AutoML and Vibecoding: Why Real Engineers Still Rule Feature Engineering

Hey, you know, with all these fancy AI tools popping up everywhere, it's like, really tempting to just let the machines handle everything in ML and coding. But as a CS student grinding through projects and interview prep, it still feels obvious that hand‑crafting features – yeah, the old‑school way – has its place, even with AutoML and vibecoding blowing up. The tools got insanely good, but they didn’t make understanding data and systems optional; they just punished people who don’t understand it even harder.

What’s AutoML and Automated Features Anyway?

If you’ve ever wished for a “just build the model for me” button, that’s basically the idea behind AutoML. It tries to automate big chunks of the ML pipeline: data preprocessing, feature engineering, model selection, and hyperparameter tuning. Instead of you manually doing train/validation splits, grid search, or trying ten different model classes, AutoML pipelines run big searches over models and configs and then hand you a “best” candidate.

Automated feature engineering is the part where software takes raw tables and automatically generates features: aggregations, ratios, time‑based windows, one‑hot encodings, interactions, and so on. Tools like Featuretools can create thousands of features from relational data and have even shown up to a 10x reduction in development time compared to fully manual feature crafting in some comparisons. That’s crazy leverage if you just want to get a baseline model running and you’re not ready to spend three evenings obsessing over the perfect “churn_score_v3” column.

The trade‑off, though, is kind of obvious when you think about it. Automatically generated features often:

• Include redundant or highly correlated features that bring marginal value.
• Miss domain‑specific logic that you’d instantly spot if you know the business.
• Create feature spaces that are harder to interpret, especially in regulated or high‑risk domains.

So AutoML is like that really fast teammate who writes a ton of code, but doesn’t always understand why your product manager is obsessed with some tiny edge case.

Vibecoding and the AI Coding Revolution

Vibecoding is basically coding by vibes instead of by, you know, painstakingly reading every line. The term is often used for workflows where you describe what you want in natural language and let an LLM handle most of the implementation, treating code as something you steer with prompts rather than micro‑managing. You’re not obsessing over every for‑loop; you’re iterating: “nah, make it more modular”, “add pagination”, “log more stuff here”.

This is getting serious backing from big tools:

• Claude‑style code assistants: They can read large chunks of your repo, generate new files, refactor, and add tests while explaining what changed.
• Google Antigravity: Marketed as a next‑gen AI IDE, it uses multiple agents that coordinate across your editor, terminal, and browser to plan and execute tasks, from scaffolding services to wiring integrations. It’s pitched explicitly as “agent‑first”, where devs focus on higher‑level architecture and agents handle implementation.
• Jules: An asynchronous, repo‑aware coding agent that clones your codebase into a secure VM, reads it, and then builds features, fixes bugs, writes tests, and opens PRs with visible plans and reasoning. You describe a task, it decomposes it, runs stuff in the cloud, and reports back.

The vibe here is: talk to your tools like coworkers, not compilers. You say “add a new recommendation widget on the home page using our existing analytics service”, and an agent chain does most of the typing. From a distance, it looks like proper software engineering is dissolving into high‑level steering.

But vibecoding has some well‑documented dark sides too. Teams report hidden technical debt, elevated bug rates in AI‑heavy sections, and longer onboarding times because the rationale behind generated code is unclear. The code “works” until it hits a weird production edge case that nobody fully understood because nobody really read it closely.

Job Fears: Are Devs Getting Replaced?

Let’s talk about the anxiety part, because pretending that devs are totally chill about this is just fake. Studies and industry reports have highlighted some trends that feel scary at first:

• AI coding tools significantly reduce the time needed for straightforward implementation tasks, which hits junior roles hardest.
• Some predictions talk about a future where the majority of lines of code are generated by AI rather than humans.
• Bootcamps and training programs are already shifting curricula towards prompt design, tool orchestration, and AI‑assisted workflows.

So yeah, if your entire identity is tied to being “the person who writes boilerplate by hand”, vibes are not in your favor.

But other analyses from industry and finance firms paint a more nuanced picture. Large orgs adopting AI coding tools report:

• Net productivity gains and increasing demand for engineers who can architect systems, define interfaces, and do deep debugging.
• New roles around AI tooling, governance, evaluation, and “AI ops” – making sure these agents behave in production.
• A shift away from repetitive implementation toward higher‑leverage tasks like designing experiments, owning systems end‑to‑end, and mentoring AI‑augmented juniors.

In other words, AI makes “just typing code” a weaker moat, but it amplifies people who can think clearly about product, data, and architecture. The work changes; it doesn’t vanish.

And there’s this important angle: risk and liability. Security standards like NIST’s SSDF still emphasize careful review of generated code, and reports keep warning that AI can happily reintroduce classic vulnerabilities like injection flaws or insecure patterns pulled from public code. Companies don’t want to be the ones who shipped a massive breach because someone vibed too hard on a backend without proper review.

So yeah, some dev work will be automated away, especially cut‑and‑paste “CRUD app number 57” tasks. But engineers who understand systems and data deeply are becoming more like tech leads for a swarm of agents, not less necessary.

Why Hand‑Craft Features? The Real Engineer Edge

Feature engineering is kind of the perfect example of where the line between “tool user” and “engineer” shows up. At its core, feature engineering is choosing how raw data is transformed into inputs that models can actually use. You’re deciding what the model sees – that’s a big deal.

Manual feature engineering is like cooking from scratch: you inspect distributions, understand your columns, and design features like:

• Ratios (e.g., “spend per active day” instead of just “total spend”).
• Aggregations (e.g., “transactions last 30 days”, “max latency this week”).
• Domain‑specific flags (e.g., “is holiday”, “is promo period”, “sensor reading out of safe range”).

It’s slower, can be error‑prone, and is often not reusable across datasets. But it gives you full control and often yields features that make sense to both you and your stakeholders.

Automated feature engineering, on the other hand, uses frameworks to systematically generate large numbers of features from relational or time‑based data. It might create every possible aggregation over windows, every interaction, and then help you select the best ones, sometimes improving predictive performance and slashing development time by up to 10x. For hackathons, MVPs, or baseline models, that’s insanely useful.

The thing is, you don’t want to confuse speed with understanding. AutoML doesn’t know your business KPIs, regulatory constraints, or which “weird outlier” is actually the core user segment you care about. It tends to:

• Treat all patterns in the data as equally valid, even if they’re artifacts.
• Favor complex ensembles that are harder to explain and debug.
• Miss nuanced domain‑specific constructs that a human expert could craft.

So the real edge of handcrafted features is this: you deliberately inject your understanding of the domain, product, and failure modes into the data your model sees.

When to Grab the Keyboard Yourself

Here’s where it really makes sense to stop vibing and actually think like an engineer.

1. Domain‑heavy problems with subtle behavior

If your problem lives and dies on domain knowledge, AutoML is a tool, not your brain. Think fraud detection, recommendation systems tuned to real user behavior, complex B2B workflows, or anything deeply vertical.

• AutoML won’t know that a “failed login at 3 AM from a new device” is more suspicious than “two failed logins at lunchtime”.
• It won’t automatically create features that align with how your ops team thinks about the business.

You design those features, then maybe let AutoML search around that space to fine‑tune models and interactions.

2. High‑stakes, high‑regulation environments

In finance, healthcare, and other regulated spaces, interpretability and explainability are not “nice to have”, they’re requirements.

• AutoML ensembles can be opaque, making it hard to explain how certain predictions were made.
• Regulators and auditors often want to see features that map to clear concepts, not a tangle of generated interactions.

Hand‑crafting features gives you control over what signals enter the model, so you can defend them in documents, audits, and post‑incident reviews.

3. When your data is messy, weird, or multimodal

Real‑world problems often involve logs, sequences, audio, images, or mixtures of structured and unstructured data. AutoML tools are strongest on clean, tabular datasets; performance and controllability drop as structure gets complex.

Examples where manual feature work still shines:

• Defining windowed aggregations over event streams that match real workflows.
• Designing meaningful spectral or rhythmic features for audio projects.
• Building cross‑source features that connect, say, clickstream data with CRM events.

Reviews of automated feature engineering note that current methods still struggle to rival expert‑designed features on highly specialized tasks.

4. When performance ceilings and latency matter

AutoML is great for “good enough quickly”, but many guides point out that motivated experts can often beat its best models with custom feature engineering and tuning. If you’re:

• Chasing leaderboard‑level performance.
• Constrained by strict latency budgets.
• Operating at scale where every small improvement saves serious money.

…then handcrafted, targeted features plus manual model design are often what let you squeeze out that last 5–10%.

5. When you’re drowning in technical debt from vibecoding

A bunch of teams have started documenting exactly how vibe coding goes wrong. Common issues include:

• Hidden technical debt in sections with high AI‑generated code density.
• Security vulnerabilities because devs assume “if the tests passed, it’s fine”.
• Architecture drift where each AI session introduces slightly different patterns.

The fixes people recommend look suspiciously like… traditional engineering: code review, debt tracking, scheduled refactoring sprints, and clear architecture boundaries. That same mindset applies to features: you can absolutely let AutoML propose stuff, but you should still review, simplify, and deliberately own what ends up in production.

AutoML vs Manual: How to Actually Decide

Here’s a simple mental model you can use when you’re staring at a dataset wondering if you should vibe or grind.

Situation	AutoML / Automated features	Hand‑crafted features
Quick baseline or hackathon	Use AutoML to generate features and get a model running in hours, not days.	Maybe add 1–2 simple, high‑impact features you know matter.
New product with unclear requirements	Let AutoML explore model space while you focus on understanding user behavior.	As patterns emerge, lock them into handcrafted features that reflect product insights.
Mature product in regulated domain	Use AutoML cautiously for candidate exploration and stress tests.	Keep a curated, human‑understood feature set as the backbone of production models.
Messy, multimodal data	AutoML can assist on the tabular parts.	Manually design cross‑modal and custom domain features that tie everything together.
Heavy vibecoding codebase	Let AI help refactor and analyze feature usage.	Humans decide which features stay, which go, and how they’re documented.

This isn’t about “AutoML bad, manual good”; it’s more like “AutoML fast, manual precise”. You pick depending on how expensive being wrong is.

Being a “Real Engineer” in the AutoML + Vibecoding Era

So what does it actually mean to be a “real engineer” when Claude, Antigravity, Jules, and AutoML are publicly flexing?

It looks something like this:

• You use the tools, but you don’t outsource your brain. You can explain what your features mean, what assumptions your model is making, and how your system fails.
• You think in systems, not snippets. AI can write a function; you understand how it affects latency, security, observability, and product metrics.
• You treat AI agents like strong juniors. You give them clear tasks, review their work, and push back when things feel off.
• You know when to slow down. For high‑risk features, weird data, or critical systems, you’re willing to drop the vibes and do the boring, careful, manual work.

AutoML and vibecoding are not the end of engineering. They’re more like a giant multiplier on whatever you already are. If you really understand features, data, and systems, these tools turn you into someone who ships more and faster. If you don’t… they just help you get lost at scale.