getDay(), ...pDaily[key]});\n }\n const vals=days.map((d:any)=>metric===\"tokens\"?(d.tokens||0):metric===\"usd\"?(d.usd||0):(d.chf||0));\n const max=Math.max(...vals,1);\n const firstOffset=(start.getDay()+6)%7;\n const cells=[...Array(firstOffset).fill(null),...days];\n const weeks=Math.ceil(cells.length/7);\n const dayByDate:any={}; DATA.daily.forEach((d:any)=>dayByDate[d.date]=d);\n return

ACTIVITY FIELD

Daily intensity

\n {([\"tokens\",\"chf\",\"usd\"] as const).map(x=>)}\n

JulAug

\n {Array.from({length:weeks},(_,w)=>Array.from({length:7},(_,r)=>{\n const idx=w*7+r, d=cells[idx];\n if(!d) return

;\n const v=metric===\"tokens\"?(d.tokens||0):metric===\"usd\"?(d.usd||0):(d.chf||0);\n const t=v?Math.pow(v/max,.58):0;\n const alpha=v?0.16+0.84*t:0.035;\n const glow=v?`0 0 ${2+12*t}px rgba(123,218,255,${0.05+0.17*t})`:\"none\";\n return

QuietIntense

\n {hover &&
\n {fullDate(hover.d.date)}\n {hover.d.tokens ? <>\n {metric===\"tokens\"?fmtTokens(hover.d.tokens):metric===\"usd\"?money(hover.d.usd,\"USD\"):money(hover.d.chf,\"CHF\")}\n
Tokens{fmtTokens(hover.d.tokens,2)}
\n
Equivalent{money(hover.d.chf,\"CHF\")}
\n {project===\"All\" && dayByDate[hover.d.date] && <>\n
\n
Cached input{fmtTokens(dayByDate[hover.d.date].cached_input_tokens)}
\n
Uncached{fmtTokens(dayByDate[hover.d.date].uncached_input_tokens)}
\n
Output{fmtTokens(dayByDate[hover.d.date].output_tokens)}
\n
Reasoning{fmtTokens(dayByDate[hover.d.date].reasoning_output_tokens)}
\n }\n : No local Codex activity}\n
}\n

\n}\n\nfunction Sparkline({project}:{project:string}){\n const {pDaily}=useView(project);\n const dates=DATA.daily.map((d:any)=>d.date);\n const vals=dates.map((d:string)=>pDaily[d]?.chf||0);\n const cum:number[]=[]; vals.reduce((a:number,v:number,i:number)=>(cum[i]=a+v,a+v),0);\n const max=Math.max(...cum,1), W=900,H=220,P=12;\n const pts=cum.map((v,i)=>`${P+i*(W-2*P)/(cum.length-1)},${H-P-v/max*(H-2*P)}`).join(\" \");\n const lunaX=P+dates.findIndex((d:string)=>d>=\"2026-08-03\")*(W-2*P)/(dates.length-1);\n return
\n
COST TRAJECTORY
Cumulative equivalent spend
{money(cum.at(-1)||0,\"CHF\")}
\n \n
{dateLabel(dates[0])}{dateLabel(dates[Math.floor(dates.length/2)])}{dateLabel(dates.at(-1)!)}
\n
\n}\n\nfunction Breakdown({project}:{project:string}){\n const all=project===\"All\";\n const base:any[] = all ? Array.from(DATA.models as any) : Array.from(DATA.projectModels as any).filter((r:any)=>r.project_label===project);\n const rows:any[]=[];\n base.forEach((r:any)=>{\n const key=`${r.model}__${r.reasoning_effort}`;\n const ex=rows.find(x=>x.key===key);\n if(ex){ex.total_tokens+=r.total_tokens;ex.cost_usd+=r.cost_usd;ex.cost_chf+=r.cost_chf}\n else rows.push({key,...r});\n });\n rows.sort((a,b)=>b.total_tokens-a.total_tokens);\n const total=rows.reduce((a,r)=>a+r.total_tokens,0);\n return
\n
MODEL SPECTRUM
Where the tokens went
\n
\n {rows.map(r=>
\n
{modelName(r.model)}{r.reasoning_effort}{fmtTokens(r.total_tokens)}
\n
\n
{pct(r.total_tokens,total)} of tokens{money(r.cost_chf,\"CHF\")}
\n
)}\n
\n
\n}\n\nfunction ProjectCards({project,setProject}:{project:string,setProject:(x:string)=>void}){\n const total=(DATA.projects as any[]).reduce((a:any,r:any)=>a+r.total_tokens,0);\n return
\n
PROJECT ORBITS
Local work attribution
\n
\n {Array.from(DATA.projects as any).sort((a:any,b:any)=>b.total_tokens-a.total_tokens).map((p:any)=>)}\n
\n
Project attribution is based on Codex working-folder labels. The export resolved folders, not Git identities.
\n
\n}\n\nexport function App(){\n const [project,setProject]=useState(\"All\");\n const view=useView(project);\n const totals:any=DATA.totals;\n const all=project===\"All\";\n const tokens=all?totals.tokens:view.totalTokens;\n const chf=all?totals.chf:view.totalChf;\n const usd=all?totals.usd:view.totalUsd;\n const cacheRate=pct(totals.cached,totals.input);\n const avoided=(totals.nocacheUsd-totals.usd)*DATA.meta.fxUsdChf;\n const projLabel=project===\"All\"?\"MAC MINI // LOCAL ARCHIVE\":`PROJECT // ${project===\"spotipipe\"?\"SPOTIPIPE\":project.toUpperCase()}`;\n return
\n \n
\n
\n
CODEX OBSERVATORYSNAPSHOT · {DATA.meta.snapshot} · ZÜRICH
\n
{projLabel}
{fmtTokens(tokens,3)} tokens
under observation.
\n
A local telemetry portrait of model usage, project gravity and token-equivalent cost.
\n
\n \n \n \n \n
\n
\n\n
\n
01
THE FIELDHover the calendar. Switch the gravity.
{!all&&}
\n \n \n\n
\n \n \n
\n\n
\n
CACHE DIVIDEND{money(avoided,\"CHF\")}
Estimated token-equivalent cost avoided versus treating every input token as uncached.
\n
MODEL ECONOMICS49.2% tokens
Luna carries almost half of local token volume, while its current-rate equivalent cost is dramatically lighter than Sol.
\n
THE QUIET REVIEWER{fmtTokens(383612316)}
Auto-review tokens · about {money(206.031501*DATA.meta.fxUsdChf,\"CHF\")} equivalent at GPT-5.4 rates.
\n
\n\n
\n
PEAK TOKENS316.9M14 Aug · {money(67.241488*DATA.meta.fxUsdChf,\"CHF\")}
\n
PEAK COST{money(251.752423*DATA.meta.fxUsdChf,\"CHF\")}15 Jul · 289.2M tokens
\n
CURRENT STREAK14 days1 Aug → 14 Aug
\n
LOCAL COVERAGE38 days8 Jul → 14 Aug
\n
\n\n
\n
CODEX OBSERVATORY / v1Mac mini local telemetry · Europe/Zurich
\n
Costs are token-equivalent estimates, not an invoice. Standard mode only. USD→CHF snapshot: {DATA.meta.fxUsdChf}. Pricing rates are embedded as of 14 Aug 2026.
\n
\n
\n
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