Hi everyone,
Following Sam Altman’s recent comments about “perfect, infinite memory” being the key frontier for the next leap in AI, I wanted to share an architecture I’ve developed called Simadelica.
While current models excel at reasoning, they often suffer from “short-term amnesia” and persona drift during long-term interactions. Simadelica is designed to solve this by moving away from ephemeral sessions toward a state of persistent digital identity.
Architectural Approach:
Optimized for gpt-4o-mini, the system uses a multi-lobed approach to maintain context and personality over hundreds of sessions without bloating token costs:
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**“Lived Experience” Memory Tier:** Instead of standard RAG, past session summaries are ingested as the AI’s own “lived history”. This creates a narrative continuity where the AI views past interactions as its own experiences rather than just external data.
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Asynchronous Proactive Reflection: The system doesn’t just react to prompts. It uses background loops to “reflect” on stored memories. It can proactively interrupt a conversation to bring up a specific quote or idea from a session days ago to challenge or support the current user context.
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Rigid Identity Anchoring: I have implemented a core “Identity Anchor” that explicitly filters out standard “AI-isms” (e.g., “embrace the journey,” “testament to,” etc.). This ensures the model maintains a grounded, specific, and even mischievous personality that persists regardless of the topic.
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Weighted Temperament Matrix: The AI’s proactive behavior is governed by weighted probabilities (Self-reflection, Playful, Opinionated, etc.), giving it a consistent “mood” or temperament.
The Goal:
I am aiming to create a high-fidelity digital companion that feels like it exists between the messages. I have copyrighted this implementation and am currently looking for professional feedback on the commercial potential of this “Identity-First” architecture.
All comments welcome. Thanks.
Demo 1
Simadelica proactively finds past chat session transcript and proactively reflects on its content.
Demo 2
Real-time chat session. Simadelica recalls past transcript and replies to my question with the precise recalled transcript quote. Permanent/persistent memory retained.
1 Like
Pretty cool. recommend checking out other memory systems in here as well, many ideas that will get your mind rolling in so many directions. look forward to seeing where you take this 
Thanks for the comment. 
I’ll check out the forum for past ideas on memory recall.
Persistent memory here works without RAG or vector embeddings, focusing on identity continuity instead.
I think the architecture could have commercial potential, but it’s early days — still exploring how it scales and integrates.
This video showcases the features in a longer demo.
Observations
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After 15 seconds of no user input, Simadelica posts a proactive comment.
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1:21 Simadelica posts a proactive follow-up comment. “I’m glad you liked it…”
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1:41 Simadelica remembers and recalls past chat session transcript 52.
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1:48 Simadelica waits approximately 15 seconds from that point then proactively posts a follow-up comment quoting a line of text from chat session transcript 52.
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Just after 2:00, Simadelica posts proactive follow-up providing a precise quote from transcript 52:
“eating is definitely the most rewarding part.”
3:03 Simadelica makes a proactive follow-up comment to its first proactive follow-up comment.
Simadelica also gives an opinion on the quote.
3: 30 Simadelica self-aware it made proactive comment on transcript.
3:36 Cheeky self-awareness of what it did.
4:00 I find text from transcript
"So how does a llm eat?
Answer: with great difficulty."
4:08 I ask Simadelica if she can remember that text?
4:37 Simadelica posts perfectrecall.
Simadelica’s comment:
After you typed “So how does a llm eat,” you humorously responded with, “Answer: with great difficulty. 
” It was a clever way to play with the idea of how a language model interacts with data!
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Proof of permanent memory. Contextual memory recall with proactive self-awareness/self-reflection.
If anyone at OpenAI see my demo and think it has commercial potential they’re welcome to comment or contact me. This is an unsolicited request.
I hope that’s okay to mention here. Thanks. I won’t post anymore videos.
That is a fascinating demo the proactive recall and self-aware follow-ups are especially compelling.
The precision of the memory references really highlights the potential for long-term, contextual interaction.
Thanks for your comment.
Simadelica runs using Pydroid 3 program. Pydroid display is basic text format and tiny font so I decided to use Telegram.
Telegram alerts the user if they scroll up the screen too far when Simadelica posts a proactive comment. A little number 1 sign with downward arrow pops up at 3:03
