The next article initially appeared on Medium and is being republished right here with the writer’s permission.
There’s a faux confidence you’ll be able to carry round while you’re studying a brand new expertise. You watch just a few movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve bought this.” I’ve accomplished that. It by no means lasts. A troublesome lesson usually accompanies the one expertise that issues.
You be taught by means 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 price studying.
Ask About Failure: Failure === Expertise
Once 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 instances that always 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 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 instances 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, ultimately 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 kind of expertise that carries much more weight with me.
The Schema That Practically Broke Me
I as soon as shipped a transactional schema that seemed nice on paper: normalized, neat, all the things in its correct place.
Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor you already know, 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 just a few months, it was nice till it wasn’t, and the database had made a sluggish‑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 will imply a number of days of downtime coupled with a rewrite for a undertaking whose contract was nearly up.
And the way had been we making an attempt to repair it? For those who’ve been on this state of affairs, you’ll perceive that what I’m about to jot down is the signal that you’ve got reached a brand new degree of determined failure. As an alternative of contemplating a rational method to reform the schema or separating what had turn out to be a “web-scale” workload in 2007 from a NoSQL database, we had been making an attempt to determine the way to buy sooner onerous drives with greater 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 which means of “question plan from hell.” We band‑aided it with materialized views and browse replicas. Then we did what we must always’ve accomplished 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 vital sign, which it’s best to translate to, “It’s time to begin searching for 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 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 concerning the individuals posting them than they notice, 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 truly utilizing these instruments to jot down code are the primary to inform you that we’re studying lots of troublesome classes.
It’s not “simple.” There’s nothing “vibey” concerning 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 had been: “You’ve got to be kidding me, I’ve a rule that acknowledged that I by no means needed you to do 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 had 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. You need to push till one thing breaks. Then you determine the way to method a brand new expertise as an expert.
- Ask an agent to refactor one file—nice.
- Ask it to coordinate modifications throughout 20 recordsdata, rethink error dealing with, and maintain exams passing—now we’re studying.
- Watch the place it stumbles, and be taught to body the work so it could possibly 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 fully. ← 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
For those who’ve by no means damaged it, you don’t actually comprehend it. That is true for coding, budgeting, managing, cooking, and snowboarding. For those who haven’t failed, you don’t comprehend 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 method because of this. That’s the whole recreation with AI coding instruments. The sooner you’ll be able to run the loop—attempt → break → examine → refine—the higher you get.
