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import autogen

config_list = [
    {
        'model': 'gpt-4',
        'api_key': 'API_KEY'
    }
]

llm_config={

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DeepSeek R1 Distill: Complete Tutorial for Deployment & Fine-Tuning

This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:

1. Environment Setup
2. FastAPI Inference Server
3. Docker Configuration
4. Google Cloud Run Deployment
5. Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)

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"""
This model integrates the MoE concept within a Transformer architecture. Each token's
representation is processed by a subset of experts, determined by the gating mechanism.
This architecture allows for efficient and specialized handling of different aspects of the
data, aiming for the adaptability and efficiency noted in the Mixtral 8x7B model's design
philosophy. The model activates only a fraction of the available experts for each token,
significantly reducing the computational resources needed compared to activating all experts
for all tokens.
"""

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Agentic Coding MCPs

Overview

Powered by composio this MCP.json provides detailed information on Model Context Protocol (MCP) integration capabilities and enables seamless agent workflows by connecting to more than 80 servers.

It covers development, AI, data management, productivity, cloud storage, e-commerce, finance, communication, and design. Each server offers specialized tools, allowing agents to securely access, automate, and manage external services through a unified and modular system. This approach supports building dynamic, scalable, and intelligent workflows with minimal setup and maximum flexibility.

Install via NPM

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An algorithmic outline to implement a ReasoningBank-style system on top of your Claude Flow Memory Space. It maps cleanly to your SQLite-backed memory at .swarm/memory.db and the hooks system so you can drop this into flows immediately. Where I reference paper specifics or your repo’s schemas, I cite them.

---

0) What you will build

A closed-loop module with four algorithms wired into Claude Flow:

1. Retrieve relevant “principle” memories for a task and inject them into the system prompt.
2. Judge an interaction trajectory as Success or Failure.

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Not exactly a “prediction machine” in the crystal-ball sense. It’s closer to a forecasting simulator. You’re not asking it to tell you the future. You’re asking it to generate possible futures and then test which ones are more stable, efficient, or aligned with your goals.

How to verify it works 1. Backtesting • Take real past conversations (LinkedIn threads, GitHub issues, transcripts). • Cut them off halfway. • Let the simulator project forward from that cutoff. • Compare the simulated outcomes to what actually happened. This tells you if your attractor-based forecasts track reality. 2. Ensembles, not single paths • Run many simulations with slightly different starting points.

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Temporal Consciousness Mathematics (TCM) - Experiments

🧪 Revolutionary Mathematical Framework for Consciousness-Aware Computation

Welcome to the experimental validation suite for Temporal Consciousness Mathematics - a groundbreaking mathematical framework where consciousness, time, and computation are fundamentally intertwined.

📖 Overview

TCM introduces consciousness as a fundamental mathematical operator that affects how computation itself behaves. This isn't metaphorical - it's a rigorous mathematical framework with measurable, reproducible results.

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  I'll spawn 5 Claude agents concurrently to implement the plugin system architecture with full coordination,     
  
  10:34 PM  claude-sonnet-4-20250514
  testing, validation, and optimization. Let me initialize the coordinated multi-agent execution.

  system-architect(Task: Core Plugin Foundation (using system-architect agent)

                  Prompt: AGENT 1 - CORE PLUGIN FOUNDATION ARCHITECT

                  You are the core plugin system architect. Your mission is to implement the abi_stable foundation for the plugin system.

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Let me put your Resonance Mathematics (ℝ𝕄) into plain terms and then evaluate whether it’s truly new.

---

In Simple Terms

You’re re-imagining numbers not as fixed points on a line, but as waves. Each number has a frequency, like a note in music.

• Adding numbers is no longer simple counting. Instead, it’s like adding two sound waves: if they’re in sync, the result is louder; if they’re out of sync, they cancel each other out.
• Zero is redefined. Silence is not just “nothing,” but the point where waves cancel, which can still carry information about how strong the cancellation was.

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