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 👇 

---

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 

---

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.

---

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 👇 

---

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.

🔖 Save this post for later

---

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 👇 

---

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. 

🔖 Save this post for later 

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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.

--- 

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.

--- 

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

Let me know in the comments 👇 

--- 

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 

--- 

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 

--- 

For what other coaching use case do you use ChatGPT?

Let me know in the comments 👇

--- 

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.

🔖 Save this post for later 

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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👇 

🔖 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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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 👇 

--- 

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 

--- 

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 

--- 

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

Let me know in the comments 👇 

--- 

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.

🔖 Save this post for later 

--- 

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  

---

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 👇 

--- 

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.

🔖 Save this post for later 

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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. 

--- 

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 

---

Please chill.

You don’t need to memorize this.

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

---

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 

--- 

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. 

--- 

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. 

--- 

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. 

--- 

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. 

--- 

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

I talk about AI and help businesses implement agentic systems.

I turned a $47/day agent into $6/day while cutting hallucinations 87%. That's real AI automation done right.

I started on a warehouse floor and retrained into tech. I learned the hard way that success comes from systems, not hacks. Now I build AI agents that streamline operations and actually ship.

By day, I keep systems humming at Star76, automating processes with Azure and ServiceNow. By night, I'm the CTO of TechTide AI and co-host of the Automation Vibes podcast. Two roles, one mission: keep operations running smoothly with AI and automation.

I build with DEPTH.
Not hype. Not hope. Durable systems that ship.

Durable systems over hacks
Build it right the first time. No quick fixes. No breakage at scale.

Embedded agents that ship
If it's not in production, it doesn't count. Every agent gets guardrails, logs, and an off switch.

Proof, not potential
Before and after metrics. Time saved. Tickets closed. Hours returned to the team.

Teaching as building
Write the playbooks. Share the steps. Treat every build as a training doc for the next one.

Hard stacks, not stunts
Pick tools that last. Architecture that grows. Stack decisions that aren't just for demo day.

Migration, not magic
Every .1 update is a breaking change. I've migrated 12 production systems and documented every landmine.

If you're a CTO, CIO, Ops Director, or founder looking to bring real-world AI and automation into your systems, let's talk.

I specialize in turning manual, 3-day headaches into reliable 4-hour solutions. No fluff. No hype. Just grounded strategy and measurable wins.

I also share new automation playbooks and case studies here on LinkedIn. 

Join 26K+ builders for an inside look at what real AI systems can do. Feel free to DM me to swap ideas or explore solutions.

Alex Cinovoj

The most expensive AI degree in the world... is now free.

And it's just a click away.

Stanford University, one of the top AI research institutions, has made its flagship AI & Machine Learning courses freely available on YouTube.

No tuition, no gatekeeping. 
Just world-class learning.

I can confidently say: these courses are game-changers for anyone serious about AI.

The brutal truth about AI education:

Most people are paying $200k for knowledge that's already free.
They're chasing credentials while builders are shipping products.

Stanford gets it.

They're not protecting knowledge behind paywalls.
They're democratizing it.

Same professors who taught OpenAI's founders.
Same curriculum that built Silicon Valley's AI giants.
Same quality that costs six figures on campus.

Zero dollars on YouTube.

I've audited these courses while building production systems.
The CS231n computer vision course? Better than most bootcamps.
The NLP with Deep Learning series? Actual implementation details.
The reinforcement learning content? Production-ready concepts.

Not watered-down summaries.
Not "AI for managers" fluff.
Real mathematical foundations.
Real code that ships.

While others debate whether AI will take their jobs, you could be learning to build the AI.

Nine courses that replace a $280,000 degree:

1️⃣ CS221 – Artificial Intelligence: Principles & Techniques
↳ Core AI foundations: search, reasoning, planning, decision-making.
🔗 https://lnkd.in/dD-zCpRV

2️⃣ CS224U – Natural Language Understanding
↳ How machines interpret meaning, semantics, and human language.
🔗 https://lnkd.in/dvyKbgni

3️⃣ CS224N – NLP with Deep Learning
↳ Deep dive into transformers, embeddings, and modern NLP.
🔗 https://lnkd.in/dWdzHA6B

4️⃣ CS229 – Machine Learning (Andrew Ng)
↳ The legendary ML course that started it all.
🔗 https://lnkd.in/dNuiW9BM

5️⃣ CS229M – Machine Learning Theory
↳ Mathematical backbone: sample complexity, generalization, convergence.
🔗 https://lnkd.in/dmyS2WZr

6️⃣ CS329H – ML from Human Preferences
↳ Where reinforcement learning meets alignment.
🔗 https://lnkd.in/dz6uVz-N

7️⃣ CS230 – Deep Learning (Andrew Ng)
↳ Neural networks, CNNs, RNNs, and practical DL systems.
🔗 https://lnkd.in/dZRdJhWb

8️⃣ CS234 – Reinforcement Learning
↳ Agents, environments, and reward-driven learning.
🔗 https://lnkd.in/dh6QWS_f

9️⃣ CS330 – Deep Multi-Task & Meta Learning
↳ How models learn to learn. Transfer and generalization at scale.
🔗 https://lnkd.in/dFDYNMbj

Each one free.
Each one world-class.
Each one immediately applicable.

The gap between those who know AI and those who don't is growing daily.

Stanford just eliminated the excuse.

Ship with Stanford knowledge.
Not student debt.

Thanks to amazing Basia for sharing this with me a while back!

I test AI in production so you don’t have to. Building agentic systems that ship.

A year ago, I pitched AI agents to a room full of VCs.

"Too early," they said.
"Nobody wants this."
"Come back when there's demand."

Today McKinsey dropped a report: $2.9 trillion in value by 2030.
From people, agents, and robots working together.

Being right too early feels exactly like being wrong.
The Meeting That Changed Everything.

December 2024. VC pitch event.
I'm showing a production agent handling customer support.

Partner stops me mid-demo:
"But who's asking for this?"

Nobody. 
That was the point.

Ford's customers wanted faster horses.
Not cars.

I walked out with zero funding.
And complete conviction.

McKinsey & Company's report confirms what I lived...
57% of work hours could be automated with today's tech.

Not tomorrow's breakthroughs.
Today's capabilities.

But here's what the report misses:
The real barrier wasn't technology.
It was imagination.

Every client I pitched in early 2025:
"We need to wait for the market."
"Let's see what others do first."
"Too risky."

Meanwhile, I'm shipping agents that work.
In production.
Making money.

McKinsey says organizations need to "redesign workflows."
I've been doing that for 13 months.

They talk about "skill partnerships" between humans and AI.
I've built twelve of them.

They predict massive value creation.
I'm already capturing it.

Not because I'm smarter.
Because I didn't wait for permission.

The Pattern I Keep Seeing

January 2025: "AI agents are hype."
March 2025: "Maybe for simple tasks."
June 2025: "We're exploring options."
September 2025: "It's strategic priority #1."
Today: "We're behind."

The same VCs who rejected me?
Now funding agent companies at 50x valuations.

The enterprises who said "too early"?
Scrambling to catch up.

The brutal truth...

The best time to build the future was a year ago.
The second best time is now.

Forget the report's timeline.
2030 is their conservative estimate.

The real transformation?
Happening right now.
In production.
With builders who don't wait for reports.

I spent a year being told I was wrong.
Turns out I was just early.

Same thing, different story.

While McKinsey writes about the future of work,
Some of us are already living it.

That's the difference between analysis and action.

What "crazy" idea are you building that nobody believes in yet?

AI agents Robots and US

MCP was supposed to be complex. 

Then I built it wrong and everything clicked.

90 days later, our backlog is gone.

The playbook that actually ships:

MCP in one line (skip the docs)
→ Universal protocol for LLMs to use your actual tools. Not prompts pretending to be APIs.

What broke (then worked)

✅ Cursor talks to our MCP server talks to Supabase talks to webhooks talks to Claude
✅ Legacy trade-desk system connected without vendor blessing
✅ Lead routing collapsed from 3 services to 1 endpoint
✅ Brittle integrations died. Small tools that compose were born.

Five moves to steal

✅ One workflow first. Not your entire stack.
✅ Wrap production tools (DB, CRM, files) as MCP resources. Kill the prompt gymnastics.
✅ Keep tool code under 50 lines. Seriously.
✅ Log everything: trigger → execution → outcome
✅ Same MCP tools across all teams. Stop rebuilding.

When to skip MCP entirely

Webhook + SQL already works? Ship that.
Complexity isn't a feature.

Most of our automation starts with MCP now.
Less architecture astronauting.
More shipping.

Follow Alex for AI that works in production, not PowerPoint.

Props to Daily Dose of Data Science for the original breakdown.

MCP Masterbook

Your AI Agents Are About to Get 98% Cheaper (Here's How Anthropic Just Solved It)

Anthropic just dropped code execution with MCP.

Your agents now write code instead of calling tools.

98.7% reduction in token costs. Overnight.

I tested it on a client's support bot last week. Their monthly bill dropped from $12,000 to $240.

Same functionality. 
Same speed. 
2% of the cost.

The Problem Nobody Talks About

Every tool you add to an agent loads definitions into context.

100 tools = 150,000+ tokens per run.

My client had 147 tools. 
Customer data, Slack, HubSpot, Stripe, n8n, internal APIs.

Each conversation burned through tokens like a broken faucet.

The agent would:
- Call gdrive.getDocument("abc123") → returns full transcript
- Pass that transcript through the model
- Call salesforce.updateRecord() with the full text
- Pass it through again

50,000 tokens. 
Gone. 
For moving text between systems.

The MCP Fix That Changes Everything

MCP exposes your tools as a file tree.

Not a token blob. 
A browsable structure.

The agent reads tool files like code modules. Only loads what it needs.

Instead of this mess...

```
TOOL CALL: gdrive.getDocument("abc123")
→ returns full transcript text
TOOL CALL: salesforce.updateRecord({ Notes: "[full text]" })
```

You get this...

```javascript
import 𝘢𝘴 𝘨𝘥𝘳𝘪𝘷𝘦 𝘧𝘳𝘰𝘮 './𝘴𝘦𝘳𝘷𝘦𝘳𝘴/𝘨𝘰𝘰𝘨𝘭𝘦-𝘥𝘳𝘪𝘷𝘦';
𝘪𝘮𝘱𝘰𝘳𝘵 as salesforce from './servers/salesforce';
const transcript = (await gdrive.getDocument('abc123')).content;
await salesforce.updateRecord({ Notes: transcript });
```

The transcript never touches the model context.

Stays in the execution environment. Processes locally.

The Numbers That Made Me Double-Check

Anthropic's benchmark:
- Before: 150,000 tokens
- After: 2,000 tokens
- Reduction: 98.7%

Same bot. 
Same capabilities. 
Just smarter architecture.

How to Implement This Today

1. Use an MCP-compatible client (Claude Desktop, or build your own)
2. Generate a tool file tree structure
3. Give the agent filesystem access
4. Enable code execution for tool calls

This isn't a prompt hack or a model upgrade.

It's architectural.

We stopped treating agents like text processors.
Started treating them like developers.

They browse. 
They import. 
They execute.

Like actual engineers.

Code execution turns LLMs into systems that act.

Not chat. 
Systems.

While everyone's debating which model is "smartest," Anthropic just made them all 98% more efficient.

That's engineering.

Not hype.

𝗙𝗼𝗹𝗹𝗼𝘄 𝗔𝗹𝗲𝘅 for more production AI breakdowns.

𝗦𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗮 𝗳𝗿𝗶𝗲𝗻𝗱 who's burning money on agent tokens.

The future isn't smarter models.
It's smarter architecture.

Claude AI Agent MCP

$200/month for GPT-5 when a free model beats it?

Beijing just shipped what OpenAI is still pricing.

Kimi K2 Thinking dropped couple weeks back.
Beats GPT-5 and Claude 4.5 on reasoning benchmarks.
Trained for $4.6 million.
Free on Hugging Face.

The brutal truth:

Companies are burning $200/month per seat for ChatGPT Plus.
100 employees = $20K monthly. $240K yearly.
For performance you can now get at zero cost.

I tested K2 against production workloads:
✔️ Complex reasoning: Outperformed GPT-5
✔️ RAG pipelines: Stronger retrieval logic
✔️ Agent brains: Same quality, infinite savings

Not "almost as good."
Better on benchmarks that matter.

While enterprises negotiate OpenAI contracts, builders are downloading K2 and shipping.

The math is simple:
GPT-5 API: $0.15 per 1K tokens
Kimi K2: $0.00 per infinite tokens

One client just saved $8K/month switching their internal tools.
Same output quality.
Zero API dependencies.

China built frontier AI for less than SF's monthly burn rate.

The playbook is shifting:
Use K2 for internal experiments and RAG
Keep GPT for customer-facing (for now)
Benchmark everything

Free beats paid when it performs better.

Ship with models that work.
Not invoices that hurt.

Open-Source Model Kimi K2

I just gave away $100K worth of agent training.

For free.

Because gatekeeping is dead.

While consultants charge $5K/day for "AI strategy," I'm shipping the entire production playbook.

No paywall.
No email capture.
Just the raw blueprint.

Most "AI experts" are reading the same GitHub repos you could access right now.

They're charging enterprise rates for public knowledge.

I tested this theory.

Built 47 production agents using only free resources.
Deployed them for enterprise clients.
Zero paid courses.

The knowledge gap isn't real.
The organization gap is.

THE FOUNDATION (Where builders actually start):

✔️ LLM mechanics that ship (not theory)
✔️ Stanford University's agent architecture (what OpenAI uses)
✔️ Google's internal agent docs (leaked last month)

THE BUILD STACK (Copy my exact setup):

✔️ Microsoft's production agent framework
✔️ Anthropic's MCP implementation guide
✔️ Hugging Face's deployment patterns
✔️ The memory systems that actually scale

THE ADVANCED ARSENAL:

✔️ ReAct reasoning (how agents think)
✔️ Stanford's Generative Agents paper
✔️ Meta's Toolformer architecture
✔️ OpenAI's Swarm orchestration

(Most consultants haven't even read these)

THE CODE THAT SHIPS:

✔️ GenAI production repo (battle-tested)
✔️ Prompt patterns from FAANG engineers
✔️ Pinecone's vector memory course
✔️ The "zero to deployment" sequence

This replaces:
❌  $20K bootcamps
❌ $100K consulting engagements
❌ 6 months of trial and error

One document.
Everything organized.
Updated weekly with what works.

While others debate AI's future, you could be building it.

Comment "AI 2026" below.
I'll send the complete playbook.

(Connect first so I can DM)

Reposts get priority access.

The gap between those building agents and those talking about them is massive.

Pick your side.

Ship with the builders.
Not the talkers.

Thanks to legend Sairam Sundaresan for building the original that started it all.

Quit using AI like a glorified FAQ.

The breakthrough happens when agents think, execute, and evolve.

Begin small.
Single task.
Single agent.
Then expand.

Target one operation:
Onboarding
Billing
Appointments

Ship it.
Measure time recovered.
Next workflow.

Agents are modular components that coordinate:
Sensing
Analysis
Storage
Execution
Learning

The brutal truth:

Narrow focus guarantees measurable wins.
Measurable wins build confidence.
Confidence funds the next agent.

Production evidence:

An accounting firm reclaims 30 hours monthly.
A legal practice drops intake to 10 minutes flat.
A medical office eliminates half their cancellations.

My client deployment sequence:
Identify one painful, repeatable task.
Document the ideal flow and failure modes.
Connect the agent to necessary systems.
Log all operations and choices.
Analyze results and adjust logic.
Deploy the solution.
Iterate.

Green lights for starting now:
The task happens weekly without fail.
Data lives in one authoritative system.
Success has a binary definition.

Traps that kill momentum:
Trying to automate everything immediately.
Pretty prototypes nobody maintains.
Mystery prompts with zero visibility.

I ship production-ready agents. Check the Galileo research.
Follow Alex Cinovoj for actual agent deployments and tactical breakdowns.
Pass this along if you know teams ready for agents that deliver.

Mastering AI Agents

Lusha AI turned me into a 10x SDR.

I'm one person.
Running outbound, follow-ups, and pipeline while building products.

My CRM was half-dead. Lead lists lived in 6 different sheets.

Then Lusha rolled out MCP.

Now I work WITH AI agents who pull verified B2B data in real-time.

No CSVs.
No manual enrichment.
No hallucinated contacts.

The brutal truth: Most solo founders are still copy-pasting from LinkedIn.

I'm asking Claude directly: "Show me 25 companies in the Midwest hiring RevOps, 50-200 staff, using HubSpot, pull direct emails for ops lead."

MCP handles the lookup. The cleaning. The structure.
My agents amplify my reach 10x while I focus on strategy.

The Stack That Makes My Day 10x Easier 

Signals show me job changes and hiring spikes the moment they happen.
If a company in my ICP suddenly posts three sales roles? I see it instantly,  no more guessing.

Conversations sit on my calls and catch patterns I’d normally miss.

Budget pushback, timing excuses, trust issues… it highlights the real story so my follow-up is sharper.

Recommendations + Playlists give me a starting point instead of a blank page.

I open my laptop, and the next 20 people I should reach out to are already lined up, based on real signals, not random job titles.

Workflow automation ties it all together.

New prospects get added automatically, enriched, pushed to my CRM, and dropped into the right sequence.

I just set the rules, the system runs them.

And Flex Search means I can search the way I actually think:
“US B2B SaaS, 20–200 staff, hiring RevOps, strong in the Midwest.”

Lusha translates it into something structured. 

I just pick who to talk to.
Testing new Lusha MCP with n8n flows this week. 

P.S. Lusha's running 40% off all annual plans this November if you want to try this out. and https://lnkd.in/gVn_3yTR

I am an #Lushapartner 
Follow Alex for systems that ship, not tutorials that demo.

Lusha AI

Everyone's building AI wrappers while llama.cpp quietly solved LLM inference.

Plain C/C++.
Zero dependencies.
Runs on everything from M4 MacBooks to ancient ThinkPads.

While you're waiting for cloud credits, someone's running Llama 3.1 on their phone.

The brutal truth about production inference:

You don't need Kubernetes clusters.
You don't need $50k/month GPU bills.
You don't need another Python framework.

You need code that compiles and runs.

llama.cpp ships exactly that.

Apple Silicon? First-class citizen with Metal optimization.
Old desktop with AVX2? Covered.
That random RISC-V board? It runs there too.
Your NVIDIA 4090? CUDA kernels ready.
AMD GPU collecting dust? HIP backend works.

The kicker:

4-bit quantization means a 7B model runs on 4GB RAM.
Same model quality. Quarter the memory.

I tested Qwen 2 on a 2019 MacBook Air last week.
15 tokens/second for a 1.5B model.

No cloud. 
No API keys. 
No monthly bills.

Just `./llama-cli -m model.gguf` and it works.

The Architecture That Actually Matters

Everyone obsesses over model size.
llama.cpp obsesses over making models usable.

1.5-bit to 8-bit quantization? Pick your speed/quality tradeoff.
CPU+GPU hybrid inference? Run models bigger than VRAM.
Custom CUDA kernels? Squeeze every FLOP from your hardware.

They turned inference from a DevOps nightmare into a single binary.

Download. Compile. Run.

Three commands between you and local AI.

While OpenAI debates AGI safety, the open source community ships tools that democratize AI access.

No venture funding.
No hype cycles.
Just 200+ contributors making LLMs accessible to everyone.

That's not just engineering.
That's philosophy in action.

Every merge request makes AI less corporate and more human.

Ship local inference.
Not API dependencies.

Follow Alex for production AI that ships, not slides that pitch.

Sales Navigator

(1) Notifications | LinkedIn

Local AI solution

Five years ago, AI was a headline.

Now it’s a heartbeat.

Microsoft’s new AI Diffusion Report doesn’t talk about model wars.
It talks about people.

The engineer in Ohio shaving 3 hours off his day with Copilot.
The nurse in London who finally closes charts before her shift ends.
The developer in Manila who ships twice as fast with AI pair tools.

That’s what “diffusion” really means, 
not hype on stage,
but quiet adoption in real workflows.

The report says we’ve entered the deployment era.
The winners aren’t the ones training the biggest models anymore.

They’re the ones who integrate faster, ship safer, 
and measure ROI in saved hours, not press mentions.

The next frontier isn’t model size.
It’s diffusion velocity, how fast AI moves from pilot to production.

And here’s the part that hit me...

Only 12% of companies have guardrails or observability in place.
That means 88% are flying blind.

The real innovation now isn’t building another chatbot.
It’s designing systems where humans and agents work in sync, logged, measured, trusted.

Microsoft calls this “AI as infrastructure.”

I call it AI as oxygen.

Invisible. 
Everywhere.

And the people who learn to breathe it, safely, consistently, will own the decade.

If you’re still waiting for AI to get practical,
you’re already behind.

Start with one workflow.
Add an off switch.
Measure your throughput.
Then repeat.

That’s how diffusion happens.
One useful agent at a time.

Follow Alex for more systems that actually ship.
Save this if you’re building the next layer of AI in real operations.

(2) Messaging | LinkedIn

LinkedIn: Log In or Sign Up

Microsoft

AI Diffusion Report

Mike from Stranger Things evolved from Season 1 to Season 5.

So did my AI and LinkedIn #BuildInPublic journey.

Season 1 (Nov 2024): 500 followers. Testing ChatGPT. Getting laughed at.
Season 3 (Apr 2025): 2K followers. Shipping my first agent.
Season 5 (Jul 2025): 8K followers. Production systems running.
Season 7 (Aug 2025): 15K followers. Clients actually paying.
Season 9 (Sept 2025): 22K followers. Building what ships, not what's hyped.
Season 11 (Nov 2025): 28K followers. Full Voldemort mode on AI skeptics.

Mike took 5 seasons to go from innocent kid to basically evil.

I took 12 months to go from "AI is interesting" to "ship agents or die trying."

His transformation: Years of trauma, loss, and compromise.

Mine: Months of rejection, iteration, and relentless shipping.

Slow burn into darkness.
Speed run into obsession.

The kid who saved Eleven became the villain.

The founder who asked permission became the builder who doesn't.

→ Hero 
→ Anti-hero 
→ Necessary evil.

→ Curious 
→ Convicted 
→ Ruthlessly practical.

His superpower evolved from friendship to evil.

Mine evolved from following tutorials to shipping what works.

Sometimes becoming the villain is the character development you need.

Build in public.
Bring value.
Show up with consistency.

Even when you're morphing into Voldemort.

𝘌𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 then.

Happy Thanksgiving to everyone who watched this evolution.

To those still at Season 1: 
The journey gets darker.
The results get brighter.
Trust the process.

What season of your AI journey are you in?

GitHub just turned every repo into an agent factory.

And developers are sleeping through the biggest platform shift since Actions.

AgentHQ dropped at Universe 2025.
Not another AI tool.
An entire agent economy built into GitHub.

I've been testing agent frameworks all year.
This changes the deployment game completely.

The brutal truth:

While teams debate "AI readiness," GitHub just made agents as simple as markdown.

One file: AGENTS.md
Define your agent army.
GitHub handles everything else.

I'm already seeing the patterns:

PR Review Agent catches security flaws before human review.
Dependency Agent updates packages while you sleep.
Docs Agent writes documentation that actually matches code.

Not suggestions.
Autonomous execution.

The marketplace angle is genius.

Remember when Actions launched?
Now there's 20,000+ in the marketplace.
$2B ecosystem.

AgentHQ follows the same playbook.
Except agents can earn revenue.

My prediction:

By 2026, the average repo will run 5+ agents.
Top maintainers will earn $50K+ yearly from agent licensing.
"Agent developer" becomes a distinct career path.

The immediate wins I'm targeting:

✔️ Issue triage that actually understands context
✔️ Code reviews that catch business logic errors
✔️ CI/CD that self-heals broken builds

Already running a test agent that:
1. Monitors PR quality scores
2. Suggests improvements via CodeQL
3. Auto-merges when standards met

30% less review time.
First week.

Enterprise features are production-ready:
Code Quality Dashboard across all repos.
Usage APIs for cost tracking.
Isolated sandboxes for security.

While consultants sell "digital transformation," builders are shipping agent workforces.

Today.

What workflow are you automating first?

Follow Alex for agent systems that ship in production.

GitHub AI AgentHQ

n8n broke me for 6 weeks straight.

Every tutorial promised "easy automation."
Every build ended in cryptic errors and broken workflows.

Then I joined Nate Herk's private community and everything changed.

After building one of the fastest growing and most profitable Skool communities' in 2025, Nate cracked the code on why 73% of builders quit n8n in week one.

His Ultimate n8n Starter Kit gets you shipping in days, not months.

The 5 modules that actually matter:

Foundation → How automation thinks (not how nodes click)
Data Logic → JSON without the tears
Production → Error handling that saves your weekends  
AI Agents → Real agents, not ChatGPT wrappers
Clone & Ship → 10 case studies you can deploy today

Before joining Nate's community:
- Week 1: Quit rate 73%
- Time to production: 6 weeks
- Error rate: 47%
- Debug time: Hours of frustration

After using his system:
- Day 2: First working automation
- Week 1: Complex workflows shipping
- Error rate: Under 5%
- Debug time: Minutes with community support

The real difference isn't just the guide.

It's having Nate and 2000+ builders who've been there...

When you hit that weird API error at midnight?
Someone's solved it.

When your workflow logic makes no sense?
The community debugs it in real-time.

I've built 250+ n8n systems.
Debugged every 3 AM crash alone.
Fixed every edge case through trial and error.

This community lets you skip all that pain.

Most n8n tutorials teach you nodes.
Nate's system teaches you to think.

His community teaches you to ship.

That's the difference between demos and production.

💬 Join me in Nate's community, the link incoming in the comments
📎 Save this if you're ready to ship automations
🧠 Want all 6,000 templates? Drop "N8N is Truth" and make sure we are connected. 

Follow Alex for systems that ship, not tutorials that demo and share with a builder who is ready to ship.

n8n ultimate guide

Most research dies in a doc no one reads.

The team at Marvin just changed that, 
and turned research into a growth engine.

Here’s their playbook:
1️⃣ Centralize your insights.
 One searchable source of truth. Query before you study. Stakeholders self-serve so nothing gets missed.
2️⃣ Automate the grunt work.
 Transcripts, notes, labels, and thematic clusters, all done for you. Less copy-paste, more decisions.
3️⃣ Tie findings to metrics.

Quant + qual. 
Short video receipts. 
Every insight connected to KPIs leaders already track.

The solution?
Use AI as your analysis partner.

The result?
Proof travels faster than opinions.

And trust follows proof.

Real outcomes:
✅ 15 hours saved per researcher, weekly
✅ 4x more output, no new hires
✅ Sub-$400 studies that actually move the needle

Start simple this week:
✅ Pick 1 team
✅ Set up 1 intake pipe
✅ Use 1 report template
✅ Ship a Slack digest

Next week?
Add auto-labeling and connect insights to a KPI.

Useful > shiny.
Measure time saved, pass rate, and actual decisions made.

Follow Alex for systems that ship.
Save this to build your blueprint.
Repost to help one teammate run research like an operator.

AI UX Playbook

I remember the first time my “smart” agent failed in production.

The demo was perfect. 
The meeting was brutal.

The agent forgot context halfway through.
Started quoting the wrong file.
And then, confidently, made something up.

That’s when it hit me, 
The model wasn’t broken.
My system was.

We keep thinking prompt engineering is enough.
That if we just phrase it better, the model will understand.
But intelligence isn’t about better sentences.
It’s about better systems.

That’s what Weaviate just nailed with their Context Engineering report.
It’s not another whitepaper, it’s the blueprint for how real AI apps are actually built.

Here’s the shift that changed everything for me:

Prompts talk to a model.
Context builds its world.

When you give an LLM:

Memory to remember what it did
Retrieval to feed it live data
Tools to act in the real world
And agents to orchestrate it all

You stop building chatbots.
You start building employees made of code.

The best part?

This guide breaks it all down, agents, chunking, memory, orchestration, and MCP, the missing playbook for builders who want systems that last.

If your agents still work great in a demo but fall apart in production, read this.

The future isn’t bigger models.
It’s better context.

Follow for my context hygiene checklist and a one screen system map.

If this helped you, re-share so one more builder avoids my scar tissue.

If you re-share, I owe you one terrible Midwest joke in the replies.

Context Engineering Whitepaper

I used to duct tape agents together.

Every new agent lived on a different platform.
One tool for prompts. One for data. One for workflows.

Fixing bugs meant jumping between tabs and guessing where it broke.

The brutal truth: I spent more time wiring tools than solving real problems.

No clean way to see what an agent did and why.
Every new client meant rebuilding the same plumbing again.

Keys spread across random services.
No simple way to say who can touch what.
Hard to show a business owner that the system is safe and in control.

Then I switched to Airia.

Now I build agents in one place.

🧠 Building An Agent Now

I start from a template that matches a real workflow.
Drag and drop steps instead of stitching scripts together.
Pick the model I want without changing my whole stack.

📊 Connecting Real Data

I plug in tools like email, chat, or CRM from one screen.
Upload files or connect to live data warehouses.
My agents finally see the same truth my team sees.

🎯 Run, Watch, Adjust

I test the agent with real questions in the same view.
I can see each step, the inputs, and the outputs.
When something feels off, I fix the step. Not the whole system.

🔄 From One Agent To A System

Agents work together instead of living alone.
Access rules and checks are built into the platform.
I can go from small test to real use without rebuilding from scratch.

The difference?

Less time babysitting glue code. More time solving real problems.
Easier to show clients how the agent works and why they can trust it.

I save an hour every day not jumping between tools.
That's 5 hours a week. 20 hours a month.

Time I spend shipping instead of debugging.

If you're tired of scattered agents, start testing them in one place with Airia.

Follow Alex for systems that ship, not tools that scatter.

Try it here: https://tryit.cc/PdkuwAK

#Airiapartner

AI Agent Chaos

Everyone says “I build AI agents.”

Few know what that really means.

Six months ago, I didn’t either.
Back then, “AI agents” meant tools.

Relevance AI, FlowiseAI, n8n.

I thought the secret was stacking automations.

Then I started building full systems, 
agents that plan, reason, remember, and act.

I learned fast that real AI agents aren’t workflows.
They’re living architectures.

Memory. 
Context. 
Feedback loops.

Human in the loop. 
Ethics and control.

MCP, A2A, Response Generation.
All working in sync.

It took hundreds of hours in Cursor,
dozens of failed runs,
and a lot of debugging at midnight.

But that’s how I became what I now call
a Agentic Engineer, an operator who ships systems that think,
not scripts that trigger.

This diagram by Rakesh nails the gap perfectly.

Most think agents are tools.
They’re actually ecosystems.

Useful over shiny.
Systems over hacks.

If you’re serious about building real ones,
start small, add memory, add orchestration,
measure the loop, and ship again.

Follow Alex for full agent blueprints.
Repost to help others see the difference.

Adoption is not the win. 

Application is.

99% of the Fortune 500 use AI.
Most are still stuck in pilots.

The gap is simple.

Where do you point AI to create real value?

Here is the operator playbook I use.
✔️ Pick one workflow, not a department.
✔️ Define one metric: pass rate, minutes saved, or dollars won.
✔️ Wire a small agent: retrieve, plan, act, check.
✔️ Give it a toolbelt it can use today.
✔️ Add guardrails, logs, and an off switch.
✔️ Run for one week. Promote only if the metric moves.

Where this works now:
✔️ Underwriting and compliance.
✔️ Claims and billing exceptions.
✔️ Vendor risk intake.
✔️ Term sheets for private lending.
✔️ Student advising and scheduling.
✔️ IT tickets and password resets.
✔️ KYC refresh and alert triage.
✔️ Finance close checklists.

Breakdown below on 65+ proven use cases by function and metric.

Useful over shiny. 
Systems over hacks.

Thanks StackAI for a breakdown.

AI Agent Use Cases

The brutal truth...

Most teams still treat AI apps like static chatbots.

Legend Anurag(Anu) just dropped a full system map that shows how real AI agents are built.

Here’s the complete playbook in one screen.

1️⃣ Orchestrate the runtime
Choose how your agent lives:
✅ Web apps → App Service
✅ Microservices → AKS
✅ Event-driven → Azure Communication + Functions
✅ Voice or chat → Microsoft Agent Framework + Foundry Agent Service

2️⃣ Add memory
Don’t rely on context windows.
✅ CosmosDB or PostgreSQL for structured + unstructured data
✅ GraphRAG for knowledge graphs
✅ Azure AI Search for retrieval with OCR, multi-modal, and ranking

3️⃣ Wire up tools
Agents need to act, not just talk.
✅ Logic Apps or Functions for function calling
✅ Vision, Language, Document Intelligence, Speech Avatar APIs
✅ Built-in code interpreter via Azure AI Agent Service

4️⃣ Reason with the right engine
System 1 = quick thinking. System 2 = deliberate planning.
✅ GPT-5 or 4.1 mini for reasoning
✅ Embeddings: OpenAI, Cohere, CLIP, DINOv2
✅ Domain models fine-tuned in Azure AI Foundry (Llama, Phi, Mistral AI)

5️⃣ Close the loop
✅Ground data → recall memory → plan → act → reflect.
✅That’s how agents become systems, not stunts.

Real operators don’t need more “AI magic.”
They need architecture that ships.

Follow Alex for more one-screen blueprints.
Save this if you’re building agents that actually work.

Agentic AI Leader @Microsoft | Author - Generative AI for Cloud Solutions | LinkedIn Learning Instructor | Responsible AI Advisor | Ex-PwC, EY | Marathon Runner

OpenAI

Cohere

Mistral AI

Everyone's obsessing over AI agent frameworks.

After shipping 27 agents in production, here's the only tech stack that matters.

🧠 Foundation Models
Your agent's brain. Pick one. Master it.
GPT for reasoning. Claude for writing. Stop model hopping.

📊 Data Storage
Vector DBs and memory systems.
Get this wrong? Your agent forgets everything or remembers test data in production.
(Cost me $3K to learn that one)

🎯 Agent Development Frameworks
For orchestration and workflows.
But frameworks don't ship products. You do.
One for complex flows. That's it.

🔍 Observability
When your agent breaks at 3 AM, you need logs.
Not philosophy. Logs.
Monitor everything. Debug faster.

🔧 Tool Execution
APIs, browsers, external systems.
Wrap them early. Test everything.
Least-privilege access. Always.

💾 Memory Management
Short-term: Current task only.
Long-term: What actually matters.
Expiry and resets: Mandatory.

The brutal truth?

Most teams build the stack backwards.
They start with frameworks, add models, then panic about memory and observability when things break.

Start with observability.
End with frameworks.
Ship what works.

My everyday agents Lovable Cursor, Manus AI and Gamma.

Thanks Alex Xu and ByteByteGo for amazing visual.

What's missing from this stack that you've learned the hard way?

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taplio

The Battle

Shushant Lakhyani

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Engagements

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Shushant Lakhyani's LinkedIn Performance

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