barqawiz

I explore why black-box prompts are risky in healthcare and how AI graphs offer a clearer path to safe and reliable medical support. The ideas come from the systems we’ve been developing at MedWrite to make clinical AI easier to trace and verify.

This PyData tutorial walks through building multi-agent AI systems using directed acyclic graphs to orchestrate NLP, vision, recommendation models, and any model.

Key sections:

- When to use multi-agent systems (6:37) - IntelliNode framework demo (9:35) - Hands-on labs with nutrition assistant (19:42) - MCP integration patterns (34:30)

Thanks toomuchtodo, this is helpful

create a virtual character in JavaScript that give identity to your AI project.

Key Features: - Manual Mode: Control the character through on-screen buttons. - AI-Controlled: Connect with an AI assistant to enable automatic actions. - AI Characters: Customizable visual expressions and animations.

Github: https://github.com/Barqawiz/Tamagotchi

I added this as an issue here: https://github.com/Barqawiz/Tamagotchi/issues/5

Feel free to contribute.

Thank you for the suggestion! I’ll add an example in the README.

The character is rendered with JavaScript (no images), this enable the AI assistant to manipulate the animation directly.

Another approach is to use the pre-set button with different reactions, it can be given directly to the AI model through its tool functionality. commonly available in models like ChatGPT and Claude.

I'm happy to release some free IntelliNode keys for the Hacker News community.

Go to the app and click "Settings," then "Set One Key," and enter:

intelli-o21c27c218c0a750f440f

This will let you use the html simulator and other tools with GPT-4 Omni for free.

That's great suggestion, I can provide few prompts each for each IP.

The tsunami stones of Japan serve as a reminder of the timeless battle between human memory and nature's fury.

An insightful take on the evolution of computing. should we also consider the device's ability to execute conditional operations?

Mini AGI can be created in two ways: one large model with a breakthrough that can take the current transformer to the next level. Or multi-models with different abilities that work to achieve user tasks and break into multiple steps.

The second option is more achievable in the short term, for this, I create a framework that uses graph theory to navigate the AI models as multiple tasks and handle the relations between different models.

This example shows how to create a data science teaching assistant using open-source Gemma and stable diffusion: https://www.kaggle.com/code/jaguar00/data-science-teaching-a...

This article describes the concept in detail: https://towardsdatascience.com/graph-theory-to-harmonize-mod...

Gemma is an open-source large language model (LLM) toolkit that allows you to customize pre-trained models for specific tasks. Use intellinode python module to fine tune the model and integrate with stable diffusion.

Integrating the capabilities of various AI models unlocks a symphony of potential use cases, but it is complicated when integrating different models providers and types like image, text.

I developed a python framework to orchestrate the AI models integration as a graph, read the article for more details.

To use this framework: pip install intelli

I've developed a tool to expedite the AI integration process into pre-existing systems from a timeline of one to two months, to a matter of a few simple clicks. This was born out of my repetitively writing code to connect AI capabilities to other systems on top of language models and database indexing.

The result? I've exported these actions into a microservice to deploy and integrate with any system.

I would very much appreciate your feedback on this tool. Does the flow make sense? Is there any other service you believe could possibly speed up the AI integration process to your apps?

This project provides high-level functionality on top of the popular LLMs, allowing you to decouple the models and switch between them based on your use case.

The available functionality:

- Chatbot: define the provider as chatGPT or llama chat.

- Evaluation: Run evaluation across multiple models by sending a query and array of target answers. This will call the models in the background, generate the vector, and compare the distances. This supports cohere, openai, replicate, and sage maker models.

- Semantic Search: Apply search beyond the keywords using vectors.

- Direct models: directly access the model providers like Sage maker llama or hugging face.

- Offline model loader: under development.

The micro service published to docker hub:

```

docker pull intellinode/intelliserver:latest

docker run -p 80:80 -e API_KEY=$API_KEY -e ADMIN_KEY=$ADMIN_KEY intellinode/intelliserver:latest

```

I am working to add more features to the micro-service, Let me know which functions you think going to be useful.

chatGPT models upgrade to support function calls as part of the user conversation, which streamlines automation without the need for third-party dependencies. To showcase the potential of GPT function calling, this article explores a real-world example involving AWS S3 daily tasks.

it is visible now

Thanks <3

thanks for the response, will check the showdead option

you are correct, i am asking about hackernews

Are you looking to integrate AI and build startup prototypes faster than ever? You can access state-of-the-art AI models and create engaging materials with just one line of code using open-source IntelliNode:

`Gen.generate_image_from_desc(prompt, openaiKey, stabilityKey);`

In my recent blog post, I show you how to integrate the AI model into your node projects and build a [Jasper ai]-like platform for content generation, connecting multiple models from Google, Openai, and Stability to UI in a few minutes.

it is in the roadmap backlog

lightning-fast access to the latest deep learning models ChatGPT, Diffusion, Cohere, and more using node.js

unified code to access ChatGPT, WaveNet, diffusion and more.

MedWrite creates personalized patient treatment plans from a doctor’s electronic health record.

Leveraging Intelligent Java to Build a Climate Awareness App

Language models in the biomedical and clinical tasks study is not only to evaluate the existing language models, but also to develop proposed research areas to improve the usage of existing models in medical systems.

ultimate tool for Java developers looking to integrate with the latest language models and deep learning frameworks like GPT-3, DALLE, Cohere with few lines.