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I'll provide you with a full Python-based CLI implementation that interacts with both GitHub's REST API (for classic projects) and GraphQL API (for new projects). The CLI will support authentication via Personal Access Token (PAT) and include operations such as listing projects, creating projects, adding items, and updating items. I'll structure the implementation in a modular format to ensure maintainability and scalability. I'll update you once it's ready.

GitHub Projects CLI Tool

GitHub Projects CLI is a Python command-line tool that lets you manage GitHub Projects both in the classic format and the newer Projects v2 format. It supports authentication via Personal Access Token (PAT) and provides commands to list projects, create new projects, add items (issues/PRs or notes) to projects, update project details, move items within a project, and delete projects. The tool uses the GitHub REST API for classic projects and the GraphQL API for Projects v2, ensuring full coverage of project management

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DREAM Ai

Introduction

The AI by Dreaming framework is an innovative approach that allows models not only to think in text but also to leverage a visual world model to reason across multiple modalities—including text, audio, and other sensory information.

By integrating Multimodal Visualization-of-Thought (MVoT) with Guided Symbolic Problem Optimization (GSPO), the framework enables an AI to generate interleaved reasoning traces that include both textual and visual elements. This dual-modality mimics human cognitive processes—where we often use diagrams or mental imagery to solve complex problems—and supports recursive self-optimization.

In essence, the AI not only “talks through” a problem but also “imagines” it, consolidating memory and refining its reasoning through an internal process similar to dreaming.

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Quantum Agent Manager

Introduction

What if you could instantly see all the best solutions to a complex reasoning problem all at once? That’s the problem I’m trying to solve with Quantum Task Manager. Traditional AI approaches like reinforcement learning struggle with interconnected decision-making because they evaluate actions sequentially, step by step. But quantum computing can consider all possibilities simultaneously, making it an ideal tool for agent-based task allocation.

Using Azure Quantum, this system leverages pure mathematical optimization and quantum principles to find the best way to distribute tasks among autonomous agents. Most people don’t fully understand how quantum computing works, but in simple terms, it can represent and evaluate every possible task assignment at the same time, using superposition and interference to amplify the best solutions and discard bad ones. This makes it fundamentally different from other scheduling or learning-based approaches.

What makes

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🪰 Deep Codestral

by rUv. cause he could.

Deep Codestral is an innovative re‑imagining of Codestral 25.01—Mistral AI’s latest coding model renowned for its speed and efficiency.

Originally designed to rapidly generate and complete code, Codestral 25.01 now forms the basis for Deep Codestral when tuned for “architect mode.”

In this configuration, the model excels not only in executing coding tasks but also in explaining each design decision through detailed, multi‑step reasoning. This approach provides complete transparency in design choices, empowering engineers and architects to understand the rationale behind every suggestion.

Created by rUv simply because he could—Deep Codestral is designed to push the boundaries of AI reasoning.

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A complete set of requirements—covering UX, CLI, and code—that builds on the previous pipeline for training and optimizing ONNX models with test‑time compute methods using DSPy. This document specifies user stories, command‐line interface arguments, and sample code snippets to guide implementation.

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1. Overview

The goal is to build a unified tool (or pipeline application) that:

• Trains a model using DSPy (with integrated or hybrid PyTorch training).
• Exports the optimized model to ONNX format.

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#!/usr/bin/env -S node --no-warnings=ExperimentalWarning --enable-source-maps

// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
// and may be used to improve Anthropic's products, including training models.
// You are responsible for reviewing any code suggestions before use.

// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).