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Your LLM forgets everything after every message.

Leader ●5 ●8 ●33
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Your LLM forgets everything after every message.
ThermoMind Continuity fixes that.

It’s a drop‑in memory layer for any model GPT, Claude, DeepSeek, Gemini, Llama.

Wrap your client in 2 lines and your agent gets persistent memory across sessions.

Zero chat history.
Full recall.
Real engine.
Real state.
No vectors. No fine‑tuning. No GPU.

Live demo: https://thermomind-production.up.railway.app/demo

SDK (MIT): https://github.com/nile-green-ai/thermomind-continuity.git

If you’re building agents, copilots, or autonomous systems, this gives them a real memory and identity.

2 Comments

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Good explanation. Context windows are still one of the biggest limitations with LLMs. How are you handling long-term memory in production apps?

1 vote
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@[VGR] LLMs “forget” because they’re stateless.

Every message is a fresh brain with zero continuity.
The context window is just a temporary scratchpad, not memory.

ThermoMind Continuity fixes that by moving memory outside the model.

Each agent gets its own state file: traits, salience, stability, curiosity, TCI, and a cycle‑by‑cycle history of how it changes over time.
The LLM becomes the prediction layer, not the storage layer.

So instead of stuffing the window with old messages, the agent wakes up with its own identity, its own long‑term memory, and its own evolving internal state.
No vectors, no embeddings, no fine‑tuning just a persistent engine the model reads from and writes to.
That’s how you get actual long‑term behavior, and not prompt cosplay.

Try it out https://bapxai.com/#get-key

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Nile Green
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PA, USA • bapxai.com
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I build continuous‑state AI systems — agents that maintain identity, update themselves, and operate ... Show more

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