AgentGPT - Deploy AI Agents in Your Browser - High Signal AI #8

Curated learning resources | Search engine for developers | GitHub Repos to watch | AI Funding News | Weekly Reading List | AI Projects Showcase | Tech Updates Digest

This is the 8th issue of High Signal AI. As the newsletter is growing, I’d love to hear your thoughts, comments, suggestions.

One reason I obsess about building quality training materials is that I think learning must be a habit. Learning a little every week is important to get through the volume of learning we all need, and additionally to keep up with changing technology. High-quality training that’s also fun supports a healthy learning habit!  - Andrew Ng

The World Needs High-Quality AI Education More Than Ever

1. Highlight of the week 🏆: AgentGPT

2. Developer tool of the week

3. GitHub repositories you must check out

4. AI startups that raised funds

5. High value learning resources

6. Reading list for the week

7. What people are building with AI

8. Updates from dev libraries, hubs and tools

---

Highlight of the week 🏆: AgentGPT

AgentGPT by Reworkd AI is a platform that allows users to create and deploy AI agents with specific goals.

Users can name their agent, set a goal, and deploy it for tasks such as generating reports, planning trips, or creating study plans.

It is currently in beta and focuses on automating various tasks using AI agents.

This is an open source project from the Rewrkd team which allows to deploy autonomous agent in the browser:

• Purpose: Assemble, configure, and deploy autonomous AI agents in the browser

• Setup:

  • CLI Integration: Easy setup via Command Line Interface

  • Environment Variables: Integration support

  • Database: MySQL setup

  • Components: Backend (FastAPI) and Frontend (Next.js)

• Prerequisites: Node.js, Git, Docker, OpenAI API keys, and optional services

• Tech Stack: Next.js, FastAPI, Prisma, TailwindCSS

• Open Source: Over 30.6k stars and 9.2k forks on GitHub

---

Dev tool to get high quality search results

https://devv.ai

Google’s search results are filled with sponsored, irrelevant and low-quality results, we need a better search engine.

Devv AI is an advanced search engine tailored for developers, offering three modes: Chat, Web, and GitHub.

• In Chat Mode, users can interact with AI for tasks such as writing documents and code refactoring.

• Web Mode enables the AI to browse the web for up-to-date answers.

• GitHub Mode, currently in beta, allows seamless interaction with repositories for contextualized search and assistance.

• Devv AI aims to enhance productivity and accuracy in developer workflows.

Here’s an example of a query that I searched for and I liked the results I got:

---

GitHub repositories you must check out

• Perplexica is an open-source AI-powered search engine designed as an alternative to Perplexity AI. It uses advanced machine learning algorithms like similarity searching and embeddings to provide accurate search results with cited sources. Key features include:

  • Local LLMs Support: Use models like Llama3 and Mixtral via Ollama.

  • Two Main Modes: Copilot Mode (generates different queries for better results) and Normal Mode (standard web search).

  • Focus Modes: Specialized modes for tasks like academic research, writing assistance, and multimedia searches.

• LitGPT by Lightning-AI is a comprehensive library designed for pretraining, finetuning, and deploying large language models (LLMs) at scale. It supports over 20 high-performance LLMs, offering a developer-friendly environment with minimal abstractions for easy debugging and optimal performance.

  • Flexible Workflows: Allows pretraining, finetuning, evaluation, deployment, and testing of LLMs.

  • Model Variety: Supports models like Llama, Falcon, Mistral, and more.

  • State-of-the-Art Optimizations: Includes Flash Attention v2, multi-GPU support, CPU offloading, and TPU/XLA support.

  • Command Line Interface: Facilitates advanced workflows through simple commands.

---

AI startups / businesses that raised funds

• AI-powered startup Regard secured $61 million to enhance its platform that detects missed illnesses and boosts hospital revenue.

• Beeble AI raised $4.75 million to launch a virtual production platform for indie filmmakers.

• Substrate, a NYC-based AI infrastructure startup, closed an $8 million seed funding round to expand its market for inference solutions and enhance its AI capabilities for software engineers.

• AI healthcare startup Cloudphysician has raised $10.5 million to enhance their AI platform, RADAR. Cloudphysician partners with hospitals to manage ICU and emergency department patients, aiming to bridge the gap in critical care with advanced AI technology and 24/7 monitoring​.

---

High Value Learning resources 📚

• The "Understanding Deep Learning" book on GitHub provides a comprehensive guide to deep learning concepts and applications.

  • It includes 68 interactive Python notebooks with exercises that cover essential topics such as background mathematics, supervised learning, shallow networks, and more.

  • Each notebook is designed to help readers understand and implement deep learning models through hands-on practice.

• The "Prompt Compression and Query Optimization" course from DeepLearning.AI teaches how to use MongoDB's features to build efficient Retrieval-Augmented Generation (RAG) systems.

  • The course focuses on optimizing prompts and queries to address challenges related to scaling, performance, and security in AI applications.

---

Reading list for the week

• The article The World Needs High-Quality AI Education More Than Ever emphasizes the growing need for high-quality AI education, particularly as AI technologies become more integral to various industries.

  • Key takeaways include:

    • Increased Demand for AI Skills: There is a significant and growing demand for professionals skilled in AI, necessitating accessible and effective education programs.

    • Accessibility of AI Education: Platforms like DeepLearning.AI aim to democratize AI learning, making it accessible to a wider audience, not just to those with a technical background.

    • Practical and Hands-On Learning: The emphasis is on providing hands-on, practical training that prepares learners for real-world applications, enhancing their ability to implement AI solutions effectively.

    • Continuous Learning and Adaptation: As AI technologies rapidly evolve, ongoing education and upskilling are crucial for professionals to stay current and competitive in the field.

• What We’ve Learned From A Year of Building with LLMs - Large language models (LLMs) have become "good enough" for real-world applications, and their accessibility has been enhanced by provider APIs, allowing a broader audience to integrate AI into products. The article highlights the learnings from the pioneers like Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu

  • Learnings from Experience: Insights are categorized into:

    • Tactical: Practical tips for prompting, retrieval-augmented generation (RAG), flow engineering, evaluations, and monitoring for both practitioners and hobbyists.

    • Operational: Guidance on organizational and day-to-day aspects of shipping AI products, including team building and sustainable deployment strategies.

    • Strategic: Long-term perspectives with advice for founders and executives, such as focusing on system development over model tweaks and avoiding premature investments in GPUs.

  • Practical Guide: The guide aims to help others avoid common pitfalls and accelerate their development processes by sharing lessons learned from building with LLMs over the past year.

---

What people are building with AI 🧑‍💻

• qdurllm by AstraBert is a local search engine that allows users to upload and interact with content from their favorite websites on their desktop.

  • It uses Qdrant for vector database storage, Langchain for URL content management, and llama.cpp-Gemma for text generation, all integrated within a Docker application.

  • Users can search and chat with the uploaded content through a Gradio interface.

• Agent-Examples by Definitive-AI showcases a collection of AI-generated outputs, demonstrating the tool's ability to automate AI agent development.

  • The repository includes examples of multi-agent systems such as content writing agents, email management agents, and LinkedIn engagement agents.

  • Each example contains the generated code and comprehensive documentation, illustrating the system's capabilities in creating various AI agents from interviews and process documentation.

---

Updates from dev libraries, hubs and tools

• Crawlee Enhances Web Data Collection: The launch of Crawlee for Python has been announced, featuring consolidated interfaces for HTTP and Playwright, along with automated scaling and session handling, as outlined on GitHub and Product Hunt.

  • Supporting both web scraping and browser automation, Crawlee presents itself as a powerful solution for Python developers involved in data extraction for AI applications, LLMs, RAG systems, or GPTs.

• Innovative Techniques Transform LLM Efficiency: Scientists have introduced methods to remove matrix multiplication in large language models, preserving high performance for models with billions of parameters while drastically lowering memory requirements. Tests demonstrate up to a 61% decrease in memory usage compared to standard models.

  • The novel Test-Time-Training layer design substitutes traditional RNN hidden states with a machine learning model. This approach achieves linear computational complexity and performance on par with or exceeding leading transformer models, as revealed in a recent social media post.

• LlamaCloud Simplifies Data Handling: A beta version of LlamaCloud has been introduced, offering a managed solution for processing, indexing, and retrieving unstructured data. Interested parties can now join a waiting list for early access.

  • Incorporating LlamaParse for sophisticated document processing, LlamaCloud aims to facilitate data synchronization across various backend systems to enhance LLM integration.

• Unsloth AI Boosts Model Training Efficiency: Unsloth AI's newly launched documentation site explains how to enhance speed by 100% and cut memory consumption by 70% during the finetuning of large language models such as Llama-3 and Gemma, while maintaining performance quality.

  • The platform offers guidance on dataset creation and model deployment, and addresses gguf library challenges by recommending compilation from the llama.cpp repository.

---