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 while you’re studying a brand new expertise. You watch a number of movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve bought this.” I’ve achieved that. It by no means lasts. A tough lesson typically accompanies the one expertise that issues.

You be taught by way of failure—falling flat in your face, trying 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 concerning the worst database schema you ever created. What did it educate 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 circumstances that continuously pull in opposition to 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 feels 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 instances over the course of a number of years. Joins turned 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 right way to clear up among the foolish logic questions which are more and more well-liked in job interviews, that is the kind 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 wonderful on paper: normalized, neat, all the pieces in its correct place.

Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor you understand, 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 wonderful 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 venture whose contract was virtually up.

And the way have been we making an attempt to repair it? When you’ve been on this scenario, you’ll perceive that what I’m about to jot down is the signal that you’ve got reached a brand new stage of determined failure. As an alternative of contemplating a rational strategy to reform the schema or separating what had develop into a “web-scale” workload in 2007 from a NoSQL database, we have been making an attempt to determine the right way to buy sooner onerous drives with increased IOPS.

I realized lots of issues:

• I realized that upgrading {hardware} (shopping for a sooner machine or dropping one million {dollars} on onerous drives) will solely delay your disaster. The actual 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 skim replicas. Then we did what we must always’ve achieved from day one: arrange an precise reporting path.
• If you’re having to optimize for a question plan each week? Your database is sending you an essential sign, which you must translate to, “It’s time to start out in search of another.”

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 concerning the individuals posting them than they understand, as they hardly ever 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 per week, these of us who’re really utilizing these instruments to jot down code are the primary to inform you that we’re studying lots of tough classes.

It’s not “simple.” There’s nothing “vibey” concerning the course of, and if you’re 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 wished you to try this, and also you simply ignored it?”

Each time I see individuals get excited concerning 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 “simple” as they’re reporting.

The failure muscle you construct with databases is identical one you want with AI coding instruments. You possibly can’t tiptoe in. It’s a must to push till one thing breaks. Then you determine the right way to strategy a brand new expertise as knowledgeable.

• Ask an agent to refactor one file—nice.
• Ask it to coordinate adjustments throughout 20 recordsdata, rethink error dealing with, and hold exams passing—now we’re studying.
• Watch the place it stumbles, and be taught to body the work so it will possibly 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 fully. ← That is costly, but it surely’s the way you be taught.

The trick isn’t avoiding failure. It’s failing in a managed, reversible method.

The Meta Lesson

When you’ve by no means damaged it, you don’t actually realize it. That is true for coding, budgeting, managing, cooking, and snowboarding. When you haven’t failed, you don’t realize it. And the general public 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 the complete recreation with AI coding instruments. The sooner you possibly can run the loop—strive → break → examine → refine—the higher you get.