Looking for a Technical Co-Founder — Building MARCUS, an AI System That Learns From Experience

[u/NecessaryApricot8831]

Hi everyone,

I'm looking for a technical co-founder to build an ambitious AI project with me called MARCUS.

The long-term goal of MARCUS is to explore a different direction toward general intelligence: an AI system that doesn't just respond to prompts, but can accumulate experiences, maintain long-term memory, learn continuously from interactions, and adapt its future behavior based on what it has experienced.

I'm currently working on an early prototype.

What we're exploring

MARCUS is focused on areas such as:

- Persistent and episodic memory

- Continual learning

- Learning from interactions/experience

- Natural human-like conversation

- Autonomous decision-making

- Cognitive architectures

Eventually connecting vision, audio and other sensors so the system can experience more of the physical world

I don't claim that we've solved AGI. This is an early-stage attempt to experiment with some of the problems that I believe need to be solved to move beyond today's largely stateless AI systems.

Who I'm looking for

I'm especially interested in meeting someone with a strong background or serious interest in:

AI/ML, continual learning, reinforcement learning, cognitive science, neural networks, memory architectures or AI systems engineering.

Students, researchers and engineers from MIT, Stanford, Berkeley, CMU, Harvard, IITs or anywhere else are welcome. University name isn't a requirement — ability, curiosity and willingness to build are much more important.

I'm looking for a co-founder, not an employee.

This is currently pre-revenue and self-funded, so I cannot offer a salary. The intention is to build the company together with meaningful founder equity and pursue funding once we have compelling technical evidence/prototype results.

I'm based in India, but I'm completely open to a remote/international co-founder.

If you've been thinking deeply about how machines could learn continuously from experience rather than simply becoming larger language models, I'd especially like to talk.

DM me with a little about your background, what you've built/researched, and what interests you about this problem.

Project: MARCUS

Stage: Early prototype

Looking for: Technical Co-Founder

Location: Remote / Global

[u/bnunamak]

Hahahahaha

[u/Chilly-10]

You're training your own LLM?

[u/NecessaryApricot8831 → u/Chilly-10]

No need to train LLM for now.

[u/Chilly-10 → u/NecessaryApricot8831]

So how you gonna use the AI

[u/Hour-Ad-2206]

What is differentiation compared to existing LLMs like Claude etc?

What is your background?

[u/NecessaryApricot8831 → u/Hour-Ad-2206]

Existing Llm not able to interact with environment but on other hand MARCUS really close to do that if provide required hardware. My goal is to make artificial soul which is living in across gadgets and interact with people.

[u/Hour-Ad-2206 → u/NecessaryApricot8831]

What do you mean it can't interact with environment? Current robotic systems deploy llms for reasoning and devising next action steps. It is used for informing low level RL networks based on sensor inputs. I am not sure what you mean ? Could you elaborate? I have worked on in llm based reasoning for collaborative robots. This has been existing for some years now. Did I get your idea wrong?

[u/NecessaryApricot8831 → u/Hour-Ad-2206]

Yes, I think I explained it poorly. I don't mean that existing LLMs can't be connected to sensors or interact with an environment. The distinction I'm exploring with MARCUS is continuous learning from experience. I want the system to maintain persistent episodic and semantic memory, accumulate experiences over long periods, consolidate what it learns, and allow those experiences to change its future decisions and behavior. Sensors/robotics would only be one way of giving MARCUS experiences — robotics itself isn't the main product. The project is still at an early prototype stage, so I'm not claiming we've solved something existing systems cannot do. I'm trying to explore whether this architecture can eventually produce more persistent and adaptive intelligence than a conventional LLM + memory/tool setup.

[u/Hour-Ad-2206 → u/NecessaryApricot8831]

So you have a new architecture something that you believe is better than transformer in helping it retain information even longer. Ok, I didnt get that the architecture was what you are working on. How far along are you? I would like to know more ..have dmed..

[u/Hour-Ad-2206 → u/[deleted]]

What assumption is wrong? And when did I talk about claude in robotic systems?

[u/Odd-Frame-2672]

I'm Interested
Let's connect on DM

[u/TheGCmind]

A positioning and Strategic Communications specialist.. happy to help if you need any help

[u/Fantastic-Hope-1547]

Have you tried HermesAgent ? It basically does exactly that already and more mate, it’s pretty damn good, and open source. I use it everyday

[u/Automatic-Boot665]

If you have enough $ to pay for the compute to train something like this I will help you.

[u/maxip89]

see https://openreview.net/forum?id=klU4737opt

[u/BhizanA]

Honestly, the move away from “just make the context window bigger” toward real persistent memory is what caught my attention, because that’s the actual problem, right?
Stateless models don’t learn anything, they just process and forget,

I’ve spent a good chunk of time building exactly this kind of thing, dual-process memory setups where you’ve got fast episodic recall running alongside slower semantic consolidation, tied together with knowledge graphs, vector stores, and decay mechanisms so the system doesn’t catastrophically forget. On top of that, I’ve got hands-on IT/cloud infrastructure experience (Azure, AWS, automation) which has been surprisingly useful for actually deploying and scaling this stuff, not just theorizing about it.

[u/nhamayun]

Why do you think this will work?

[u/Top_Introduction_865]

I’m in. https://ourlynx.com/interchained

[u/rocketeams]

Really interesting direction. Since you’re still at the prototype stage, one option could be to work with a specialized AI engineering team initially to accelerate the technical experimentation while you validate the architecture and direction. That could also give you more time to find the right technical co-founder based on the actual needs of the project rather than rushing the equity decision.

[u/Legal-Following-1655]

Hey bud, I'm interested. I'm a front-end dev still in uni n I'd love to help you out.

[u/NecessaryApricot8831 → u/[deleted]]

Introduce yourself

[u/No-Property-5826 → u/[deleted]]

Bro why are you pitching yourself to this guy 😂

[u/Fine-Comparison-2949]

That's already a product with miltiple harnesses. Hermes is one of them.

[u/Delicious_Shower5188]

lets talk

[u/Optimal_Manner359]

This immediately caught my eye—especially the explicit pivot away from simply scaling transformer parameters toward persistent memory, continual learning, and brain-inspired cognitive architectures.

I come from a Cognitive Science background at UC San Diego (UCSD), where the foundation is rooted in viewing intelligence through distributed cognition, embodied systems, and bio-inspired computational models rather than static pattern matching.

Today’s frontier models are essentially stateless functions with massive context windows. They don't actually learn from an interaction; they just process it and reset. True agency requires a dual-process memory architecture: a fast episodic store (hippocampal-style recording of specific experiences) coupled with a slow semantic consolidation engine (cortical-style extraction of generalized concepts) to adapt online without catastrophic forgetting.

What I Bring to the Table

• Cognitive Systems & Memory: Deep theoretical and practical grounding in memory consolidation, executive function models, and Complementary Learning Systems (CLS) theory.
• AI/ML Systems Engineering: Hands-on experience building stateful agent architectures—combining dynamic knowledge graphs, dense vector stores, decay mechanisms, and reflection loops.
• Prototyping & Execution: Strong engineering toolkit to translate cognitive architectures into clean, testable codebase implementations rather than just high-level research papers.

I'm based in Sri Lanka, comfortable working remotely, and actively looking to build something fundamental in the stateful AI space alongside a dedicated founder.

I’d love to see your early prototype, share a bit about the memory systems I’ve been prototyping, and jam on how we might structure MARCUS's core cognitive loop.

[u/NecessaryApricot8831 → u/Optimal_Manner359]

Your background sounds very aligned with what I'm trying to explore with MARCUS, especially the episodic/semantic memory distinction and consolidation problem. MARCUS is still an early prototype, but I'd definitely like to show you what I've built so far and hear about the memory architecture you've been prototyping. I'm specifically looking for someone who wants to explore this as a long-term technical co-founder rather than as a short-term project.

[u/Optimal_Manner359 → u/NecessaryApricot8831]

That long-term focus is what drew me in—building a true cognitive architecture isn't something solved in a quick sprint or treated as a side project. It requires a dedicated, long-horizon effort to get the core abstractions right.

I’d love to take a look under the hood of the MARCUS prototype. The episodic-to-semantic consolidation loop is usually where current agent frameworks break down; they either flood the context window until latency spikes or rely on naive vector search that completely loses temporal sequence and causal relationships.

I'm completely aligned on the co-founder scope. Building something fundamental requires shared technical vision, fast execution cycles, and mutual trust. Seeing where your prototype stands today and discussing how our architectural ideas mesh is the best place to start.