Content Marketing: Curated articles on content curation and its supported applications! We use our curation tools to power curated newsletters and conversational chatbots like the one you see on this page.
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Publisher: AI News & Strategy
2025-11-05The Mental Models of Master Prompters: 10 Techniques for Advanced Prompting
(00:00) Teaching AI is really hard. Teaching advanced prompting is even harder. This video will make it easier. My goal is to equip you with an understanding of the mental models, the principles that advanced prompters use. We're going to go beyond t...(00:00) Teaching AI is really hard. Teaching advanced prompting is even harder. This video will make it easier. My goal is to equip you with an understanding of the mental models, the principles that advanced prompters use. We're going to go beyond t... -
Publisher: Philosophical Trials
2025-09-18Noam Chomsky on Language Evolution and Semantic Internalism Philosophical Trials 14
In this interview, Noam Chomsky reflects on his intellectual journey, from his accidental introduction to linguistics to his revolutionary ideas about language as an internal cognitive system rather than merely a tool for communication. He contrast...In this interview, Noam Chomsky reflects on his intellectual journey, from his accidental introduction to linguistics to his revolutionary ideas about language as an internal cognitive system rather than merely a tool for communication. He contrast... -
Publisher: IBM Technology
2025-09-08Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents
Prompt engineering is crafting the instruction text for an LLM (instructions, examples, formatting) to steer its output. Context engineering is the system-level work that programmatically assembles everything the model sees at inference—prompts, re...Prompt engineering is crafting the instruction text for an LLM (instructions, examples, formatting) to steer its output. Context engineering is the system-level work that programmatically assembles everything the model sees at inference—prompts, re... -
Publisher: IBM Technology
2025-09-017 AI Terms You Need to Know Agents RAG ASI More
This video explains seven essential AI terms—Agentic AI, Large Reasoning Models, Vector Databases, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), Mixture of Experts (MoE), and ASI (Artificial Superintelligence). It describes ho...This video explains seven essential AI terms—Agentic AI, Large Reasoning Models, Vector Databases, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), Mixture of Experts (MoE), and ASI (Artificial Superintelligence). It describes ho... -
Publisher: Ben AI
2025-08-06The "Boring" AI Business Model Making Millionaires in 2025
(00:00) In 2025, the AI automation agency market is around 11 billion. The SAS market around 300 billion. But there's a \$3 trillion market being disrupted by AI that nobody's talking about, the service industry. And while most attention in AI goes ...(00:00) In 2025, the AI automation agency market is around 11 billion. The SAS market around 300 billion. But there's a \$3 trillion market being disrupted by AI that nobody's talking about, the service industry. And while most attention in AI goes ... -
Publisher: Nick Saraev
2025-08-06$2.4M of Prompt Engineering Hacks in 53 Mins (GPT, Claude)
(00:00) Here is 6 years of prompt engineering in 53 minutes. I started working with AI in 2019 using GPT-2. Since then, I’ve built several service and consulting businesses: one did \$92,000 a month, another $72,000 a month, and my current one did...(00:00) Here is 6 years of prompt engineering in 53 minutes. I started working with AI in 2019 using GPT-2. Since then, I’ve built several service and consulting businesses: one did \$92,000 a month, another $72,000 a month, and my current one did... -
Publisher: Novara Media
2025-08-05The Problem With ChatGPT
In this conversation, host Aaron Bastani interviews AI critic Gary Marcus about the real risks of current large language models and the challenges of bringing about safe, aligned AI. Gary explains that while LLMs offer impressive capabilities, they...In this conversation, host Aaron Bastani interviews AI critic Gary Marcus about the real risks of current large language models and the challenges of bringing about safe, aligned AI. Gary explains that while LLMs offer impressive capabilities, they... -
Publisher: IBM Technology
2025-07-27Can AI Think? Debunking AI Limitations
In this video, IBM Technology tackles the big question—can AI truly think? The discussion debunks common misconceptions about artificial intelligence and explores its reasoning capabilities and limitations.0:01 – Host (IBM Technology):Welcome to ...In this video, IBM Technology tackles the big question—can AI truly think? The discussion debunks common misconceptions about artificial intelligence and explores its reasoning capabilities and limitations.0:01 – Host (IBM Technology):Welcome to ... -
Publisher: IBM Technology
2025-07-26Context Optimization vs LLM Optimization: Choosing the Right Approach
This video explains two key approaches to optimizing large language models (LLMs): context optimization (using prompt engineering and retrieval augmented generation or RAG) and model optimization through fine tuning. Using a retail store analogy, the...This video explains two key approaches to optimizing large language models (LLMs): context optimization (using prompt engineering and retrieval augmented generation or RAG) and model optimization through fine tuning. Using a retail store analogy, the... -
Publisher: IBM Technology
2025-07-25How Large Language Models Work
This video explains what large language models (LLMs) are, how they work, and their practical business applications. It covers the foundation of LLMs in pre-training with vast amounts of data, transformer architecture, and iterative training method...This video explains what large language models (LLMs) are, how they work, and their practical business applications. It covers the foundation of LLMs in pre-training with vast amounts of data, transformer architecture, and iterative training method... -
Publisher: HubSpot
2025-07-17Benefits of Sales Enablement
This post explains how sales enablement—providing your sales team with the right knowledge, content, and tools—can drive increased revenue, efficiency, and overall success. It outlines what sales enablement is, details its benefits such as impr...This post explains how sales enablement—providing your sales team with the right knowledge, content, and tools—can drive increased revenue, efficiency, and overall success. It outlines what sales enablement is, details its benefits such as impr... -
Publisher: IBM Technology
2025-07-17What is a Context Window? Unlocking LLM Secrets
(00:00) In the context of large language models. What is a context window? Well, it's the equivalent of its working memory. It determines how long of a conversation the LLM can carry out without forgetting details from earlier in the exchange. And a...(00:00) In the context of large language models. What is a context window? Well, it's the equivalent of its working memory. It determines how long of a conversation the LLM can carry out without forgetting details from earlier in the exchange. And a... -
Publisher: IBM technology
2025-07-17AI Inference: The Secret to AI's Superpowers
(00:01) What is inferencing. It's an AI model's time to shine its moment of truth, a test of how well the model can apply information learned during training to make a prediction or solve a task. And with it comes a focus on cost and speed. Let's ...(00:01) What is inferencing. It's an AI model's time to shine its moment of truth, a test of how well the model can apply information learned during training to make a prediction or solve a task. And with it comes a focus on cost and speed. Let's ... -
Publisher: IBM Technology
2025-07-14What is AI Search? The Evolution from Keywords to Vector Search & RAG
(00:00) AI search is transforming how we locate and consume information online, but how? Well, back in the day, search engines were pretty simple because they were based more or less just on keyword search. They matched words in a user's query to ...(00:00) AI search is transforming how we locate and consume information online, but how? Well, back in the day, search engines were pretty simple because they were based more or less just on keyword search. They matched words in a user's query to ... -
Publisher: IBM Technology
2025-06-30How to Make AI More Accurate: Top Techniques for Reliable Results
In this video, IBM Technology explains several techniques to improve AI accuracy. The speakers discuss methods such as Retrieval Augmented Generation (RAG), choosing the proper model, Chain of Thought prompting, LLM chaining, Mixture of Experts (MoE...In this video, IBM Technology explains several techniques to improve AI accuracy. The speakers discuss methods such as Retrieval Augmented Generation (RAG), choosing the proper model, Chain of Thought prompting, LLM chaining, Mixture of Experts (MoE... -
Publisher: Brendan Dell
2025-06-29Everyone is Lying About AI - Heres Proof
0:00 Brendan: Apple recently released a research report claiming to debunk much of the hype around the current AI craze. The report, entitled "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of P...0:00 Brendan: Apple recently released a research report claiming to debunk much of the hype around the current AI craze. The report, entitled "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of P... -
Publisher: FAR.AI
2025-06-24Mechanistic Interpretability: A Whirlwind Tour
Neel Nanda presents a tour of mechanistic interpretability, arguing that machine learning models develop human-comprehensible algorithms even without explicit guidance. He explains how techniques like sparse autoencoders help uncover hidden model str...Neel Nanda presents a tour of mechanistic interpretability, arguing that machine learning models develop human-comprehensible algorithms even without explicit guidance. He explains how techniques like sparse autoencoders help uncover hidden model str... -
Publisher: Lex Fridman
2025-06-24Mechanistic Interpretability explained
In this discussion, Chris Olah explains mechanistic interpretability, a field focused on understanding the algorithms inside neural networks by “growing” them rather than programming them directly. He walks through how features and circuits emerg...In this discussion, Chris Olah explains mechanistic interpretability, a field focused on understanding the algorithms inside neural networks by “growing” them rather than programming them directly. He walks through how features and circuits emerg... -
Publisher: IBM Technology
2025-06-23AI vs Human Thinking: How Large Language Models Really Work
This video compares AI and human cognition, exploring key differences in learning, information processing, memory, reasoning, error, and embodiment. It explains that while humans learn dynamically and interact with the world through sensory experienc...This video compares AI and human cognition, exploring key differences in learning, information processing, memory, reasoning, error, and embodiment. It explains that while humans learn dynamically and interact with the world through sensory experienc... -
Publisher: MIT
2025-06-10Nobel Laureate Busts the AI Hype
MIT economist Daron Acemoglu argues that, despite the hype, AI is unlikely to automate more than about 5% of tasks or add more than 1% to global GDP this decade. He advises business leaders to focus on using AI to augment human expertise and create...MIT economist Daron Acemoglu argues that, despite the hype, AI is unlikely to automate more than about 5% of tasks or add more than 1% to global GDP this decade. He advises business leaders to focus on using AI to augment human expertise and create... -
Publisher: AI Engineer
2025-06-09AI Snake Oil - Building and evaluating AI Agents
The speaker discusses why AI agents often underperform in the real world, highlighting three main challenges: evaluation is difficult, static benchmarks are misleading, and reliability often lags behind capability. The talk emphasizes the need fo...The speaker discusses why AI agents often underperform in the real world, highlighting three main challenges: evaluation is difficult, static benchmarks are misleading, and reliability often lags behind capability. The talk emphasizes the need fo... -
Publisher: IBM Technology
2025-06-075 Types of AI Agents: Autonomous Functions and Real-World Applications
This video explains five main types of AI agents—from simple reflex agents to adaptive learning agents—and how each operates using different decision-making processes in various environments. It also covers the evolution from rule-based systems t...This video explains five main types of AI agents—from simple reflex agents to adaptive learning agents—and how each operates using different decision-making processes in various environments. It also covers the evolution from rule-based systems t... -
Publisher: IBM Technology
2025-06-07MCP vs API: Simplifying AI Agent Integration with External Data
This video explains the Model Context Protocol (MCP) and how it standardizes the integration of large language models (LLMs) with external data and tools, comparing it to traditional APIs. It also highlights MCP’s dynamic discovery, uniform interfa...This video explains the Model Context Protocol (MCP) and how it standardizes the integration of large language models (LLMs) with external data and tools, comparing it to traditional APIs. It also highlights MCP’s dynamic discovery, uniform interfa... -
Publisher: IBM Technology
2025-06-07RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
This video explains three approaches to improving large language model outputs: Retrieval Augmented Generation (RAG), Fine-Tuning, and Prompt Engineering. It covers how each method works, the benefits they offer, and the trade-offs involved in applyi...This video explains three approaches to improving large language model outputs: Retrieval Augmented Generation (RAG), Fine-Tuning, and Prompt Engineering. It covers how each method works, the benefits they offer, and the trade-offs involved in applyi... -
Publisher: IBM Technology
2025-06-07LangChain vs LangGraph: A Tale of Two Frameworks
This video compares LangChain and LangGraph—two open source frameworks for building applications with large language models. It explains each framework’s architecture, components, and state management approaches, and outlines the scenarios where ...This video compares LangChain and LangGraph—two open source frameworks for building applications with large language models. It explains each framework’s architecture, components, and state management approaches, and outlines the scenarios where ... -
Publisher: IBM Technology
2025-06-07What is a Vector Database? Powering Semantic Search & AI Applications
This video explains vector databases and how they enable semantic search by representing unstructured data like images, text, and audio as mathematical vector embeddings. The speaker details how data is transformed into high-dimensional vectors and e...This video explains vector databases and how they enable semantic search by representing unstructured data like images, text, and audio as mathematical vector embeddings. The speaker details how data is transformed into high-dimensional vectors and e... -
Publisher: AI Engineer
2025-06-07RAG Agents in Prod: 10 Lessons We Learned
Douwe Kiela, CEO of Contextual AI, shares his insights on deploying RAG agents in production for enterprises. He emphasizes that success depends on building robust systems around language models, specializing to capture enterprise expertise, and de...Douwe Kiela, CEO of Contextual AI, shares his insights on deploying RAG agents in production for enterprises. He emphasizes that success depends on building robust systems around language models, specializing to capture enterprise expertise, and de... -
Publisher: IBM Technology
2025-06-07RAG vs. CAG: Solving Knowledge Gaps in AI Models
This video explains two methods—Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG)—to overcome the knowledge limitations of large language models. It details how each technique processes and utilizes external information, c...This video explains two methods—Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG)—to overcome the knowledge limitations of large language models. It details how each technique processes and utilizes external information, c... -
Publisher: MIT
2025-06-06AI Snake Oil What Artificial Intelligence Can Do, What It Cant, and How to Tell the Difference
(00:01) ASU OZDAGLAR Opening Remarks "Maybe we should get started, right? Hi, everyone. It's a pleasure to welcome you to tonight's talk with Professor Arvind Narayanan. The Schwarzman College of Computing is honored to co-host this event with MI...(00:01) ASU OZDAGLAR Opening Remarks "Maybe we should get started, right? Hi, everyone. It's a pleasure to welcome you to tonight's talk with Professor Arvind Narayanan. The Schwarzman College of Computing is honored to co-host this event with MI... -
![The Dark Matter of AI [Mechanistic Interpretability]](https://optimalaccess.com/wp-content/uploads/2025/11/4c439586029c8a19c68b65c5f1204c99_1-480x270.jpg)
Publisher: Welch Labs
2025-06-05The Dark Matter of AI [Mechanistic Interpretability]
This video explores how researchers use mechanistic interpretability—especially sparse autoencoders—to uncover hidden, human‐understandable features in large language models. It highlights the challenges of pinning down internal model behavio...This video explores how researchers use mechanistic interpretability—especially sparse autoencoders—to uncover hidden, human‐understandable features in large language models. It highlights the challenges of pinning down internal model behavio... -
Publisher: LinkedIn
2025-05-255 Questions AI Can Never Answer for You
The article "5 Questions AI Can Never Answer for You" by Bill Jensen challenges professionals to take ownership of their own AI adoption in an increasingly automated workplace. Rather than waiting for companies to mandate AI upskilling, Jensen emph...The article "5 Questions AI Can Never Answer for You" by Bill Jensen challenges professionals to take ownership of their own AI adoption in an increasingly automated workplace. Rather than waiting for companies to mandate AI upskilling, Jensen emph... -
Publisher: MIT Sloan
2025-05-20Philosophy Eats AI: What Leaders Should Know
In this discussion, David Kiron and Michael Schrage argue that true AI success hinges not on technical sophistication alone but on grounding AI initiatives in solid philosophical frameworks—teleology (purpose), ontology (nature of being), and epist...In this discussion, David Kiron and Michael Schrage argue that true AI success hinges not on technical sophistication alone but on grounding AI initiatives in solid philosophical frameworks—teleology (purpose), ontology (nature of being), and epist... -
Publisher: Campaign Monitor
2025-05-13What are good open rates CTRs CTORs for email campaigns
This post provides an in‐depth look at the most critical email marketing metrics—open rate, click-through rate (CTR), and click-to-open rate (CTOR)—explaining what each metric means, how they are calculated, and what constitutes a “good” p...This post provides an in‐depth look at the most critical email marketing metrics—open rate, click-through rate (CTR), and click-to-open rate (CTOR)—explaining what each metric means, how they are calculated, and what constitutes a “good” p... -
Publisher: beehiiv Blog
2025-05-09The State of Email Newsletters by beehiiv (2025)
The webpage presents beehiiv’s comprehensive 2025 State of Email Newsletters report, highlighting major industry trends, detailed performance statistics, and actionable strategies for creators, publishers, and businesses. Key points include:• Th...The webpage presents beehiiv’s comprehensive 2025 State of Email Newsletters report, highlighting major industry trends, detailed performance statistics, and actionable strategies for creators, publishers, and businesses. Key points include:• Th... -
Publisher: This Is World
2025-05-05Lost in the Hype: AI Will Never Become Conscious
0:00 – Roger Penrose: You have to be careful. First, the name is wrong. It’s not artificial intelligence—it’s not intelligence. True intelligence involves consciousness. I’ve always promoted the idea that these devices are not conscious an...0:00 – Roger Penrose: You have to be careful. First, the name is wrong. It’s not artificial intelligence—it’s not intelligence. True intelligence involves consciousness. I’ve always promoted the idea that these devices are not conscious an... -
Publisher: SEI Blog
2025-04-22What is Explainable AI
There is a whole field in AI Study called Interpretability / Explainable AI. It turns out that engineers don't really know how AI is generating its answers. The blog post "What is Explainable AI?" by Violet Turri explores the concept and significan...There is a whole field in AI Study called Interpretability / Explainable AI. It turns out that engineers don't really know how AI is generating its answers. The blog post "What is Explainable AI?" by Violet Turri explores the concept and significan... -
Publisher: Google
2025-04-22Comprehensive Guide to Prompt Engineering
The document is a comprehensive guide on "Prompt Engineering," authored by Lee Boonstra and contributed to by various experts. It provides insights into crafting effective prompts for large language models (LLMs), particularly focusing on the Gemini ...The document is a comprehensive guide on "Prompt Engineering," authored by Lee Boonstra and contributed to by various experts. It provides insights into crafting effective prompts for large language models (LLMs), particularly focusing on the Gemini ... -
Publisher: The AI Drid
2025-04-16Metas AI Boss Says He DONE With LLMS...
Yann LeCun says LLMs are limited for reaching AGI because text/next-token prediction can't capture the continuous, high-dimensional physical world. He advocates world models — joint embedding predictive architectures (e.g., VJepa) that learn abstra...Yann LeCun says LLMs are limited for reaching AGI because text/next-token prediction can't capture the continuous, high-dimensional physical world. He advocates world models — joint embedding predictive architectures (e.g., VJepa) that learn abstra... -
Publisher: YouTube
2025-04-08AI Agents Clearly Explained
0:03 – Jeff Su:“AI. Agentic capabilities. An AI agent. Agentic workflows. Most explanations of AI agents are either too technical or too basic. This video is for people with no technical background who use AI tools.”0:30 – Jeff Su:“You want...0:03 – Jeff Su:“AI. Agentic capabilities. An AI agent. Agentic workflows. Most explanations of AI agents are either too technical or too basic. This video is for people with no technical background who use AI tools.”0:30 – Jeff Su:“You want... -
Publisher: luiza newsletter
2025-03-31Anthropics Fair Use Boomerang
The blog post by Luiza Jarovsky focuses on a legal situation involving Anthropic and its recent court filings in response to a copyright lawsuit related to AI. The main arguments presented in the post highlight Anthropic's claims regarding the tran...The blog post by Luiza Jarovsky focuses on a legal situation involving Anthropic and its recent court filings in response to a copyright lawsuit related to AI. The main arguments presented in the post highlight Anthropic's claims regarding the tran... -
Publisher: openAI
2025-03-11Prompt Engineering
This guide provides strategies to improve responses from large language models like GPT-4o. Experimentation is encouraged to find the most effective methods. Six Strategies for Better Results 1. Write Clear Instructions Models cannot infer your inte...This guide provides strategies to improve responses from large language models like GPT-4o. Experimentation is encouraged to find the most effective methods. Six Strategies for Better Results 1. Write Clear Instructions Models cannot infer your inte... -
Publisher: The Media Copilot
2025-03-01Can AI save local news? The promise and peril of AI-powered journalism
(00:00) Mark Riley: I think people should lean into the people behind these paywalls—the personalities driving opinion. Those doing well on Substack will thrive in this environment. People will crave human intelligence and human opinion. I see...(00:00) Mark Riley: I think people should lean into the people behind these paywalls—the personalities driving opinion. Those doing well on Substack will thrive in this environment. People will crave human intelligence and human opinion. I see...
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