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micro.webology.dev | ||
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ezyang.github.io
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| | | | | Outside of very tactical situations, current models do not know how to stop digging when they get into trouble. Suppose that you want to implement feature X. You start working on it, but midway through you realize that it is annoying and difficult to do because you should do Y first. A human can know to abort and go implement Y first; an LLM will keep digging, dutifully trying to finish the original task it was assigned. In some sense, this is desirable, because you have a lot more control when the LLM does what is asked, rather than what it thinks you actually want. | |
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blog.lmorchard.com
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markodenic.com
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| | | | | Free programming books, algorithms, public APIs, and much more. | |
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blog.owulveryck.info
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| | | This article details my journey in building a custom chat host for AI agents, moving away from existing solutions to gain a deeper understanding of the underlying technologies. I implement a chat engine using Google's Vertex AI and Go, focusing on compatibility with the OpenAI API to integrate with tools like Big-AGI. The article covers the core architecture, including my use of ChatSession and GenerativeModel from the Vertex AI SDK. It delves into the implementation of the /v1/chat/completions endpoint, highlighting the challenges of streaming responses and integrating function calls. I also describe a workaround for handling function calls in a streaming context and introduce the concept of a callable interface to prepare for implementing the Model Context Protocol (MCP) in future work. The goal is to move the tools outside of the agent. This will be detailes in the last part of this series. | ||
