The Hidden Limits of ChatGPT: What the Sandbox, Context Window, and Memory Really Mean

I’ve noticed a lot of people wondering why GPT forgets things, or why memory doesn’t feel like “real” long-term memory, and after pushing this thing harder than most people do, I’ve learned where the walls really are. They’re invisible if you never hit them, but once you push big projects, you run straight into them whether you want to or not.

When I started building a Sysadmin Black Book, basically a full repair and recovery manual, I assumed the AI could hold the whole thing in its head while helping me write it. But the deeper I went, the more I realized how the system actually works, and it explained every problem I kept seeing.

The first thing people don’t realize is that everything runs in a sandbox, nothing is happening on your PC. When you tell GPT to run Python or generate a PDF, it’s all happening inside a tiny boxed-in space with limits on time, memory, CPU, file size, you name it. If you push too hard, the model doesn’t crash dramatically, it just quietly hits the ceiling, and suddenly it starts forgetting formatting, shortening answers, timing out, or repeating itself. It’s not “getting tired,” it’s simply trapped inside hard boundaries that the user never sees.

Then there’s the context window, which is the real working memory. Once the conversation grows too large, older information literally falls out of view. Gone. The model isn’t refusing to remember your earlier instructions, it just can’t see them anymore. That explains every time my book project started drifting or losing structure the longer the session went on. The early rules rolled right off the edge of the window and the model was working blind to them.

People also misunderstand the Memory feature. Folks try to shove giant character bios or multi-page histories in there and then wonder why GPT doesn’t “remember” them. Memory isn’t loaded all at once. The model only pulls what it thinks fits into the active conversation, and it still has to fit inside that same context window. If you store huge chunks in memory, it will never load them fully because it physically can’t.

Projects help, but they don’t remove the limits. I learned that quickly. When I asked GPT to generate big PDF chapters, it slammed into the sandbox limits. When I tried to keep a whole manual in play at once, it slammed into the context limits. The only thing that worked was breaking the book into small parts, generating one piece at a time, saving them myself, and assembling them later. Once I did that, the results were great because I wasn’t asking GPT to hold the whole universe in its head at the same time.

The truth is, GPT is incredibly powerful, but it’s not limitless. It’s more like having a brilliant assistant working in a small room. You can hand them anything you want, but if you stack too many boxes in the room, the older boxes get shoved out the door.

If you understand that, the whole system makes more sense, and you stop fighting it.

Anyone else run into this? I’d like to compare notes with others who’ve pushed it past the comfortable surface level.