Coze's launch by ByteDance, a major tech player, into the competitive AI bot space, marks a significant event, deserving of breaking news status If you are creating content for TikTok or use CapCut, this is a must-try tool for you.
GENERATIVE AI
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AI-Powered Knowledge Base Management and Integration Features
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The platform supports complex knowledge base interactions, with options for online data fetching and integration from various sources. You can crawl a website every day to update the content of your knowledge base or utilise Google search and Twitter trends.
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Granular control and model selection in Coze AI agents
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Advanced settings in Coze offer granular control over bot responses, with options for temperature adjustment and variable use for logic. GPT4 and GPT-4-turbo are also available. For FREE.
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Coze platform provides free access to AI bot building
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Coze stands out by letting users explore and build their own AI bots, without needing a subscription, making AI more accessible for FREE
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ByteDance Launches Coze AI Bot Platform
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It is not viral on AI Twitter but it should be ByteDance (a company behind @tiktok_us
) has launched Coze, an AI bot platform that directly competes with OpenAI's GPTs, enabling users to create and deploy custom AI bots. For FREE. -
Understanding as Mapping: LLMs and Causal Model Weights
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I think of understanding as a mapping between observables and causal models. Weights can be a way to represent understanding. LLMs don't always understand, but Gary is himself simulating understanding via prompt completion, which is why he always gets to the same conclusion.
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Training tiny character RNN for Office script generation
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update: training a tiny char RNN to write lines from The Office (no BOS token yet so these samples always start with M; using ancestral sampling for now)
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LangGraph Adds Built-in Persistence for AI Agents
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LangGraph: Persistence LangGraph comes with built in persistence. This means you can easily add "memory" to any LangGraph agents This is done by passing in a checkpointer to a LangGraph object. Then, the state of all runs are saved via that checkpointer We added an example
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LangChain Deep Dive: LLMs, Agents, and Evaluations with Lance Martin
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GenAI solutions with LangChain: Lance Martin on LLMs, agents, evals, and more! Thanks to @neo4j for having our very own @RLanceMartin on their http://
GraphStuff.FM podcast! A great deep and technical dive into a lot of advanced topics! https://
graphstuff.fm/episodes/genai
-solutions-with-langchain-lance-martin-on-llms-agents-evals-and-more
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FuseLLM: Advancing Multi-Model LLM Combination with Probabilistic Approach
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A couple weeks ago, we open-sourced a merged and stacked LLM, which is combining multiple pre-trained LLMs to come up with a new, more performant model. FuseLLM takes this to the next level and explores combining models from a probabilistic distribution perspective. The paper