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 once 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 obtained this.” I’ve achieved that. It by no means lasts. A troublesome lesson typically accompanies the one expertise that issues.
You study 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 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 instances 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 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 palms 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 site 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 operating a database. Whereas they won’t know the way to resolve a number of the foolish logic questions which might be more and more standard in job interviews, that is the form of expertise that carries much more weight with me.
The Schema That Almost Broke Me
I as soon as shipped a transactional schema that regarded high quality on paper: normalized, neat, all the things in its correct place.
Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor , 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 couple of months, it was high quality 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 might repair something, as a result of that may imply a number of days of downtime coupled with a rewrite for a venture whose contract was virtually up.
And the way had been we making an attempt to repair it? In case 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 a substitute of contemplating a rational strategy to reform the schema or separating what had turn into a “web-scale” workload in 2007 from a NoSQL database, we had been making an attempt to determine the way to buy quicker exhausting drives with increased IOPS.
I realized lots of issues:
- I realized that upgrading {hardware} (shopping for a quicker machine or dropping one million {dollars} on exhausting drives) will solely delay your disaster. The true repair is unavoidable—large 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 achieved 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 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 folks posting them than they notice, as they not often 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 per week, these of us who’re really utilizing these instruments to jot down code are the primary to let you know 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 wished you to do this, and also you simply ignored it?”
Every time I see folks get excited concerning 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 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 may’t tiptoe in. You must push till one thing breaks. Then you determine the way to strategy a brand new expertise as an expert.
- Ask an agent to refactor one file—nice.
- Ask it to coordinate adjustments throughout 20 recordsdata, rethink error dealing with, and hold assessments passing—now we’re studying.
- Watch the place it stumbles, and study to body the work so it will probably 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 approach.
The Meta Lesson
In case you’ve by no means damaged it, you don’t actually realize it. That is true for coding, budgeting, managing, cooking, and snowboarding. In case 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 strategy consequently. That’s your complete sport with AI coding instruments. The quicker you’ll be able to run the loop—strive → break → examine → refine—the higher you get.
