AI Resources & FAQs

Our Exclusive Content:

AI Reports:

• State of AI: Explore the rapid advancements of AI, its transformative impact across industries, and the crucial considerations for the future of AI.
• AI Coding Capabilities: Discover the capabilities and limitations of AI coding tools, exploring their role in augmenting developers and the critical need for quality assurance.
• User-Owned AI: A Blockchain-Powered Future: Explore the potential of blockchain technology to address the challenges of centralized AI control by enabling user-owned, transparent, and secure AI systems.
• Model Context Protocol (MCP): Understand the open-source framework that helps developers build modular, interpretable, and controllable AI applications using LLMs.
• AI Beyond the Hype: Six Unprecedented Differences from Past Trends: Decode the rapid advancements and profound shifts driven by AI across technology, industries, and global dynamics.
• The Usage-Based Revolution: Comparing Subscripition to Usage-Based AI Models: Explore how AI is fundamentally changing SaaS pricing models, moving businesses from traditional seat-based subscriptions to more dynamic, usage-based and hybrid approaches.

AI Videos:

• Cybersecurity Insider Threats Made with VEO 3 (Vol. 1): This video highlights insider threats within organizations, and all video clips were done with Veo 3.
• Cybersecurity Insider Threats Made with VEO 3 (Vol. 2): This video highlights insider threats within organizations, and all video clips were done with Veo 3.

Curated Content:

Industry Guides & Playbooks:

• The Chief AI Officer's Handbook: Master AI leadership with strategies to innovate, overcome challenges, and drive business growth, exploring everything from AI system design to ethical compliance.
• Gartner's Executive AI Governance Playbook A comprehensive guide to AI governance, offering strategies to balance value and risk. This playbook assists CIOs in designing, implementing, and adjusting AI governance, ensuring compliant, ethical, trustworthy, and responsible AI.

Videos:

• The Alarming Reality of AI Deepfakes – A Must-Watch Warning for Everyone: This video exposes the growing threat of AI deepfakes in digital media, using entirely synthetic yet realistic visual examples.

Diagram of How AI Agents Work

Understanding the workflow and components required to build and deploy AI agents.

Foundation Model Pros and Cons

This is a short, introductory overview of eleven different foundation LLM models and their main benefits and drawbacks — as described by Google's Gemini.

Last update: August 20, 2025

GPT-4 (OpenAI)

• Pros: State-of-the-art performance, especially in complex reasoning, creative writing, and handling nuanced instructions. It's often the benchmark for other models.
• Cons: High cost and limited public access through APIs, making it less accessible for many developers and researchers. It also has a tendency to "hallucinate," or generate factually incorrect information, and its training data can reflect societal biases.

Gemini (Google DeepMind)

• Pros: Native multimodal capabilities allow it to seamlessly process and understand text, images, audio, and code. It's designed for advanced problem-solving, planning, and long-context understanding.
• Cons: As a newer model, its full capabilities are still being explored, and public documentation is less extensive than for some established competitors.

PaLM 2 (Google AI)

• Pros: Known for its strong factual accuracy and ability to follow instructions precisely. It's a versatile model used in many Google products, with a focus on reducing harmful outputs.
• Cons: While improved, it can still produce inaccurate or misleading information. Its performance can be inconsistent across different, highly specialized tasks.

Claude (Anthropic)

• Pros: Safety and harmlessness are its core design principles, making it a reliable choice for applications where avoiding harmful content is critical. It excels at long-form text generation and maintaining a consistent conversational style.
• Cons: Its focus on safety can sometimes make it seem less creative or adventurous compared to other models. It may not be the top performer in complex, open-ended problem-solving scenarios.

Llama 2 (Meta AI)

• Pros: An open-source powerhouse with strong performance, allowing for extensive customization and fine-tuning. This makes it a popular choice for both researchers and developers who want full control over the model.
• Cons: It requires significant computational resources for training and fine-tuning, which can be a barrier for individuals or smaller organizations. It can also produce factually incorrect responses.

Falcon 180B (Technology Innovation Institute)

• Pros: High performance with relatively few parameters, efficient training and inference.
• Cons: It's a newer entrant, so it's less battle-tested and its full range of capabilities and limitations are still being discovered by the community. It's a resource-intensive model.

Stable LLM (Stability AI)

• Pros: Completely open-source and designed to be highly customizable, especially for creative applications like image and text generation. Its open nature fosters community innovation.
• Cons: It is generally less accurate and coherent than larger, more mature models. It is best suited for specific creative tasks rather than general-purpose reasoning.

Grok (xAI)

• Pros: Unique personality and a focus on answering "spicy" or unconventional questions. It has real-time access to information from the X platform, giving it a more current knowledge base than many competitors.
• Cons: Less robust documentation and integrations compared to more established models. Its less-filtered approach can sometimes lead to unexpected or off-brand responses, and it's less adept at detailed, long-form creative writing.

Mixtral 8x7B (Mistral AI)

• Pros: It uses a "Mixture of Experts" (MoE) architecture, which gives it the performance of a much larger model while being highly efficient to run. This makes it a powerful, open-source model that is more accessible for local deployment.
• Cons: While its architecture is efficient for inference, it still requires a significant amount of VRAM (GPU memory) to run, which can be a limiting factor for some hardware.

Command R+ (Cohere)

• Pros: Geared specifically for enterprise use with a strong focus on Retrieval-Augmented Generation (RAG), which allows it to provide verifiable, citation-backed answers. It excels at tool use and automating business workflows.
• Cons: It is a closed-source, proprietary model, which limits the ability of developers to customize or fine-tune it for specific applications beyond what the API allows.

DeepSeek (DeepSeek AI)

• Pros: Exceptional performance in coding, mathematics, and complex reasoning. It uses an efficient "Mixture of Experts" (MoE) architecture, making it highly capable while being more accessible and cost-effective to run than some other large models. Its open-source nature allows for extensive customization.
• Cons: As a Chinese company, concerns have been raised about potential censorship and political bias on sensitive topics. While the model itself is open-source, the official API may employ filtering. Its ecosystem is still maturing, with less extensive documentation and integrations compared to more established models.

List of AI-driven Image Generators

This is a short, introductory overview of five AI driven image generators and their main benefits and and drawbacks — as described by Google's Gemini.

• Midjourney: Strengths: Exceptional image quality, strong artistic style, and ability to generate highly detailed and imaginative images. Weaknesses: Can be challenging to use for beginners, and image generation can be inconsistent.


• Stable Diffusion: Strengths: High level of customization, open-source nature, and ability to generate a wide range of image styles. Weaknesses: Can produce lower image quality compared to some competitors, and requires more technical expertise to use effectively.


• Dall-E3: Strengths: User-friendly interface, strong image generation capabilities, and ability to generate realistic images. Weaknesses: Can be limited in artistic style compared to some other models.


• Adobe Firefly: Strengths: Seamless integration with Adobe Creative Cloud, strong focus on commercial use, and ability to generate high-quality images. Weaknesses: Relatively new model with limited features compared to some competitors.


• Stable Diffusion XL (SDXL): Strengths: Significant improvement over Stable Diffusion in terms of image quality, detail, and realism. Weaknesses: Still under development, with potential for further enhancements./li>

List of AI Podcasts, Blogs and YouTube Channels That We Learned From

Below are a few top AI learning resources for beginners and others to learn about the business, technologies and language of AI. The content providers typically offer you both web blog or audio podcast versions.

They all provide valuable content on AI models, use cases, AI risks, governance, legislation, AI technologies and more. They also have important information about and interviews with the technology, business and political leaders associated with AI.

Any business or technologist today must speak the AI dialect. The sooner you start, the sooner you'll be fluent. It will be a boon to your career if you immerse yourself into this important subject. Please consider us as your AI and cybersecurity partner.

• AI Breakdown Daily AI news with clear and concise explanations of AI issues and events, making it accessible for beginners.They also have an "AI school."
• OpenAI Research - Updates and research findings from OpenAI.
• Bens Bites - Great repository for all things AI
• Super Human AI - So much valuable info — check it out.
• a16z Provides insights into the world of technology and business, with a focus on AI and its impact.

AI Glossaries

AI is starting to develop its own dialect...like the dialects of IT and cybersecurity.

Any business or technologist today must speak the AI dialect. The sooner you start, the sooner you'll be fluent. It will be a boon to your career if you immerse yourself into this important subject. Please consider us as your AI and cybersecurity partner.

Here are a couple AI glossaries that are different than each other, but together adequately cover the waterfront.

• Expert.AI Glossary
• a16z Glossary

Additionally, we present the AI Lexicon: A Concise Glossary — a curated set of key terms and concepts that we believe are essential for understanding and leveraging AI in today's business and cybersecurity landscape. This focused glossary reflects both foundational knowledge and emerging trends we've identified through hands-on experience.

• AI (Artificial Intelligence): A broad field encompassing the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.


• AI Agents: AI systems that can perform complex tasks with minimal human intervention.


• AI Compliance: Ensuring that AI systems and their deployment adhere to relevant laws, regulations, and organizational policies.


• AI Crawlers: Automated programs driven by AI that scan and index web content to gather vast amounts of data, often used to train large language models. These can dominate website traffic and lead to defensive measures like blocking.


• AI Ethics: A field that addresses the moral and societal implications of AI, including issues such as bias, data privacy, and transparency.


• AI Governance: The policies, processes, and technology necessary to develop and deploy AI systems responsibly. CEO oversight of AI governance is correlated with higher bottom-line impact from generative AI use.


• AI Maturity: The state of fully integrating AI into organizational structures and processes to realize its full potential. Despite high AI adoption rates, achieving AI maturity remains a significant challenge.


• API (Application Programming Interface): A set of protocols and tools for building software applications. Many LLM companies provide APIs to access their models. Tracking API usage can be a metric for gauging LLM adoption.


• Automated Vulnerability Detection: The capability of AI tools to scan code for potential security flaws and vulnerabilities. This is a key feature for maintaining the security of software applications.


• Context-aware Code Completion: An AI feature that suggests and completes code snippets based on the surrounding code and project context. This helps developers write more efficiently and accurately.


• Context Window: The amount of information (measured in tokens) that an LLM can consider when generating a response. A larger context window allows the AI to maintain coherence over longer interactions and process more information.


• Foundation Models: AI models trained on a broad range of unlabeled data that can be adapted or fine-tuned for a wide variety of downstream tasks. LLMs are a type of foundation model.


• Generative AI (GenAI): A category of AI that can generate new content, including text, images, code, and music, at levels that can rival human creativity. Examples include ChatGPT, Midjourney, and Suno.


• Hallucinations: Instances where an AI model generates incorrect or nonsensical information that is not grounded in the training data.


• Inference Speed: How quickly an AI model can make predictions after being trained.


• Large Language Models (LLMs): Deep learning models with a vast number of parameters, trained on massive text datasets, enabling them to understand and generate human-like text. Examples include GPT-4, Gemini, Claude, and Llama 2.


• Multimodal AI: AI models that can process and generate multiple types of data simultaneously, such as text, images, and audio. GPT-4o is an example of a multimodal model.


• Prompt Engineering: The process of designing and refining input prompts to guide AI models, especially generative AI and LLMs, to produce desired and high-quality outputs. Effective prompt engineering is crucial for successful AI use cases.


• Reasoning AI: AI systems that can perform logical thinking, problem-solving, and decision-making beyond simple pattern recognition. Models like OpenAI's o1 and Google's Gemini 2.0 Flash are capable of reasoning.


• Reskilling: Training employees with new skills to adapt to changes brought about by AI and automation, rather than replacing them.


• Retrieval Augmented Generation (RAG): A technique that enhances the accuracy and reliability of LLM responses by grounding them in external knowledge sources retrieved at the time of inference. This helps to reduce hallucinations.


• Robots Exclusion Protocol (Robots.txt): A standard used by websites to communicate to web crawlers which parts of the site should not be accessed. The ai.robots.txt project offers resources specifically for blocking AI crawlers.


• Token: The basic unit of text that LLMs process. Words and parts of words are often broken down into tokens. The number of tokens in a prompt and response can affect processing speed and cost.


• Training Speed: How quickly an AI model can learn from data.


• User-Agent: A string of text that web browsers and other client applications, including web crawlers, send to identify themselves to servers. AI crawlers may spoof user-agents to evade detection.


• Vibecoding: An emerging development paradigm where users create functional software, websites, or digital experiences using natural language prompts instead of traditional code. Vibecoding tools leverage generative AI to interpret intent and transform plain-language input into working applications.

Company AI Policy Elements

Here are a few elements which must be considered as part of any company's internal AI policy. If you work with us, we'll provide you with a professional AI policy.

AI Policy Elements:
1. AI systems must be used ethically and transparently.
2. Data privacy and security must be maintained at all times.
3. Regular audits and evaluations for AI accuracy, bias, and fairness must be conducted.
4. Continuous training and upskilling of staff on AI capabilities and limitations must occur.
5. Clear accountability and governance structures for AI-related decisions must be put into place.
                    

Is It Easy to Build a Business AI Agent?

When we got started eight months ago, that's what we were hearing. Everyone was saying that all the tools, APIs and other technical elements had already been developed for folks like us and all we had to do was...just do it.

Well, for us at least, a company with a technical team and a strong knowledge of development processes, we did not find it easy. We found that if we persisted and pushed through our misperceptions and the bad advice we were getting...we COULD do it. And we did. What you see on the Agent Farm website is exactly what we can deliver to you. We did not find it to be easy.

If you have a technical team, the support of your management, and the internal discipline and resources to pursue developing, deploying and managing an agent, you CAN do it. But is that the best use of your time and resources? If you work with us, over time you and your team will learn much and may be able to take over various aspects of the project from us. We are happy to train you in this regard. Or we can lead and execute on this project. Whatever works for you, will work for us. We can support you in any way that you like.

Writing Effective Prompts — Science and Art

Assuming you have identified and processed the relevant data to meet your use case, now you have to "engineer" a prompt that will extract the answers you expect and present those answers in a way that will accomplish your objectives. This is another tricky piece of the puzzle.And this is not easy either.

A well-crafted prompt is crucial for effective AI agent interaction. Here are a few key elements to consider:

• Clear and concise objective: Clearly state the desired outcome or goal.
• Role or persona: Define the AI agent's role or perspective for context.
• Constraints or limitations: Specify any boundaries or restrictions.
• Contextual information: Provide relevant background or context.
• Iterative refinement: Be prepared to iterate the prompt based on initial results.
• Evaluation criteria: Define how to measure the success of the output.