How to Turn Hidden Developer Analytics into Business Intelligence Gold with Tableau, Power BI & Modern Data Warehousing
October 1, 2025How ‘Cherry-Picking’ Market Inefficiencies Can Give Quant Traders a Real Edge in HFT
October 1, 2025As a VC, I’m always scanning for that one signal—something beyond the pitch deck, the TAM, or the growth charts—that tells me a startup is built differently. And one of the strongest signals? How they make technical decisions. Not just *what* they build, but *how* they build it. The way a team approaches technical trade-offs, often under pressure and with limited resources, is one of the clearest predictors of whether they’ll earn a 5x or a 50x return. This isn’t about stacks or frameworks. It’s about **cherry-picks**—small, high-conviction technical calls that compound into massive valuation advantages.
The Art of the Cherry-Pick: What It Really Signals to VCs
Think of a cherry-pick like a numismatist spotting a misgraded rare coin at auction. Most bidders see the same thing. A few see something more—the subtle double strike, the off-center strike, the overlooked variety—and that’s where the real value hides. In tech, the **cherry-pick** is that same instinct, applied to engineering. It’s a deliberate, under-the-radar decision that unlocks outsized value: faster iteration, lower costs, unexpected scalability, or defensibility built into the code itself.
At seed and Series A, when your P&L is still lean and your user graph just starting to climb, I’m not just looking at revenue. I’m looking for **evidence of technical precision**—not just engineering skill, but judgment. The best founders don’t just pick tools. They pick *opportunities* most others miss.
What Is a Technical ‘Cherry-Pick’ in a Startup?
This isn’t about using the “coolest” framework or chasing the latest AI trend. A true technical cherry-pick is:
- A high-conviction bet on an underutilized solution—like a niche library, a custom data flow, or a lightweight alternative to a bloated standard.
- Made under pressure, when time and resources are tight, and the wrong call could sink the product.
- Delivers 10x the impact for 1/10 the effort—less code, less cost, less ops, more speed.
- Backed by reasoning, not hype—they can explain *why* this choice, and why not the popular one.
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It’s not about perfection. It’s about **precision**. Like a coin collector who spots a doubled die obverse, the best founders spot technical inefficiencies—and exploit them.
Why Technical Cherry-Picks Matter in Technical Due Diligence
When I sit down for technical due diligence, I’m not asking, “Are you using React?” I’m asking, “*Why* did you use it? And what *else* did you consider?” The same way a coin dealer’s blind spots create value for a sharp-eyed collector, **the industry’s collective technical biases create blind spots for startups that think differently**.
1. The Signal of ‘First-Mover’ Technical Foresight
I once backed a fintech that needed real-time event processing—but didn’t want the overhead of Kafka. So instead of defaulting to the standard, they built on **NATS + PostgreSQL’s logical decoding**. Same reliability. 95% of the throughput. At 1/10 the cost and operational weight.
Code snippet (logical decoding setup in Go):
// Capture WAL changes from PostgreSQL
decoder := pglogrepl.NewLogicalDecoding(primaryConn, "event_slot", pglogrepl.WALLocation(0))
for {
msg, err := decoder.Read()
if err != nil { continue }
go processEvent(msg.Payload) // lightweight event handler
}
Why this was a cherry-pick:
- They knew Kafka was overkill for their scale and audit needs.
- They reused existing infrastructure—no new clusters, no new ops burden.
- They got auditability and replayability for free, a must in regulated environments.
Result? 40% lower cloud spend. Faster deployments. And a moat: competitors couldn’t match their simplicity without a full rewrite.
2. The Power of ‘Right Tool, Right Job’ (Not Popularity)
Another founder I backed chose **Dgraph** over Neo4j for patient relationship data. Neo4j is the default for graphs—but Dgraph’s distributed design and GraphQL-native API fit perfectly with their stack.
- Horizontal scaling with near-zero ops.
- 60% faster queries on complex patient history.
- Seamless integration with their React frontend.
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When I asked, “Why not Neo4j?” they said: “Clustering costs a fortune, and it’s fragile. Dgraph gives us 80% of what we need at 30% of the cost.” That’s not just smart. That’s a **technical cherry-pick rooted in cost, speed, and long-term flexibility**.
3. The ‘Hidden Gem’ of Custom Tooling
One of my best returns came from a health tech startup that built a **custom compliance CLI** in Python. No fancy SaaS. Just a clean script that:
- Scanned code for exposed SSNs, patient IDs, and other PII.
- Turned 2-day audit prep into 2-minute reports.
- Cut compliance overhead by 70%.
Code snippet (simplified):
def scan_pii(file):
patterns = [r'\b\d{3}-\d{2}-\d{4}\b', r'\b[A-Z]{2}\d{6}\b']
for line in open(file):
if any(re.search(p, line) for p in patterns):
log_violation(file, line)
This wasn’t just automation. It was a **strategic bet on developer speed and regulatory defensibility**. At Series B, when competitors were stuck in compliance purgatory, they were already selling faster. The CLI? It became a selling point.
Cherry-Picks as Valuation Multipliers
At early stages, valuation isn’t just about market size. It’s about **how efficiently you turn capital into defensible value**. Cherry-picks accelerate that.
1. Lower Burn, Faster Iteration
Smart technical choices reduce cloud costs, simplify ops, and speed up releases. A 30% cloud cost cut from a better data architecture? That’s 4+ months of extra runway. Time to refine, test, and grow.
2. Higher Optionality
Cherry-picks create **architectural flexibility**. The Dgraph team later pivoted into real-time analytics with zero rework. The event-sourced fintech added audit trails overnight. These weren’t just efficiencies. They were **future features in waiting**.
3. Stronger Defensibility
The best cherry-picks become part of the product. That compliance scanner? Customers cared about speed and trust. Competitors couldn’t match it without rebuilding. The tech *was* the moat.
What VCs Look for in a Tech Stack (Beyond the Hype)
When I evaluate a stack, I’m not checking a vendor list. I’m asking:
1. Decision Rationale
Is this choice *intentional*? Or just habit? Choosing Elixir for real-time resilience? That’s a cherry-pick. Using Node.js because “everyone does”? Not so much.
2. Operational Leverage
Can a small team run this? Kubernetes is great. But for a 5-person startup, it can be a time sink. A cherry-pick makes ops lighter, not heavier.
3. Scalability Without Over-Engineering
Are they using event sourcing because it’s trendy—or because it solves a real need? The fintech used it for auditability, not throughput. That’s the difference.
4. Data as a Strategic Asset
The best cherry-picks unlock data. The Dgraph team found patient patterns no one else saw. The event-sourced fintech built a data warehouse in days. Data isn’t just stored. It’s *leveraged*.
How to Signal Cherry-Picks in Your Pitch
During fundraising, skip the buzzwords. **Show the decisions.**
- “We switched to Dragonfly for Redis-like performance at 1/3 the cost.”
- “We wrote a custom query optimizer—70% faster API responses.”
- “We moved data processing to WebAssembly on the client. Cut server costs by 60%.”
Frame each as a **measured bet**, not a boast. VCs don’t want to hear about tools. We want to hear about *judgment*.
Conclusion: The Cherry-Pick Is the New Competitive Moat
Today, every startup has a tech stack. A few have cherry-picks. In a world of “me-too” SaaS and AI clones, **technical cherry-picks are the difference between a solid exit and a legendary return**.
- They show deep technical insight—the ability to see value where others don’t.
- They signal operational discipline—efficiency built into the code, not just the budget.
- They build defensibility—architecture as a competitive edge.
At seed and Series A, I’m not betting on the product. I’m betting on the team’s ability to spot—and act on—technical inefficiencies. Like a collector who finds a rare coin in a stack of common ones, the best founders don’t just build. They *curate*. They find the overlooked, the undervalued, the quietly brilliant.
So next time you’re building, pause. Ask: *What’s the cherry-pick here? What small, smart decision today will look like genius in two years?* That’s the signal I’m looking for.
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