Portfolio

Systems you can feel — not just screenshots.

Interactive labs from Stackline systems work — WASM shopper analytics, hybrid LLM keyword generation — plus MapTask.

WASM raceLLM workbench

Live lab · Stackline pattern

WASM vs naive JS — shopper analytics race

At Stackline we hit ~1.8s median filters on 1M+ purchase rows. The winning architecture was hybrid: backend coarse aggregation + Rust/WASM typed-buffer kernels for final client filtering. This lab replays that tradeoff on synthetic shopper cohorts.

Slow path (JS objects)

—

main-thread churn

WASM path (typed + kernel)

—

loading…

Speedup

—

same filter, same cohort

Bad viz path

Object graph + main-thread filter → canvas

—

WASM viz path

Float64Array → WASM filter → canvas

—

Note: the live WASM module here is a compact stand-in for the production Rust kernels. The architecture and numbers below match the Stackline shopper analytics / WASM hybrid story (1.8s → ~300ms, −30% AWS, 80% faster drill-downs).

typescriptNaive dashboard reducer
before
// ❌ Slow path — object churn on the main thread
function aggregateShoppers(rows: Shopper[], minSpend: number) {
  // Multiple full passes + allocations (classic dashboard footgun)
  return rows
    .filter((r) => r.spend >= minSpend)
    .map((r) => ({ ...r, label: r.retailer + ":" + r.channel }))
    .reduce(
      (acc, r) => {
        acc.count++
        acc.sumSpend += r.spend
        acc.sumRetention += r.retention
        return acc
      },
      { count: 0, sumSpend: 0, sumRetention: 0 }
    )
}
// Stackline baseline on ~1M rows: ~1.8s median
rustRust WASM filter kernel
wasm
// ✅ Rust → WASM kernel (Stackline filter step)
#[no_mangle]
pub extern "C" fn filter_scatter(
    input: *const f64,  // [x, y, spend] * n
    len: usize,
    threshold: f64,
    output: *mut f64,   // compacted [x, y] *
) -> u32 {
    let mut kept = 0usize;
    unsafe {
        for i in 0..len {
            let base = i * 3;
            let spend = *input.add(base + 2);
            if spend >= threshold {
                *output.add(kept * 2) = *input.add(base);
                *output.add(kept * 2 + 1) = *input.add(base + 1);
                kept += 1;
            }
        }
    }
    kept as u32
}
typescriptHybrid client orchestration
shipped
// ✅ Hybrid path — backend aggregates, WASM finishes client-side
async function loadCohort(companyId: string, range: DateRange) {
  // 1) Server does coarse aggregation (cuts payload ~10–50×)
  const page = await api.getAggregatedCohort(companyId, range)

  // 2) Pack into typed buffers (no object graph on hot path)
  const packed = packScatterBuffer(page.rows) // Float64Array

  // 3) WASM filters / transforms off the expensive JS object path
  const wasm = await loadAnalyticsWasm()
  const { points, count, ms } = wasmScatter(wasm, packed, threshold)

  // 4) Canvas draws only the filtered cohort (virtualized)
  return { points, count, computeMs: ms }
}
// Result: 1.8s → ~300ms compute, −30% AWS cost from smaller payloads

Live lab · Stackline Drive

LLM keyword workbench — hybrid beats “just add AI”

Ambiguous ask: “Can we use AI for keywords?” Pure LLM suggestions drifted into semantically related but commercially useless terms. The shipped system ranked historical winners, expanded with grounded generation, then validated — unlocking +19% ad performance and −37% ad spend.

Naive LLM

—

ungrounded expansion

Hybrid rank + LLM

—

rank → expand → validate

Relevance lift

—

hybrid vs naive on this seed

Shipped impact

+19% / −37%

ad performance / ad spend (Drive)

Streaming suggestions (hybrid · 0 rows)

Batched reveal · approve/reject · 0 relevant · 0 noise

KeywordCTR est.Conf.FlagAction
Run a path to stream keyword suggestions
typescriptNaive LLM-only
before
// ❌ "Just add AI" — ungrounded LLM expansion
async function suggestKeywords(seed: string) {
  const prompt = `Suggest Amazon keywords for: ${seed}`
  const raw = await openai.chat(prompt) // creative, but commercially noisy
  return raw.map((text) => ({ text, confidence: 0.5 }))
  // Failure mode: "shoe lace tutorial", weak CTR, blown API spend
}
typescriptHybrid rank + RAG expand
shipped
// ✅ Hybrid: rank → embed/RAG expand → validate (Drive)
async function suggestKeywords(seed: string, history: CampaignRow[]) {
  // 1) Gradient boosting / LambdaRank on historical CTR/ROAS
  const ranked = rankKeywords(history, seed) // LightGBM-style

  // 2) Embed winners → semantic expand (RAG), not open-ended chat
  const neighbors = await vectorSearch(embed(ranked), { k: 40 })

  // 3) LLM expands grounded neighbors only
  const draft = await llmExpand(neighbors)

  // 4) Rules + brand-safety validation before UI
  return validateCommercial(draft)
}
// Impact: +19% ad performance, −37% ad spend, $100K+ revenue
typescriptProgressive UI batches
fe
// Progressive batches — UI never blocks on 1000+ suggestions
const useKeywordGeneration = (seed: string) => {
  const [rows, setRows] = useState<KeywordSuggestion[]>([])
  const [generating, setGenerating] = useState(false)

  const run = async () => {
    setGenerating(true)
    setRows([])
    const { suggestions } = hybridSuggest(seed)
    for await (const batch of streamBatches(suggestions, 10)) {
      setRows((prev) => [...prev, ...batch])
      await new Promise((r) => setTimeout(r, 0)) // breathe for paint
    }
    setGenerating(false)
  }
  return { rows, generating, run }
}

Shipped product

MapTask

Geo-aware task coordination for retail, construction, energy, and field ops — secure assignment, location context, and transparent handoffs.

MapTask product screenshot