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.
The goal is to build a unified tool (or pipeline application) that:
We stand at the brink of a new political age. In the shadows of Silicon Valley boardrooms and Washington backrooms, an unlikely alliance has taken shape. The Dark Enlightenment – an obscure neo-monarchist ideology born on internet forums – has crept from fringe blogs into the corridors of power. When I first heard whispers about tech CEOs and White House aides reading the same forbidden tracts, I knew something extraordinary was unfolding. This chronicle that follows is a firsthand journey into that unfolding drama, written in the heat of events by an intrepid observer who witnessed the transformation up close. It reads like a political thriller because, in many ways, it is one – except every bit of it is based on real people and real ideas shaping our world.
To set the stage, let me sketch the key players and ideas at work, so you can follow the wild narrative that ensues:
| use wsl for all local terminal when running in windows. | |
| never hardcode .env variables in dockerfiles or code. | |
| User query: {base_task} --keep it simple | |
| Context from Previous Research (if available): | |
| Key Facts: | |
| {key_facts} |
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
