WARNING: THESE ARE DRAFT POSTS WHICH 'ESCAPED'. It's likely much of what's below won't work; I generate these as how-to for ME and then do all the steps and get the sample app working...You've been sneaky and seen them! they'll likely be ready mid-December.…
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WARNING: THESE ARE DRAFT POSTS WHICH 'ESCAPED'. It's likely much of what's below won't work; I generate these as how-to for ME and then do all the steps and get the sample app working...You've been sneaky and seen them! they'll likely be ready mid-December.…
WARNING: THESE ARE DRAFT POSTS WHICH 'ESCAPED'. It's likely much of what's below won't work; I generate these as how-to for ME and then do all the steps and get the sample app working...You've been sneaky and seen them! they'll likely be ready mid-December.…
WARNING: THESE ARE DRAFT POSTS WHICH 'ESCAPED'. It's likely much of what's below won't work; I generate these as how-to for ME and then do all the steps and get the sample app working...You've been sneaky and seen them! they'll likely be ready mid-December.…
Welcome to Part 4! We've set up our GPU (Part 2), understand embeddings and vector databases (Part 3), and have the overall architecture (Part 1). Now it's time to build the ingestion pipeline - the system that processes all your blog posts and makes them…
Welcome to Part 3! We've got our GPU stack working (Part 2), and we understand the architecture (Part 1). Now it's time to dive into the magic that makes semantic search possible: embeddings and vector databases. NOTE: This is part of my experiments with AI…
Welcome to Part 2! In Part 1, we laid out the vision for building a writing assistant that uses your blog as a knowledge base - like how lawyers use LLMs trained on case law to draft documents. Now it's time to get our hands dirty with the foundation: making…
Buckle in because this is going to be a long series! If you've been following along with this blog, you'll know I'm a bit obsessed with finding interesting ways to use LLMs and AI in practical applications. Well, I've got a new project that combines my love of…
In my 8-part "Lawyer GPT" series, I showed you how to build a complete local RAG-based writing assistant using GPU acceleration, local LLMs, and vector databases. It's powerful, private, and runs entirely on your hardware. But let's be honest - not everyone…
In my LLMApi project I wanted to be able to support asking for LOTS of data; however LLMs have a very limited amount of data they can output at one time; and aren't particularly fast about doing it. So I needed to add a clever way of pre-generting that data…
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