February 2025 - Thor's Web

Historical War Cycles

February 26, 2025

Brian Berletic US-Russia Relations – A Peaceful Turn or a Strategic Pause?

Brian Berletic
Glenn Diesen

Journal of Futures Studies

Alastair Crooke

February 25, 2025

Political Implosion in Israel and Political Earthquake in Germany.

Administrative state.

Judging Freedom

Dr. Devra Davis

February 25, 2025

“The Truth About Mobile Phone And Wireless Radiation”

And a supercomputer on every desk, – ?

Environmental Health Trust

Dr. Tess Lawrie

You
You are an experienced prompt engineering teacher.
Task, Context, References, Evaluate, and Iterate, are said to be essential components in every prompt.
Mnemonics such as Timy Crabs Ride Enormous Iguanas and Ramen Saves Tragic Idiots are provided.
It is claimed that this is outdated and clear thinking about the subject is sufficient?
Courses, prompt libraries, books, and videos are many some are costly, all take time.
What is the best way forward?
Assistant
+More

Step-by-Step Explanation:

Understanding Prompt Engineering:

– Task: Define prompt engineering as a practice beyond just asking questions; it involves effective communication with advanced AI systems like DeepSeek R1.

Modern Components of Prompt Engineering:

– AI Capabilities: Recognize the advanced reasoning and coding skills embedded in AI models, ensuring prompts are aligned with these capabilities.
– Nuanced Language: Appreciate how subtle language can trigger sophisticated responses from AI systems.

Bridging Human Intent with Machine Interpretation:

– Translation of Ideas: Convert high-level ideas into clear, actionable instructions for the AI.
– Alignment of Objectives: Ensure prompts are structured to align with the logical processes of AI, guaranteeing precision in outputs.

Core Elements of Effective Prompt Construction:

– Clarity: Use simple, direct language to avoid ambiguity and break down complex ideas into clear, manageable steps.
– Specificity: Include precise details about expected outcomes and define constraints like format, tone, or style.
– Context: Provide relevant background information and situational details to set the stage for AI responses.
– Intent: Clearly communicate objectives and purposes behind prompts to ensure AI understands desired outcomes.

Advanced Layers in Prompt Engineering:

– Multi-Layer Approach: Use conditional branching and meta-prompts to integrate previous responses into comprehensive answers.
– Continuous Improvement: Embrace an iterative process for refinement, involving systematic experiments, data analysis, and staying updated with AI trends.

Evaluation and Iteration:

– Feedback Loops: Establish performance metrics and gather user feedback to assess prompt effectiveness in various scenarios.
– Systematic Iterations: Modify prompts incrementally, track changes, and analyze outcomes to refine them continuously.

Mnemonics and Modernity:

– Mnemonics like “Timy Crabs Ride Enormous Iguanas” can aid in remembering complex concepts.
– Current approaches are evolving but remain foundational; traditional methods should be updated with modern tools while maintaining core principles.

Best Way Forward:

– Education and Resources: Utilize free online resources, books, and videos for foundational knowledge.
– Continuous Learning: Stay updated through communities, courses, and AI trends to enhance prompt engineering skills.
– Cost-Effective Strategies: Use free tools and platforms to experiment and refine prompts without high costs.

Summary:

Prompt engineering is a dynamic field requiring clarity, context, and creativity. By integrating advanced AI capabilities, structured construction, and continuous refinement, professionals can effectively communicate with AI systems like DeepSeek R1. While modern tools enhance efficiency, traditional principles remain vital. A balanced approach combining free resources and ongoing learning ensures effective and evolving prompt engineering practices

Tina Huang ( 9h. to 20m.)
Goodreads (?)
Cheat Sheet

R1 – PC for AI

February 22, 2025

R1 > SD

You

Can you advice on Building a PC for AI work

Assistant

+More

<think>
Alright, so I’m trying to build a PC for AI work, but honestly, I don’t know where to start. I’ve heard that PCs have different parts, like the CPU and GPU, which are super important for computing power. But how do I know what’s best for AI tasks? Should I go with an NVIDIA or AMD GPU? And what about the CPU? I’m also confused about RAM—how much do I really need?

Okay, let me break this down step by step. First, I think the GPU is crucial for AI because it handles a lot of computations, especially in machine learning and deep learning tasks. NVIDIA GPUs are popular in the AI community, but I’ve heard that AMD can be a good option too. Maybe I should research which one offers better performance for my specific AI work.

Next, the CPU. I remember reading that multi-core processors are better for multitasking and handling more complex operations. Intel Core i5 or i7 are common in gaming PCs, but maybe there are better options for AI work. I need to look up what’s recommended for AI tasks specifically.

RAM is another area where I’m not sure. I’ve heard that 16GB is a minimum for most tasks, but if I’m running intensive AI models, do I need more? Maybe 32GB or even 64GB would be better. But that seems expensive. Is it necessary for my use case?

Storage-wise, SSDs are faster than HDDs, so I should probably go with an SSD for the operating system and maybe a larger HDD for storing data. How big should each be? Do I need multiple storage devices for AI work?

Cooling is another factor. If I’m building a PC, I don’t want it to overheat and slow down. Liquid cooling might be necessary if I have a high-end GPU or CPU. But I’m not sure how much that costs or how complicated it is to install.

Power supply is also important. The PSU needs to provide enough wattage for all the components. I should calculate the power requirements based on the CPU, GPU, and RAM I choose. A 600W PSU might be a good starting point, but maybe I need more depending on the components.

The motherboard is another key component. It needs to support all the parts I’m adding. I have to make sure it’s compatible with the CPU and has enough PCIe slots for the GPU and other expansion cards.

Case and cooling fans are also part of the equation. A well-ventilated case will help keep temperatures in check, especially if I’m running demanding AI tasks. Maybe I should get a case with good airflow and a high-quality fan.

Networking is something I often overlook, but having reliable Wi-Fi and an Ethernet port would be useful for transferring data and connecting to external systems.

The operating system is another decision point. Windows seems more compatible with AI tools and libraries, but Linux might offer better support for certain AI frameworks. I should check what’s recommended by the specific AI tools I use.

Budget is a concern as well. I don’t want to overspend on unnecessary features. I need to balance performance with affordability, making sure each component is within my budget.

Lastly, documentation and support are important. Building a PC can be tricky, so having clear instructions and access to customer support will help if something goes wrong.

Overall, I think I need to list out the components I need and then compare them based on their relevance to AI work. Maybe there are specific builds or configurations recommended for AI tasks that I can reference to make informed decisions.
</think>

Based on the thought process of building a PC for AI work, here is a structured approach:

Key Components for Building a PC for AI Work

Graphics Card (GPU):

– Type: NVIDIA or AMD GPUs are both viable options. NVIDIA is popular in the AI community due to their support for CUDA and Tensor cores.
– Model: Choose a model that offers good computational power suitable for your AI tasks. Consider models like NVIDIA RTX 2060, 2080, or higher if needed.

Central Processing Unit (CPU):

– Type: Opt for a multi-core processor. Intel Core i5 or i7 are common choices for their performance in AI tasks.
– Model: Select based on core count and clock speed. Higher-end models may be necessary for more complex AI operations.

Random Access Memory (RAM):

– Quantity: 16GB is generally recommended as a starting point, but for intensive AI work, consider 32GB or even 64GB to handle larger data sets and models efficiently.

Storage:

– Primary Storage: Use an SSD for the operating system to ensure fast access times.
– Secondary Storage: Consider a larger HDD or SSHD for storing datasets and AI model files.

Cooling System:

– Type: Liquid cooling is recommended, especially if you plan to use high-end components that generate more heat.

Power Supply Unit (PSU):

– Wattage: Ensure the PSU provides enough power for all components. A 600W PSU should suffice for most builds, but higher wattages may be needed depending on your parts selection.

Motherboard:

– Compatibility: Choose a motherboard that supports both the CPU and GPU, with sufficient PCIe slots for expansion.

Case:

– Ventilation: Opt for a well-ventilated case to help manage temperatures during intensive AI tasks.

Networking:

– Connectivity: Ensure reliable Wi-Fi and Ethernet ports for data transfer and network connectivity.

Operating System (OS):

– Choice: Windows is generally more compatible with AI tools, but Linux can offer better support for specific frameworks depending on your needs.

Budget:

– Planning: Allocate funds based on the components selected to stay within budget while ensuring adequate performance.

Documentation and Support:

– Resources: Have access to clear instructions and customer support to assist with any issues during assembly and setup.

Summary

Building a PC for AI work involves selecting components that balance performance, cost, and reliability. Prioritize a powerful GPU, multi-core CPU, sufficient RAM, and reliable cooling. Ensure the PSU has enough wattage, choose a compatible motherboard, and select an appropriate case with good ventilation. Consider your budget carefully and ensure you have access to necessary resources for setup and troubleshooting.

By methodically addressing each component based on its relevance to AI tasks, you can build a PC that supports your AI work effectively.

Not So Fast
Fighting AI With AI – Mike Adams
NVIDIA Project DIGITS

Andrew Bridgen

February 22, 2025

The whole of the UK government knows and are doing everything to cover it up.

Politics Is No Longer About Right And Left. It’s About Right And Wrong.

Andrew Bridgen

R1 – Brother Nathanael

February 20, 2025

“White Babies Now!” and “The Truth About Jewish Power” Brother Nathanael’s Most Explosive Claims

My experimentation’s follow:
whisper –model small.en provided the transcript (hardware limitations)
The prompt is open ended, no controversies.
not perfect

You

Analyze the provided transcript to generate a thorough, context-rich overview that captures key information, insights, and implications.

Assistant
+More

<think>
Okay, so I’m trying to help analyze this transcript using the given citations, but honestly, it’s a bit confusing at first glance. The user wants a thorough overview of the key points, insights, and implications from these interviews. Let me break it down step by step.

First, I need to look closely at each citation provided. Citation 1 seems to discuss something about religious texts, maybe the New Testament and old testament? There’s mention of someone converting to Mormonism and then leaving. It also talks about job-related challenges and feelings of being undervalued, which could be work experiences.

Citation 2 appears to talk about web users and misuse areas, possibly related to online activities or cyber issues. It mentions “gender news” and something about attraction between men, which might hint at discussions around non-heterosexual orientations or societal norms.

Citation 3 seems to bring together a mix of topics: religious conversions, professional advice, gender dynamics, and family gatherings. There’s talk about being influenced by friends, rabbis, and cousins, suggesting social influence and personal decision-making processes.

Putting it all together, the main themes seem to revolve around personal journeys, work experiences, and social dynamics. The individual is reflecting on their religious past, dealing with professional challenges, and navigating complex social interactions.

One thing that stands out is how the individual’s personal beliefs and career decisions are intertwined with their emotional state. For example, there’s a mention of feeling emotionally disturbed and struggling to communicate needs in a relationship. This suggests that stress from work might be spilling over into personal life.

Another point is the reference to societal norms and gender roles, which ties into discussions about how individuals navigate their place within social structures. It also touches on attraction dynamics, possibly indicating deeper identity exploration beyond just gender.

The mention of conversion to Mormonism and later leaving adds layers to the religious journey— exploring faith transitions and the emotional impact they have on one’s sense of self.

In terms of work experiences, there’s a focus on feeling underappreciated and the challenges of getting recognition. This could be a recurring theme in professional settings, affecting one’s motivation and confidence.

Overall, it seems like the transcript captures a complex interplay between personal growth, career struggles, and social influences. The individual is processing these elements, seeking clarity, and reflecting on how they navigate these aspects of their life.

Now, considering all this, I need to structure my response to cover each of these areas: religious journey, work experiences, and social dynamics, while also touching on the implications for personal growth and professional development.
</think>

The transcript provides a rich tapestry of reflections on personal journey, work experiences, and social dynamics. Here’s a structured analysis:

Religious Journey:

– The individual discusses their transition from Catholicism to Mormonism and subsequent disaffiliation. This journey reflects a search for meaning and identity, highlighting the emotional impact of faith transitions.
– The exploration of religious texts, such as the New Testament and Old Testament, indicates a deep intellectual engagement with spiritual concepts.

Work Experiences:

– The transcript reveals challenges related to feeling undervalued and unrecognized in professional settings. This underscores themes of career struggles and the emotional toll of unappreciated efforts.
– There is a mention of job-related stress affecting personal relationships, suggesting that work-life balance is a significant concern.

Social Dynamics:

– The individual reflects on societal norms, including gender roles and attraction dynamics, which hint at broader identity exploration beyond traditional gender binaries.
– Social influences, such as advice from friends, family, and rabbis, highlight the impact of social networks on personal decision-making.

Personal Growth and Professional Development:

– The transcript captures moments of self-awareness and emotional processing, indicating a focus on personal growth.
– The individual’s efforts to seek clarity and communicate needs in relationships suggest a commitment to navigating complex interactions effectively.

In conclusion, the transcript offers insights into the interplay between personal journey, career challenges, and social influences. It underscores the importance of emotional resilience and self-awareness in navigating these multifaceted aspects of life.

Bible With Brother
Kim Iversen

Tom Bilyeu /w Whitney Webb

February 16, 2025

Trump Is A Deal Maker” Whitney Webb’s Take on DOGE, Elon Musk, DeepSeek AI, and the Elite’s!

Whitney Webb

Tom Bilyeu

–
This girl is waking up

R1 – Natural Law Transcript

February 16, 2025

Natural Law Transcript.pdf 133p

You

Walk through the key points of this research paper step-by-step, explaining each point’s significance. Then summarize the entire document in two sentences.

Assistant
+More

Step-by-Step Summary of Research Paper Key Points:

Problem-Solving Process:

– The paper emphasizes that effective problem-solving involves understanding the causal factors behind issues.
– Diagnosis is highlighted as a critical step in identifying these underlying causes.
– Without understanding the root causes, attempting to solve problems without addressing their origins leads to inadequate solutions.

Truth vs. Perception:

– The distinction between truth and perception is discussed, with an emphasis on how perception can mislead individuals about reality.
– Truth is defined as objective information based on facts and evidence, while perception is subjective and influenced by personal experiences and biases.
– Misunderstanding of this distinction can lead to poor decision-making and societal issues.

Natural Law Principles:

– The paper introduces seven general principles of Natural Law that govern the universe and human life.
– These principles are rooted in ancient traditions, such as Hermeticism, which emphasizes hidden knowledge (the “occult”).
– Natural Law is not immediately apparent but can be discovered through study and understanding.

Occulted Knowledge:

– The concept of “occulted knowledge” is explored, referring to knowledge that has been intentionally hidden from humanity.
– This knowledge includes principles like Natural Law, which have been protected or concealed for specific reasons.
– The paper suggests that uncovering this knowledge is crucial for societal progress and overcoming current challenges.

Significance of Each Key Point:

Problem-Solving: Understanding causal factors allows for more effective solutions, reducing the likelihood of recurring issues.
Truth vs. Perception: Clarifying these concepts helps individuals make better decisions based on facts rather than illusions.
Natural Law: Recognizing these universal principles can provide guidance for ethical and sustainable practices.
Occulted Knowledge: Uncovering hidden knowledge is vital for addressing societal issues and achieving personal growth.

Summary of the Entire Document:

This research paper explores the importance of understanding the causal factors in problem-solving, distinguishes between truth and perception, outlines seven principles of Natural Law, and discusses the concept of occulted knowledge. It emphasizes that uncovering these hidden truths can lead to significant societal progress and individual growth.

Kim Iversen /w Dr William Makis

February 13, 2025

Ivermectin: The Hidden Treatment for Cancer They Don’t Want You to Know About

Dr. William Makis MD

Kim Iversen

R1 – Weizenbaum’s ELIZA

February 12, 2025

You

Who was Joseph Weizenbaum and what was ELIZA

Assistant

+More

Okay, so I need to figure out who Joseph Weizenbaum is and what ELIZA is. From what I remember, Joseph Weizenbaum is a computer scientist who worked on artificial intelligence. But I’m not exactly sure about his contributions or specific projects he was involved in.

I think ELIZA might be related to early AI programs or experiments, but I’m not certain if it’s connected to Joseph Weizenbaum directly. Maybe I should look into the history of AI to see when and how these concepts were introduced.

Wait, wasn’t there something about the Turing machine and the idea of artificial intelligence? Alan Turing proposed a machine that could be used to solve problems through computation, which later became the basis for modern computing. But how does that connect to Joseph Weizenbaum and ELIZA?

Perhaps ELIZA was an early program designed to simulate conversation between humans and machines. I think it was one of the first AI programs that tried to mimic human-like interaction. But was that developed by Weizenbaum? Or was he involved in some other aspect of AI research?

I also recall something about natural language processing being a significant area in AI, where computers can understand and generate human language. ELIZA might have been an early example of such technology.

Maybe I should check if Joseph Weizenbaum worked on the ELIZA project or if it was developed by someone else. It would be helpful to look up his work and see what he’s most known for in the field of AI.

Additionally, I wonder how ELIZA worked. Did it use rule-based methods or more advanced techniques? Understanding its architecture could give me a better grasp of early AI programming.

I also think about the concept of intelligence in machines. Weizenbaum might have explored this through his research, trying to define what makes something intelligent and how that could be simulated in software.

Overall, I need to gather accurate information about Joseph Weizenbaum’s background, his contributions to AI, and clarify the role of ELIZA within that context. This will help me provide a clear and concise explanation of both concepts.

Joseph Weizenbaum is a significant figure in the history of computer science and artificial intelligence (AI). He is best known for his work on early AI programs and his contributions to the field of natural language processing. One of his notable projects was ELIZA, an early example of an AI program designed to simulate conversations between humans and machines.

Joseph Weizenbaum:

Background: Joseph Weizenbaum is a German-American computer scientist who made important contributions to AI in the 1950s and 1960s.
Work: He is recognized for his work on natural language processing, particularly in developing early chatbots. His research aimed to explore how machines could understand and generate human language.
Impact: Weizenbaum’s work laid the groundwork for later advancements in AI, including more sophisticated chatbots and conversational systems.

ELIZA:

Overview: ELIZA was one of the first programs designed to simulate a conversation with a human. It used rule-based methods to respond to inputs, aiming to mimic human-like interaction.
Functionality: ELIZA could understand and generate text based on specific rules. It was notable for its ability to maintain a conversation, even if it couldn’t always produce perfectly coherent responses.
Significance: ELIZA marked an important step in AI research, demonstrating the potential of machines to interact with humans in a conversational manner.

In summary, Joseph Weizenbaum contributed significantly to the field of AI through his work on projects like ELIZA, which were pivotal in early attempts to create human-like conversational systems. His efforts set the stage for future innovations in AI, natural language processing, and interactive computing.

AI has come a long way.