The next article initially appeared on Medium and is being republished right here with the writer’s permission.
There’s a pretend confidence you possibly can carry round whenever you’re studying a brand new know-how. You watch a number of movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve obtained this.” I’ve finished that. It by no means lasts. A troublesome lesson usually accompanies the one expertise that issues.
You be taught via failure—falling flat in your face, trying on the mess, and determining why it broke. Something that feels too straightforward? It in all probability was, and also you didn’t exit the method with something price studying.
Ask About Failure: Failure === Expertise
Once 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 educate you to keep away from?
It’s probably not a trick. Anybody who’s been knee‑deep in relational databases is aware of there’s no excellent schema. There are competing use instances that continuously 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 crew inadvertently optimizes the schema (normally years later) for the reporting use case solely to make the transactional workload unworkable.
The proper 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 site 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 may not know the best way to remedy a number of the foolish logic questions which can be more and more standard in job interviews, that is the type of expertise that carries much more weight with me.
The Schema That Almost Broke Me
I as soon as shipped a transactional schema that appeared fantastic on paper: normalized, neat, every part in its correct place.
Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor you realize, my fairly 3NF mannequin, now linked to each elementary classroom in America, was getting used like a million-row Excel spreadsheet to summarize an accounting report. For a number 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 might repair something, as a result of that may imply a number of days of downtime coupled with a rewrite for a mission whose contract was virtually up.
And the way have been we attempting to repair it? Should you’ve been on this scenario, you’ll perceive that what I’m about to write down is the signal that you’ve got reached a brand new stage of determined failure. As a substitute of contemplating a rational method to reform the schema or separating what had develop into a “web-scale” workload in 2007 from a NoSQL database, we have been attempting to determine the best way to buy quicker laborious drives with greater IOPS.
I realized loads of issues:
- I realized that upgrading {hardware} (shopping for a quicker machine or dropping one million {dollars} on laborious drives) will solely delay your disaster. The true repair is unavoidable—huge horizontal scaling is incompatible with relational databases.
- I realized the that means of “question plan from hell.” We band‑aided it with materialized views and browse replicas. Then we did what we should always’ve finished 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 vital sign, which it is best to translate to, “It’s time to begin in search of 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 Should Do with Cursor and Copilot?
I’m seeing lots of people writing on LinkedIn and different websites about how wonderful vibe coding is. These celebratory posts reveal extra in regards to the folks posting them than they understand, as they hardly ever acknowledge the fact 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 really utilizing these instruments to write down code are the primary to inform you that we’re studying loads of troublesome classes.
It’s not “straightforward.” There’s nothing “vibey” in regards to the course of, and in case you are doing it proper, you might be 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 needed you to try this, and also you simply ignored it?”
Each time I see folks get excited in regards to the newest, best fad factor that’s altering the world, I’m additionally the primary to note that possibly they aren’t utilizing all of it. In the event that they have been, they’d perceive that it’s not as “straightforward” as they’re reporting.
The failure muscle you construct with databases is similar one you want with AI coding instruments. You possibly can’t tiptoe in. You must push till one thing breaks. Then you determine the best way to method a brand new know-how as an expert.
- Ask an agent to refactor one file—nice.
- Ask it to coordinate adjustments throughout 20 recordsdata, rethink error dealing with, and preserve exams passing—now we’re studying.
- Watch the place it stumbles, and be taught to body the work so it could actually succeed subsequent time.
- Spend a whole weekend on a “wild goose chase” as a result of your agentic coder determined to disregard your Cursor guidelines utterly. ← That is costly, nevertheless it’s the way you be taught.
The trick isn’t avoiding failure. It’s failing in a managed, reversible means.
The Meta Lesson
Should you’ve by no means damaged it, you don’t actually realize it. That is true for coding, budgeting, managing, cooking, and snowboarding. Should you haven’t failed, you don’t realize it. And most people speaking about “vibe coding” haven’t.
The folks I belief most as engineers can inform me why one thing failed and how they adjusted their method consequently. That’s your entire sport with AI coding instruments. The quicker you possibly can run the loop—attempt → break → examine → refine—the higher you get.