I thiiiink the answer is no, but also this looks awesome and it's clear you're a pro at this, so I'd love the help. I want a user to click my GPT link, and I want a message from the GPT side to already be there like "tell me your name and what you have in your fridge" or w/e
AGENTS
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Testing Visualization GPTs: Where DALL-E and Code Interpreter Fail
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It's probably specific to me, but I was testing out a ton of visualization GPTs, like Whimsical and Canva. aka where Dall-E and Code Interpreter both fail
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ReWOO Agents: Reasoning Without Observations Framework
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Agents that Reason WithOut Observations (ReWOO)⍱ Take the “plan + execute” agent design up a notch via variable assignment. Can generate better plans (and avoid re-planning) while letting task executors focus. JS: https://
github.com/langchain-ai/l
anggraphjs/blob/main/examples/rewoo/rewoo.ipynb
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Python: https://
github.com/langchain-ai/l
anggraph/blob/main/examples/rewoo/rewoo.ipynb
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LLM Agents Demonstrate Autonomous Website Hacking Capabilities
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10/ LLM Agents can Hack – shows that LLM agents can automatically hack websites and perform tasks like SQL injections without human feedback or explicit knowledge about the vulnerability beforehand.
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OS-Copilot Framework Builds Generalist Computer Agents
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6/ OS-Copilot – a framework to build generalist computer agents that interface with key elements of an operating system like Linux or MacOS; it also proposes a self-improving embodied agent for automating general computer tasks.
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Top ML Papers Week: Sora, Gemini 1.5, Agents
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The Top ML Papers of the Week (Feb 12 – Feb 18): – Sora
– Gemini 1.5
– OS-Copilot
– TestGen-LLM
– Large World Model
– LLM Agents can Hack
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OMNI: Open-endedness Models Human Notions Interestingness
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OMNI: Open-endedness via Models of human Notions of Interestingness Zhang et al.: https://
arxiv.org/abs/2306.01711 #ArtificialIntelligence #DeepLearning #MachineLearning -
OMNI: Open-endedness Models Human Interestingness Notions
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OMNI: Open-endedness via Models of human Notions of Interestingness Zhang et al.: https://
arxiv.org/abs/2306.01711 #ArtificialIntelligence #DeepLearning #MachineLearning -
Plan and Execute Agents with LangGraph Tutorial
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Plan and Solve Agent 𝌣 Have your agent's ever gotten caught in a loop? They may get better performance through a “divide-and-conquer” approach and explicit planning. This tutorial walks through building a simple “Plan and execute”-style agent in LangGraph. The Planner LLM
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Practical LangChain Expression Language Guide for AI Chains
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Practical Hands-on with Langchain Expression Language (LCEL) Good article by Anurag Misrah covering LCEL and some practical things you can do with it: 1) Run Two Chains in parallel 2) Insert input dynamically in Runtime
3) Dynamic Routing & more! https://
medium.com/@anuragmishra_
27746/practical-hands-on-with-langchain-expression-language-lcel-for-building-langchain-agent-chain-2a9364dc4ca3
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