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I had very little time today, but gave a quick glance to the custom tool tutorial for CrewAI. I basically want to figure out how to create my own Youtube video summary tool, since the default Youtube loader with CrewAI doesn’t work reliably well. (Or I can’t figure
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Apple will be introducing large language models to their devices later this year. They have a high-level write-up here on the technical bits of how they got to where they are. The post includes detail on how they conducted pre-training, post-training, optimization, and dynamic model adaptation. Some key bits I
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Building and running AI agents is a messy process. I completed a draft crew of agents that can take a product name and provide summary of Youtube reviews. You can see a sample output below. The first image is my terminal command, with the agents running. Below is the final
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Today was another day of AI reading as I was travelling a bunch. looked over the arguments for when to use retrieval-augmented generation (RAG) and when to fine-tune large language models. Both approaches have pros and cons but using them both can render some weaknesses irrelevant while not necessarily solving
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I’m travelling today, so no code. On days of no-code, I read about AI. The topic today: what does a Machine Learning Engineer (MLE) do at work? Hacker News has an insightful thread about this with comments from people in the industry who are MLEs. Below are a few
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I’ve been using large language modes to help me write code for the last 18 months. I’ve created apps, scraped data from websites, transcribed podcasts, amongst other things. A few years ago, this work would take me months of learning and effort. But LLMs have been a gamechanger.
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Today I continued working on the Youtube review agent app project idea but I’m having issues with some of CrewAI tools. Once I resolve this I’ll have a better update.
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I watched this critical video of the CrewAI agent framework (as well as other frameworks like AutoGen) and I’m now rethinking a few design ideas on how to use agents. The main issues are that these frameworks can be slow, expensive, and inconsistent. These issues will eventually be resolved
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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