evalops/orbit-agent
A brutally honest "high‑orbit" startup advisor you can text or run from the CLI. Built with DSPy, it provides opinionated, YC-style advice and...
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A brutally honest "high‑orbit" startup advisor you can text or run from the CLI. Built with DSPy, it provides opinionated, YC-style advice and...
Code for intro to DSPy blog post | Language: Python | License: MIT License
A collection of example AI programs built using DSPy and maitained by the Langtrace AI team. | Language: Python | License: MIT License
SuperOptiX: GEPA DSPy Optimizer hands-on demo - User DSPy GEPA in SuperOptiX - Use with local llama and Owen models ... | Channel: Superagentic AI
GEPA + DSPy + SuperOptiX : Quick Demo. | Channel: Superagentic AI
Learn to build a DSPy classification pipeline: load data, evaluate a base model, apply optimization, and compare results to a ... | Channel: AI...
Language: Python | License: MIT License
RL-driven framework that composes modular DSPy pipelines and teleprompters to improve LLM reasoning (experiments use GPT-2). | Language: Jupyter...
MCP server for enhancing knowledge base search and retrieval | Language: Python
Is Prompt Engineering really the best way to build robust applications? Well, DSPy is here to challenge that. Let's look at a ... | Channel:...
In this session, we'll explore DSPy, a new framework for building and optimizing agentic apps. We will also continue our series on ... | Channel:...
Is DSPy just another prompt optimization tool? According to Prashanth Rao from Kuzu, that's only part of the story. In this clip, he ... | Channel:...
DSPy implementation for detecting metaethical breaches in AI systems through systematic evaluation of moral reasoning patterns | Language: Python |...
An AI-powered platform that uses an agentic workflow to automatically generate Project Requirement Documents (PRDs). | Language: Python | License:...
this repo is for linkedin learning course: Structuring Language Model Interactions with APIs and DSPy | Language: Jupyter Notebook | License: Other
Advanced RAG Pipelines and Evaluation: Self-Reflective RAG, Corrective RAG, Adaptive RAG, Sub-Query Generation and Routing, DSPy, DeepEval |...
Speaker : Mike Taylor author of book "Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs" Event: ... | Channel: London...
AI ENGINEER ROADMAP [ learn AI Engineering in 2025 ] ▻ https://zazencodes.com/ NEWSLETTER [ weekly video email ] ... | Channel: ZazenCodes
Language: Jupyter Notebook | License: Creative Commons Zero v1.0 Universal