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.
AGENTS
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Balancing Context Length, Speed, and Cost in AI Systems
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Karpathy Building JARVIS Follows Nakajima at OpenAI
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@karpathy (Building JARVIS @OpenAI) is now following @yoheinakajima
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Custom Python Loop Implementation for Three Agents
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Yeah I took it out for this version. It’s now just a custom Python loop w the three agents.
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Alternatives to Langchain and ZapierNLA for AI Integration
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I’ve tried Langchain and ZapierNLA, any other suggestions?
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Self-modifying AI agent code recursive implications
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No, I mean if the code of this agent/script is being recursively modified by itself?
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AutoGPTs Will Organize into Specialized Autonomous Organizations
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All of that is just one agent/thread. People coalesce into organizations so they can specialize and parallelize work towards shared goals. Imo this is likely to happen to AutoGPTs and for the same reasons, strung into AutoOrgs, with AutoCEO, AutoCFO, AutoICs, etc.
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GPT Calls as Thoughts: Building Agents with Reflection and Memory
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1 GPT call is a bit like 1 thought. Stringing them together in loops creates agents that can perceive, think, and act, their goals defined in English in prompts. For feedback / learning, one path is to have a "reflect" phase that evaluates outcomes, saves rollouts to memory,
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Is AI Really Recursively Self-Improving Its Own Code?
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Is it really recursively self-improving? So not just generating code, but also modifying its own code?
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AutoGPTs: The Next Frontier of Prompt Engineering and AI Systems
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Next frontier of prompt engineering imo: "AutoGPTs" . 1 GPT call is just like 1 instruction on a computer. They can be strung together into programs. Use prompt to define I/O device and tool specs, define the cognitive loop, page data in and out of context window, .run(). https://t.co/EKy84pa5bB
— Andrej Karpathy (@karpathy) 2 avril 2023Next frontier of prompt engineering imo: "AutoGPTs" . 1 GPT call is just like 1 instruction on a computer. They can be strung together into programs. Use prompt to define I/O device and tool specs, define the cognitive loop, page data in and out of context window, .run().
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Agent AI Corrects Generated Programs Using Test Feedback
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By patching you mean how the agent corrects the generated program? We get the feedback from the tests and feed it back to LLM, to make an intelligent decision on what needs to get fixed.