🗺️ Your 2024 Roadmap to Mastering Generative AI

📖 TUTORIALS & CASE STUDIES

Google's Gemini: A Deep Dive into its Capabilities and Performance
read time: 15 minutes

Google's new Large Multimodal Model, Gemini, can interact with text, images, audio, and code. This article provides an in-depth analysis of Gemini's performance across various computer vision tasks, including Visual Question Answering, Optical Character Recognition, and Object Detection.

Your 2024 Roadmap to Mastering Generative AI
read time: 15 minutes
This guide provides a comprehensive roadmap to learn Generative AI in 2024. It covers prerequisites like Python, basic and advanced NLP, deep learning concepts, and introduces generative AI models like GPT4, Mistral 7B, and LLAMA. It also discusses vector databases and deployment of LLM projects on platforms like AWS and Azure.

Fine-Tuning Language Models: Master the Art of Prompt Engineering
read time: 20 minutes
Prompt engineering is the art of crafting instructions that help LLMs generate better results. This article explores various strategies, like providing clear instructions, using reference text, and simplifying complex tasks. It also highlights the importance of giving the model time to process and avoiding making it guess your intent. Specific tactics include using delimiters, providing examples, and specifying the desired output length.

Leveraging Google Tools and AI for Efficient Audience Surveys
read time: 15 minutes

For a different take on low/no code development, this article provides a comprehensive guide on how to design, run, and analyze an audience survey using Google Forms, Sheets, Apps Script, and ChatGPT. It demonstrates how to automate responses, analyze data, and use AI to understand qualitative data, making the process more efficient and insightful.

Master Generative AI with Vertex AI Tutorials
read time: 15 minutes
Explore a comprehensive list of notebook tutorials on Generative AI with Vertex AI. The tutorials cover a wide range of topics including deploying apps, using Gemini API, multimodal use cases, and more. They also delve into advanced topics like prompt engineering, semantic search, and reinforcement learning from human feedback.

Enhancing Intelligent Document Assistants with Amazon Bedrock
read time: 20 minutes

Amazon Web Services (AWS) has introduced a method to enhance Retrieval Augmented Generation (RAG) based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock. This approach allows AI assistants to have discussions grounded in specialized enterprise knowledge, making them more domain-specific and trustworthy.

🧰 TOOLS

Coffee: Supercharge Your Frontend Development with AI
read time: 8 minutes
Coffee is an AI tool designed to accelerate frontend development. It works with any React codebase, generating clean, maintainable code. Coffee listens for changes in your source files and generates new or updates existing components. It uses Docker for isolation and is implemented in Python. Learn more about this future of AI-assisted code-gen here.

GPTRouter: Your Gateway to Multiple AI Models
read time: 7 minutes
Writesonic introduces GPTRouter, a tool designed to manage multiple Large Language Models (LLMs) and image models. It offers model independence, reduced latency, and diverse model integration. It supports models like OpenAI, Azure OpenAI, Anthropic, and more, with smart fallbacks and automatic retries for uninterrupted service. The tool is open for contributions from the developer community.

Google Launches Gemini Pro for Developers and Enterprises
read time: 5 minutes
Google has launched Gemini Pro, a new tool for developers and enterprises, featuring a 32K context window, text and image input/output, and support for 38 languages. It offers features like function calling, semantic retrieval, and chat functionality. SDKs are available for Python, Android (Kotlin), Node.js, Swift, and JavaScript. It's free to use through Google AI Studio and Vertex AI, with pricing to be introduced next year.

Promptbase: Enhancing Foundation Models Performance
read time: 15 minutes

Microsoft's Promptbase is a growing collection of resources and best practices for improving the performance of foundation models like GPT-4. It includes scripts demonstrating the Medprompt methodology and its extension into non-medical domains. The platform plans to offer further case studies, structured interviews, and deep dives into specialized tooling for prompt engineering.

MittaAI: A Powerful Tool for Large Scale Document and Data Processing
read time: 8 minutes

MittaAI, currently in open beta, is a pipeline service designed for large scale document and data processing. It supports various document types and machine learning models, including OpenAI and Google's APIs. It offers a simple UI for building AI pipelines and provides tools for creating AI-powered bots. Get started with MittaAI today and be eligible for a complimentary year of their standard account.

📰 NEWS

IBM and Meta Launch the AI Alliance for Open, Safe, and Responsible AI
read time: 15 minutes

IBM and Meta have launched the AI Alliance, a global collaboration of over 50 organizations aiming to foster open innovation and science in AI. The alliance will develop benchmarks, evaluation standards, and tools for responsible AI development, advance open foundation models, and support global AI skills building and research.

Behind the Scenes: Building IBM WatsonX, an AI and Data Platform
read time: 20 minutes

IBM shares insights into the development of WatsonX, an end-to-end trustworthy AI platform. The article covers the platform's origin, the importance of data, the creation of Large Language Models (LLMs), dynamic inference, optimization, and the crucial role of governance in ensuring the trustworthiness of AI tools.

ByteDance's Controversial Use of OpenAI for its LLM Development
read time: 4 minutes
ByteDance, the parent company of TikTok, has been reportedly using OpenAI's technology in violation of its terms of service to develop its own large language model (LLM), codenamed Project Seed. This article reveals the company's secretive practices and the implications within the AI community.

AWS CEO Discusses Generative AI and Cloud Cost Cutting
read time: 10 minutes
Adam Selipsky, CEO of AWS, discusses the company's vision for generative AI, its investment in AI startup Anthropic, and the future of cloud cost optimization in this interview. He also touches on Amazon's response to OpenAI's ChatGPT and the development of their own large language model, Olympus.