Shushant Lakhyani

Freelance marketer building TechnoBizzVault, where I help professionals discover modern tools so they can be productive without burning out.

Highly skilled content creator with a strong track record in newsletter growth, social media growth, and social media content creation. 

Proven success in building an audience on LinkedIn and Twitter/X and creating engaging content for newsletters. 

Experienced in creating content for LinkedIn, Twitter/X, and newsletters. 

Proficient in content creation and AI, no-code, and automation tools. 

Achievements:
✅ 150,000+ followers on LinkedIn and Twitter/X combined. 
✅ Helped Horizon AI reach 46,000+ newsletter subscribers from 1,800 subscribers in 6 months 
✅ Sold information products to more than 20,000 customers on Gumroad. 
✅ Worked with 5+ clients for ghostwriting 
✅ Gained 100M+ impressions on LinkedIn, Twitter/X, and Medium combined. 

Passionate about technology, content creation, and online business. 

Currently, working on building and growing TechnoBizzVault, an email newsletter where you discover hidden digital gems for free. 

Also, working as a freelance social media growth manager for a leading media brand in AI.

ClickUp Micro-Product Builder

Stop wasting hours on manual data analysis. 

Here are 10 AI tools that will help you get more data analysis work done in less time. 

🔖 Save this post for later 

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1. Quadratic 

Chat with your data and get insights in seconds.

Link: https://lnkd.in/dKHMmQtW  

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2. Rows 

Access the power of AI to analyze, summarize, and transform data. Build better spreadsheets, faster.

Link: rows.com/ai 

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3. SheetAI

Describe what you want in plain English, and watch the magic happen in your spreadsheet with hours of analysis work get done in minutes.

Link: https://www.sheetai.app/ 

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4. ChatCSV

Your new personal data analyst. Upload a CSV and start asking questions. It is an Ask Me Anything for Spreadsheets.

Link: https://www.chatcsv.co/ 

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5. Genius Sheets

Use natural language to connect to internal data and run analysis never before.

Link: https://lnkd.in/dVtyX7xb  

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6. Equals

Go from blank sheet to insight by just prompting.

Link: equals.com/ai 

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7. Numerous AI

It provides the simplest, most powerful and cost-effective solution for using ChatGPT inside Google Sheets.

Link: https://numerous.ai/ 

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8. Gigasheet

Leverage the simplicity of spreadsheet functions to seamlessly manage and analyze vast datasets in their entirety.

Link: https://www.gigasheet.com/ 

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9. ChartPixel

Get AI-assisted charts, presentations slides and actionable insights.

Link: https://chartpixel.com/ 

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10. Arcwise

Use AI with your business context built in, and a model you own, all from your spreadsheet. 

Link: https://arcwise.app/ 

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What other AI analysis tools would you like to recommend?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use the best AI analysis tools 

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Stop wasting hours on manual data analysis. 

Here are 10 AI tools that will help you get more data analysis work done in less time. 

🔖 Save this post for later 

1.  Quadratic 

Chat with your data and get insights in seconds.

Link: https://www.quadratichq.com/   

1. Rows 

Access the power of AI to analyze, summarize, and transform data. Build better spreadsheets, faster.

Link: [rows.com/ai](http://rows.com/ai) 

1. SheetAI

Describe what you want in plain English, and watch the magic happen in your spreadsheet with hours of analysis work get done in minutes.

Link: https://www.sheetai.app/ 

1. ChatCSV

Your new personal data analyst. Upload a CSV and start asking questions. It is an Ask Me Anything for Spreadsheets.

Link: https://www.chatcsv.co/ 

1. Genius Sheets

Use natural language to connect to internal data and run analysis never before.

Link: https://www.geniussheets.us/  

1. Equals

Go from blank sheet to insight by just prompting.

Perplexity Deep Research is a game changer. 

You get a top tier business consultant in your pocket. 

Here are 10 Perplexity Deep Research prompts to turn you into a superhuman.

🔖 Save this post for later 

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10 types of prompts you’ll get in the carousel: 

1. Market Analysis for a Specific Industry 
2. Competitor Benchmarking 
3. Investment Opportunity Evaluation 
4. Technology Trends Report 
5. Marketing Strategy Development 
6. Health Research Summary 
7. Sustainability Practices in Business 
8. Travel Planning Guide 
9. Policy Impact Analysis 
10. Startup Feasibility Study 

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Additional Tip-1: Click on the nexus symbol to activate Deep Research before submitting your prompt.

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Additional Tip-2: I’ve not added roles to these prompts, because role prompting doesn’t matter a lot in 2025. But if you think they help you, feel free to add roles to these prompts.

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Additional Tip-3: These are prompt templates, you need to fill in the blanks to make these prompts personalized to your use case. 

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Additional Tip-4: You can also try these prompts on ChatGPT Deep Research.

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Additional Tip-5: Download the PDF and get it transcribed by AI to use these prompts. 

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Why I like Perplexity Deep Research?
✅ Saves hours by automating in-depth research and analysis.
✅ Delivers expert-level, comprehensive reports in just 2–4 minutes.
✅ Synthesizes information from hundreds of credible sources for accuracy.
✅ Enables easy export and sharing of research as PDFs or Perplexity Pages.
✅ Ideal for complex tasks in fields like finance, marketing, and technology.

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Do’s of using Perplexity Deep Research:
✅ Define your research goal.
✅ Use clear, detailed queries.
✅ Check all citations.
✅ Export and share results.
✅ Refine with follow-up questions. 

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Don’ts of using Perplexity Deep Research:
❎ Don’t skip fact-checking results.
❎ Don’t use vague prompts.
❎ Don’t ignore source quality.
❎ Don’t exceed prompting limits.
❎ Don’t assume full coverage.

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Have you tried to use Perplexity Deep Research?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use Perplexity Deep Research 

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li_at

JSESSIONID

Talks about #ai, #nocode, #onlinebusiness, #contentcreation, and #contentcuration

Perplexity Deep Research Prompts

Manas Ranjan Jena

Here’s a quick short summary of everything that happened in AI last week.

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What do you think will happen in AI this week?

Let me know in the comments 👇  

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost this post to help your network stay updated 

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AI Updates: Oct 27 - Nov 2, 2025

AI skills aren’t built overnight.

They’re built project by project.

Here are 15 AI projects that will help you level up your AI tool skills.

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Have you started to work with AI?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network master working with AI 

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AI Projects

You’re not lazy. 

You’re just not using AI enough.

Here are 12 new AI hacks you can’t miss. 

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What other new AI hacks would you recommend adding here?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use AI better 

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12 New AI Hacks

You’ve used ChatGPT. 

But I'm sad to see you don’t really know how it works.

What happens behind the scenes will blow your mind.

Here’s how ChatGPT works.

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What more do you want to know about how ChatGPT works?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network learn how ChatGPT works 

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How ChatGPT Works Made Easy

You think ChatGPT is just ok for resume creation. You’re wrong. 

ChatGPT is much better than you think.

Here are 10 ChatGPT prompts that help you with resume creation.

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---

10 types of ChatGPT prompts covered here:

1. Crafting a Professional Summary 
2. Listing Key Skills 
3. Detailing Work Experience 
4. Education Section 
5. Creating a Technical Skills Section 
6. Highlighting Certifications and Training 
7. Showcasing Achievements 
8. Formatting for Different Industries 
9. Crafting a Cover Letter 
10. Proofreading and Polishing

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Note: 

You should use ChatGPT to create a better resume, 
But always add your human touch to your resume. 
That still matters a lot.

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What do you think of using ChatGPT for resume creation?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network create better resume with AI 

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ChatGPT Prompts For Resume Creation

You don’t need a PhD to master AI.

I’m sad to see you still think it’s too technical for you.

Here’s the zero to hero AI generalist roadmap. 

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I’m an AI generalist IRL.

I try prompts for different use cases and use them for freelance work, my content brainstorming, and also share them on LinkedIn. 

--- 

I try different AI tools and stick to the ones that help me in the long term and I also keep switching to new and more powerful tools. 

I have to build and use GPTs and prompts for various use cases of my freelance work.

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I use various AI tools to help me not only at work, but in daily life too. 

Like getting help with fitness, relationship management, life design, and personal growth.

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What more would you like to add to this AI generalist roadmap?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network be an AI generalist 

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Complete AI Generalist Roadmap

Everyone wants a personal coach. 

You already have one.

Here are 10 ChatGPT prompts that will help you use ChatGPT as your personal coach.

🔖 Save this post for later 

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Here are the 10 use cases covered in the carousel:

1. Goal-Setting & Strategy Coach
2. Accountability & Habit Coach 
3. Productivity & Time-Blocking Coach
4. Career Strategy & Job Search Coach 
5. Public Speaking & Communication Coach 
6. Writing & Content Coach
7. Learning & Skill Mastery Coach
8. Fitness & Nutrition Coach
9. Negotiation & Difficult Conversations Coach
10. Interview & Mock Practice Coach 

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For what other coaching use case do you use ChatGPT?

Let me know in the comments 👇

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost this to help your network use AI 

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ChatGPT Prompts To Use ChatGPT As Your Coach

AI isn't taking your job.

But someone with these skills might.

These are the 10 AI skills you need in 2025 to stay ahead.

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What other AI skill would you like to add here?

Comment below 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network work on their AI skills 

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AI Skills To Master

You’re not slow your system is.

But AI systems help you fix that real quick. 

Here are 10 new AI hacks you can’t miss👇 

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What other new AI hacks would you recommend adding here? 

Let me know in the comments  👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use AI 

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New AI Hacks

Here are 10 new AI hacks that will help you get more work done in less time. 

🔖 Save this post for later 

---

What other new AI hacks would you recommend adding here?

Let me know in the comments 👇 

---

Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use AI 

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Your brain isn’t slow.

You’re just using outdated learning hacks.

Here are 10 techniques to supercharge your learning.

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What other new learning techniques would you recommend adding here?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network learn efficiently 

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Best Learning Techniques

Every failed launch has the same problem. 

It’s not the product; it’s the strategy. 

Here are 10 ChatGPT prompts that will help you fix your go-to-market strategy instantly.

🔖 Save this post for later 

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Here are 10 different prompts you’ll get in the carousel:

1. GTM Strategy Blueprint 
2. Customer Segmentation & Persona Creation 
3. Positioning & Messaging Matrix 
4. Competitive Intelligence Deep Dive 
5. Pricing & Packaging Strategy 
6. Channel & Distribution Strategy 
7. Demand Generation & Marketing Playbook 
8. Sales Strategy & Playbook 
9. Launch Strategy & Timeline 
10. Metrics, KPIs & Reporting Dashboard 

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What do you think of using AI for go-to-market strategy?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost this to help your network use AI for go-to-market strategy 

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ChatGPT Prompts For Go-To-Market Strategy

Perplexity isn’t another regular AI tool. 

It’s your 24/7 working brainstorming and productivity assistant. 

Here’s the ultimate Perplexity guide you can’t miss. 

🔖 Save this post for later 

---

Do you use Perplexity regularly?

Let me know in the comments 👇 

---

Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost this post to help your network master using Perplexity AI 

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Ultimate Perplexity Guide

ChatGPT is insanely helpful for marketing.

Sadly most people use the worst prompts.

10 ChatGPT prompts that can save you hours of manual marketing work.

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10 types of prompts you get in this carousel: 

1. Email marketing campaign 
2. Social Media ad copy 
3. Content calendar planning 
4. Sales funnel script 
5. Influencer collaboration pitch 
6. Product launch strategy 
7. Market research survey 
8. Blog content brief 
9. Brand storytelling script 
10. SEO meta descriptions for products  

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use AI for marketing 

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ChatGPT Prompts For Marketing

Lisa B.

More than 150,000 custom GPTs are available in ChatGPT. 

Sadly, most people waste their time on the bad ones.  

Here are 10 GPTs you must use to save hours of work.

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1. Data Analyst 

Drop in any files and it can help analyze and visualize your data. 

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2. VideoGPT by VEED 

Type a description, set your style, and create full videos with subtitles and voiceovers. 

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3. Presentation and Slides GPT: PowerPoints, PDFs 

Make PowerPoints with a Slides AI PowerPoint Generator. 

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4. AI PDF Drive 

Upload and chat with all your files, create polished PDFs (presentations, reports, resumes, letters) right from the GPT. 

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5. Consensus 

Search references, get simple explanations, write articles backed by academic papers. 

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6. TutorMe 

Your personal AI tutor by Khan Academy. 

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7. Code Copilot 

It helps you code smarter and build faster. 

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8. Form and Survey Maker By Outgrow 

Build and share live surveys. 

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9. Whimsical Diagrams 

Explains and visualizes concepts with flowcharts, mindmaps and sequence diagrams. 

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10. Write for Me 

Write tailored, engaging content with a focus on quality, relevance and precise word count. 

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You can find all these GPTs in ChatGPT. 
But don’t limit yourself to it.
You can build your own GPTs too.

All you need to do is share your knowledge.

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Test and work on making the outputs of GPTs only limited to your knowledge.
And use it in the long term.

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And yes, you can build GPTs to use at work too.
You can share it with your team and let them use it.

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This is not it.
If you want to share your GPT with the world,
You can share it for free.

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And if you want to monetize your GPT, 
You can use a platform like Chipp or CalStudio.

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And don’t worry, you don’t need to learn coding to build GPTs.
You can build them from just prompts.

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Apart from that,
If you need to search a new GPT,
Use the search bar in the GPT store.
Write the keyword.
You’ll get the GPTs for the problem you’re facing.
It’s that easy.

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What other GPTs would you like to recommend?

Let me know in the comments 👇 

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Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use the best GPTs 

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Your competition has a secret assistant.

It never sleeps and never complains.

Steal these 10 ChatGPT prompts and beat them at their game.

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I use ChatGPT every day for personal assistance.

And I save my personal assistance chats in projects.

This helps me keep a track too.

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Tip-1: These are prompt templates, so you’ll have to fill in the blanks to create prompts for your situation.

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Tip-2: I’ve not included roles in these prompts, because they don’t matter much in 2025. But you can feel free to add roles to these prompts if role prompting works well for you. 

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Tip-3: To get these prompts in text format, upload this PDF document on Mistral and ask it to transcribe the PDF. I chose Mistral because I think it is the best for PDF transcription. 

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Do you use ChatGPT as your personal assistant?

Let me know in the comments 👇 

--- 

Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

♻️ Repost to help your network use ChatGPT as their personal assistant 

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ChatGPT Prompts To Use ChatGPT As Your Personal Assistant

AI looks magical.
But it’s not magic.
It’s maths with a disguise.

Not rocket science.
Not just coding.
Not out of reach.

Here’s how generative AI models really work, broken down so simply you’ll never see it the same again.

🔖 Save this post for later 

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Please chill.

You don’t need to memorize this.

But it always helps to know and understand how do AI models really work. 

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Do you use AI models regularly?

Let me know in the comments 👇 

--- 

Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network learn AI 

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How Generative AI Models Work

Stop wasting hours on design.

Canva’s AI now helps you complete it in minutes.

Here are 18 powerful Canva AI features you can’t miss.

🔖 Save this post for later 

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What Is Canva?

Canva is a powerful graphic design tool to help you create designs easily in minutes.

Use it here: canva.com 

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1. Magic Media

Text to Image:
Generate unique images from text descriptions using advanced AI models like Stable Diffusion. You simply write what you want, and Canva creates an image to match your prompt. 

Text to Video:
Create short video clips from text prompts, making it easy to visualize ideas or concepts without filming or editing manually.

Text to Graphics:
Instantly generate custom graphics and illustrations from written prompts, perfect for infographics or branded visuals. 

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2. Magic Design

Instantly produces design templates based on your input (such as a photo or text description).

The AI suggests layouts, color schemes, and styles, which you can further customize for your needs. 

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3. Magic Write

An AI-powered writing assistant that helps you draft, rewrite, or improve content.

It’s useful for social media posts, blog articles, presentations, and more. 

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4. Magic Edit

Allows you to alter images with text prompts, such as changing colors, swapping backgrounds, or adding new elements, no manual editing required. 

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5. Magic Eraser

Instantly removes unwanted objects or distractions from images, cleaning up visuals with a single click. 

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6. Magic Grab

Turns static images into editable ones by isolating and moving subjects or objects within a photo. Also includes "Grab Text" to make static text editable. 

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7. Magic Expand

Expands the borders of any image using AI, useful for extending backgrounds or making images fit new dimensions. 

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8. Magic Morph

Transforms text, elements, or shapes within your design using AI-generated effects and transitions, enabling creative visual changes. 

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9. Magic Animate

Automatically animates your designs, applying smooth and coordinated animation styles to text and objects for engaging presentations and videos. 

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10. Magic Switch

Converts designs into different formats (like turning a presentation into a social media post) with a single click, adapting layouts and content automatically. 

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And more covered in the carousel below 

I use Canva every day for design related use cases.
And I’m not a pro designer. 

But I can still create designs easily in minutes with Canva.
And Canva’s AI features have made design work easier than ever. 

--- 

Do you use Canva regularly?

Let me know in the comments 👇 

--- 

Discover hidden digital gems for free: https://lnkd.in/daKFh4Gr 

🔁 Repost to help your network use Canva’s AI features 

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18 Powerful Canva AI Features

Helping Buyers Acquire & Scale Small Businesses with AI | AI-Powered Deal Flow, Valuations, & Scalability

I help organizations design and implement AI that works like an operator thinks.
Not another tool. A system you can measure, trust, and scale.

Every project starts the same way: map how the work actually moves inputs, rules, outputs.
Then, build one workflow end-to-end, add guardrails, and prove the lift before scaling it.
It’s how AI moves from pilot to production with confidence.

My work spans solo operators to enterprise teams, helping them turn AI from a buzzword into a repeatable advantage that's measured in hours saved, errors reduced, and results delivered faster and more consistently.

I believe AI succeeds when it earns trust:
Clear steps, auditable logs, a human in the loop, and metrics that matter.
That’s how adoption sticks and performance compounds.

Let's connect if you care about building AI that actually works, clarity over hype, & proof over promises.

Follow for practical frameworks, shipped results, and insights from real implementations.

Brianna Bentler

AI crossed a line this year.

It’s no longer experimental. It’s operational.

Stanford’s new AI Index 2025 shows what that really means:

78% of businesses now use AI.
Inference costs dropped 280× since 2022.
And 9 out of 10 top models now come from industry, not academia.

AI isn’t a “future” problem. It’s an everyday system to manage with real costs, risks, and returns.

The winners aren’t chasing hype. They’re measuring hours saved, errors cut, and handoffs that finally make sense.

Useful over shiny. One process at a time. 

Follow Brianna Bentler for practical AI workflows and measurable business results.

I help owners and coaches start with AI | AI news you can use | Women in AI

Artificial Intelligence Index Report 2025

Stop debating if your AI is “good.” Measure it.

LLM-as-a-Judge is how operators do it at scale.

It turns fuzzy reviews into consistent scores.
So you can ship, improve, and prove ROI.

What it is: an LLM that grades other outputs.
When to use: subjective, multi-criteria, high volume.
When not to: clear ground truth or legal high-stakes.

Three ways to score, pick one:
✅Single-output with a reference for grounding.
✅Single-output without a reference for style fit.
✅Pairwise comparisons for ranking variants.

Bias is real. Plan for it.

Length, order, authority, and self-favor creep in.

Mitigate with controls, not vibes.
✅ Randomize candidate order.
✅ Cap and normalize length.
✅ Hide sources and identities.
✅ Use 3 judges and average.

Make the judge predictable.
Write criteria in plain language.
Force a strict JSON schema for scores.

Reject outputs that break the schema.
Require a brief rationale with evidence.

Then validate the validator.
Test on easy, tricky, and adversarial cases.
Track precision, recall, AUROC, and agreement.

Run it next to humans and compare.
Scale without breaking the bank.
Use a small evaluation model for real-time checks.

Spot-audit with a larger model weekly.

Operators: start with one workflow this week.

Ship the judge, log every decision, improve weekly.

Save this and share with one teammate who owns QA.

Mastering LLM-as-a-Judge

I get asked every day how I stay on top of AI reports, agent updates, and the latest news.

Short answer: I built my own agents to feed me.

Long answer: I wanted a single dashboard tying it all together.

I tried rebuilding it from my database.

Fought with layouts.

Tweaked themes. Still looked wrong.

Then I stumbled on Reflex.

I rebuilt the whole thing with one prompt in under 5 minutes.

Same data.

New skin.

Cleaner flow.

No app-hopping.

No brittle config.

Just one Python stack with live reload doing the work.

The unlock wasn’t a tool.

It was a system:

capture → refine → ship, all in one track.

If you want to try it yourself, here is the link.

https://lnkd.in/ejiUHb7x

Follow Brianna Bentler for “useful over flashy” AI that ships.

Most marketers still measure work in hours.

The winners in 2025 ship in minutes.

Charlie Hills and I set out to prove it.

We mapped 9 workflows every team runs, plus the AI stacks that compress half-day projects into coffee-break tasks.

Here is the playbook:
✅Slide decks in minutes: Manus AI, Gamma, ChatGPT, Kimi
✅Carousels in minutes: Gamma, aiCarousels, Taplio, Supergrow
✅Voice intake in minutes: Relevance AI, Vapi, Lindy, Bland
✅CRM hygiene in seconds: ChatGPT connectors, Claude connectors
✅Video clips in seconds: OpusClip, VEED, Submagic
✅Automations in seconds: n8n, Zapier, Relevance, Make
✅Web apps in seconds: Lovable, Blink.new, Replit, Base44
✅Research in seconds: Manus, ChatGPT, Perplexity, Gemini, Claude
✅Images in seconds: Ideogram, Midjourney, Gemini, ChatGPT

The outcome is simple.
❌Traditional marketing = hours of execution.
✅Vibe marketing = minutes to market.

This is not about cutting corners.
It is about trading 3-hour slogs for 3-minute sprints and letting consistency compound.

Charlie deserves huge credit for turning this into a visual you can scroll, learn, and apply today. 

I am grateful to be part of it.

If this reframed how you work, share it forward.

Teams that learn speed first will win.

Vibe Marketing 2025

Agentic AI isn’t a tool. It’s your next operating model.

IBM’s new study makes it clear.
Most leaders still optimize.
Winners redesign.

They let agents decide the repeatable work.
Humans decide what matters.
Result: net-new capabilities—not tweaks.

By 2027, autonomy inside workflows will double.
Transformation cohorts perform 32× better.
That’s not hype. It’s operating math.

Where to start this quarter:
 ✅ Finance: rolling forecasts + variance agents
 ✅ Supply chain: dynamic pricing + predictive reroutes
 ✅ HR: skills mapping + adaptive learning loops
 ✅ Customer experience: touchless support with audit trails

Design for trust.
Log every decision, handoff, and confidence score.
Measure new KPIs, not old busywork.

Agent-to-human handoff rate.
Decision accuracy.
Reasoning coherence.

Cybersecurity and ethics are baked in from day one.
Small, workflow-specific models outperform generic fluff.

One process at a time.
Ship. Measure. Iterate.

This is how operators from solopreneurs to global enterprises win the next decade.

Follow Brianna Bentler for operator-grade AI that scales.
Share with a teammate who’s designing their next system.

Agentic AI's Strategic Ascent 

AI at work is no longer experimental. The leaders are scaling workflows into operating leverage.

According to the World Economic Forum, 65% of companies now use generative AI in at least one function. 

Adoption is up. Budgets are rising. 

But scaling? That’s where most stall.

Why? Because scale isn’t about tools, it’s about systems.

The top performers do three things well:

✅Start with one high-value workflow
✅Build tight loops: clean inputs, clear finish lines
✅Track outcomes: hours saved, retries, $ per run

They don’t boil the ocean. 

They ship, measure, and expand.

If you’re a small firm wondering where to begin, here’s the play:

✅Choose one use case
✅Add relevance checks and human review
✅Measure ruthlessly
✅If it works, scale it

Context + capability = compounding ROI.

Start small. Ship it. Then scale.

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AI in Action: Beyond Experimentation to Transform Industry

AI leadership isn’t about bigger models. It’s about better systems, built around people.

❌Every year, the noise around AI gets louder.
❌Faster chips. Smarter models. Higher benchmarks.

But the University of Utah’s new AI Leadership Report cuts through it with one clear signal:

✅The real winners won’t be the ones with the best tech.
✅They’ll be the ones who build trust, talent, and governance around it.

Five insights that stood out:
1️⃣Agentic AI is already here
These aren’t sci-fi pilots. AI agents are designing trial protocols, monitoring safety, and optimizing manufacturing today.
2️⃣Skills are shifting fast
This isn’t about hiring more PhDs.
It’s about hiring people who can design guardrails, interpret outcomes, and orchestrate humans + machines. Think: clinicians who validate AI. Lawyers who translate code into policy. PMs who understand compliance.
3️⃣Trust is everything
If your AI can’t be explained, it won’t be adopted.
Transparency isn’t a buzzword, it’s a business requirement.
4️⃣Small bets beat moonshots
Start with narrow, high-friction workflows.
A 10% gain on a repetitive task > a failed pipeline overhaul.
5️⃣Hybrid teams will win
The future team isn’t all engineers. It’s “bench scientist + AI conductor.”
Cross-disciplinary beats siloed.

Why this matters beyond life sciences:

✅This is the playbook for law firms.
✅For accountants.
✅For local governments.
✅For any service org with data-heavy, compliance-driven workflows.

So don’t chase the shiniest model.
Build the most human-centered system.

Start small.
Measure trust.
Earn adoption, one process at a time.

And if you’re leading AI at your org, ask this:

What’s one high-friction process you could automate tomorrow, without risking trust?

AI Trust Stack

🚨 Gartner warns: 40% of enterprise AI agent projects will fail by 2027.

Here’s the part nobody is talking about:

AI agents aren’t just chatbots. They make decisions, call tools, and act on their own.

That’s why:
 → 77% of enterprise executives say agents will reinvent systems
 → By 2030, half of enterprise software will run on them
 → Yet 40% of projects will be canceled without proper risk controls

The missing piece? Explainability.
If you can’t answer why an AI acted, then you’re not in control; it is.

AI should extend your team, not replace your governance.

Question for you:
Would you trust an AI in your business if you couldn’t explain why it acted?

The Ultimate Guide for Agentic AI Security

Just found the best free AI agents course on the internet.

10 parts. 12 short lectures. From “what are agents?” to multi-agent orchestration.

I’ve taken 47 AI courses. This is the one of the only one's I’d recommend to my team.

Why it stands out:
Most courses teach you to build expensive chatbots. This one teaches you to implement systems that actually work. Built by practitioners who ship in production, not tutorial watchers.

Start at Part 1: What Are AI Agents. By Part 10 you’ll know why most agents fail and how to avoid it.

Follow for practical AI automation. 

Re-share to help someone skip a $2K “certification.”

P.S. Which part are you diving into first?

Thank you Aishwarya Naresh Reganti for making this and Paolo Perrone for bringing this to my attention last week. 

Sharing this to spread the word.

Most teams think “AI is AI.” The differences are bigger than they realize.

Here’s the pattern I keep seeing in real workflows:

GenAI helps you create.
AI Agents help you automate.
Agentic AI helps you think, plan, and act across multiple steps.

Three tiers. Three different outcomes.

1️⃣ GenAI
Instant content or code.
Prompt in. Output out.
Great for single tasks, not for running a process end-to-end.

2️⃣ AI Agents
This is where operational leverage starts.
Agents connect to your tools, move data, trigger events, and monitor results.
They take structured jobs off your plate and make the workflow consistent.

3️⃣ Agentic AI
The emerging layer.
Systems that reason across steps, remember context, call the right tools, and execute without being micromanaged.
This is the closest thing we have to software that operates like a team member.

For operators, here’s the bottom line:

GenAI saves minutes.
AI Agents save hours.
Agentic AI changes how the work gets done.

The real unlock isn’t choosing one. It’s knowing which layer you need for the outcome you want this month.

Credit to Greg Coquillo for the visual that inspired this breakdown.

Follow Brianna Bentler for operator-grade AI and agent workflows built for clarity, scale, and measurable results.

P.S. Which layer are you relying on today, and where does it break?

Most “agentic AI” talk still sounds like a PhD lecture.
This roadmap cuts through that.

🟩 Green = the skills that matter
🟨 Yellow = the optional layers
🟪 Purple = the tools that actually ship work

Here’s how I use frameworks like this when teams are serious about implementation:

We pick one workflow, trace it down the map, and design the smallest agent that produces reliable, measurable output. Not a demo. A system that reduces time, errors, or handoffs.

This pattern holds across all kinds of environments—anywhere people need clearer decisions, cleaner handoffs, or repeatable outcomes, this roadmap becomes the backbone.

If you want this turned into a simple 60-day plan, one accountable owner, real guardrails, and metrics that prove it works, DM me and I’ll walk you through it.

Credit to Brij kishore Pandey for the Agentic AI Roadmap 2025.
Follow Brianna Bentler for operator-grade AI roadmaps that make systems clearer, not more complicated.

Last week a Fortune 500 CEO called me.
"We spent $47M on AI. Nothing's different."

McKinsey's new report explains why.
88% of companies use AI.
Only 6% see real value.

I audited their AI stack.
73 pilots. 12 vendors. Zero integration.
They'd automated tasks, not transformed operations.

The brutal truth McKinsey & Company found after surveying 1,993 leaders…

📊 The $2 Trillion Pilot Graveyard
65% stuck testing forever.
Running experiments like science projects.
Measuring adoption instead of impact.

My client had 47 customer service bots.
Each handled one type of ticket.
When customers had complex issues, they got bounced between 5 different bots.

Response time actually got worse.

⚡ What the 6% Do Differently
They don't add AI to workflows.
They rebuild workflows for AI.

Regular companies: 47 specialized bots
The 6%: One orchestrated system

Regular companies: Track bot usage
The 6%: Track problem resolution speed

Regular companies: Protect existing processes
The 6%: Kill what AI makes obsolete

🎯 The Agentic Architecture Gap
62% are "experimenting" with AI agents.
Less than 10% have them actually working.

Because they're building agents like apps.
Isolated tools doing isolated tasks.

The winners build orchestration.
Agents managing agents.
Systems that learn from outcomes.
Architecture that scales.

We rebuilt my client's entire support system.
From 47 bots to 7 Vertical AI agents working as a team.

Results in 90 days:
- Response time: 4 hours → 7 minutes
- Resolution rate: 67% → 91%
- Cost per ticket: $47 → $3.20

McKinsey's data shows companies 3.6X more likely to succeed and focus on transformation, not tasks.

Stop adding AI to broken systems.
Start breaking systems to rebuild with AI.

The gap between 88% adoption and 6% value isn't about the technology.
It's about architecture.

Most companies used AI like email in 1995.
The 6% are building the operating system for 2030.

Which one are you?

Follow Brianna Bentler for AI strategies that actually ship value.
Save if you're tired of pilots that go nowhere.

The State of AI in 2025

We’re entering a phase of AI no one is structurally prepared for.

Agentic AI isn’t just “the next wave of automation.”
It’s the first system that forces leaders to manage technology the same way they manage people.

Massachusetts Institute of Technology and Boston Consulting Group (BCG)’s new global study makes it clear:
Adoption is exploding faster than strategy.

– Agentic AI reached 35% deployment in just two years
– Another 44% of companies are rolling it out next

But here’s what most teams aren’t ready for:

Agentic AI is both a tool and a coworker.

It scales like software.
Adapts like a person.
Depreciates like an asset.
Learns like a team member.

That duality creates four core tensions every operator must solve:

1️⃣ Scalability vs adaptability
Optimizing for pure efficiency kills the adaptive behavior that makes agentic systems valuable.

2️⃣ Experience vs expediency
These systems depreciate (model drift) and appreciate (learning) at the same time. Traditional ROI frameworks fail.

3️⃣ Supervision vs autonomy
AI works independently but must be supervised like a direct report. Most governance models can’t handle that.

4️⃣ Retrofit vs reengineer
Most AI wins start small, then expose the need to redesign the entire workflow.

The companies leading this shift do one thing differently:
They stop asking “Where should we add AI?”

They start asking “How do we reorganize around a teammate that learns, acts, and decides?”

♻️ Repost to help more leaders close the gap between adoption and strategy.
Follow Brianna Bentler for operator-grade AI built on clarity and control.

P.S. Which tension is holding your workflow back today?

The Emerging Agentic Enterprise

This is the real reason AI works in pilots but fails in production.

The new AI Standards for Global Impact report makes it clear:

AI isn’t failing because it’s moving too fast.
It’s failing because organizations are trying to scale it without the standards that make it safe, measurable, and predictable.

Three gaps stood out:

1️⃣ No shared language
Testing, reliability, and risk mean different things depending on the team. You can’t govern what you can’t define.

2️⃣ Lab tests don’t match reality
Models pass neat benchmarks, then fall apart in complex workflows with real pressure, edge cases, and human handoffs.

3️⃣ Principles aren’t the problem. Implementation is.
Ethics frameworks are everywhere. Operational guidance is not.

Meanwhile:

Governance and standards evolve separately.
 Policy says “be responsible.”
 Standards say “be measurable.”
 Workflows say “be reliable.”
 They rarely meet in the same system.

If you’re an operator, this explains the friction you feel.
The problem isn’t adoption.
The problem is the missing infrastructure underneath adoption.

AI doesn’t scale by adding more AI.
It scales by building the testing, governance, and clarity that sit below it.

♻️ Repost to help more teams understand what’s actually breaking their AI.

Follow Brianna Bentler for operator-grade AI built on clarity and control.

P.S. Where does your team still rely on intuition instead of a standard?

The Annual AI Governance Report 2025

Operators don’t win with “smarter chat.” 

They win with accountable agents that ship outcomes.

This new HubSpot  × Mindstream  report made it plain...

AI that answers is table stakes.
AI that owns a workflow is ROI.

What’s actually working (and why it sticks):
✅ Treat agents like reps: clear job, SLAs, pass bar, dashboard.
✅ Multi-agent orchestration > one clever bot. Sub-tasks, handoffs, guardrails.
✅ Memory matters. Agents that learn between sessions stop repeating mistakes.
✅ Your moat is data. Clean emails, calls, and docs beat more compute.
✅ Speed compounds. The edge is shipping a year ahead, every year.

Proof beats hype:
✅ Customer agents clearing >50% of tickets and cutting handle time ~40%.
✅ Prospecting agents booking at crazy scale (think thousands per quarter).
✅ Teams measuring agent KPIs right next to human KPIs, and keeping what wins.

How to roll this out like an operator (one process at a time):
✅ Pick a low-blast-radius workflow (missed-call follow-up, intake routing, weekly ops recap).
✅ Wire the toolbelt it can use (search, CRM write, email/SMS send, files).
✅ Run the loop: plan → act → check → retry with guardrails (rate limits, timeouts).
✅ Put it on a scoreboard next to humans (resolution %, time, quality).
✅ Review weekly. Promote what beats baseline. Retire what doesn’t.

No fluff. 
Just ROI.

Follow Brianna Bentler  for operator grade AI and,
reshare with the teammate who owns your ops dashboard.

30+ Brutally Honest AI Predictions From Industry Leaders

Most people think AI levels the playing field.
It doesn’t, yet.

Unpopular opinion:
AI isn’t the great equalizer; it’s the great separator.

A new The Brookings Institution study just ranked 387 U.S. metros by their AI readiness.
San Francisco and San Jose dominate.
The next 28 cities, such as Seattle, Boston, Austin, and New York, follow fast.
But half the country is barely moving.

And the same pattern is playing out inside companies.
A few are moving fast. Most are stuck in pilot mode.

The data boils it down to three levers of readiness:

Talent – People who can use AI to make decisions, not just prompts.
Innovation – Teams that test small and document the wins.
Adoption – Workflows that actually run in production.

You don’t need a billion-dollar lab to start.
You need one measurable process and a feedback loop.
That’s how AI spreads from one operator to an entire org.

Whether you run solo or lead thousands, the question is the same:
Which pillar are you building next: Talent, Innovation, or Adoption?

Follow Brianna Bentler for practical AI implementation from solopreneurs to CEOs focused on measurable results.

Mapping The AI Economy

AI moved from experiment to infrastructure.

Q1 2025: 87% call AI a top priority, 76% already use it.

Digital economy ≈ 15% of world GDP, and rising.
Compute is the choke point now, not features.

Inference needs petaflops; training pushes exaflops.
Liquid cooling and >100kW/rack are the new normal.

We’re moving from assistants to agents.
42% have agentic AI live; 80%+ within three years.

Multi-agent teams beat single agents by double-digit quality.
Physical AI is leaving the lab for the shop floor.

Early deployments show double-digit cost and throughput gains.
Regulators are crystallizing; compliance clocks are ticking.

Energy and carbon are first-order constraints, not footnotes.
Nations are drafting sovereign AI plays to avoid dependence.

Operator moves (one process at a time):

✅ Map workloads by FLOPs, latency, and privacy.

✅ Split train/fine-tune/infer; design for multi-vendor accelerators.

✅ Start HIL; set pass-rate, time-to-resolution, and escalation rules.

✅ Hybrid LLM: API + on-prem SLM + RAG to cut costs 30-40%.

✅ Budget liquid cooling and power orchestration from day one.

✅ Make compliance a feature: logs, audit trails, DPIA-ready docs.

✅ If you touch physical ops, pilot one humanoid cell with private 5G.

Momentum is high; constraints are real, treat compute as strategy.

Pick one workflow this week and ship the first win.

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Share with a teammate who runs ops.

Global Artificial Intelligence Report (2025)

Stop forcing code.
Start vibe coding.

It’s how operators from solo founders to enterprise teams build software that actually ships.

One workflow, one finish line, one stack.
End-state first: define “done” in a single sentence.

Then wire the boring parts so the flashy parts work.

The system behind the vibes:
 ✅ Lovable to sketch the app in minutes (UI, auth, glue).
 ✅ Cursor to tighten logic, types, and tests.
 ✅ Supabase for data, auth, and migrations that don’t drift.
 ✅ Rules files so AI follows your patterns, not its mood.

Work loop:
 ✅ Plan → generate → review → test → ship → log.
 ✅ Guardrails: strict types, Zod validation, real error messages.
 ✅ Quality gates: lint, type-check, unit tests, and a clean build.
 ✅ Artifact-or-it-didn’t-happen: screenshot, metric, or clip every time.

Why it works:
 ✅ Less context-switching, fewer bugs, faster MVPs, consistent output.
 ✅ You keep the rhythm; the IDE keeps the receipts.

Start with one feature this week.
Define the finish line in 12 words.

Draft in Lovable, refine in Cursor, commit with tests.
Ship to staging, measure one metric, and write the mini-post.

Repeat until it feels boring, because boring is scalable.

Useful over shiny.
Vibes with proof.

Follow Brianna Bentler for operator-grade AI that ships.

Comprehensive Guide: Setting Up LLM-Integrated IDEs 

Architecture > prompts.

New study: neuro-symbolic-causal agents beat LLM-only.

Translation for operators: add rules + causal checks.

Why it matters: prompts swing outcomes wildly.

LLM-only = brittle; can even lose money/trust.

System that ships looks like this:

✅ LLM proposes actions
✅ Rules layer blocks violations (floors, margins)
✅ Causal engine simulates profit & trust deltas
✅ Only safe, high-ROI actions execute

Proof (52-week e-com sim):

LLM-only: volatile, bias-prone, trust hits.
LLM+rules: safer, still leaves profit on table.
Neuro-symbolic-causal: highest profit, steadier weeks.

Operator playbook:

Set hard invariants (no exceptions).
Score actions on profit + trust before acting.
Track weekly profit, trust, and volatility like finance.
Build prompt-agnostic systems, not prompt templates.

Follow Brianna Bentler for “no fluff, just ROI” teardowns.

Beyond Prompt Engineering 

Most AI projects never make it past pilot.

But Microsoft’s new AI Diffusion Report just confirmed what we’re seeing on the ground: the shift is finally happening.

✅ 78% of large enterprises now run at least one generative AI workflow in production.
✅ Mid-market adoption is at 46% and rising fastest where governance and metrics come first.
✅ The biggest wins aren’t new models, they’re redesigned workflows.

Microsoft calls it the “post-pilot maturity” phase.
That’s when real ROI shows up, after teams re-map ownership, measure
outcomes, and build human-in-the-loop review.

The headline stat: productivity gains of 14–22% per hour once systems are re-engineered, not just layered with copilots.

In short, diffusion isn’t about access to tools. 
It’s about clarity, trust, and redesigning work so people and AI operate as one system.

If you’re leading ops, start here:
 1️⃣ Audit where time actually goes.
 2️⃣ Pick one process you can log end-to-end.
 3️⃣ Add AI only after you can measure it like a P&L line.

That’s how diffusion becomes proof, not hype.

Follow Brianna Bentler for practical AI systems that move teams from pilot → production → scale.

 P.S. Which process in your world still runs on hope instead of measurement?

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