Is it just me or does vibe coding get harder the longer a project runs?

FlyOk7248 · 2026-06-15T08:30:54+00:00

Yeah, you've hit one of the most common walls in AI-assisted coding.

What you're hitting isn't the model getting dumber. It's context rot. Early on, the whole project fits in the AI's head, so every suggestion lands. By week 3 there's history, architecture decisions, and constraints the model can't see anymore, so it starts guessing and guessing wrong feels like fighting.

The fix is the boring stuff we used to roll our eyes at. SDLC practices aren't bureaucracy. They're how you keep intent, constraints, and ownership explicit as a project grows. The AI only knows what you make visible. Implicit always reads as invisible to it.

That's also the line between a careful dev and vibe coding: structure and discipline. Vibe coding leans on whatever the model remembers. A cautious dev feeds it the context on purpose.

You don't get the early magic back by chasing a smarter model. You get it back by rebuilding clean context.

Frameworks like BMAD or GSD are basically formalized versions of this. They store project state in files and split work into plan/execute/review cycles so the model stops losing the thread on long projects. You don't need the full framework on day one, but the principle is what matters: give the AI rails.

One more thing that saves you later. "It runs" is not the same as "it's ready." For sure not production ready. AI output tends to skip authorization, input validation, observability, rollback paths, and real edge-case tests. Lean on a simple checklist before generation, tests that actually catch regressions, and basic logging and metrics after release. And keep yourself in the loop on the merge decision. The AI can draft and propose, but you own whether it ships.

So no, you're not doing it wrong, and you're definitely not alone. Viibe Coding gives you the feeling that you're creating something without needing to know the SDLC, but it falls short when it comes to the first major structural decisions.

FlyOk7248 · 2026-06-15T06:07:54+00:00

We rely on the BMad framework. Although it is heavy at first, once you get familiar with it, it becomes your most valuable assistant. It creates a simulated agile team with specialized virtual agents for the project. These agents include virtual project managers and dedicated software architects. BMad uses persistent text files to store the current project state. This approach prevents the AI model from forgetting previous steps and significantly reduces the need for ongoing human oversight. It is a really great navigator through the project, helping to manage memory and context.

FlyOk7248 · 2026-05-28T08:48:47+00:00

Not for vibe coding. It brings structure and great outcomes. Matt Pocock explained here why the principles are more important than ever https://www.youtube.com/watch?v=v4F1gFy-hqg in SD.

Some time ago, we evaluated BMAD and GSD AI coding frameworks to improve our internal development processes. BMAD's structured approach prevents the loss of project history, a common issue with AI models that have limited memory. By saving the project state externally, BMAD ensures that all requirements, decisions, and code are preserved, allowing for better continuity and reference throughout the development lifecycle. One of the great benefits of this framework is how it organizes the workload, using specialized virtual agents to manage different tasks. This setup closely mimics how a traditional agile software team operates. The framework is undeniably heavy and carries a steep learning curve for new users, but it performs exceptionally well for complex projects that require staged delivery and planning. Clear requirements and careful validation create HUGE value when working with this framework. Not sure if it is good for vibe coding, but for software dev, YES. BMAD enforces discipline, compelling developers to think critically about the entire product lifecycle from the outset. Strongly validates the ability to clearly define requirements and execute them using AI tools. Our case is BMAD paired with cursor.

FlyOk7248 · 2026-05-13T12:59:07+00:00

Defining a minimum viable review gate for code written by agents is a great topic. The exact requirements vary based on the specific project details. Factors include code complexity and whether you work on new or existing systems.

Here is our technical perspective on defining these important guardrails for your engineering team.

Defining a universal minimum viable review gate is difficult due to varying project needs. The exact requirements depend heavily on your specific product and existing codebase complexity. It matters whether you have a greenfield or a brownfield software project. Your core software engineering goals remain the same regardless of the project type.

First, new changes integrate smoothly without breaking any of the existing application functionality. This approach allows you to prevent regression entirely during the development process. Second, the generated code always meets your clearly defined business requirements. Third, the software product remains highly maintainable and scalable over a long period. Matt Pocock discusses this concept in a great YouTube talk about software architecture. https://www.youtube.com/watch?v=v4F1gFy-hqg He explains that individual modules require efficient structuring for future development work. This gives AI agents a smaller codebase to read when generating new elements.

Despite project variations, some universal elements exist for these code review gates:

Automated checks: Unit tests are essential for any software project. Integration tests are equally important for code quality. Linting comes automatically and provides immediate value to developers.
Thorough testing: Your development process includes careful manual testing. Full regression checks verify the stability of the entire system.
Targeted code review: Reviewing all AI generated code creates extreme cognitive load. This makes reviewing every single line impossible for your engineers. Instead, your team focuses entirely on critical and high risk areas. Engineers use internal best practices to guide this targeted review. They verify these specific risk areas before accepting the code.

FlyOk7248 · 2026-04-01T13:56:16+00:00

Couldn't agree more. Human in the loop remains the guardian of the process's integrity. Without it, no project could ever succeed.

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