Welcome — and thank you for being part of the very first Neural Gains Weekly. I’m building this alongside you, and every issue is designed to make AI a little more useful and accessible.

Follow us on X and TikTok as I dive into the world of social media and attempt to build viral content with AI tools. If you’ve got suggestions or requests, email me at admin@mindovermoney.ai — your input will help shape future content.

Let’s dive right in with Signals over Noise, where we highlight what matters from the last week in AI news.

1) OpenAI introduces ChatGPT Pulse

Summary: OpenAI launched ChatGPT Pulse, a personalized daily feed that proactively summarizes what you need to know and suggests next actions based on your activity.

Why it matters: It’s a shift from reactive chat to assistant that starts the conversation—useful for investors and operators who want auto-generated morning briefs, research nudges, and workflow follow-ups without prompting.

2) Microsoft adds Anthropic models to 365 Copilot

Summary: Microsoft integrated Anthropic’s Claude models into Microsoft 365 Copilot and Copilot Studio, giving enterprises a choice alongside OpenAI’s models.

Why it matters: Model choice = pricing and performance leverage. Teams can compare outputs, reduce single-vendor risk, and pick the best model for research, reporting, or compliance workflows. 

3) Meta launches “Vibes,” an AI-video feed in the Meta AI app

Summary: Meta debuted Vibes, a short-form feed dedicated to AI-generated videos with remix tools and cross-posting to Instagram/Facebook.

Why it matters: AI-native media is getting its own distribution rails; expect new reach mechanics (and potential ad formats) that marketers and creators can tap—plus a faster feedback loop for AI video experimentation.

4) DeepMind unveils Gemini Robotics 1.5 with multi-step reasoning for robots

Summary: DeepMind’s latest robotics models plan several steps ahead and can even consult the web to complete complex real-world tasks.

Why it matters: Agentic + embodied AI keeps moving from demos toward deployment—implications for logistics, home assistance, and industrial automation that could reshape labor and productivity over the next cycle. 

5) Judge approves $1.5B settlement between Anthropic and authors

Summary: A federal judge preliminarily approved a $1.5B settlement resolving claims that Anthropic used pirated books to train Claude; payouts cover past works and set a notable precedent.

Why it matters: Copyright liability is getting priced in. Clearer rules of the road reduce legal overhang for model training—and push vendors toward licensed data, which matters for risk-aware enterprises.

Let's kick this off with the basics and start to build a foundational understanding of this technology. We're going to start with highlighting relationship between three main concepts within the field of artificial intelligence. By understanding the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), every headline, product pitch, and “AI feature” suddenly makes sense—so you can spot what’s real, what’s hype, and what’s useful for your life, productivity and finances.

The family tree: AI is the umbrella. ML is a subset of AI (learning from data). DL is a subset of ML (neural networks with many layers). 

Artificial Intelligence (AI):

• What it is: Software that performs tasks we associate with human traits—perception, reasoning, decision-making, language.
• Why it matters: It frames everything else you’ll learn; ML and DL live inside AI.
• Everyday examples: Voice assistants answering questions; customer-service chatbots; maps suggesting faster routes.
• Personal finance tie-ins: Bank apps flag unusual spending; bill reminders that reduce late fees.

Machine Learning (ML):

• What it is: Techniques that learn patterns from data to make predictions/decisions without hard-coded rules.
• Why it matters: ML powers most real AI you feel—recommendations, ranking, fraud detection.
• How it differs from rules: Rules say “IF X THEN Y.” ML says “Given many past examples, learn what usually signals Y.”
• Everyday examples: Email spam filters that improve as you mark messages; product/movie recommendations; auto-photo grouping.
• Personal finance tie-ins: Fraud models catching out-of-pattern charges; automatic budgeting that learns new merchants.

Deep Learning (DL):

• What it is: ML using multi-layer neural networks to learn very complex patterns in images, audio, and language.
• Why it matters: DL enables modern breakthroughs—FaceID, speech-to-text, and today’s large language models (LLMs).
• Everyday examples: Phone identifies people in photos; accurate voice dictation; apps that read receipts or IDs.
• Personal finance tie-ins: Mobile check deposit reading amounts; LLMs turning long earnings calls into quick bullet summaries.

Common misconceptions to head off:

• “AI = robots that think like humans.”
  • Reality: It’s a toolbox; most systems are narrow and task-specific.
• “ML just finds spurious correlations.”
  • Reality: It can—if you evaluate poorly. Good practice uses clean splits, sensible metrics, and real-world tests.
• “DL is only for big tech.”
  • Reality: Cloud tools and modern laptops make DL-powered features accessible to small teams and solo builders.

Quick recap (one-liners):

• AI: The goal—make software act smart at tasks.
• ML: The approach—learn from data instead of writing rules.
• DL: The powerhouse—many-layer neural nets that excel at vision, speech, and language.

🛠️ AI Budget Analyzer with ChatGPT + Google Sheets🛠️

Why this matters: Most people know they should budget, but few have time to categorize every transaction. That’s where AI shines — in just 10 minutes, you can feed your bank data into Google Sheets, let AI categorize your spending, and instantly see where your money is really going.

Step 1: Export Your Transactions (2 minutes)

• Log in to your bank or credit card portal.
• Download your most recent transactions (CSV or Excel).
• Open the file in Google Sheets.
  • File -> Open -> Upload

Step 2: Set Up the First AI Helper (3 minutes)

• Install the GPT for Sheets add-on (free tier available).
  • From the Google Sheet: Extensions -> Add-ons -> Get add-ons -> Search “GPT for sheets” -> Install Talarian version
• Once installed, you’ll have a new formula in Sheets: =GPT()
• This lets you ask AI to analyze any cell’s text.

Step 3: Auto-Categorize Spending (3 minutes)

In a new column, type: =GPT("Categorize this expense into Food, Housing, Transportation, Shopping, or Other:", A2)

NOTE: A2 is only an example and you’ll want to update the above formula with the cell related to transaction description that corresponds with the first charge.

• Drag the formula down → AI will label every transaction.
• Label the column ‘AI Label’
• You can refine categories (e.g., “Groceries vs. Dining Out”) as needed in the formula.

Step 4: Spot Overspending - the Second AI Helper (2 minutes)

• Gemini is also available directly in Google Sheets
• Click the ‘Ask Gemini’ button at the top right to start a prompt
• Ask Gemini to analyze how much total spend is associated with each ‘AI label’ category
  • Follow up with additional questions and ask for specific visuals to be created
• In seconds, you’ll see:
  • Where your money goes.
  • Which category is creeping up (subscriptions? eating out?).

The Payoff: In under 10 minutes, you’ve built an AI-powered budget analyzer.

• No manual categorizing.
• Instant insights into spending habits.
• A live system you can refresh monthly.

💡 Pro tip: Add this to your calendar as a 10-minute “money check-in” at the start of each month.

👉 Your turn: Try this today and reply back with your biggest surprise from your spending — you’ll be shocked how fast patterns emerge once AI does the heavy lifting.

I’m not an AI expert. I don’t write code. My degree is unrelated to computer science. My career path hasn’t been intertwined with emerging technologies. If all of this is true (which it is) and part of my life experience, then why am I starting an AI-powered newsletter?

The answer: education and empowerment. A guiding principle in my personal and professional life is a thirst for knowledge and understanding. I tend to dive in headfirst when a topic appeals to me and find ways to consume information to develop a better understanding. This comes from various outlets such as podcasts, articles, newsletters, and conversations with friends. There is nothing better than a fall night in Florida spent hanging by the fire pit and deep-diving on random topics with those close to you. I started paying more attention to AI developments at the end of 2024 and realized something big was brewing. I quickly prioritized and committed to building a learning plan for myself to catch up with experts and to understand how this technology works. I’ve consumed many hours of podcasts, read articles & research papers, followed social media ‘experts’, and have experimented with AI tools. Throughout this journey, my frustration has been building with narratives that seem to dominate space in the public domain:

• AI is going to take all of our jobs soon and there is nothing you can do about it
• AI is dangerous and will lead to a collapse of the economy and potentially the end of the world
• AI makes me $10,000/week and here is a playbook for you to replicate my success
• AI can automate anything, all you need to do is watch this YouTube video

I could list more, but you get the idea, and I’m sure you’ve come across headlines that fall into one of these buckets. Most content aims for engagement over substance, reflecting the extreme ends of the AI spectrum. My frustration was boiling over. I was growing tired of filtering through content to find meaningful information to aid my learning journey. This is how MindOverMoney.ai was born and why I set out to build content to help people like me - curious and motivated individuals who want to learn and understand the power of AI and how it can be implemented in their everyday life. 

I’m dedicated to building in public - offering an inside look into the process of developing the website and launching Neural Gains Weekly. This transparent approach will hopefully help you get a better understanding of what is possible and join me in building with AI. Every week, you can expect insights into my interactions with various AI tools that helped me deliver content and explain the good and bad parts of the process. Let’s dive into the first lesson and likely the most crucial concept as we begin our journey together.

Experiment, experiment, experiment! If you learn nothing else from my ramblings, understand that the best way to learn AI is by doing. And no, I don’t mean simply using ChatGPT, Gemini, or Claude as search engines or to create funny pictures of your pets (we’ve all done it). I mean really explore and test the features available to everyone on the free tiers. My biggest mistake since ChatGPT launched on November 30, 2022, was ignoring updates and new features being released. To compound this, over the last year, AI features and capabilities seem to be moving at warp speed, a phenomenon that seems impossible to keep up with. I only began experiencing ‘aha moments’ once I committed to using, learning, and building with AI. It was a process to get to this first newsletter release, a process that started in July 2025. My journey was not a straight line, and multiple plans were changed as I forced myself to become more efficient with AI. I finally found the right process and direction to bring this vision to life. The biggest and earliest hurdle was choosing the right tools to build with. I finalized my current tech stack, which consists of:

• Ghost for website hosting
• Namecheap for domain register and setup
• ChatGPT Plus - 5o Thinking for:
  • Prompt support
  • Content roadmap
  • Content execution
• Gemini 2.5 Pro for:
  • Veo video creation
  • Nano Banana image tool
  • Content editor
• Github for editing my custom code for the Ghost formatting
• Stripe for payment processing
• HeyGen for custom AI avatars 

At first, it was overwhelming trying to navigate these tools to build out my vision. But you quickly learn that experience and experimentation are the best ways to accelerate understanding. It took me multiple prompts and hours of brainstorming with ChatGPT before I finally found clarity and direction to get moving on the right path (check out the Prompt Library —spoiler: prompt engineering will be a constant focus in Founder’s Corner). It made me more efficient because I started to understand how ChatGPT worked and adapted my interactions to drive results. I’m still learning and refining my skills to ensure the output is valuable and accurate, but I wouldn’t be where I am today without forcing myself to evolve the way I use AI and not be afraid to fail.

Sorry for the long format this week, but I felt it was important to dive deeper into my motivation and intent behind this publication. I’m thankful that you’ve subscribed and taken the time to read the first edition and the origin of my journey. My goal is to deliver valuable content and build a community of like-minded ‘doers’ that see AI as an opportunity. Feel free to share Neural Gains Weekly with friends, colleagues, family and anyone else you feel would benefit. You can also contact me directly at admin@mindovermoney.ai or connect with me on LinkedIn. I’m open to suggestions and ideas to inject into the content roadmap. Enjoy and see you next week!

Bonus content - Enjoy a video overview of the first Neural Gains Weekly and share with your network!