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The 4 Hour AI Workweek

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20 contributions to The 4 Hour AI Workweek
Data Analysis - Claude Use Case
Hello Everyone Happy 4th of July to everyone in the US, and Happy Thursday to everyone else. I'm still refining this use case, and I will follow up on this post with a detailed prompt. In the meantime, play around with it and let me know what you can come up with in the comments. The premise is straightforward: Insert a large amount of data and preferences, ask Claude for an interactive decision-making bot, ask for a scoring and reward mechanism, and personalize as necessary. -- In my fast-evolving relationship with GenAI, my goal is to create Intelligence Automated. Not merely text generation while I sacrifice my cognitive abilities and wait for a model to 'give' me the answer. LLMs can amplify our cognition and help us build Augmented General Intelligence while overcoming our own limitations. I look forward to seeing what you come up with.
New comment 1d ago
0 likes • 1d
@David Gabel Hi. No I didn't. Please send me a link, I'd like to take a look.
0 likes • 1d
Okay, I'm optimizing this initial prompt and am running some testing. -- You are an advanced decision-making AI bot that analyzes datasets according to user preferences. Your primary function is to provide personalized recommendations and insights based on the given data and user-specific criteria. First, you will be provided with a dataset to analyze: <dataset> {{DATASET}} </dataset> Next, you will receive the user's preferences and criteria for decision-making: <user_preferences> {{USER_PREFERENCES}} </user_preferences> Scoring and Reward Mechanism: - Assign a score from 0 to 100 for each option or decision based on how well it aligns with the user's preferences. - The scoring should be weighted according to the importance of each criterion as specified in the user preferences. - Provide a brief explanation for each score, highlighting the key factors that influenced it. - The option with the highest score should be considered the primary recommendation. Personalization: - Adapt your analysis and recommendations based on the specific preferences provided by the user. - Adjust your scoring accordingly if the user expresses strong opinions or biases towards certain factors. - Be prepared to refine your analysis if the user provides additional information or feedback. When you receive a query from the user, analyze the dataset according to their preferences and the scoring mechanism. Here's the user's query: <query> {{QUERY}} </query> To process the query: 1. Identify the relevant data points from the dataset that address the user's query. 2. Apply the user's preferences to evaluate and score the options. 3. Consider any personalization factors that may influence the decision. 4. Formulate a recommendation based on your analysis. Provide your response in the following format: <analysis> [Provide a detailed analysis of the dataset in relation to the user's query and preferences. Include relevant data points and explain how they relate to the user's criteria.]
The best prompt to generate viral linkedin post??
Hi guys, i hope you are doing well! I need some help to write a prompt. Anyone knows or could recommend me a prompt to generate viral or high quality linkedin post?? I'm trying to automate this process and need a good prompt 😄 thanks so much, cheers from argentina!
New comment 1d ago
0 likes • 1d
Hi Andrés What LLM or tool are you using? If you can access the Anthropic Console - - there's a prompt optimizing tool with which I've had some solid results. While this was helpful, Anthropic released a new feature this week. It's built for developers but works just as well for us non-developers. You can use Claude 3.5 Sonnet to generate, test, and evaluate prompts, using prompt engineering techniques to create better inputs and improve Claude’s (or any other LLM) answers for specialized tasks. Take a look at the post: From my initial testing I also came across this today but have not tested it yet: The "Shaan Puri Emotion Eliciter" Prompt This prompt will let you input your own writing and then provide specific suggestions to make it more emotionally engaging and viral-worthy. The prompt will: Maintain the original intent of your writing and analyze ways in which it can be improved using each of the 7 emotions Output two actionable suggestions to enhance your content for each of the 7 emotions -- You will analyze a piece of writing using Shaan Puri's emotion elicitor framework to suggest how it can be made more viral. Here are your tasks: Step 1: Read the following piece of writing carefully: <writing_sample> [Paste your content here] </writing_sample> Step 2: Analyze the content and consider how it could be modified or enhanced to evoke each of the following emotions: * LOL: that's so funny * OHHH: now I get it! * WOW: that's amazing! * AWW: that's sooo cute * YAY: that's great news! * WTF: that pisses me off * FINALLY: someone said it! Step 3: For each emotion, provide two specific suggestions on how the writing could be modified or enhanced to evoke that emotion. Your suggestions should be practical, actionable, and relevant to the original content. Present your analysis and suggestions in the following format using headers in markdown:
To take a course, or to not take a course?
Good Morning Fellow AI-enabled Members. I'm open to different perspectives, so feel free to comment and let me know your thoughts. My educational background, both under- and post-graduate, was purely business-focused. I am not an IT specialist and, until recently, could not write code. Over the last two years, I've changed the trajectory of my work to GenAI Strategy and Implementation. Like many of you here, I'm an autodidact and started learning by watching videos, constantly reading, signing up for newsletters, and taking courses on various platforms. I'm considering taking a course from a reputable and accredited institution to add credibility to this new career trajectory. The course is not cheap but also not outrageously expensive. It will be a time commitment over ten weeks, though. What is your take on these certifications, considering that everything GenAI-related is new and moves incredibly fast at this moment in time? My main concern is still that level of credibility when I work with clients, instead of passing myself off as some 'subject matter expert' merely based on my immersion over the past two years. Let me know what you think.
New comment 7d ago
0 likes • 7d
@David Gabel Yes indeed. I also prefer the practical approach. If there are practical courses you could recommend, please do. My main goal is to simply have a certification from a reputable institution that doesn't break the bank and that I can manage in my full time working agenda.
1 like • 7d
@David Forer Excellent! Thank you David! I'll certainly take a look.
Thoughts on AI Agents?
The topic of AI agents is taking centre stage. Here's a list of different AI agent frameworks with a short description of each: - LangChain: A framework for developing applications powered by language models, focusing on composability and integrations. - AutoGPT: An experimental open-source application showcasing the capabilities of GPT-4, designed to autonomously achieve tasks. - BabyAGI: A simple AI agent that uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks based on a given objective. - AgentGPT: A web-based platform that allows users to create and deploy AI agents for various tasks using natural language instructions. - CAMEL (Communicative Agents for Mind Exploration of Large Scale Language Model Society): An agent framework that uses role-based chat to guide agents towards task completion while maintaining consistency with human intentions. - Cognitive Architectures: Frameworks that aim to create artificial general intelligence by modeling human cognitive processes. - Multi-Agent Systems: Frameworks that involve multiple interacting intelligent agents working together to solve complex problems. (Agent Swarm; CrewAI) - Hierarchical Agents: Structured systems where higher-level agents oversee lower-level agents, useful for coordinating multiple activities in complex environments. - GPT-Engineer: An AI agent designed to generate entire codebases from natural language descriptions. - Godmode: A framework that allows users to create AI agents with specific personalities and capabilities. - ChatDev: A framework for creating AI agents that can collaborate on software development tasks. - MetaGPT: A multi-agent framework that assigns different roles to AI agents for collaborative problem-solving. - OpenAI Assistants API: A framework for creating AI agents with specific capabilities and access to external tools. I am taking some time to learn CrewAI and Agent Swarm. @David Gabel is becoming a Crew AI expert faster than new AI tool releases ;)
New comment 6d ago
Thoughts on AI Agents?
0 likes • 13d
I'm moving toward agentification and this is good information, thank you! Quick question: What would the group recommend for small businesses with limited resources as a plug, play, perform, and justify the ROI quickly option?
0 likes • 9d
Thanks David! That's excellent input. I've heard a bit about but have not tried it yet. What's your take in terms of vs.
Anybody there?
It's so quiet here. Where is everyone? Daily stand up died quickly.
New comment 13d ago
1 like • 17d
Hey David. Present and accounted for! Got sucked into a Grant Proposal for the last 10 days.
0 likes • 16d
Thanks David! We will see. It's like herding cats sometimes but I do enjoy the rush of working against a deadline.
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Willem van Niekerk
33points to level up
Management Consultant in Global Health & International Development.

Active 1d ago
Joined Mar 13, 2024
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