Vibe coding is an informal term used to describe a style of development where a large language model writes most of the code interactively. Instead of starting with detailed designs or carefully planned implementations, you describe intent. You explain what you want to build, how it should behave, or what needs to change, and the LLM generates working code in response.
When it works, it feels great. Ideas turn into running applications quickly. Complex behavior appears with surprisingly little effort. For prototypes, demos, and exploratory projects, vibe coding can feel like a genuine productivity breakthrough.
This article is not an argument against that experience.
It is about what happens after many hours of success, when a project that started simple and promising quietly becomes frustrating, slow, and far more time-consuming than expected.
What a Checkpoint Is, and Why It Comes Up So Early
Before going further, it helps to define checkpointing up front, because it enters the picture sooner than most people expect.
A checkpoint is a deliberate snapshot of project state that exists outside the conversation with the LLM. It captures what matters so the LLM no longer needs to rely on a long, fragile prompt history to be effective. Though I do not believe this is a common or agreed upon developer term.
Put simply, a checkpoint is how you reduce dependence on conversational memory.
Checkpointing usually becomes relevant when:
- The project has real structure and nuance
- You are invested enough that walking away is not realistic
- The conversation begins to slow down or degrade
- Starting a new session feels unproductive because too much context is lost
Checkpointing helps, but it is important to be clear about what it does and does not solve.
The Wall You Do Not See Coming
It started out simple.
I asked for a fairly complex application, and after some trial and error, the LLM produced an impressive solution. It worked. The structure made sense. It felt like a strong foundation to build on.
The problems did not show up immediately.
They emerged as I tried to improve and mold that initial solution to better match my vision. Each change was reasonable on its own. Each adjustment felt like progress. But over time, the system became harder to reason about. Even small improvements began taking longer than expected. Side effects appeared in places I did not anticipate.
The code itself was not failing. My ability to comfortably understand and evolve it was.
As the hours added up, time started slipping away. What should have been straightforward refinement turned into extended troubleshooting. The conversation grew longer and increasingly dependent on its own history. Eventually, I hit a wall, not because the application was too complex, but because the thread itself reached a practical limit.
The prompt session could no longer carry the project forward.
Responses slowed. The browser struggled. Continuing in the same thread became frustrating, but starting a new session felt just as unproductive. I had invested too much time to walk away, yet the context that made progress possible was now working against me.
Losing Understanding Is the Real Cost
The most frustrating part of this experience was not that the code stopped working. It was that my understanding of the code gradually eroded.
Each change added complexity. And there are always changes. New features, adjustments, fixes, and refinements all introduce additional logic and assumptions. As the system grows, your mental model inevitably lags behind, even if the application continues to run.
Working code is important, but understanding is nearly as important. Without understanding, you cannot confidently improve the system, add new features, or truly take ownership of the result.
Asking the LLM to explain its own code, add comments, or walk through logic helps. Building incrementally helps. These practices improve comprehension, but they do not change the underlying constraint.
The Real Limitation Is Runway, Not Technique
After hitting the wall once, I tried again with a different approach. I rebuilt the project more incrementally. Smaller steps. More explanation. More effort to understand each piece as it was added.
I hit the same wall again.
That experience changed how I framed the problem.
This is not about starting big versus going step by step.
It is about how long a single conversational thread can remain effective.
In my experience using ChatGPT through the web interface, long-running sessions eventually degrade. Context becomes heavy. The UI struggles. At some point, progress slows regardless of how careful or disciplined the development approach is.
You run out of runway right when you are most invested.
Why a New Session Helps, and Why It Still Falls Short
When a thread becomes unusable, starting a new prompt session is the obvious move. This is where checkpointing helps.
A good checkpoint gives the LLM something concrete to work from instead of guessing. It reduces ambiguity. It shortens the time needed to get back to productivity.
But it is important to be honest about the tradeoff.
A new session, even with a strong checkpoint, is less capable than the original thread.
Subtle intent is gone. Prior reasoning is flattened. The LLM is more likely to:
- Offer general recommendations instead of targeted guidance
- Miss edge cases that were discovered earlier
- Suggest changes that quietly break existing behavior
Checkpointing improves survivability, but it does not restore continuity.
What Makes a Good Checkpoint
A good checkpoint captures structure, not history.
At a practical level, that usually means documenting:
- File names and file structure
- What each file is responsible for
- A limited set of key files
- Key data structures such as table headers or object shapes
- The current working state of the application
- Known limitations or fragile areas
- What you are trying to do next
Using the LLM itself to generate this summary can work well if done before the thread becomes unstable. Storing it alongside the code, especially in a repository, makes collaboration and return visits far easier.
Checkpointing is useful. It just is not a silver bullet.
Modularity May Be the More Durable Answer
One takeaway from this experience is that large, tightly coupled problems do not map well to long conversational sessions.
A more sustainable pattern may be to design projects as a set of smaller, loosely coupled modules. Each module can be:
- Designed and developed within a single session
- Fully understood before moving on
- Checkpointed or handed off independently
- Revisited later without reconstructing the entire system
This shifts the burden away from conversational memory and toward explicit boundaries.
In that sense, checkpointing helps you recover. Modularity helps you avoid the problem in the first place.
About Tools and the Future of This Limitation
My experience here is based on ChatGPT in a web browser. Other models, interfaces, or IDE-integrated tools may extend the runway significantly. Claude may tolerate longer context. GitHub Copilot avoids conversational memory entirely by grounding itself in files.
I do not believe this is a permanent limitation.
I suspect this is a limitation of vibe coding today, not of AI-assisted development as a whole. Better memory models, improved tooling, or hybrid approaches may reduce or eliminate this constraint over time.
For now, it is something to design around.
Closing Thoughts
Vibe coding remains powerful. It enables speed, experimentation, and creativity that would have felt unrealistic not long ago.
But long-running projects expose limits that are easy to miss early on. Understanding erodes. Conversations grow heavy. Threads run out of runway just when you are most invested.
Checkpointing helps, but it does not solve everything. Modularity helps even more. Both are ways of adapting to the tools we have today, not statements about what will always be true.
For now, treating conversations as temporary, understanding as essential, and structure as intentional is what turned this from a frustrating time sink into a workable learning experience.
Final Sanity Check
At one point, I stopped and did a sanity check. I wanted to be sure this was not just me misusing the tool or imagining a limitation that was not really there. So I asked ChatGPT directly what might be happening.
The response was reassuring.
ChatGPT described this as a known limitation of long-running, conversational coding sessions. As a project grows, the conversation itself becomes the workspace. Each new response has to reason across an increasingly dense history of code, corrections, and prior decisions. Over time, that weight starts to matter.
The model does not simply forget earlier context, but its ability to reason precisely across all of it degrades. Responses slow down. Suggestions become more generic. Subtle intent that was clear earlier becomes harder to preserve. In browser-based sessions, the interface itself can also contribute to the problem as threads grow large.
What stood out most was that this is not unique to one model or platform. Larger context windows or different interfaces can delay the issue, but the same pattern eventually appears once a single conversational thread grows large enough.
That explanation matched my experience almost exactly. It helped reframe checkpointing and modular design not as workarounds, but as practical ways of working within the limits of vibe coding today.