Would you let Meta's AI look at you?

Hi ,

Meta just launched Muse Spark, the first model from Meta Superintelligence Labs.

Built from scratch over nine months. Powers Meta AI across the app, website, and soon WhatsApp, Instagram, Facebook, Messenger, and AI glasses.

Handles complex reasoning in science, math, and health. Launches multiple subagents in parallel to tackle problems simultaneously.

See a photo of food and it estimates calories. Scan a product and it compares alternatives. Build a custom website or mini-game from a single prompt.

Private API preview available to select partners now.

Today's prompt turns your messy call transcripts into clean action items automatically. Plus how AI figures out what your market will actually pay. Then Meta's brand new AI model that sees the world through your camera.

🔥 Prompt of the Day 🔥

AI Meeting Notes Action Extractor: Use ChatGPT or Claude

Create one client call follow-up system.

"Act as a client success specialist. Create one meeting notes system for [CLIENT CALLS] that extracts actions and prevents forgotten commitments.

Essential Details:

• Meeting Type: [KICKOFF/REVIEW/STRATEGY]

• Recording Tool: [ZOOM/FATHOM/GRANOLA]

• Attendee Count: [TEAM SIZE]

• Follow-Up Speed: [HOURS POST-CALL]

• CRM Integration: [WHERE NOTES GO]

• Deliverable Format: [EMAIL/DOC/SLACK]

Create one extraction system including:

• Transcript processing workflow

• AI action item detection prompts

• Owner assignment logic

• Deadline extraction rules

• Follow-up email generator

• Task management integration

Never drop the ball on commitments."

Variables:

CLIENT CALLS: What type of calls you run

MEETING TYPE: Kickoff, review, or strategy

RECORDING TOOL: Zoom, Fathom, Granola, or other

FOLLOW-UP SPEED: How fast you send follow-ups after the call

CRM INTEGRATION: Where your notes and tasks live

Why This Works:

Every dropped commitment costs a client relationship. AI processes your transcript automatically. Pulls every action item. Assigns owners. Extracts deadlines. Drafts the follow-up email. Pushes tasks to your CRM. Nothing falls through the cracks.

✅ Tips and Tricks Thursday ✅

AI Pricing Psychology Analyzer

Setting prices based on costs alone leaves money on the table.

Most businesses guess at what the market will bear.

AI reveals what people actually pay.

The Problem

You pick a price. Hope it converts. Drop it when it doesn't.

No data. No strategy. Just guessing.

Meanwhile competitors are running pricing experiments you can't see. And customers are paying more elsewhere for less.

Why Cost-Based Pricing Fails

Customers don't buy based on your costs. They buy based on perceived value.

A $97 product feels cheap to one customer and expensive to another. The difference is positioning, not price.

Cost-based pricing ignores that entirely.

The Solution

Feed competitor pricing pages into AI. Add your own conversion data.

Ask AI to identify pricing gaps in your market.

Where are customers underserved? Where is pricing too high for the value delivered? Where is there room to charge more?

What AI Analyzes

Competitor price points and packaging structures.

Your conversion rates at different price points.

Value perception signals from customer reviews and feedback.

Pricing language that converts vs. language that kills deals.

How To Build Tiered Pricing

Ask AI to suggest tiered structures based on value perception — not just feature lists.

Good — Better — Best isn't just a packaging strategy. It's a psychology play.

Middle tier anchors the decision. Top tier makes middle feel reasonable. Bottom tier filters bad-fit customers.

AI maps which features belong at which tier based on what customers actually value.

Test Price Point Messaging

Same price. Different framing. Completely different conversion rates.

"$297/month" vs. "Less than $10 a day" vs. "One client pays for this."

Test variations for different customer segments. AI identifies which framing resonates with which audience.

What To Do

Export competitor pricing pages. Paste into Claude or ChatGPT.

Add your conversion data by price point.

Ask AI to identify gaps, suggest tiers, and recommend messaging variations.

Test one pricing change this month. Track conversion rate before and after.

Refine based on data. Not guessing.

Strategic pricing multiplies revenue without more customers.

Did You Know?

The most popular uses of consumer AI shifted dramatically in just one year. Therapy and emotional support, life organisation, and finding purpose now top the list — displacing idea generation and search, which had dominated just twelve months earlier.

🗞️ Breaking AI News 🗞️

Meta Launches Muse Spark — First Model From Meta Superintelligence Labs

Meta just introduced Muse Spark.

First model in the new Muse series. Built by Meta Superintelligence Labs from the ground up over nine months.

Powers Meta AI app and website now. Rolling out to WhatsApp, Instagram, Facebook, Messenger, and AI glasses in coming weeks.

Private API preview available to select partners.

What's New

Two modes depending on the task. Instant for quick answers. Thinking for complex problems requiring deep reasoning.

Launches multiple subagents in parallel. Planning a trip — one agent drafts the itinerary, another compares destinations, a third finds activities. All simultaneously.

What It Can See

Strong multimodal perception built in. Meta AI can see and understand what you're looking at.

Snap a photo of food — estimates calories and ranks nutritional value.

Scan a product — compares it to alternatives automatically.

Coming to AI glasses — assistant sees and understands the world around you in real time.

Health Capabilities

Health is one of the top reasons people turn to AI.

Muse Spark developed with a team of physicians to handle health questions including those involving images and charts.

More detailed responses on common health questions and concerns.

Shopping Mode

Draws from styling inspiration and brand storytelling already happening across Meta's apps.

Surfaces ideas from creators and communities people already follow.

Discover what to wear, how to style a room, what to buy for someone — powered by content from your network.

Why This Matters

Meta rebuilt their entire AI stack in nine months. That's an aggressive timeline.

Muse Spark is described as small and fast by design — a foundation model with larger versions already in development.

For users: Meta AI just got significantly more capable across every surface they already use daily.

For businesses: Shopping mode and creator integration means AI-powered product discovery inside the world's largest social platforms.

For the AI market: Meta is positioning personal superintelligence as the end goal. Not just a smarter chatbot.

What This Means

If you use Meta products: Muse Spark is rolling out now. Instant and Thinking modes available in the US first.

If you sell products online: Shopping mode surfacing creator content means AI-driven discovery is coming to your customers' feeds.

If you build on Meta's platform: Private API preview is open to select partners. Apply now.

Meta just raised the stakes on what a personal AI assistant should look like.

Over to You...

Meta AI can now see what you see. Does that excite you or concern you?

Hit reply with your take.

To AI that understands your world,

Jeff J. Hunter 
Founder, AI Persona Method | TheTip.ai

P.S. Want to turn AI Agents into a consulting offer? Book your AI Certified Consultant strategy 👉 here.

NEW: “AI Money Group” to Learn to Make Money with AI