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Why LLMs - Chapter 2 of PHP and LLMs The Book
This is the start of 10 part series as the "PHP and LLMs" book uses NotebookLm to create discussions around each chapter.
These episodes are created using https://notebooklm.google.com/
- 🎉 Buy the book now it is 40% done https://bit.ly/php_llms
- 📰 Join the news letter https://sundance-solutions.mailcoach.app/php-and-llms
Eight years ago, I did a Machine Learning and PHP video on YouTube, and it is still my most popular video. Back then, AWS made a service that started to make "Machine Learning" easy to host and create APIs around. For some reason, the potential to use this API to parse sentiment or tag content got my attention. But quickly, it faded because I still needed to know Machine Learning. I still needed to train models to do specific tasks.
But then, as we all know, OpenAI released an API that we could use like any API and get results. No training unless I wanted to and no Machine Learning expertise —- just read the docs and throw your prompt at it. It was then that I realized that this could make my work more accessible and allow me to create things for myself and customers I could not even imagine doing before.
About two years ago, I heard about LangChain, a Python framework that enabled developers to build LLM-centric workflows and automation. It honestly got me worried.
👉🏻 Ai Automation Consulting https://dailyai.studio
👉🏻 Join the NewsLetter https://videos.dailyai.studio/
👉🏻 Buy the book "PHP and LLMs - the practical guide" https://bit.ly/php_llms
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By Alfred Nutile
5
11 ratings
This is the start of 10 part series as the "PHP and LLMs" book uses NotebookLm to create discussions around each chapter.
These episodes are created using https://notebooklm.google.com/
- 🎉 Buy the book now it is 40% done https://bit.ly/php_llms
- 📰 Join the news letter https://sundance-solutions.mailcoach.app/php-and-llms
Eight years ago, I did a Machine Learning and PHP video on YouTube, and it is still my most popular video. Back then, AWS made a service that started to make "Machine Learning" easy to host and create APIs around. For some reason, the potential to use this API to parse sentiment or tag content got my attention. But quickly, it faded because I still needed to know Machine Learning. I still needed to train models to do specific tasks.
But then, as we all know, OpenAI released an API that we could use like any API and get results. No training unless I wanted to and no Machine Learning expertise —- just read the docs and throw your prompt at it. It was then that I realized that this could make my work more accessible and allow me to create things for myself and customers I could not even imagine doing before.
About two years ago, I heard about LangChain, a Python framework that enabled developers to build LLM-centric workflows and automation. It honestly got me worried.
👉🏻 Ai Automation Consulting https://dailyai.studio
👉🏻 Join the NewsLetter https://videos.dailyai.studio/
👉🏻 Buy the book "PHP and LLMs - the practical guide" https://bit.ly/php_llms