4 Trending Products/Services You Can Tap Into This Week (Prompts Included)

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Every week we see new product and service trends pop up, but the real question is: how do we, as builders and agency owners, actually capitalize on them?

I've broken down 4 massive trends that are blowing up right now on Google. But we're not just talking about them. I'm giving you the exact business ideas and the LLM prompts to start building an MVP this week using n8n and AI coding agents.

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TREND #1: THE AI-POWERED STUDY ASSISTANT

The Rundown: Tools like Mindgrasp are exploding in the college market. Students upload any study material (lecture notes, PDFs, YouTube videos) and the AI spits out quizzes, flashcards, and summaries. With over 85% of students using AI, this market is a blue ocean of opportunity.

The Opportunity: Niche Down for Massive Value

Instead of a general study tool, build an "AI MCAT/LSAT/CFA Tutor." Target high-stakes exams where students are already spending thousands on prep. You'll provide a hyper-focused tool that turns dense prep books and lectures into interactive study materials.

Your Action Plan:

1. Build a simple web app where a user can upload a PDF or paste text from their study materials.

2. Fire off a webhook to an n8n workflow that processes the text.

3. The workflow uses an LLM to identify key concepts and generate quiz questions and flashcards.

4. The results are stored in a database (like Supabase) and displayed back to the user on the web app.

Your Prompts:

--- PROMPT FOR N8N WORKFLOW DESIGN: START ---

Act as an expert n8n automation architect. I need to design a workflow for my AI Study Assistant app.

The trigger is a Webhook that receives a JSON object containing 'userId' and 'studyText'.

Here is the step-by-step logic I need you to outline in a clear, actionable format:

1. Parse the input: Extract the 'studyText'.

2. Generate Key Concepts: Send the 'studyText' to a Claude 3.5 Sonnet node. The prompt should ask it to extract the top 10-15 most important concepts, terms, and definitions from the text. The output should be a clean list.

3. Create Quiz Questions: Take the original 'studyText' and the 'Key Concepts' and send them to a second LLM node. The prompt should be: "Based on the following text and key concepts, generate 10 multiple-choice quiz questions with 4 options each. Mark the correct answer with an asterisk (*). Format the output as a JSON array of objects."

4. Store in Database: Use the Supabase node to insert the generated quiz questions into a 'quizzes' table, linked to the 'userId'.

5. Confirmation: Send a success response back to the webhook.

Please outline the configuration for each node, including the specific prompts for the LLM nodes.

--- PROMPT END ---

--- PROMPT FOR AI CODING AGENT: START ---

Build me a simple web application using Next.js and Tailwind CSS.

The app should have one page with the following components:

1. A heading that says "AI Exam Prep Assistant".

2. A large, styled textarea input for users to paste their study notes.

3. A button below the textarea that says "Generate My Quiz".

4. When the button is clicked, it should take the text from the textarea and send it via a POST request to a placeholder n8n webhook URL: `https://your-n8n-instance.com/webhook/quiz-generator`.

5. Below the button, there should be a "Results" section that is initially empty. After the API call is successful, this section should display the quiz questions returned from the API.

6. Style the page to be clean, modern, and focused, suitable for studying.

--- PROMPT END ---

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TREND #2: GREEN AMMONIA & CLEAN ENERGY TECH

The Rundown: This isn't just about science; it's about money. Companies like Amogy are cracking ammonia for clean power, attracting hundreds of millions in funding (including from Amazon). The market is predicted to see $2.3 TRILLION in infrastructure investment.

The Opportunity: Build an AI Intelligence Hub

You're not building a chemical plant. You're building the shovel in the gold rush. Create an "AI Green Energy Investment Dashboard." This tool scrapes news, research papers, and SEC filings, using AI to summarize and categorize opportunities in the green ammonia space for investors and analysts who need to stay ahead.

Your Action Plan:

1. Set up a scheduled n8n workflow that runs daily.

2. The workflow pulls data from RSS feeds of energy news sites, Google News alerts for keywords like "green ammonia," and academic search engines.

3. An LLM node summarizes each article/paper, extracts key entities (companies, investment amounts, locations), and performs sentiment analysis.

4. All this structured data is pushed into a database to power a dashboard.

Your Prompts:

--- PROMPT FOR N8N WORKFLOW DESIGN: START ---

Act as an expert n8n automation architect. I need to build a daily market intelligence workflow for the green ammonia industry.

The trigger is a Schedule Node set to run every day at 8 AM.

Here's the logic:

1. Fetch News: Use the RSS Feed Reader node to pull from 5-10 renewable energy news feeds.

2. Fetch Research: Use an HTTP Request node to query an academic search API like Consensus AI with terms like "green ammonia production," "hydrogen fuel cell," etc.

3. Process Each Item (Loop): For each article and research paper, send the content to an LLM Node (Claude 3.5 Sonnet) with the following prompt: "You are a financial analyst. Analyze the following text and return a JSON object with these keys: 'summary' (a 3-sentence summary), 'companies_mentioned' (an array of company names), 'investment_amount' (any dollar amount mentioned), 'sentiment' ('Positive', 'Neutral', or 'Negative'), and 'key_takeaway' (the single most important point)."

4. Store Data: Use the Airtable Node to add the structured JSON output as a new record in a base called "Green Energy Intel."

Please detail the setup for each step.

--- PROMPT END ---

--- PROMPT FOR AI CODING AGENT: START ---

Build a data dashboard front-end using React and Tailwind CSS.

The app will display data fetched from a backend API.

1. The main page should feature a title: "Green Ammonia Market Intelligence."

2. Create a `Card` component that accepts props for `summary`, `companies_mentioned`, `sentiment`, and `key_takeaway`. The sentiment prop should conditionally change the card's border color (green for Positive, gray for Neutral, red for Negative).

3. The main page should make a `fetch` call to a placeholder API endpoint (`/api/intel`) and display the returned data as a grid of these `Card` components.

4. Include filter buttons at the top for "News," "Research," and "Investments."

5. Use mock data in a JSON file to develop the UI before connecting to a real API.

--- PROMPT END ---

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TREND #3: SUSTAINABLE PET FOOD (WITH A TWIST)

The Rundown: People are obsessed with their pets and increasingly obsessed with sustainability. Insect Dog Food is a booming niche because it hits both notes. It has a tiny carbon footprint compared to traditional protein, and the market is growing like crazy.

The Opportunity: AI-Personalized Pet Nutrition

Don't just sell bug-based food; sell a solution. Create an "AI-Powered Custom Meal Plan for Dogs." Build a DTC brand where customers take a quiz about their dog (breed, age, activity level, allergies), and your AI generates a personalized blend of insect-based and other sustainable ingredients. This is a subscription-model machine.

Your Action Plan:

1. Build a beautiful landing page with a quiz. Use a tool like Typeform or build a custom one.

2. When the quiz is submitted, it triggers an n8n workflow.

3. The workflow sends the quiz answers to an LLM pre-loaded with canine nutrition info to create the perfect meal plan.

4. The results are used to create a custom product in your e-commerce store (e.g., Shopify) and add the customer to a personalized email flow.

Your Prompts:

--- PROMPT FOR N8N WORKFLOW DESIGN: START ---

Act as an n8n expert. I'm building a personalized dog food subscription service using Shopify.

The trigger is a Webhook from a quiz on my website. The webhook will contain data like `dogName`, `breed`, `age`, `weight`, `activityLevel`, and `allergies`.

Here is the workflow:

1. AI Meal Plan Generation: Take the webhook data and send it to an LLM node (Claude 3.5 Sonnet). The prompt needs to be a persona-based system prompt: "You are a world-class canine nutritionist. Given the following dog profile, create a personalized weekly meal plan. Our food is based on a mix of black fly larvae protein, sweet potatoes, and carrots. Adjust the portion sizes and ingredient ratios for optimal health. Output the plan in a clear, simple JSON format with keys: 'daily_calories', 'morning_meal_grams', 'evening_meal_grams', and 'nutrition_notes'."

2. Create Shopify Customer: Use the Shopify Node to create a new customer record with the user's details.

3. Personalized Email: Use the Mailchimp Node to add the customer to a specific audience and send them a "Welcome" email that includes the 'nutrition_notes' generated by the LLM for a personal touch.

Please describe the prompt and data mapping needed for each step.

--- PROMPT END ---

--- PROMPT FOR AI CODING AGENT: START ---

Create a multi-step quiz component using React and Tailwind CSS.

The quiz should be visually appealing and guide the user through a series of questions about their dog.

- Step 1: "What's your dog's name and breed?" (Two text inputs)

- Step 2: "How old are they and what's their current weight?" (Number inputs)

- Step 3: "Describe their daily activity level." (Buttons: 'Couch Potato', 'Active Walker', 'Super Athlete')

- Step 4: "Any known allergies?" (Textarea)

- Final Step: A summary of their answers and a "Get My Custom Plan" button.

Use `useState` to manage the state of the quiz answers. When the final button is clicked, package the state into a JSON object and POST it to a placeholder n8n webhook URL.

--- PROMPT END ---

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TREND #4: THE RISE OF THE FACELESS AI CHANNEL

The Rundown: Creators are making bank on YouTube without ever showing their faces. Channels like Bright Side get millions of views with stock footage and AI voiceovers. Now, with AI video generation tools like Invideo and Lumen5, anyone can become a content factory.

The Opportunity: Hyper-Niche, Data-Driven Content Automation

Forget generic trivia. Build a "Hyper-Local AI Real Estate News Channel." Create a system that automatically generates weekly video market updates for specific cities or even neighborhoods ("What's Happening in Denver Real Estate This Week?"). Real estate agents and investors will pay for this content as a service or subscribe for the alpha.

Your Action Plan:

1. Set up an n8n workflow that triggers weekly.

2. It pulls the latest housing market data for a target city from a real estate API (like Zillow's or a public data source).

3. An LLM node writes a short, engaging script based on the data trends.

4. The script is sent to an AI voice API (like ElevenLabs) to generate a voiceover.

5. The script and audio are then sent to an AI video generation API, which mashes it up with relevant stock footage of that city.

6. The final video is automatically uploaded to a YouTube channel via the YouTube API.

Your Prompts:

--- PROMPT FOR N8N WORKFLOW DESIGN: START ---

Act as a master n8n automation engineer. I want to fully automate a faceless YouTube channel for real estate market updates.

The trigger is a Schedule Node, set to run every Monday.

Here is the complex, multi-API workflow:

1. Get Market Data: Use an HTTP Request Node to call a real estate API for 'Denver, CO' to get data like 'median_sale_price', 'days_on_market', 'inventory_count' and their week-over-week change.

2. Write Video Script: Send the data to an LLM Node (Claude 3.5 Sonnet) with the prompt: "You are a real estate analyst creating a YouTube script. The tone is informative but engaging. Write a 150-word script based on this data. Start with a hook. End with a call to action to subscribe."

3. Generate Voiceover: Take the script text and send it to the ElevenLabs Node to generate an MP3 audio file.

4. Create Video: Use an HTTP Request Node to call an AI video generation platform's API (like Invideo's). The API call should include the script, the audio file URL, and keywords for stock footage like 'Denver skyline', 'happy family', 'real estate sign'.

5. Upload to YouTube: Once the video is rendered (this may require a 'Wait' node and a second workflow), use the YouTube Node to upload the final video file with a title like "Denver Real Estate Market Update | [Current Date]" and a description generated by another LLM call.

Outline this entire pipeline.

--- PROMPT END ---

--- PROMPT FOR AI CODING AGENT: START ---

Build a simple internal control panel using SvelteKit and Skeleton UI (a Tailwind component library).

The purpose of this tool is to manually trigger the YouTube video generation workflow.

1. The UI needs one text input field labeled "Enter City Name".

2. A button next to it that says "Generate Weekly Video".

3. When the button is clicked, it should make a POST request to our n8n workflow's trigger webhook, sending the city name in the JSON body.

4. Below the button, add a status log area. It should display messages like "Workflow triggered for Denver...", "API call successful.", "Error: ...". This will be used for monitoring.

5. Keep the design simple and functional.

--- PROMPT END ---

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Which one of these are you going to build? Let me know in the comments.

More helpful posts from Ai Titus --> https://www.skool.com/@aititus?g=ai-automation-society&fl=posts

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