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Today, I continued testing my crew of agents that will find and summarise product reviews. I probably won’t have anything publicly ready until the weekend. Meanwhile I came across this interesting article about how deep learning is being used to predict the weather. Traditional systems in use today rely
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Today I started work on a crew of LLM agents that will search the internet and YouTube for reviews about products. For example, if I was planning to buy a pair of noise cancelling headphones, I would search YouTube for product reviews. With AI agents, I can automate this process
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I’m completely “coursed out”! Over the last 2 and a bit months I finished 7 online courses: * IBM Machine Learning Professional Certificate (Started on Day 1, finished on Day 17) * AI for Everyone (Started on Day 28, finished on Day 33) * Python for Data Science, AI & Development (Started
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Today was AI reading day. The question to explore: “When will AI systems be able to do all jobs better than humans?” Initially, I was unsure how to approach this question. However, findings from a comprehensive survey of 2,778 AI experts from the top AI journals and conferences provide
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I’m working through a short course on agentic RAG and in my research, I came across a detailed article that explains how and why agentic RAG is better than traditional RAG. (RAG stands for retrieval augmented generation — it’s a way to provide knowledge to LLMs so that their
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I’m completing just one more short course at DeepLearning.ai before getting to personal projects. So I made a start today on the “Building Agentic RAG with LlamaIndex” course. The key topic is a data framework called LlamaIndex. It helps developers and enterprises load, index, and query their data
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I completed reading and working through the StatQuest book I bought on Day 2 of this challenge. “Completed” is a stretch word in this context, though! I could barely follow the final chapter on neural networks. Still, I did get an appreciation for how much of an art form building
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One of the best things I did last year was to get into Python programming. I want to get better at it, and so will continue to build side projects with it. For example, today I automated something that could take many hours to process and instead, I got a
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I’m now 69% of the way through the AI agents course, and I added a new idea to the list of side projects I’d like to build after the course. I look forward to this because I can already see how agents can automate both personal and work-related
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I was very short on time today but I went through a primer on neural networks (I still don’t fully understand them) and made progress on the AI Agents course. I’ve completed about 40% of the agents course and should be fully done with it by day 70.
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I completed the short course on prompt engineering that I started 3 days ago. It was a light introduction to prompt engineering and the key ideas I took away were: * Write clear and specific instructions. * Note: clear and specific doesn’t mean short. In this course and in other areas,
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I watched a video lesson on prompt engineering today. I’m 23% of the way through this short course and already have a few useful tips on how to get more from LLMs. Basics include: * Clear instructions. * They can be short, but often need to be long for more complex