Vibe coding workflowImpact: Medium
Oleksandr Kuzmenko, EditorPublished 6 min readAI-assisted · editor-reviewed
TL;DR
- Review agent-generated code for structural bloat; prefer minimal schema changes over verbose application abstractions.
- Prevent cognitive fatigue by capping continuous AI coding sessions and enforcing manual implementation for core domain logic.
- Factor in GDPR and US CLOUD Act implications before piping sensitive internal repository architectures to cloud LLMs.
Key facts
- Author Machine Learning Experience
- 10+ years in production ML
- Regulatory Constraint Cited
- US CLOUD Act extraterritorial reach
- Primary Cognitive Pattern
- Accumulation of cognitive debt and brainfog
The Cognitive Debt and Review Burden. Relying on coding assistants for prolonged sessions frequently triggers mental fatigue and disorientation, often referred to as 'LLM brainfog'. Microsoft research and multiple cognitive studies indicate that knowledge workers suffer reduced confidence and accumulate cognitive debt when continuously delegating reasoning tasks. In code review, this manifests as fatigue: scrutinizing hundreds of lines of plausible, syntactically flawless agent output demands more sustained mental effort than writing dense, intentional code from scratch.
Superficial Quality versus Minimal Architecture. A primary defect in current agentic coding is structural verbosity. When prompted for bug fixes or refactoring, models like Claude frequently construct complex application-layer abstractions, modern framework hooks, and redundant test suites instead of modifying core database schemas or applying minimal migrations. While the generated output looks polished, it widens the literacy gap: less experienced developers mistake boilerplate for thoroughness, baking unnecessary technical debt directly into production repositories.
Compliance and Long-Term Ecosystem Lock-in. Entrusting code generation to US-based proprietary providers introduces non-trivial compliance risks for teams subject to the General Data Protection Regulation (GDPR). Even when vendors commit to regional data boundaries, the US CLOUD Act permits federal authorities to compel disclosure of stored information. Developers must balance whether the temporary boost in drafting speed justifies dependency on subsidized commercial APIs that obscure low-level problem solving.
When to use
- When unblocking execution bottlenecks by asking targeted conceptual questions.
- When prototyping user interfaces where rapid iteration takes priority over long-term code density.
When NOT to use
- When drafting core database schemas, security boundaries, and high-density business logic.
- When compliance requires absolute data residency outside the jurisdiction of foreign extraterritorial laws.
What to do today
- Enforce strict pull request diff limits to reject unsolicited AI-generated boilerplate and verbose tests.
- Conduct architectural spikes and data modeling migrations manually before invoking coding agents.
- Audit corporate compliance regarding US CLOUD Act jurisdiction over proprietary codebase prompts.
Step names are taken from What to do today.
Step names are taken from What to do today. Enforce strict pull request diff limits to reject unsolicited AI-generated boilerplate and verbose tests.. Conduct architectural spikes and data modeling migrations manually before invoking coding agents.. Audit corporate compliance regarding US CLOUD Act jurisdiction over proprietary codebase prompts.
- Enforce strict pull request diff limits to reject unsolicited AI-generated boilerplate and verbose tests.
- Conduct architectural spikes and data modeling migrations manually before invoking coding agents.
- Audit corporate compliance regarding US CLOUD Act jurisdiction over proprietary codebase prompts.
What the community says
One reason is that there is zero point in posting this type of article to HN anymore. The replies are always identical. Nothing is gained. Rest assured, though, that the sentiment grows ever stronger.
I mentioned before, code is definitely worse, but end product is better. High quality code doesn't always lead to a high quality product. And bad code doesn't always lead to a bad product.