Andreas Ramos | Adjunct Professor of Digital Marketing | See andreas.com

Stuff about AI

My links, courses, eBooks, FAQs, PDFs, and so on about AI.

I'm often asked, how to keep up? LLM genAI (Large Language Model Generative Artificial Intelligence) is evolving extremely fast (much faster than HTML in the mid-90s). I constantly talk with people: developers, founders, investors, etc.; I go to events; I speak at events, I teach university courses; and I talk with lots of other people.

If you see a useful article or whatever about AI, let me know. I'll look at it and if it fits, I'll add it.

My eBooks and Webpages

Useful Books and Articles about AI

  • God, Human, Animal, Machine by Meghan O’Gieblyn. Intelligence and sentience have a complex history in philosophy, theology, and science. What do these mean? The best book on the origins and implications of these ideas.
  • Tim Lee explains LLMs in clear language. Large Language Models Explained. This is a Must-Read to get a basic understanding of AI LLM.
  • A survey of 2,778 AI researchers who had published peer-reviewed research in the prior year in six top AI venues (NeurIPS, ICML, ICLR, AAAI, IJCAI, JMLR) on their predictions of AI. See Survey of 2,778 AI Researchers (January 2024).

News and Events in AI

  • Twitter is the main place for AI news. Stuff is announced or discussed in tweets. Here's my list of useful AI accounts in Twitter. Follow me on Twitter at @Andreas_Ramos
  • See the OpenAI Forum for community discussion. Ask questions. Get answers. Go community.openai.com.
  • Newsletters by AlphaSignal, The Deep View, and The Rundown. (However, these newsletters mostly summarize Twitter.)
  • Jeremiah Owyang's List of AI Events. Over 300 meetings or conferences every month in Silicon Valley (Palo Alto and San Francisco). There are also events in New York NYC, Seattle, and London. Come to SV for five days and go to lots of events. You'll learn more in five days here than a year elswhere.
  • Agenda Hero has additional events in Silicon Valley.

My Courses for Digital Marketing and AI

  • Course in Digital Marketing with AI. I teach a six-hour course at the DMAnc on the use of AI in digital marketing.
  • Courses in SEO with AI. I teach an eight-hour course on how to use AI for SEO at the DMAnc.
  • Courses in Digital Ads with AI. I also teach a six-hour course on how to use AI for digital ads at the DMAnc.
  • These university-level courses are hands-on and practical. I show the prompts and results. You get everything: code, examples, links, etc. I've been teaching these since January 2023.

The Top LLM GenAI for Chat and Images

  • The current top LLM AIs are OpenAI ChatGPT-4o, Anthropic Claude-3, Microsoft Copilot, Meta AI , Google Gemini. The subscription versions ($20 per month) are much better.
  • These are similar with differences in options, quality, and capabilities. These are called “foundation models” or "frontier models". Some 70,000 AI tools are built on top of these (such as Perplexity , etc.). Tip: Microsoft Copilot in Creative mode uses GPT-4 for free.
  • Rayan also adds Mistral, an AI from France.
  • The top image AI: Dall-e, Microsoft Copilot, Microsoft Designer, ImageFX, and Meta Image. Try also Pikaso
  • Perplexity.ai is also a very good AI for search. It combines its own LLM AI, along with OpenAI ChatGPT, Claude-3, Mistral, and Llama-3 AIs with search data from the Microsoft Bing search engine.
  • This list changes frequently. ChatGPT-5 may appear in the next few months. I'll try to keep this up-to-date. Current status June 10th, 2024.

As I said, if you see something useful, let me know and I'll add it.

Multi-Agent System (MAS)

  • Multi-Agent System (MAS): Combine multiple AI persona to create virtual teams with roles such as CEO, CTO, CFO, directors, managers, reviewers, testing, documentation, and so on. The team of AI persona collaborates to process tasks and produce results.
  • What can an MAS do? Here are two examples of using an MAS to create a translations company or a software company. See arxiv.org/pdf/2405.11804 and arxiv.org/abs/2307.07924.
  • What does this mean for you? You'll be able to create an MAS agent to duplicate what you do. It can do your work, learn for you, attend meetings on your behalf, make decisions, receive and send messages, and produce reports, software, translations, and so on.
  • You'll be able to buy or rent MAS agents. Need software to do something in your company? Rent an MAS, describe what you need, and it makes custom software (with design, code, testing, and user manual) in 410 seconds (6.8 minutes) at a cost of US$0.30 (thirty cents). (Think about the implications for the software industry and the hundreds of thousands of programmers.)
  • An MAS won't be just five or ten agents. Langchain expects an MAS may have 200 to 300 agents, each with specialized roles, tasks, and responsibilites.
  • Whatever can be done remotely can be turned into an MAS.

Libraries and Frameworks for AI Projects

Yohei Nakajima @yoheinakajima asked AI builders of their favorite go-to libraries and frameworks for AI projects. A Hive Mind Survey Summary.

MAS Multi Agent System Frameworks:

  • AgentOpsAI: This tool simplifies the debugging and monitoring of MAS multi agent system operations, making it easier to read through prompts and visualize spend. @AlexReibman
  • AgentForge: Designed for rapid iteration of cognitive architectures with support for multiple LLM APIs per prompt and easy VectorDB implementation via ChromaDB. @JohnSmith4Reel
  • crewAIInc: A flexible and powerful framework for enabling MAS multi agent system task completion with support for any OpenAI API-compatible endpoints. @whoabuddydev, @as_cybersamurai
  • craftgen: Offers flexibility with its actor model and event-driven architecture, making human-in-the-loop workflows easy. @Necmttn
  • Lyzr: An MAS multi agent system framework mentioned for its capabilities in building multi-agent systems. @theAIsailor
  • Policy Synth: An open-source multiscale MAS multi agent system library. @robertbjarnason
  • GraphAI: Used for data flow programming to build genetic apps, enabling LLMs to generate genetic applications. @snakajima

Libraries for Data Management and Vector Databases:

  • Pinecone: Used for vector databases and praised for its serverless capabilities. @EricBDelisle, @jjackyliang, @theAIsailor
  • ChromaDB: Integrated with AgentForge for easy VectorDB implementation. @JohnSmith4Reel
  • Redis, Qdrant, Postgres: Commonly mentioned databases for AI projects. @who_mansu
  • Greenhouse ECS: An ECS server framework built to be programmable by LLMs. @EricBDelisle

Web and UI Frameworks:

  • Streamlit: Popular for creating web apps quickly from ideas, making it a go-to tool for many. @josephs_tez, @SaidAitmbarek, @theAIsailor, @hTrapVader
  • Gradio: Useful for creating a UI in a few lines of Python, often used to showcase work in team meetings. @AI_NewsWaltz
  • Wordware: Not a traditional framework but appreciated for building AI agents quickly and easily, even for less technical users. @unable0_
  • PreactJS: Used for front-end application interfaces.
  • NodeJS: Often paired with PreactJS for front-end development.

Language Models and NLP Tools:

  • Langchain and Llama Index: Common starting points for fast iterations in AI projects. @TheHamedMP, @topmass
  • DeepgramAI and AssemblyAI: Mentioned for their NLP capabilities. @traviscorrigan
  • Instruct: A lightweight library for structured outputs with LLMs. @who_mansu, @HamzaFarhan
  • Litellm: Another library for structured outputs with LLMs, often used alongside Instruct. @who_mansu, @yoheinakajima
  • DSPy: Highlighted for its power and versatility in AI projects. @mysticaltech
  • Magentic: A thin layer over LLM providers to simplify structured outputs and function calling. @MichaelNStruwig, @jackmpcollins
  • Openrouter: Offers multiple language models under the OpenAI API definition for easy experimentation.
  • Funcchain: An integration of Langchain with simpler typing and usage. @akatzzzzz

Developer Tools and Platforms:

  • Replit: Favored as an IDE for its versatility and integration with various tools. @theAIsailor
  • Deno: Used in the craftgen tool for its code interpreter capabilities. @Necmttn
  • Imprompt: Highlighted for its ease of use in generative AI projects. @jeffrschneider
  • Vapi_AI, usebland, vocodehq, retellai: New tools currently being explored. @traviscorrigan
  • Hacknote: Recently added a feature called reactor creator, simplifying prompt writing and model selection. @dbqsun

Evaluation and Testing:

  • LangSmith: Used for evaluation, debugging, and testing of LLM applications. @as_cybersamurai
  • Promptfoo: A testing framework to evaluate prompts and iterate on LLMs faster. @Yossi_Dahan_
  • Ragas: Used for evaluation in building RAG applications. @AI_NewsWaltz

Miscellaneous Tools:

  • NATS / Socket IO / Redis: Used for messaging and caching in AI applications. @EricBDelisle
  • Tailwinds: A CSS framework used alongside NodeJS and PreactJS for front-end development. @EricBDelisle
  • BBScript: Acts as the glue between data, frontend, and backend. @EricBDelisle
  • Obsidian: A personal knowledge management tool that integrates with OpenAI APIs. @BrianAndrenMA
  • Convokit: An NLP toolkit from Cornell for conversational analysis.
  • Google Cloud's Vertex AI: Mentioned for its multimodal embedding model. @jjackyliang
  • PEFT: A tool used frequently for various AI projects. @actualrealyorth
  • Blacksmith: Provides fine-grained automation for agents and flow generation. @MoMe36806866
  • Trafilatura: Useful for processing web data, mentioned for its utility in LLM projects. @TommyFalkowski