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xcnqa

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replied to CultriX's post about 23 hours ago
Hi all! I was hoping somebody would be willing to check out this thought experiment of mine with the aim to reduce tokens in inter-agent communications. How It Works: 1. You provide a task in natural language (NL) 2. NL-to-CCL Agent: Converts your request into a structured Compressed Communication Language (CCL) task. 3. Inter-agent communication occurs in CCL 4. CCL is translated back to NL before being presented to the user. I have a notebook with an example that claims to achieve these results: --- Token Usage Summary --- Total NL Tokens (User Input & Final Output): 364 Total CCL Tokens (for NL/CCL Conversions): 159 Total CCL Tokens (Internal Agent Communication): 194 Overall token savings on NL-to-CCL conversion portions: 56.32% ------------------------ When asking Gemini it concludes: "Yes, the methods used in this notebook are sensible. The multi-agent architecture is logical, and the introduction of a Compressed Communication Language (CCL) is a clever and practical solution to the real-world problems of token cost and ambiguity in LLM-based systems. While it's a proof-of-concept that would need more robust error handling and potentially more complex feedback loops for a production environment, it successfully demonstrates a viable and efficient strategy for automating a software development lifecycle." However, I have no idea if it's actually working or if I'm just crazy. Would really like it if someone would be willing to provide thoughts on this! The notebook: https://gist.github.com/CultriX-Github/7f9895bc5e4d99d2d4a3eb17d079f08b#file-token-reduction-ipynb Thank you for taking the time! :)
replied to CultriX's post 1 day ago
Hi all! I was hoping somebody would be willing to check out this thought experiment of mine with the aim to reduce tokens in inter-agent communications. How It Works: 1. You provide a task in natural language (NL) 2. NL-to-CCL Agent: Converts your request into a structured Compressed Communication Language (CCL) task. 3. Inter-agent communication occurs in CCL 4. CCL is translated back to NL before being presented to the user. I have a notebook with an example that claims to achieve these results: --- Token Usage Summary --- Total NL Tokens (User Input & Final Output): 364 Total CCL Tokens (for NL/CCL Conversions): 159 Total CCL Tokens (Internal Agent Communication): 194 Overall token savings on NL-to-CCL conversion portions: 56.32% ------------------------ When asking Gemini it concludes: "Yes, the methods used in this notebook are sensible. The multi-agent architecture is logical, and the introduction of a Compressed Communication Language (CCL) is a clever and practical solution to the real-world problems of token cost and ambiguity in LLM-based systems. While it's a proof-of-concept that would need more robust error handling and potentially more complex feedback loops for a production environment, it successfully demonstrates a viable and efficient strategy for automating a software development lifecycle." However, I have no idea if it's actually working or if I'm just crazy. Would really like it if someone would be willing to provide thoughts on this! The notebook: https://gist.github.com/CultriX-Github/7f9895bc5e4d99d2d4a3eb17d079f08b#file-token-reduction-ipynb Thank you for taking the time! :)
updated a model almost 2 years ago
xcnqa/test
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