The next article initially appeared on Medium and is being republished right here with the creator’s permission.
There’s a faux confidence you’ll be able to carry round whenever you’re studying a brand new know-how. You watch just a few movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve received this.” I’ve completed that. It by no means lasts. A tough lesson typically accompanies the one expertise that issues.
You study by way of 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
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 probably not 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 consistently 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 workforce 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 consumer visitors. We needed to cut 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 working a database. Whereas they may not know easy methods to clear up a few of the foolish logic questions which are more and more fashionable in job interviews, that is the kind of expertise that carries way more weight with me.
The Schema That Almost Broke Me
I as soon as shipped a transactional schema that appeared high-quality on paper: normalized, neat, every little thing 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 just a few months, it was high-quality 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 attempting to repair it? If you happen to’ve been on this state of affairs, you’ll perceive that what I’m about to put in writing is the signal that you’ve got reached a brand new stage 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 have been attempting to determine easy methods to buy sooner exhausting drives with increased IOPS.
I discovered quite a lot of issues:
- I discovered that upgrading {hardware} (shopping for a sooner machine or dropping one million {dollars} on exhausting drives) will solely delay your disaster. The true repair is unavoidable—huge horizontal scaling is incompatible with relational databases.
- I discovered 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 completed 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 you must translate to, “It’s time to start out 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 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 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 every week, these of us who’re really utilizing these instruments to put in writing code are the primary to inform you that we’re studying quite a lot of tough classes.
It’s not “straightforward.” 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 needed you to try 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 “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. It’s important to push till one thing breaks. Then you determine easy methods to method a brand new know-how as an expert.
- Ask an agent to refactor one file—nice.
- Ask it to coordinate modifications throughout 20 information, rethink error dealing with, and hold exams passing—now we’re studying.
- Watch the place it stumbles, and study to body the work so it may 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, but it surely’s the way you study.
The trick isn’t avoiding failure. It’s failing in a managed, reversible means.
The Meta Lesson
If you happen to’ve by no means damaged it, you don’t actually comprehend it. That is true for coding, budgeting, managing, cooking, and snowboarding. If you happen to 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 consequently. 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.
