Building AGI for the follows. That’s what man was put on earth for.
INNOVATION
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Context Management and Summarization in AI Systems
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Just depends on how much context to pull in. Might add a summarization step for the context but looking at a few options on where.
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User explores Pinecone vector database for AI applications
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Haven’t tried em. Started w pinecone because i heard their name most often and wanted to try it.
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AI agents dynamically generate new tasks after execution
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Yeah, it generates new tasks at the end of each task execution based on the result, after defiling against remaining task list + most relevant past tasks.
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Weekly AI Newsletter for Business Leaders to Win with AI
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Lots going on #AI every week and it’s hard for most to keep up. So we at Stories in AI started a weekly newsletter that is optimised to the business leader looking to win with #AI Tips, what’s not, focus on #generativeai to…
https://
lnkd.in/gNrKSk2j https://
lnkd.in/giMRsjf8 -
AI-Generated TikTok Content Creation Strategy Discussion
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Oh tight. Wait, should I start a Tik tok? Take requests and just post videos of the output.
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Python Framework Development and TypeScript Translation for AI
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I guess that would be a one word change in the prompts I'm using. Python basic framework is close, then maybe I'll have ChatGPT translate to Typescript.
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Relevant Context Management for Autonomous Agents
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The thread broke, but here's a simple example of how "relevant context" (most relevant tasks from past) can be provided to current task. Allowing the autonomous agent to continue generating novel ideas and next steps – that wouldn't fit within a context window.
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Balancing Context Length, Speed, and Cost in AI Systems
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The decisions you need to make… – how much context you provide
– where/what/how you summarize
– how you break up knowledge
– etc. …are a constant balance of context length, model speed and cost, use case, latency, keeping up w latest tools, and more. -
Pinecone Vector Search Enhances Task Execution Memory
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Added a new "Relevant Context" section that shows up in the "Task Execution" stage. This is to show how we're using @Pinecone vector search to provide memory from past tasks into the current task being executed. It's empty on step 1.