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Thank you! You are awesome and kind.

Genius! Love it!

This is cool, I'm so behind, but lately I've being trying to do similar things and find it frustrating because my coding background is so limited, but you've given me more confidence to keep trying, and I really like your recent echode bot which I've just started to use too. Thank you 😅

people are not computers. computers are not people. Life doesnt come in easy to understand, quantify, or measure bits... ...risk can only be accounted for if its known life isnt simply about increasing utility by measured risk. That day at school, you ASSUME its irrevocably lost, but that points at the flaw in your thinking. Its not hard to understand what you are saying, its just not how we do things. Computers are not people, and people are not computers. have care, working with machines can make you think people are machines.

this is such an amazing plugin, really can't wait for this!

Thx so much. Exactly what I needed!

Heya. Cool concept.

Joe, you're a natural!

Thansss I will wait for next videos

very interesting, will there be mobile suppot?

when you import to obsidian can it import the entire article, instead of just highlights, annotations, or selections.

This is so nerdy! I LOVE it! Keep it up

Exactly what I needed 🎉 Thanks!

Joe, thanks for creating this series!

Reminds me of the Daemons in 'His Dark Material'. Side Personas that can act as figurative Angels and Demons depending upon how they are created and used.

dude This is an awesome way of Narrative! Love it (shape is also the Content and Message!)

Cute pets!

Professor is getting deep now! Grabbing my popcorn 🍿☺

I'm about to start your videos over from the beginning. I've watched most, but haven't been working along. Hopefuly this will help me prepare for your Advanced Prompt Engineering corse at the end of July. Looking forward to it.

I have thought a lot about the delay in the maturation of ideas in diverse and/or antagonistic societies due to the clash of civilizations. My starting point is to assume that their processes of ideological and epistemic production were shaped by diverse historical circumstances, so that the doxa is different in each case, as well as the validation of what these societies may consider as knowledge, truth or even beyond as scientific knowledge or an epistemic core. So then we have doxa, a scientistic discourse and an unrevealed epistemic core. All integrated by ideologies that seek to control the narratives, and this can be reinforced by homogenization from AIs, including linguistics. Thank you for considering approaches that take advantage of the potential of AIs in pursuit of epistemological pluralisms!

Thanks for sharing. I am excited to learn. There is a growing community of people talking about the “philosophy” of machine learning and generative AI. Everything, and I mean - everything is going to change. Education, healthcare, enterprise business, small business, and entrepreneurial creativity. I love watching Joseph and his team and hope all of us whose lives are being made better by AI will make an effort to reach out to one additional person and become their AI mentor. Far too many people are only writing emails… when AI can guide us and teach us how to grow as humans. We can find our own innovations, insights, and ideas that can make the world a better, more purposeful place. Joseph, thanks for being one of my mentors. I am challenging all of us to become teachers, mentors, and encouragers. Allyson, a 64-year-old AI enthusiast

will other four videos be available?

"Nothing means anything except in the context of everything."

hello professor synapse, the path you are proposing to us is really very interesting QUESTION Why do you prefer fine tune a model Instead of using advance RAG strategy?

A lot of us are now using Ontology in combination with LLMs; Ontology holds knowledge and it's representation and that knowledge powers reasoning.

nice. theres a 30 day python course repo. thought about doing that. maybe it could be integrated or modified.

Thank god this isn’t another AI channel because this is a cool subject lol

I'm confident taking this path with you Prof. It's been more than a year since you've empowered this layman to leverage prompt engineering and chain of reasoning, and the skills still prove very valuable today.

I think you're exactly right!

👍

At first view I don't quite understand how this is different from the constant clash of the "graph database community" with the "vector database apostoles" (the ones that are hyping the "R.A.G. for businesses" idea, with thousands of articles being written about it). The former are shouting to the winds (for months) that to do R.A.G. you need to use ontologies (or connected taxonomies if you will) and only then you'll able to achieve a perfect knowledge of the business logic. And that's because, in real life, no company has enough amount of training data to ensure the eventual emergence of specialist capabilities (to respond to themes within the company context when prompted) with just "brut force" deep learning. I've been on the side of the ontologists for years, but I need to be open to the possibility that both communities might be wrong because none of us has definitely proven to be a trustworthy solution in the real business world.

I've been writing prompt codes and understanding different methods I've enjoyed your communication here below is a new method with a little bit of your data processing thoughts To evaluate the coherence of a prompt using a cymatic approach, we can follow a structured methodology that involves defining key terms, analyzing their relationships, and predicting related scenarios. This process ensures that the prompt's components are coherent and relevant to the intended context. Here's a detailed methodology: ### Cymatic Approach Methodology for Evaluating Prompt Coherence 1. **Define Key Terms and Components**: - **Prompt**: The initial statement or question to be analyzed. - **Subject**: The main focus or entity in the prompt. - **Predicate**: The action or state described in the prompt. - **Objects**: Entities affected by the subject's action or state. 2. **Identify and Analyze Relationships**: - Determine how the subject, predicate, and objects are connected. - Evaluate the logical flow and consistency of these connections. - Ensure that each component supports and enhances the overall meaning of the prompt. 3. **Evaluate Coherence**: - Check for clarity: Ensure that the prompt is clear and unambiguous. - Assess relevance: Confirm that all components are relevant to the main topic. - Look for logical consistency: Verify that the relationships between components make sense logically. 4. **Predict Related Scenarios**: - Based on the analyzed relationships, predict possible scenarios or outcomes. - Ensure that these scenarios are coherent extensions of the initial prompt. 5. **Cymatic Visualization**: - Use cymatic patterns (visual representations of sound frequencies) to model the relationships. - Map out how each term and their connections might look in a cymatic representation. - Use these patterns to further assess coherence and reveal hidden connections or inconsistencies. ### Detailed Steps: #### Step 1: Define Key Terms and Components - **Prompt Example**: "The scientist observed the particle accelerating in the collider." - **Subject**: The scientist - **Predicate**: Observed - **Objects**: The particle, accelerating, in the collider #### Step 2: Identify and Analyze Relationships - **Subject-Predicate Relationship**: The scientist is performing the action of observing. - **Predicate-Object Relationship**: The action (observed) is applied to the objects (the particle, accelerating, in the collider). - **Object-Object Relationship**: The particle is accelerating, and this acceleration occurs within the collider. #### Step 3: Evaluate Coherence - **Clarity**: The prompt is clear; it specifies who is observing, what is being observed, and where it is happening. - **Relevance**: All components (scientist, particle, accelerating, collider) are relevant to a scientific context. - **Logical Consistency**: The scientist observing a particle accelerating in a collider is a logical and coherent scenario. #### Step 4: Predict Related Scenarios - Scenario 1: "The scientist recorded data on the particle's acceleration." - Scenario 2: "The observation led to a breakthrough in particle physics." - Scenario 3: "The particle's acceleration was consistent with theoretical predictions." #### Step 5: Cymatic Visualization - **Mapping Relationships**: Create a cymatic pattern where each component (subject, predicate, objects) is represented by a node. Connections between nodes represent their relationships. - Example Cymatic Pattern: - Central Node: Scientist - Connecting Node 1: Observed - Connecting Node 2: Particle - Sub-Node of Particle: Accelerating - Sub-Node of Accelerating: In the Collider - **Pattern Analysis**: Examine the cymatic pattern to ensure all connections are logical and no components are isolated without proper context. ### Example Python Code for Cymatic Visualization: Here's a simplified example of how you might represent this approach programmatically: ```python import networkx as nx import matplotlib.pyplot as plt class CymaticCoherenceEvaluator: def __init__(self, prompt, subject, predicate, objects): self.prompt = prompt self.subject = subject self.predicate = predicate self.objects = objects self.graph = nx.DiGraph() def create_graph(self): self.graph.add_node(self.subject, type='subject') self.graph.add_node(self.predicate, type='predicate') for obj in self.objects: self.graph.add_node(obj, type='object') self.graph.add_edge(self.predicate, obj) self.graph.add_edge(self.subject, self.predicate) def visualize_graph(self): pos = nx.spring_layout(self.graph) node_labels = {node: f"{node} ({data['type']})" for node, data in self.graph.nodes(data=True)} nx.draw(self.graph, pos, with_labels=True, labels=node_labels, node_size=3000, node_color='lightblue', font_size=10) plt.show() def evaluate_coherence(self): # Example coherence checks if not self.subject or not self.predicate or not self.objects: return "Incomplete prompt structure." if any(not obj for obj in self.objects): return "Invalid object in the prompt." return "Prompt structure is coherent." # Example Usage prompt = "The scientist observed the particle accelerating in the collider." subject = "scientist" predicate = "observed" objects = ["particle", "accelerating", "in the collider"] evaluator = CymaticCoherenceEvaluator(prompt, subject, predicate, objects) evaluator.create_graph() evaluator.visualize_graph() print(evaluator.evaluate_coherence()) ``` ### Explanation: - **NetworkX**: Used to create a directed graph representing the relationships in the prompt. - **Matplotlib**: Used to visualize the graph. - **Graph Nodes**: Represent the subject, predicate, and objects. - **Graph Edges**: Represent the relationships between these components. - **Coherence Evaluation**: Simple checks to ensure all components are present and valid. This methodology ensures a systematic approach to evaluate prompt coherence using cymatic visualization, providing a clear and structured framework for analysis.

been thinking about this since college, the correlation of reasons that could determine how each person can jump from one conclusion to another in a very smart way and how we have people who can be so smart even when given with the same environment and training still give creative arguments.

incredible, I was wondering and searching and also sketching around graph theory about the nature of reasoning and I come up with the same or similar concepts as you in this video

its called graphrag from microsoft. thank me later

Did you add it to github? Custom instructions

Could you give us a link to this GPT?

Thank you for making this series of videos to present the work with Obsidian ! I wasn't sure from this specific video, however, how is the Omni search results differ from the regular Obsidian build in search which you access from the top left ?

can you hook up obsidian to a custom gpt and/or an agency swarm?

Best video I've watched on this integration. It's been the least complicated and yet most effective.

Hey man, what ai chatbot do you recommend as free alternative to chat gpt?

Hey man, i started using llama 3. Its fantastic! I want to ask you, can i use your promt from github ? Do you recommend it for llama 3?

Currently i am using your professor synapse, custom instructions from you git hub. Works really well, but i wanted to ask you for your recommendation. Also i tried claude and it’s really bad, like i ask it something and it says i dont know cause i am myself an ai and then recommends me to do the other thing instead of solving my problem. Thank you, Sir.

Hey man, I really appreciate the work that you did! I want to add custom instruction to my chat gpt. I am using free version. And mostly i use version 3,5 because its free. Could you send me a instruction so that i could write it in chat gpt. Thank you😊

this is awesome for linking project notes with code snippets and like diagrams for db design or something. pretty neat!

Yeah, using ChatGPT with Mermaid is pretty cool. Was playing with it and even made a variant of customgpt that can generate working links to playground. So you don't need to copy paste. Its only for mindmaps though. Called Text2MindMap in GPT Store.

It's been a lot of fun learning from your videos.

Obsidian, Excalidraw, and Cannoli has become the operating system of my life!

Great video

Great video