AI Disruptor

About AI Disruptor

Stop sounding like everyone else using AI. Build your own personal writing system. aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast)

Publisher

Alex McFarland

Language

en

Explicit

No

First episode

9 Jan 2025

Latest episode

15 Jul 2025

Latest episodes

16 episodes in the feed.

1. 

   15 Jul 2025

   Grok 4 dropped and X lost its mind

   AI Disruptor's Alex discusses Grok 4's launch and its real advantage of exclusive access to X data, advising listeners to test models themselves with their own context.

2. 

   24 Jun 2025

   Turn any Reddit thread into 10 validated content ideas

   This episode reveals how creators use NotebookLM to analyze Reddit threads, extracting audience intelligence and transforming discussions into validated content ideas.

3. 

   4 Jun 2025

   I interview myself to train my AI (copy my 12 questions)

   Alex, host of AI Disruptor, details a twelve-question framework designed to train AI on a user's unique voice, expertise, and communication style by systematically capturing key elements.

4. 

   5 May 2025

   Customizing Gemini to become your writing co-pilot

   This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) → Want my proven prompts? Get lifetime access to my personal Prompt Vault (https://prompt-vault-unlock.lovable.app/) – a constantly updated Notion database with the exact prompts I use for writing, research, and content creation. → Ready to accelerate your AI writing? Join the AI Writing Accelerator Waitlist (https://systems.aidisruptor.ai/) to be the first notified when v2 launches! While many of us have our go-to AI tools for writing – maybe Claude or ChatGPT – I know a lot of you are using, or are curious about, Google Gemini. You've asked how to effectively use it for writing tasks, and the good news is, you absolutely can turn it into a highly personalized and powerful AI writing assistant. Today, we'll walk through exactly how to set up Google Gemini to understand your unique needs, writing style, and knowledge base, making it a serious partner in your content creation process. We'll cover creating a custom "Gem," feeding it the right instructions and documents, and getting it ready to write for you. While tools like Claude and ChatGPT have their serious strengths , Gemini also offers a feature called "Gems" that allows you to build a persistent, customized assistant trained on your specific context. Many of the techniques we use for other AI models apply here too. By investing a little time upfront, you create an assistant that consistently reflects your voice, understands your projects, and leverages your specific knowledge, saving you significant time and effort down the road. You first need to understand and create Gemini Gems Think of Gemini Gems as the equivalent of Custom GPTs or Claude Projects. They are dedicated spaces where you can give Gemini ongoing instructions and upload reference files, creating a specialized version of the AI tailored to specific tasks, like being your writing assistant. Here’s how to create one: * Open Google Gemini (using the reasoning model is recommended, though these techniques work with others too). * Look at the left-hand menu and click on "Gems.” You might see some pre-set Gems, but we want a custom one. * Click on "Gem Manager" at the bottom . * Inside the Gem Manager, click "Create a new Gem.” * Give your Gem a name, something descriptive like "My AI Writing Assistant" or "[Your Project Name] Writer.” Personalize your Gem by crafting custom instructions This is where you tell your Gem how to act and what its purpose is. Just like custom instructions elsewhere, this guides the AI's behavior in every interaction within that Gem. In the "Instructions" section of your new Gem, maybe define: * Your Identity/Role: "My name is [Your Name], and I am a [Your Role/Profession]." * The Gem's Purpose: "You work with me to [Your Goal], such as writing my newsletter, [Newsletter Name], which focuses on [Your Niche]." * Key Directives: "Make sure at the start of every task you analyze your project knowledge for any relevant guidance." (This is crucial!) * Desired Tone: Specify the writing style you want. For example: "Use a straightforward, practical, and engaging tone." Be as detailed as you need to be. The better the instructions, the better the alignment. Further personalize your Gem by leveraging knowledge documents This is arguably the most powerful part of Gems and any AI writing system really: the ability to upload your own reference materials. Gemini will consult these documents whenever you interact within the Gem, ensuring its responses are grounded in your specific information. Docs I like to upload (you can add up to 10 files):

5. 

   4 Apr 2025

   How to create mind maps with NotebookLM

   This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Learn how to use complete AI writing systems and 3-5X your output with my AI Writing Accelerator. If you've been creating content for any length of time, you've felt that frustrating disconnect between all your existing content, sources, and ideas. Or maybe you struggle to repurpose one piece of content into multiple others. This is precisely why the new mind map feature from NotebookLM is such a revelation for content creators. Google just released this feature a few days ago, and I'm already convinced it's one of the most powerful AI research tools for writers in 2025. NotebookLM automatically generates comprehensive mind maps from your sources. The AI analyzes your content, extracts key concepts, and maps relationships between ideas in seconds—showing you connections you might never have discovered on your own. This is perfect for: * Content creators looking to maximize existing material * Researchers trying to synthesize multiple sources * Writers planning structured content * Anyone trying to extract maximum value from their content library Creating your first mind map Getting started is incredibly simple. Here's how: * Upload a source to NotebookLM (article, PDF, or paste text) * Click "Create a mind map" in the right sidebar * Within seconds, you'll have a complete visualization The system automatically identifies the main theme of your content and branches out into subtopics and related concepts. For example, when I uploaded my newsletter on "3 AI writing problems prompts can’t fix (but systems can) (https://aidisruptor.ai/p/3-ai-writing-problems-your-prompts)," the mind map instantly organized it into logical branches. The beauty is in how you can click any branch to expand it further, revealing even more detailed concepts. This gives you an immediate visual overview of your entire piece while allowing you to drill down into specific areas. Multi-source mind mapping Where this tool truly shines is when you combine multiple sources.

6. 

   27 Mar 2025

   How to create thumbnails in 5 steps with GPT-4o and Claude 3.7

   This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Today I'm sharing an exact system for creating thumbnails so you can apply it to your own newsletter, blog, or any content that needs a visual boost. The thumbnail problem most creators face Many thumbnails are forgettable at best (I’ve been there many times). Yet they're the first thing readers see when scrolling through their inbox or your publication's homepage. The problem? Creating good thumbnails traditionally requires: * Design skills most writers don't have * Time that could be spent creating actual content * Consistency across multiple editions Previous AI image generators like Midjourney promised to solve this but fell short – especially with text rendering. The text would be distorted, misspelled, or just plain weird. That's why GPT-4o's new image generation is such a big deal. As I demonstrate in the video, GPT-4o excels at rendering text accurately – something no other AI image model has mastered until now. This means we can finally create thumbnails with clear, readable headlines that pop. My systematic approach to AI content creation If you've been following me for a while, you know I'm all about systems – not just individual prompts or tools. The most powerful AI workflows combine multiple models, each doing what they do best. "I believe strongly in creating systems of content creation with AI, right? So not just a single prompt and a single tool. No, we're going to create systems." - Yours truly This thumbnail workflow is a perfect example. We'll use Claude (excelling at understanding content and crafting perfect prompts) and GPT-4o (the new king of AI image generation) together in a 5-step process. And here's the best part: While setting this up takes a bit of initial effort, once you've done it once, subsequent thumbnails take just minutes to create. The system front-loads the work so you can move incredibly fast later. Let's break down the system: Step 1: Creating your preset style in Sora First, we need to create a preset style in Sora (OpenAI's interface for GPT-4o image generation). This gives the model a visual reference for your brand's aesthetics. To do this: * Go to Sora (https://openai.com/sora/) and log in with your OpenAI account * Click "My Media" at the bottom * Click on the preset button and select "Manage" * Create a new preset by clicking the plus icon * Name your preset (e.g., "Thumbnails") * Describe your style in one line (e.g., "Bold retro propaganda style vector illustration") * Attach examples of thumbnails you like For that last step, I recommend playing around with GPT-4o image generation first to find styles you like. Create a few sample images using natural language prompts, save the ones that match your brand's vibe, and use those as examples in your preset. If you need help defining your style, you can even use ChatGPT to analyze images you like and provide a one-line style description. Step 2: Setting up project knowledge in Claude Next, we'll set up Claude to help us craft the perfect image prompts. The key here is providing Claude with expert knowledge about GPT-4o's image capabilities. I've created a document called "GPT-4o Image Prompting Best Practices" that I'm including at the bottom of this newsletter for paid members. This project knowledge document contains detailed information on how to create effective prompts specifically for GPT-4o's image generation model. To add this to Claude: * Create a project in Claude (or use an existing one) * Download the "GPT-4o Image Prompting Best Practices" document I've included * Add it to your project knowledge * Keep the name (or if you change the name, remember to update it in the prompt later) This step is crucial because it gives Claude specialized knowledge on how to craft prompts that work well with GPT-4o's image generation capabilities. Step 3: Using Claude to generate optimized image prompts Now we'll use Claude to analyze your newsletter content and create the perfect prompt for your thumbnail. I've developed a specialized prompt for this purpose, which I'm including below for paid members as well. You'll need to customize a few things in this prompt: * Your brand colors (hex codes) * Your preferred font * Your visual style (the same one you defined in Step 1) Then add your newsletter content between the XML tags, and let Claude work its magic. Claude will: * Analyze your newsletter * Extract key themes and high-impact words * Create an optimized prompt specifically for GPT-4o * Ensure the prompt aligns with your brand's visual identity The beauty of this approach is that Claude excels at understanding text content and distilling it into its essence – perfect for creating thumbnail prompts that capture what your newsletter is actually about. Step 4: Bringing it all together in Sora Now that you have a Claude-generated prompt, head back to Sora: * Create a new image generation * Paste the Claude-generated prompt into the description field * Set the aspect ratio to 3:2 (ideal for newsletter thumbnails) * Select your preset style from Step 1 * Generate the image If you've followed the steps correctly, GPT-4o will now create a thumbnail that: * Features high-impact text from your newsletter * Matches your brand's visual style * Has professional-quality typography * Visually communicates your content's main theme Step 5: Iteration and refinement Rarely will the first generation be perfect. This is where iteration comes in. You can: * Adjust the prompt slightly based on what you see * Generate variations of the same image * Mix and match elements from different generations * Request specific adjustments to color, composition, or text Each iteration gets you closer to the perfect thumbnail. And here's the best part – once you have a few thumbnails you love, add them back to your preset in Step 1. This creates a positive feedback loop where your preset gets better and better over time. PRO TIP: The first time you go through this process might take some time, as it should. But once your preset is dialed in and you've got your prompt template set up, subsequent thumbnails take just minutes to create! The efficiency payoff This system transforms what used to be a multi-hour design task into a few minutes of AI orchestration. The time savings are massive, but the consistency benefits might be even more valuable. By using this system, all your thumbnails will have a cohesive look and feel – strengthening your brand's visual identity with each new piece of content. Compare this to the old way: * Manual design: 1-2 hours per thumbnail * Basic AI generation: Quicker but inconsistent quality * This system: minutes per thumbnail with professional results Resources to get you started To help you implement this system right away, download/copy the following:

7. 

   11 Mar 2025

   How to create useful web apps with Replit (full waitlist tutorial + what I learned)

   This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Yesterday, I opened the waitlist for my upcoming AI Writing Accelerator course. What most subscribers didn't realize? I built that entire waitlist myself without writing a single line of code. I've always been that person who avoided anything code-related. My eyes would glaze over at the mention of JavaScript or React. As a content creator and writer, I told myself coding was simply "not my thing" — another way of saying I was intimidated and didn't want to invest the time to learn. But that changed last week when I finally dove into the "vibe coding" trend you've probably been hearing about. For the uninitiated: vibe coding is essentially using AI to build software applications through natural language conversations. Instead of writing code, you describe what you want, and the AI handles the technical implementation. It's like having a developer on standby who can instantly translate your ideas into functioning applications. The result? I created a professional-looking waitlist site with a real database that stores email addresses — all through conversation, zero coding required. The entire process took less than an hour, cost me less than $5, and I now have complete control over the design and functionality. Why this matters if you create content or run a business Here's why you should care about this development, even if you've never considered building your own web applications: * Freedom from dependency. How many times have you had a simple idea for a landing page, signup form, or customer portal but shelved it because you'd need to hire a developer or settle for the limitations of third-party platforms? That constraint just disappeared. * The cost difference is staggering. A basic custom waitlist built by a freelancer would cost a lot more. Even using a premium form builder with customization would run you monthly. I built mine for the cost of a fancy coffee. * Total control without the learning curve. Before this, your options were: 1) learn to code (months/years), 2) hire someone (expensive), or 3) use restrictive templates (limited). Now you can have full customization without either time investment or significant cost. * Speed to market. I had an idea for my course waitlist and launched it within 3 days. This kind of turnaround gives creators and small businesses a competitive edge that previously only tech-savvy teams enjoyed. For content creators especially, this is a game-changer. Your ideas can now become functional digital experiences without technical barriers. Replit walkthrough: the basics The tool I used is called Replit, one of several "AI coding assistants" that have emerged in recent months. The interface doesn't scream "developer tool" at all – it's essentially a chat window with your AI helper on one side and a live preview of what you're building on the other. Pricing is actually reasonable compared to hiring a developer or designer. Replit offers a free tier, but I opted for their Core plan at $25/month. This gives you unlimited apps, access to the AI agent, and $25 worth of "credits" each month. These credits are used whenever the AI makes significant changes (called "checkpoints") to your code, costing about $0.25 each time. As the AI works, you can watch as it is creating files, writing code, and solving problems in real-time. It’s not just a black box that spits out a final product – you get to see the entire process unfold. This transparency has an unexpected side effect. In my case, despite having no coding background, I started noticing patterns and understanding basic concepts just by observing. I'm certainly not claiming to be a developer now, but I've picked up enough to make simple edits and understand what's happening under the surface. What impresses me most is the conversational nature of building. When something didn't work, I didn't need to figure out complex error codes or debugging tools – I just told the AI "that's not working" or "the logo isn't showing up" and it would investigate and fix the issue, explaining what went wrong along the way. From idea to live waitlist in an afternoon Creating my waitlist was remarkably straightforward – no complicated setup or technical hurdles. I started with a simple description: "Build a waitlist site with React for me, which collects emails and full names from users. Make the design modern and professional. Use a database to store the registrations." I added some context about my AI Writing Accelerator course and the price point – that's it. The AI analyzed my request and proposed a plan, showing me what components it would create. After I approved, it got to work building the site while I watched. The initial version took only minutes to appear, complete with a form for collecting information. From there, customization was as simple as asking. When I wanted to change text, I'd say something like "Change the subheading to 'Not another prompt course. A writing system course'" and it would update instantly. When I uploaded my logo and the colors didn't quite match my brand, I simply said, "The red isn't matching my brand color. Look at my logo and adjust it" – and it analyzed the logo's color palette and updated the site accordingly. The most critical part was ensuring the database worked properly. When I first tested the form submission, I got an error. I just told the AI "When I entered my info, it gave an error message." It checked the logs, identified the database connection issue, and fixed it within minutes. I verified everything worked by submitting a test entry and confirming it appeared in the database view.

8. 

   6 Mar 2025

   How to do product comparisons with ChatGPT deep research

   Just a few days ago, OpenAI released one of their most powerful features to a much wider audience. Deep research, previously locked behind the $200/month ChatGPT Pro plan, is now available to all ChatGPT Plus subscribers at just $20/month. This is huge news if you're like me and constantly making decisions about tools, products, and software. I've been using deep research so much since it launched. Now that it's part of the Plus subscription, I immediately started experimenting with how to get maximum value from it given this plan is what most of my community uses. There is one important catch: Plus subscribers only get 10 deep research queries per month. That's why I wanted to share exactly how I use this feature to make the most of those limited opportunities. Why deep research is great for comparisons Let me take you back to the "old days" of product research (like, a few months ago). I'd find myself with 20+ browser tabs open, skimming through article after article. Each piece would contradict the last, and after an hour, I'd be more confused than when I started. The problem with traditional product research is: * They're rarely personalized to your specific use case or requirements * The information is scattered across dozens of sources, requiring you to mentally consolidate everything Deep research solves all these issues in one go. Instead of you doing the heavy lifting, ChatGPT does it for you by: * Searching across dozens of sources simultaneously * Focusing on your specific needs and use case * Consolidating everything into one coherent report * Providing a clear analysis And the time savings are dramatic. In the example I share in the video, I received a comprehensive comparison of two coding platforms in just 4 minutes. That same research would have taken me at least an hour manually, and the results wouldn't have been nearly as tailored to my specific situation. But what's really changed my relationship with this tool is realizing it's not just for tech decisions. I've used it to compare things like: * Vacation destinations based on specific criteria my family cares about * Different investment strategies * The best approach to learn a new language based on my learning style Every time, I've gotten a personalized analysis that cut through the noise and helped me make a confident decision in a fraction of the time. My step-by-step process Let me walk you through exactly how I used deep research recently to compare two AI-assisted coding platforms for a waiting list page I wanted to create. 1. Crafting the perfect comparison prompt The key to making your 10 monthly queries count is starting with a well-structured prompt. Here's the exact one I used: Notice what I included: * The specific products I want compared (Replit and Lovable) * My personal context (AI writer running a publication) * My specific goal (building a waiting list for a course) * My experience level (no coding experience) * My specific requirements (email collection and export) This context is crucial because it allows the AI to focus its research on aspects that matter to my use case, rather than generic feature comparisons. 2. Handling follow-up questions After submitting your initial prompt, ChatGPT will always ask clarifying questions. This is where the magic happens – these questions allow the AI to further personalize your results. In my case, ChatGPT asked: * Do you need specific design/customization options or is a simple form enough? * Are you looking for a free solution or open to paid plans? * Do you have a preference for how the collected emails should be stored or exported? * Would you like the comparison to include additional AI-driven features? My advice: Keep your answers concise but specific. I simply replied: * "We would like a little control over design" * "Open to paid" * "CSV" * "No need. We want to focus just on that." You don’t have to overcomplicate it, but you can also get as specific and complicated as you want! 3. Interpreting the results After about 4 minutes of processing 35 different sources, ChatGPT delivered a comprehensive comparison report that included: * A clear overview of both platforms * A comparison table with key features * Detailed sections on ease of use for non-coders * Design flexibility analysis * Pricing breakdowns for free vs. paid plans * Email collection and export capabilities * Other notable features and considerations * A conclusion with a recommendation based on my specific needs The best part? All of this was tailored specifically to my use case of creating a waiting list as a non-coder, rather than generic platform information. When reviewing your own results, pay special attention to: * The sources cited (hover over them to see more details) * Any gaps in information that might require follow-up questions * The reasoning behind recommendations, not just the recommendations themselves Making the most of your deep research queries Deep research has transformed how I approach product comparisons. Instead of wasting hours sifting through information, I can now get personalized, comprehensive analyses in minutes. The benefits are clear. Now that this powerful tool is available for just $20/month with ChatGPT Plus, I encourage you to try it yourself. Start with a comparison that's been on your mind—whether it's tech tools, home appliances, or vacation destinations. Remember to be specific about your context and requirements in your initial prompt, and don't rush through the follow-up questions. I'd love to hear how you use this feature. Comment below with your experience or the creative ways you've applied deep research to make decisions. My upcoming AI Writing Accelerator includes an entire module on using Deep Research for AI SEO content like comparisons. Sign up to the waitlist to secure a spot and early-access pricing. Get full access to AI Disruptor at aidisruptor.ai/subscribe (https://aidisruptor.ai/subscribe?utm_medium=podcast&utm_campaign=CTA_4)

9. 

   26 Feb 2025

   Prompt to generate LinkedIn data visualizations

   This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) LinkedIn contains a lot of your professional history, connections, and engagement patterns, but most of this valuable data just sits there unused. With Claude 3.7's new capabilities, particularly the extended thinking mode, you can transform this raw information into interactive dashboards and visualizations that actually tell you something useful. Like this: Or this: What's nice about this approach is that it requires zero coding knowledge, works with the free version of LinkedIn, and creates shareable visualizations you can use in presentations or reports. I've been experimenting with this technique, and the results have been really insightful. Exporting your LinkedIn data The first step is getting your data out of LinkedIn. Here's how: * Log into your LinkedIn account * Click on your profile picture and select "Settings & Privacy" * Navigate to "Data Privacy" on the left side * Look for "Get a copy of your data" and click on it * You have two options: download everything, or select specific data For this visualization project, I recommend focusing on these key files: * Connections * Messages * Profile * Skills * Endorsements * Education * Positions * Invitations LinkedIn usually takes up to 24 hours to prepare your data for download. You'll receive an email notification when it's ready. Once downloaded, you'll get a zip file containing various CSV files with your data. Important preparation step: Claude can only work with up to 20 files at once through the web interface. If you downloaded the complete archive, you'll need to clean it up first. Keep only the most relevant files listed above. (Or adjust based on your specific needs.) Setting up Claude 3.7 for visualization With your data ready, it's time to create your visualizations: * Start a new chat in Claude * Select "Claude 3.7 Sonnet" as your model * Enable "Extended thinking" mode (this makes a significant difference in the quality of visualizations) * Upload your LinkedIn data files (up to 20) * Use the prompt template I've provided below The extended thinking mode is crucial here. It gives Claude time to properly analyze your data and create meaningful visualizations rather than rushing through the process. Prompt for LinkedIn data visualization (Claude 3.7 Sonnet)

10. 

    24 Feb 2025

    How to get started with Claude 3.7 Sonnet

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Just a few minutes ago, Anthropic released their new Claude 3.7 Sonnet model, and I couldn't wait to share it with you. This release has been highly anticipated, especially with all the recent AI developments from companies like OpenAI, Google, and Perplexity that have left some wondering if Anthropic was falling behind. Let's dive into what makes this new model special, how to access it, and what you can actually do with it. What's new in Claude 3.7 Sonnet Anthropic is calling Claude 3.7 Sonnet their "most intelligent model to date" and the "first hybrid reasoning model on the market." What makes it stand out is its dual capability: * Normal mode: Produces near-instant responses (best for everyday tasks) * Extended thinking mode: Shows step-by-step thinking that's visible to you (best for complex tasks) This is a significant shift from other AI companies that have been splitting these capabilities across different models. Anthropic's philosophy is that "reasoning should be an integrated capability of frontier models rather than a separate model entirely." Accessing the new model is straightforward: * Go to your Claude interface * Click on the model selection dropdown * Select "3.7 Sonnet" (labeled as "our most intelligent model yet") * Choose between "Normal thinking" or "Extended thinking" modes * Optionally select your personal style/tone if you've created one The basic model is available on all Claude plans (including free), but the extended thinking mode requires a paid subscription. Normal mode vs. extended thinking mode From my initial testing, the difference between the two modes is substantial: Normal mode: * Incredibly fast responses (noticeably quicker than previous Claude versions) * Great for straightforward tasks like certain forms of content creation and writing * Provides concise, direct answers Extended thinking mode: * Shows its reasoning process in real-time (similar to what you've seen in ChatGPT) * Goes much deeper with analysis and ideation * Often provides more comprehensive outputs and additional formats A real-world test: content repurposing To demonstrate the practical difference, I tested both modes with the same task: analyzing a transcript and repurposing it into other content formats. With normal mode, Claude quickly processed thousands of words and immediately provided: * A main article structure * Several social media post ideas * Short-form content suggestions The output was good but relatively standard. (See below) When I switched to extended thinking mode with the exact same prompt, the results were dramatically more comprehensive: * Detailed content strategy * Multiple newsletter structures * Social media content for different platforms * A full content calendar framework * Additional format ideas including YouTube series, podcasts, and tutorials The extended thinking mode essentially provided what felt like a six-month content plan from a single transcript (See below)

11. 

    21 Feb 2025

    How to use Grok to write articles with real-time X data

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) I’ve been using Grok 3 for the past couple of days, testing it out, and I’m not gonna lie—I’m pretty impressed. At first, I didn’t think it’d be anything special, but after messing with it, it’s clear this thing’s got something. The big standout? Its real-time integration with X. It’s fast—like, really fast—pulling conversations straight from the platfo…

12. 

    20 Feb 2025

    How to get started with Grok 3

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Elon Musk’s AI company, xAI, just dropped Grok 3, its latest AI model aimed at taking on OpenAI, Anthropic, and Google. Unlike its competitors, Grok 3 is directly integrated with X (formerly Twitter) and positioned as a real-time, knowledge-driven chatbot that taps into live data. There’s plenty of noise surrounding its release—Musk has framed it as a major leap forward, and X is presenting it as an AI that competes at the highest level. But does it? And more importantly, is it worth your time and money? Let’s cut through the hype and look at what’s actually new, how you can get access, and whether Grok 3 is a serious contender in the AI arms race. What makes Grok 3 different? There’s no shortage of AI models right now. If you’re going to consider adding another to your tech stack (or even replacing an existing one), it needs to bring something new to the table. Here’s what Grok 3 offers: 1. Raw power upgrade Grok 3 is reportedly 10-15x more computationally powerful than Grok 2. In theory, this means better reasoning, faster responses, and more complex problem-solving. But AI companies love throwing around big numbers—whether this translates into a meaningful performance jump is something only real-world testing will prove. 2. Hybrid architecture The model is built to switch between quick responses and deep reasoning. This is xAI’s attempt to match what OpenAI and Anthropic are already doing—balancing speed with depth depending on the complexity of the task. 3. Deep search & think modes Grok 3 introduces two new modes: * Deep Search: Designed for pulling in live information from external sources, much like ChatGPT and Gemini’s deep research modes. * Think Mode: A breakdown-style reasoning process that aims to make the AI’s thought process more transparent. 4. Real-time Twitter/X integration This is the standout feature. Grok 3 is the only major AI model that directly pulls in data from X in real time. It can summarize recent posts, analyze sentiment, and surface trending discussions without relying on third-party search engines. This could be a huge advantage for anyone following breaking news, market trends, or real-time discourse. But it also introduces risks—X’s algorithm isn’t known for surfacing the most credible information, and relying on social media as a knowledge source has its own pitfalls. How to access Grok 3 Right now, Grok 3 is behind a paywall. Here’s what you need to know: * You need X Premium+ ($40/month in the U.S.) to access it. * It’s available inside X’s sidebar—if you’re a subscriber, you’ll see “Grok” as an option when using the platform. * The Grok website currently only has Grok 2, meaning the only way to access Grok 3 is inside X itself. * It’s still in beta, so expect ongoing updates. Musk has claimed improvements will roll out "almost daily." For now, the barrier to entry is high—most AI users aren’t paying for X Premium, and adding another $40/month subscription just to try a new model isn’t an easy sell. Here’s what I found when testing Grok 3 Here’s what stood out—both the good and the bad—in my real-time testing (see video). The interface is clean, fast and functional One thing xAI got right is the user experience. The interface inside X is smooth, responsive, and easy to navigate. Everything is neatly laid out, and interacting with the chatbot feels snappy. Compared to ChatGPT or Claude, it’s a clean, minimalist experience. If you’re already using X regularly, having Grok 3 directly in the platform makes it convenient. The features are useful but familiar Grok 3 introduces Deep Search and Think Mode, two features that are meant to boost its reasoning and research capabilities. * Deep Search pulls in live information from external sources, much like what ChatGPT, Gemini, and Perplexity offer (in different ways). It’s useful, but not revolutionary. * Think Mode displays its reasoning step-by-step, similar OpenAI’s reasoning model. Both features work as expected, but they don’t feel particularly new or groundbreaking. If you’ve used AI research tools before, you’ve seen this already.

13. 

    14 Feb 2025

    Perplexity's Deep Research isn't for serious work

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) The pace of AI releases is relentless – every minute brings something new to analyze. But this one needed immediate attention. Perplexity just launched their "Deep Research" feature, joining the trend of AI companies all seemingly adopting the same naming convention. Whether it's Perplexity, OpenAI, or Google Gemini, apparently we're all calling it "deep research" now. It’s starting to look like these companies are really rushing out unfinished products to compete. Perplexity has been disappointing me a bit. This new tool is not even on par with Google Gemini’s deep research. It has its place, but do not listen to the people saying it’s just as good as ChatGPT or even Gemini. The full Perplexity-generated report is attached further down as a PDF. Take a look for yourself. Let's cut through the noise and look at what Perplexity actually released. According to their announcement (https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research), Deep Research promises to "save you hours of time by conducting in-depth research and analysis." The pitch is familiar: it performs dozens of searches, reads hundreds of sources, and reasons through material to deliver comprehensive reports. The key differentiator? It's free. Pro subscribers get unlimited queries, while everyone else gets a limited number of daily searches. That's genuinely interesting when most advanced features come with a premium price tag. How deep research actually works The process starts simply enough. You access it through Perplexity's regular search engine – whether on desktop, web, or mobile. Select "Deep Research" from the models dropdown, and you're ready to go. When you submit a query, you get: * A real-time research plan * Visible search queries being used * Sources with hover-preview descriptions * Progress tracking with notification options I watched it work through our test query about AI copyright rules. The system shows you exactly what it's searching for, letting you see how it modifies and refines queries based on initial results. You can enable desktop notifications to keep working while it processes – a nice touch for longer research sessions. The copyright rules test

14. 

    4 Feb 2025

    How to use ChatGPT's Deep Research

    ChatGPT’s '“deep research” is a new AI agent from OpenAI that conducts multi-step research across the internet. But calling it just a "research tool" doesn't capture what makes it special. This is an AI system that: * Analyzes many online sources simultaneously * Creates comprehensive reports at research analyst level * Uses reasoning to interpret massive amounts of text, images, and PDFs * Shows you its thought process as it works It's powered by a specialized version of OpenAI's upcoming o3 model, specifically optimized for web browsing and data analysis. In this video edition, let me share what I discovered while testing it. (I finally dropped that $200 price tag to get access and test it so you don’t have to.) When you start a deep research query, you'll notice something different: instead of instant responses, you see the AI's actual research process unfolding. It shows you: * Which sources it's evaluating * How it's connecting information * Why it chooses certain references over others * Every step of its reasoning process But here's what really sets it apart I think: you can run multiple research queries simultaneously. In my testing, I had it analyzing medical robotics papers while simultaneously researching AI content creation trends. Each query maintained its own separate analysis thread, complete with independent source tracking and verification. The power of deep research comes from three key capabilities: * Multi-threaded research: Deep research can handle multiple complex queries at once. These aren't just parallel conversations - they're full research investigations running simultaneously. * Comprehensive synthesis: When deep research analyzes a topic, it identifies patterns, connects related concepts, and builds a coherent understanding across hundreds of sources. Each insight comes with clear citations and source verification. * Transparent reasoning: You can see how the AI thinks. Every research session includes a detailed log of its process - which sources it consulted, why it made certain connections, and how it arrived at its conclusions. What can you realistically use it for? Based on my testing and the official documentation, deep research excels at: * Academic literature review * Market and industry analysis * Technical documentation research * Comparative product research * Complex topic synthesis But there's a catch: this is currently a Pro-only feature ($200/month). OpenAI plans to bring it to Plus and Team plans eventually, but for now, it's limited to Pro users. Is it worth it? That depends entirely on your research needs. If you regularly spend hours diving deep into complex topics, synthesizing information from multiple sources, or need comprehensive analysis with verified citations - this could be really helpful for your workflow. Get full access to AI Disruptor at aidisruptor.ai/subscribe (https://aidisruptor.ai/subscribe?utm_medium=podcast&utm_campaign=CTA_4)

15. 

    14 Jan 2025

    How to setup and use ChatGPT's new "Tasks"

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) A few hours ago, OpenAI dropped something that's going to change how we all use ChatGPT. While everyone's been talking about building complex AI agents and automation systems, OpenAI is working towards an easier approach with their new Tasks feature. We're looking at the first step toward having a genuine ChatGPT/AI assistant that can handle ongoing tasks and future planning - something it has never been able to do before. Tasks lets you schedule future actions, set up recurring automations, and get notifications when things are done. Think of it like having a personal assistant who's always ready to handle your reminders, track information, and keep you updated on what matters. Getting started (watch the video!) Let's get you set up with Tasks in five minutes. First, you'll need to be a ChatGPT Plus, Team, or Pro subscriber. Once you're in, here's exactly what to do: * Select your model * Head to the model selector at the top of your chat * Look for "GPT-4 with scheduled tasks" * If you don't see it yet, don't worry - it's rolling out over the next few days * Enable notifications * This is crucial - you'll want to allow desktop notifications * This means you'll get updates even when you're not actively using ChatGPT * Create your first task, start with something simple: "Every Monday at 7am, give me the top five AI stories from the previous week" Pro tip: Start with tasks that are actually useful to you. The weather example that OpenAI shows? Sure, it works, but you probably have better weather apps. Think about what information you regularly need that ChatGPT is uniquely good at gathering or analyzing. All your tasks will show up in the new Tasks tab under your profile - this is where you can edit, pause, or delete them as needed. We're finally seeing AI that can work proactively for us instead of just responding to immediate queries. And this is just the beginning of what's coming. Why you shouldn't stress about AI agents right now

16. 

    9 Jan 2025

    How to digest big tech events with NotebookLM

    This is a free preview of a paid episode. To hear more, visit aidisruptor.ai (https://aidisruptor.ai?utm_medium=podcast&utm_campaign=CTA_7) Here's the thing about tech events like CES - they're overwhelming. While everyone else is struggling to piece together information from scattered sources, I've discovered a systematic way to extract exactly what matters using NotebookLM. You know how it goes. A major keynote drops, and suddenly your feeds are flooded. There's the two-hour presentation you don't have time to watch, dozens of summary articles that all say the same thing, and endless discussions trying to make sense of it all. Most people either miss important details or waste hours trying to stay informed. I've developed something better. Instead of relying on basic summaries or trying to consume everything, I use NotebookLM to combine three specific types of content that give you the complete picture: the raw keynote transcript, unique analyst perspectives, and community discussions. Each plays a crucial role in understanding what really matters. But here's what makes this approach different. NotebookLM acts as your personal research assistant, helping you extract insights that you'd miss with traditional approaches. The key is how these three sources work together to give you a deeper understanding than any single perspective could provide. In this guide, I show you exactly how this works. I've included a detailed video tutorial, but first, let's break down why this combination of sources is so powerful and how you can adapt it for any tech event you're trying to understand. The power of three perspectives Most people rely on a single source when trying to understand tech events - usually whatever summary article appears first in their feed. But here's what I've learned: the real insights come from combining three distinct perspectives that each serve a unique purpose. Think about NVIDIA's recent CES keynote (https://www.youtube.com/watch?v=k82RwXqZHY8). You could read a summary, sure. But you'd miss the raw context from the actual presentation, the critical analysis that spots what wasn't said, and the collective wisdom of developers and enthusiasts picking apart every detail. Here's why each piece matters: 1. The raw source: Your foundation When you feed a keynote transcript into NotebookLM, you're not just getting timestamps and quotes. You're creating a base layer of knowledge that lets you verify claims, spot patterns, and catch details that summaries miss. This becomes your ground truth - especially important when everyone's racing to interpret what was said. 2. The analyst perspective: Critical context I specifically look for articles with unique angles - not basic summaries. For example, while everyone was talking about NVIDIA's announcements, one analyst spotted something crucial: what wasn't announced. This second layer helps you understand market implications and read between the lines. 3. The community insight: Collective wisdom This is where things get interesting. By feeding in structured community discussions (I'll show you exactly how in the video), you tap into the collective expertise of developers, industry insiders, and power users who spot implications that journalists miss. During CES, this revealed concerns about performance claims that weren't obvious from official sources. Building your knowledge ecosystem This is where NotebookLM really shines - it's not just about collecting these three sources, but creating a dynamic system that lets you explore and extract exactly what matters to you. Here's what happens when you bring everything together:

Podcast Authority Score: 35 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

71

Social presence

0

YouTube

0

Engagement

0

Contact AI Disruptor

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

Questions about AI Disruptor