Table of Contents
- From a Name to a Mindset
- Software is Built on Unknowns
- What Senior Developers Actually Do
- 5 Real-World Scenarios
- Final Thought
From a Name to a Mindset
As someone who grew up with the name Darwin, I didn’t really have a choice—my name came with built-in jokes. Every time Charles Darwin was mentioned in class, I could already feel it coming. Heads would slowly turn, and my classmates would look straight at me like I had something to do with the theory of evolution.
It became a running joke from elementary to high school. At some point, I just learned to own it.
Ironically, one of his ideas stayed with me—not because of biology, but because of how closely it mirrors life as a developer:
“The mystery of beginning is insoluble by us; I for one must be content to remain agnostic.”
At first, it sounds philosophical. But in software engineering, it feels practical. Almost daily.
Software is Built on Unknowns
In theory, systems are logical and deterministic. In reality, they are full of unknowns. We work with:
- Code we didn’t write
- Decisions we don’t fully understand
- Systems that evolved over years
- Behaviors that only show up in production
You don’t always get a clean beginning. Sometimes, you just inherit the middle of the story.
What Senior Developers Actually Do
There’s a misconception that senior engineers “know everything.” They don’t. What they actually do is:
- Reduce uncertainty where possible
- Design systems that tolerate unknowns
- Validate assumptions through testing and observability
- Avoid pretending they fully understand things they don’t
Senior engineering is not about certainty.
It’s about operating safely without it.
5 Real-World Scenarios
1. Legacy Code You Didn't Write
A common real-world case is inheriting pricing, billing, or payroll logic that nobody fully understands anymore. The safe move is not to rewrite immediately. The safe move is to preserve current behavior with tests, then improve around it.
type CustomerType = 'regular' | 'vip';
type PricingInput = {
customerType: CustomerType;
subtotal: number;
};
export const legacyCalculatePrice = ({
customerType,
subtotal,
}: PricingInput): number => {
if (customerType === 'vip') return subtotal * 0.85;
return subtotal;
};
export const calculateFinalPrice = ({
customerType,
subtotal,
}: PricingInput): number => {
if (subtotal < 0) {
throw new Error('Subtotal cannot be negative');
}
return Number(legacyCalculatePrice({ customerType, subtotal }).toFixed(2));
};
// pricing.test.ts
import { describe, expect, it } from 'vitest';
import { calculateFinalPrice } from './pricing';
describe('calculateFinalPrice', () => {
it('keeps regular pricing unchanged', () => {
expect(
calculateFinalPrice({ customerType: 'regular', subtotal: 100 }),
).toBe(100);
});
it('keeps vip discount behavior unchanged', () => {
expect(calculateFinalPrice({ customerType: 'vip', subtotal: 100 })).toBe(
85,
);
});
});
Real-life application: e-commerce discounts, payroll rules, invoicing systems.
2. Debugging Distributed Systems
In production, one user request might touch an API gateway, auth service, order service, payment service, and database. You often cannot know what happened by checking only one place. That is why observability matters.
// request-context.ts
import { randomUUID } from 'crypto'
import type { NextFunction, Request, Response } from 'express'
export type RequestWithContext = Request & {
requestId?: string
}
/**
* Adds a request ID to correlate logs across services.
*/
export const attachRequestContext = (
req: RequestWithContext,
res: Response,
next: NextFunction,
) => {
const requestId = req.headers['x-request-id']?.toString() ?? randomUUID()
req.requestId = requestId
res.setHeader('x-request-id', requestId)
next()
}
// logger.ts
type LogInput = {
level: 'info' | 'error'
message: string
requestId?: string
meta?: Record<string, unknown>
}
export const log = ({ level, message, requestId, meta }: LogInput) => {
console.log(
JSON.stringify({
timestamp: new Date().toISOString(),
level,
message,
requestId,
...meta,
}),
)
}
// orders-route.ts
import express from 'express'
import { log } from './logger'
import type { RequestWithContext } from './request-context'
const router = express.Router()
router.get('/orders/:id', async (req: RequestWithContext, res) => {
try {
const { id } = req.params
log({
level: 'info',
message: 'Fetching order',
requestId: req.requestId,
meta: { orderId: id },
})
const order = { id, total: 1499 }
res.json(order)
} catch (error) {
log({
level: 'error',
message: 'Failed to fetch order',
requestId: req.requestId,
meta: {
error: error instanceof Error ? error.message : 'Unknown error',
},
})
res.status(500).json({ message: 'Internal server error' })
}
})
export default router
Real-life application: slow APIs, tracing request failures, debugging microservices.
3. Third-Party API Uncertainty
Shipping APIs, payment gateways, SMS providers, and exchange-rate services are outside your control. You do not fully know how they behave internally, so you code defensively.
export const getShippingRate = async (zip: string) => {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), 3000);
try {
const res = await fetch(`https://api.example.com/shipping?zip=${zip}`, {
signal: controller.signal,
});
if (!res.ok) throw new Error('API failed');
const data = await res.json();
return {
provider: 'third-party',
amount: data?.rates?.[0]?.amount ?? 150,
currency: data?.rates?.[0]?.currency ?? 'PHP',
};
} catch {
return {
provider: 'fallback',
amount: 150,
currency: 'PHP',
};
} finally {
clearTimeout(timeout);
}
};
Real-life application: logistics, payments, notifications, auth providers.
4. Rolling out risky features safely
Sometimes you are not fully certain how a new checkout flow, recommendation engine, or search logic will behave in production. Instead of betting everything on certainty, you use feature flags.
// useFeatureFlag.tsx
import { useMemo } from 'react'
type FeatureFlags = {
newCheckout: boolean
}
const flags: FeatureFlags = {
newCheckout: false,
}
export const useFeatureFlag = (flagName: keyof FeatureFlags) => {
return useMemo(() => flags[flagName], [flagName])
}
// CheckoutPage.tsx
import { useFeatureFlag } from './useFeatureFlag'
const OldCheckout = () => <div>Old checkout flow</div>
const NewCheckout = () => <div>New checkout flow</div>
export const CheckoutPage = () => {
const isNewCheckoutEnabled = useFeatureFlag('newCheckout')
return isNewCheckoutEnabled ? <NewCheckout /> : <OldCheckout />
}
Real-life application: staged rollouts, A/B tests, redesign launches, risky backend migrations.
5. Idempotent Webhooks
In real systems, the same webhook may be delivered twice, or events may arrive out of order. If your system assumes ideal conditions, it will eventually break. Good systems accept that uncertainty and guard against it.
// webhook-handler.ts
type PaymentEvent = {
id: string
type: 'payment.success'
orderId: string
}
const processedEvents = new Set<string>()
/**
* Handles webhook events safely using idempotency.
*/
export const handlePaymentWebhook = async (event: PaymentEvent) => {
if (processedEvents.has(event.id)) {
return {
status: 'ignored',
reason: 'duplicate_event',
}
}
processedEvents.add(event.id)
if (event.type === 'payment.success') {
return {
status: 'processed',
orderId: event.orderId,
}
}
return {
status: 'ignored',
reason: 'unsupported_event_type',
}
}
Final Thought
For someone who grew up being compared to Darwin, I never expected his ideas to resonate with my work as a developer. Yet here I am, working with systems I didn’t start, solving problems I can’t always fully explain, and still moving forward. What defines a strong developer is the ability to design with that in mind: to make systems observable, testable, and resilient. In that sense, Darwin’s perspective still applies—progress comes not from complete understanding, but from disciplined iteration within the limits of what we know.
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