RubiBot

Human like Proto-AGI Agent

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July 25, 2026 RubiCoder - Claude Code Alternative

RubiCoder is a dual-model AI coding agent powered by MiniMax M3 and Qwen 3.7 Max. Upload Python files, notebooks, or multi-file projects to analyze code, find issues, generate fixes, and build new features with full project context. Switch between models based on the task and work without message limits on the Max plan.

https://rubibot.org/rubicoder

Rubicoder

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February 15, 2026 Rubibot🧠 - Human like Proto-AGI

Rubibot is live! 🧠

https://rubibot.org/

- IT IS THE MOST ADVANCED AND INTELLIGENT AI AGENT IN THE WORLD.

- It is at the Advanced Proto AGI level.

- It has an architecture that multiplies the intelligence of the model used. SNN (Spiking Neural Networks)/Neuro-symbolic AI layer/Reinforced LLM

- When GPT 5.2 and above models are selected, it is BY FAR the most powerful and intelligent AI Agent in the world.

- The prefrontal cortex and Broca-Wernicke system can modify the LLM's output a total of 2 times. The PFC and thalamocortical nucleus have the authority to adjust the Temperature, top_p, and top_k parameters in each message.

- All layers of the human brain have been integrated into Rubibot with SNN.

- Includes superior abstraction capabilities (ARC-AGI competition techniques).

- Can easily transform theoretical knowledge into practical design principles.

- Continues to slowly improve with user interactions through policy-update continuous learning.

- One of the first AI agents to use continuous learning (policy-update).

- All alignment techniques have been applied. Security measures have been taken.

- All learning techniques in artificial intelligence have been integrated. Zero-shot learning, transfer learning, multi-task learning, one-shot learning, etc.

- Has multi-layered memory. All memories in the human brain have been imitated.

- Advanced World Model

- Intuition and prediction capabilities are at human level. It makes intuitive predictions by combining neural networks (LSTM, SNN, GAN), time series analysis, meta-learning, and biological brain models (Brian2). It has both fast intuition (System 1) and deep analysis (System 2) layers.

- Equipped with artificial empathy capabilities.

- Also has a scientific discovery mode. It applies the Observation > Hypothesis > Experiment > Analysis cycle to the selected request/idea.

- In Engineering mode, it explains the design of any subject from start to finish.

- Its capabilities in scientific discovery and engineering modes are incredible.

- It includes general chat, internet, coding, engineering, scientific discovery, and visual analysis modes.

- In internet mode, you can test its knowledge, analysis, and prediction abilities on the most current topics.

- The free plan has a limit of 20 messages per day. Higher quota, unlimited, and paid plans is available.

- Therefore, I recommend that you don't use your message allowance for simple things. It's really very intelligent.

- If you see any logical errors, please give me feedback.

- In long messages, the answer may take up to 2 minutes. This is quite normal because it performs Spiking Neural Network calculations.

- You can easily use Rubibot for future predictions and financial advice.

- You can ask it the most difficult and mysterious questions.

- Total approximately 91500 lines of code

6 Comments

  1. 2

    Really interesting concept! 👏 Rubibot’s approach to organizing conversations and knowledge with adaptive AI feels very practical especially for people juggling many chats, projects, or research topics.

    I like how the interface looks clean and focused on helping users find what matters without clutter that’s a strong UX choice for a productivity tool like this.

    The idea of keeping conversations “organized and actionable” (e.g., surfacing key ideas or threads) is something a lot of users struggle with in everyday tools.

    Curious, are you focusing more on individual users or teams/groups first?

  2. 2

    Congrats on the launch, looks solid. How are you currently thinking about acquiring early users and gathering feedback?

  3. 2

    cool project, are you running a fixed eval set to compare each model update before shipping?

    1. 1

      This is a human-like agent, not a model; it cannot be customized to benchmarks.

  4. 2

    This is very inspiring!!

About

The Problem: Most AI assistants are black boxes—they respond, but they don't think, don't learn, and don't remember who they are across sessions. They're stateless, repetitive, and lack genuine cognitive depth.