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OpenAI launched a feature called “function calling” last year, but it’s a feature I haven’t used and didn’t understand until today. Part of my misunderstanding was because the feature is poorly named. It made me (and many other more technical people) expect that the capability means an
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I have a 7 billion parameter large language model running locally on my Macbook thanks to Ollama! This is great when travelling and there’s no Wifi to access ChatGPT or other large language models. What I can run on my Macbook today is not as powerful as GPT4o but
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The Plateau of Generative AI? There are many people in Generative AI circles who believe that we can get to God-like AI by training models on increasingly larger swaths of data. Their view is that if we can train gargantuan models on all of the world’s data, we’ll
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I did two things today. First, I got a Youtube summarizer to work. I followed a simple tutorial here. I will create an agent tool out of this, and also try and build a RAG process around it. Second, I watched this lecture on “The Future of AI from the
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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