gargigupta97
Joined 2 years ago
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Mar 10
Recent submissions
Comment · 1 year ago
Comment · 1 year ago
There are many tutorials on chatbots and Q&A systems, but creating specialized agents that can work together to solve specific problems requires a different approach.
In this tutorial, we have built a Personal Health & Fitness AI Agent that demonstrates how to create task-specific AI agents that collaborate effectively. Using Google Gemini and Phidata, we created a system where two specialized agents - one for diet and one for fitness - work together to generate personalized recommendations based on user inputs.
This app generates tailored dietary and fitness plans based on user inputs such as age, weight, height, activity level, dietary preferences, and fitness goals.
Phidata makes this multi-agent approach straightforward by providing a framework designed for building and coordinating AI agents. It handles the complexity of agent communication, memory management, and response generation, letting us focus on defining our agents' roles and behaviors.
This tutorial includes everything you'd need to build the app - the prerequisites, step-by-step code walkthrough, app demo, and even some ideas for enhancing the app further. Do check it out if you want to really build something useful with AI.
Comment · 1 year ago
A ChatGPT-like assistant that runs entirely offline and recalls past conversations—an AI that learns from each chat and personalizes its responses, all without any internet dependency. Giving this kind of control to users is a powerful way to make AI both secure and adaptable for private use cases.
In this tutorial, we’ll build a local ChatGPT clone using Llama 3.1 8B with a memory feature, making it capable of recalling past conversations. All components, from the language model to memory and vector storage, will run on your local machine
For this app, we’re using Qdrant for vector storage, Ollama to run Llama 3.1 locally, and Mem0 to manage memory.
Find all the awesome LLM Apps tutorials with RAG and AI agents in this AI newsletter for developers.
It's absolutely free!
Comment · 1 year ago
Unwind AI would be helpful. They publish daily newsletters on AI as well as tutorials on building apps with step-by-step walkthrough. Super focused on developers. https://www.theunwindai.com/
Comment · 2 years ago
Using open-ended questions for broader insights is a strategy the blog recommends. Can anyone share an example where this approach led to surprisingly innovative or insightful responses from an AI?
This app coordinates multiple specialized AI agents - each focusing on their domain expertise - to generate cohesive game concepts where narrative, gameplay, visuals, and technical specifications work in harmony.
The entire process is automated so developers can quickly iterate on ideas and ensure all crucial aspects of game design are considered.
Game Design Agent Team:
1. Story Agent: Specializes in narrative design and world-building, including character development, plot arcs, dialogue writing, and lore creation 2. Gameplay Agent: Focuses on game mechanics and systems design, including player progression, combat systems, resource management, and balancing 3. Visuals Agent: Handles art direction and audio design, covering UI/UX, character/environment art style, sound effects, and music composition 4. Tech Agent: Provides technical architecture and implementation guidance, including engine selection, optimization strategies, networking requirements, and development roadmap 5. Task Agent: Coordinates between all specialized agents and ensures cohesive integration of different game aspects.
Check out our step-by-step tutorial. 100% opensource code: https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/a...