The next article initially appeared on Medium and is being republished right here with the writer’s permission.
There’s a pretend confidence you may carry round whenever you’re studying a brand new expertise. You watch a couple of movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve bought this.” I’ve carried out that. It by no means lasts. A troublesome lesson typically accompanies the one expertise that issues.
You be taught by way of failure—falling flat in your face, wanting on the mess, and determining why it broke. Something that feels too simple? It most likely was, and also you didn’t exit the method with something value studying.
Ask About Failure: Failure === Expertise
After I’m hiring somebody who claims relational database experience, I ask a “trick” query:
Inform me in regards to the worst database schema you ever created. What did it train you to keep away from?
It’s not likely a trick. Anybody who’s been knee‑deep in relational databases is aware of there’s no good schema. There are competing use instances that continually pull towards one another. You design for transaction workloads, however inevitably, somebody tries to make use of it for reporting, then everybody wonders why queries crawl. One other developer on the staff inadvertently optimizes the schema (normally years later) for the reporting use case solely to make the transactional workload unworkable.
The right reply normally appears like:
We constructed for transactional throughput—one of many founders of the corporate thought MySQL was a database, which was our first mistake. The enterprise then used it for reporting functions. The system modified arms a number of occasions over the course of a number of years. Joins grew to become gnarly, indices didn’t match the entry patterns, and nightly jobs began interfering with person visitors. We needed to break up learn replicas, finally introduce a warehouse, and after 5–6 years, we ended up simplifying the transactions and shifting them over to Cassandra.
That’s an individual who has lived the trade-offs. They’ve skilled a drawn-out existential failure associated to operating a database. Whereas they won’t know the way to remedy a few of the foolish logic questions which might be more and more standard in job interviews, that is the type of expertise that carries way more weight with me.
The Schema That Practically Broke Me
I as soon as shipped a transactional schema that regarded fantastic on paper: normalized, neat, every part in its correct place.
Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor , my fairly 3NF mannequin, now related to each elementary classroom in America, was getting used like a million-row Excel spreadsheet to summarize an accounting report. For a couple of months, it was fantastic till it wasn’t, and the database had made a gradual‑movement faceplant as a result of it was spending 80% of its time updating an index. It wasn’t as if I may repair something, as a result of that might imply a number of days of downtime coupled with a rewrite for a undertaking whose contract was virtually up.
And the way have been we attempting to repair it? In case you’ve been on this scenario, you’ll perceive that what I’m about to put in writing is the signal that you’ve got reached a brand new degree of determined failure. As an alternative of contemplating a rational strategy to reform the schema or separating what had change into a “web-scale” workload in 2007 from a NoSQL database, we have been attempting to determine the way to buy quicker arduous drives with greater IOPS.
I realized numerous issues:
- I realized that upgrading {hardware} (shopping for a quicker machine or dropping 1,000,000 {dollars} on arduous drives) will solely delay your disaster. The actual repair is unavoidable—huge horizontal scaling is incompatible with relational databases.
- I realized the which means of “question plan from hell.” We band‑aided it with materialized views and skim replicas. Then we did what we must always’ve carried out from day one: arrange an precise reporting path.
- In case you are having to optimize for a question plan each week? Your database is sending you an essential sign, which you need to translate to, “It’s time to begin on the lookout for an alternate.”
Lesson burned in: Design for the use case you even have, not the one you hope to have—and assume the use case will change.
What Does This Need to Do with Cursor and Copilot?
I’m seeing lots of people writing on LinkedIn and different websites about how superb vibe coding is. These celebratory posts reveal extra in regards to the individuals posting them than they notice, as they not often acknowledge the truth of the method—it’s not all enjoyable and video games. Whereas it’s astonishing how a lot progress one could make in a day or every week, these of us who’re truly utilizing these instruments to put in writing code are the primary to let you know that we’re studying numerous troublesome classes.
It’s not “simple.” There’s nothing “vibey” in regards to the course of, and if you’re doing it proper, you’re beginning to use curse phrases in your prompts. For instance, a few of my prompts in response to a Cursor Agent yesterday have been: “You’ve got to be kidding me, I’ve a rule that said that I by no means wished you to do this, and also you simply ignored it?”
At any time when I see individuals get excited in regards to the newest, biggest fad factor that’s altering the world, I’m additionally the primary to note that perhaps they aren’t utilizing all of it. In the event that they have been, they’d perceive that it’s not as “simple” as they’re reporting.
The failure muscle you construct with databases is similar one you want with AI coding instruments. You’ll be able to’t tiptoe in. You need to push till one thing breaks. Then you determine the way to strategy a brand new expertise as knowledgeable.
- Ask an agent to refactor one file—nice.
- Ask it to coordinate adjustments throughout 20 information, rethink error dealing with, and preserve assessments passing—now we’re studying.
- Watch the place it stumbles, and be taught to body the work so it may succeed subsequent time.
- Spend a complete weekend on a “wild goose chase” as a result of your agentic coder determined to disregard your Cursor guidelines utterly. ← That is costly, however it’s the way you be taught.
The trick isn’t avoiding failure. It’s failing in a managed, reversible approach.
The Meta Lesson
In case you’ve by no means damaged it, you don’t actually comprehend it. That is true for coding, budgeting, managing, cooking, and snowboarding. In case you haven’t failed, you don’t comprehend it. And most people speaking about “vibe coding” haven’t.
The individuals I belief most as engineers can inform me why one thing failed and how they adjusted their strategy in consequence. That’s your entire recreation with AI coding instruments. The quicker you may run the loop—attempt → break → examine → refine—the higher you get.
