As someone who spends many hours in long working sessions with frontier models like Claude, GPT, Gemini, and Grok, one problem continues to stand out more than almost anything else.
You can have a solid two-hour session making real progress — coding, researching, planning, whatever it is. You step away for three hours or come back the next day, and the model has only a shallow, compressed memory of what you did. Even when you paste a careful session recap or give it strong instructions pointing out what to remember, it still drops important details, decisions, and direction you established earlier.
This isn’t occasional. It happens consistently across the major models.
THE CORE PROBLEM: Context windows have grown massive on paper, yet real continuity across session breaks remains surprisingly poor. Every time a model “forgets,” it burns more tokens to rebuild context — and more tokens mean more money.
The Workarounds Heavy Users Actually Try
I’ve tried nearly every approach people talk about: dedicated operating instructions in markdown files, detailed session recap summaries, full conversation history exports, screenshots of key decisions, and external memory files with RAG setups. They all help a little, but they’re manual, tedious, and force you to play babysitter just to keep the model on track. You end up doing the remembering work yourself instead of the AI doing it.
The Uncomfortable Question
This situation raises a blunt question: are the major labs intentionally keeping long-term session memory weaker than the technology allows?
Because every time the model “forgets” and forces you to re-explain context, it burns more tokens. More tokens mean more money. It also keeps you engaged longer in their platforms. When context windows have grown massive on paper yet real continuity across breaks remains surprisingly poor, it’s fair to ask how much of this is technical limitation versus business incentive.
“The most reliable memory solution in 2026 is still copy-paste. That should embarrass everyone building frontier models.”
— David Solomon, TrainingRun.AIRight now, the most reliable approaches heavy users rely on are external second-brain tools, Projects features with custom instructions, custom RAG pipelines, and starting fresh sessions with rich context uploads. None of these feel like the seamless experience we should have in 2026.
The Takeaway: Frontier models are extremely capable inside a continuous session. Their biggest practical weakness remains poor persistent memory once you pause. This limitation is costing users real time and money every single day. Until the labs treat session memory as a first-class feature — not a workaround the user has to manage — we’re leaving massive productivity on the table.
I want an honest discussion on this. What memory techniques or tools are you actually using to maintain continuity when you return to a project hours or days later? Have you found anything that works reliably with minimal effort? Do you think the slow progress on real memory is mostly technical — or are commercial incentives playing a role?
Drop a reply below. I read every one.
This is an original editorial by David Solomon.
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