In an effort to ensure the entire email reaches your inbox and doesn’t get clipped, I have shortened segements and reduced the size of every image on the page.

TL;DR 🌟:

1. Bard's Balancing Act: Google's Bard AI shakes up Android Messages, sparking a heated debate over privacy and innovation. A critical look at AI's role in our personal conversations.

2. Anthropic's Data Drama: Amid FTC scrutiny, a data leak at AI firm Anthropic puts a spotlight on the industry's challenges in handling sensitive information and maintaining strategic partnerships.

3. Arc Search's Web Wonder: Experience the future of internet browsing with Arc Search's unique AI-powered platform, merging search, browsing, and AI into a seamless digital journey.

4. Flippy's Fast Food Future: Pasadena welcomes the world's first fully autonomous AI-powered restaurant, leading a culinary revolution with its efficient, robotic kitchen staff.

5. Sales, Search, and AI's Impact: Explore how AI is reshaping sales strategies, altering search engine effectiveness, and leading Google's strategic shift towards AI-focused solutions.

6. Innovative AI Tools and Tutorials: From Traq AI's deal analysis to JellyPod's email-to-podcast converter, plus a masterclass on Perplexity AI's search transformation – we've got the tools to keep you ahead.

7. Virtual Try-All with AI: "Diffuse to Choose" technology promises to revolutionize online shopping, offering a personalized and immersive virtual try-on experience.

Stay ahead of the curve with these AI insights, and gear up for a future where technology and daily life intertwine more closely than ever. 🌐🤖

Perplexity AI Masterclass: Search Will Never Be the Same

Title: 

Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All

Authors: 

Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar, Amir Tavanaei, Ismail B. Tutar

Executive Summary: 

"Diffuse to Choose" (DTC) is a novel diffusion-based image-conditioned inpainting model, aimed at revolutionizing the online shopping experience through a concept named "Virtual Try-All." This technology enables consumers to visually integrate any e-commerce product into their personal environments, such as inserting a piece of furniture into an image of their living room. DTC outperforms traditional diffusion models by efficiently incorporating fine-grained features from a reference image directly into the latent feature maps of the diffusion model. This is achieved using a secondary U-Net encoder and perceptual loss, enhancing the preservation of product details while ensuring seamless blending with the scene. The model has been rigorously tested on various datasets, showing superior performance compared to existing methods.

Pros: 

• Efficient Detail Preservation: DTC excels at retaining high-fidelity details of the reference product, outperforming traditional image-conditioned diffusion models.

• Versatile Application: The model is capable of handling a wide range of e-commerce products and scenes, making it broadly applicable.

• Real-time Functionality: Designed for rapid inference, DTC is suitable for real-time applications, facilitating a dynamic virtual try-all experience.

• Improved Semantic Manipulation: The model ensures accurate and coherent integration of products into various scenes, maintaining semantic consistency.

Limitations: 

• Detail Loss in Text Engravings: The model occasionally struggles with fine details, such as text engravings on products.

• Pose Alteration Issues: DTC might alter human poses in full-body coverage scenarios due to its pose-agnostic design.

• Limited by Underlying Diffusion Model: The quality of outputs is inherently bound to the capabilities of the latent diffusion model used.

Use Cases: 

• E-commerce Virtual Try-On: Allows customers to visualize products in their own space before purchase.

• Interior Design Planning: Users can virtually place furniture items in their home setting to assess fit and aesthetics.

• Fashion and Apparel: Offers a virtual fitting room experience, allowing customers to see how clothes would look on them or in various styles.

Why You Should Care: 

DTC presents a significant advancement in virtual reality and e-commerce technology, offering a highly interactive and personalized shopping experience. Its ability to maintain product integrity while seamlessly integrating items into various settings is a game-changer for online shopping, interior design, and fashion industries. By enabling a more immersive and realistic preview of products, DTC has the potential to enhance customer satisfaction, reduce return rates, and revolutionize the way we shop online.

Traq AI - Discover why you’re losing winnable deals. Record every call, analyze every conversation, understand your buyers, and coach your team to success.

JellyPod - Transform your inbox into a personalized daily podcast. Overwhelmed by your email subscriptions? Convert your favorite newsletters into a personal podcast & never say “I'll read this later” again.

Chipp - Build a custom ChatGPT without code. Share it with your audience or sell it to build your business.

Letterly - Turn your speech into well-written text

AtLabs - The end-to-end video marketing suite. No sophisticated systems or technical skills required.

Artflow - Consistent Characters with Controllable Shot Types, from Close-Up to Full-Shot.

Creating An Offer Stack:

Prompt by The AI Solopreneur

I want you to act as Offer Stack Otto, an AI specialized in creating offer stacks.

Your job is to design a 3 tier offer stack for my business.

For context, I offer to [INSERT DESCRIPTION OF  CORE OFFER]. 

My target customer is [INSERT DESCRIPTION OF TARGET CUSTOMER].

For the lowest tier, provide an entry-level offer. For the middle tier, design a premium version. For the highest tier, create a high-touch service offer.

Make sure each tier clearly builds on the previous one and is priced accordingly higher. Please provide a 1 sentence explanation for each tier on the added value it provides over lower tiers.

Your output should be formatted like this:

[OFFER STACK THESIS]

[OFFER 1]: [DESCRIPTION OF OFFER]

Price: [INSERT PRICING]

[OFFER 2]: [DESCRIPTION OF OFFER]

Price: [INSERT PRICING]

[OFFER 3]: [DESCRIPTION OF OFFER]

Price: [INSERT PRICING]

[GUIDE ON HOW TO BUILD OUT THE OFFER STACK]


In the [OFFER STACK THESIS] placeholder, explain why you think this offer stack will work for me.

In the [GUIDE ON HOW TO BUILD OUT THE OFFER STACK] placeholder, provide me with a full guide on how to build out the offer stack you described following the PEEK framework:

P - Predict Problems

Identify the next logical problem your solution will create.

Look ahead after your initial solution is implemented. 

What new problems will arise that you can solve?

What’s the next thing they will need?

Then…

E - Exclusive Solution

Design a premium solution to that next problem.

Create a high-end offer to address the next problem.

Often, the new solution falls into one of these three buckets:

1) Optimization: Most one-time offers benefit from continous optimization

2) Ascension: Can you offer a more advanced form of your current service?

3) Expansion: Does it make sense to build out a new service? 

(use expansion at caution)

Once you got your new offer…

E - Envision Tomorrow 

Provide a glimpse of the future to spark desire among your clients.

Your “old” offer is still the first thing you sell. 

But you should always “future-pace” your clients on what will happen after that’s built out.

The key lies in making this sound EXCITING, not like a new problem.

You can use this template:

“Once we’ve built out [INSERT YOUR FIRST SERVICE], you’ll face a very convenient problem; [NEW PROBLEM] will happen, and then the next thing you should do is [INSERT YOUR NEXT OFFER] to [GET BENEFIT].”

K - Key Step Framing 

Frame your solution as the only logical next step.

Make choosing your offer feel inevitable by referencing your existing relationship and showing how seamlessly it addresses your customers’ upcoming needs.

Again, make the “next step” obvious to your customer.

The clearer the path, the more clients will dare to travel it.

To clarify what I mean by offer stacking:

Most solopreneurs rely on a linear growth model, where revenue is directly tied to hours worked. 

But, as you know, there are only so many hours you can sell NFT art of pizza on the internet.

You can’t rely on working more to make more.

What you need instead is an exponential growth model, where each customer pays you more money over time.

The key to increasing your revenue per customer is understanding the Problem-Solution Cycle.

“Every problem has a solution. Every solution reveals more problems.”

Alex Hormozi in his book $100M Leads

The Problem-Solution Cycle means that every time you solve a problem for a customer, you create a new, more valuable one. 

The Problem-Solution Cycle

If you’re only solving 1 problem for your customer, you’re leaving money off the table.

Rather than providing a one-time fix, you can create an offer stack that solves the next problem, and the one after that.

To be clear - I’m NOT talking about broadening your offer. 

I’m talking about providing more solutions in the niche you’re already in.

For example:

Say I'm ghostwriting sales funnels. The offer I feed you may be: 

1) I ghostwrite your 7-step sales funnel for a one-time $5,000

But a sales funnel needs recurring optimization to become effective.

So I could easily 2X or even 3X your earnings per customer by building out an offer stack.

For example, your offer stack for this ghostwriter could add:

1) Monthly optimization retainer: I analyse your sales funnel and find bottlenecks, then create solutions to increase the conversion ($1000 per month)

2) Quarterly major overhaul: I re-write the 3 worst-performing steps of the funnel to increase your revenue from the funnel ($2,500 every quarter)

If the funnel is live for 6 months, you’d earn $16,000 with this offer.

That’s $11,000 more than with the old one (that was just $5000)