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.

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!

Alex Cinovoj

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

li_at

JSESSIONID

(1) Notifications | LinkedIn

Local AI solution

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 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.

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.

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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?

Stop paying $8,000 for AI courses.

I just compiled the vault that actually ships.

Every resource the top 1% of AI engineers use daily.
All free. All battle-tested in production.

The brutal truth:
Most paid courses teach theory.
These resources ship systems.

📦 Repositories That Actually Matter

Not the 500-star weekend projects.
The ones powering production at scale:

1️⃣ Transformers, Where GPT, BERT, T5 live → https://lnkd.in/guqm9tix
2️⃣ vLLM, Fastest inference. Period. → https://lnkd.in/gNhxxQ-5
3️⃣ LangChain, Orchestration that ships → https://lnkd.in/gZ63GrSf
4️⃣ LlamaIndex, RAG that actually works → https://lnkd.in/gzUsNZmA
5️⃣ DSPy, Prompt optimization on steroids → https://lnkd.in/gdgucs9K
6️⃣ LitGPT, Fine-tuning without the pain → https://lnkd.in/gXEmd4MQ
7️⃣ Ray, Scale to infinity → https://lnkd.in/g6pgFZfe
8️⃣ Qdrant, Vector DB that doesn't crash → https://lnkd.in/gSfs9rqK

📚 Books That Changed My Production Stack

Free. Authoritative. No fluff:

✅ Building LLMs from Scratch (Sebastian) → https://lnkd.in/g2YGbnWS
✅ AI for the Rest of Us → https://lnkd.in/giV_N6KU
✅ Understanding Deep Learning (Prince) → https://lnkd.in/g4xgvu_q
✅ LLM Engineering Handbook → https://lnkd.in/gWUT2EXe
✅ AI Engineering (Chip Huyen) → https://lnkd.in/gpfQSMCQ
✅ Hands-on GenAI → https://lnkd.in/gSRgvcKA

🎯 Agent Guides From The Source

Not blog posts. Official documentation:

🔹 Google's 70-page Agent Memory Bible → https://lnkd.in/gFvCfbSN
🔹 Anthropic's Building Effective Agents → https://lnkd.in/gRWKANS4
🔹 OpenAI's Production Agent Playbook → https://lnkd.in/guRfXsFK

The kicker:

These three guides alone replace every $2,000 "masterclass."

🚀 The Repos That Ship When Others Demo

Neural Networks: Zero to Hero → https://lnkd.in/gEzFebK7
Computer Vision Arsenal → https://lnkd.in/g7jZNFHX
Hands-on LLMs → https://lnkd.in/g7HcxTZz
Prompt Engineering Bible → https://lnkd.in/gJjGbxQr
Generative AI Goldmine → https://lnkd.in/g_tmrqTi
AI Agents for Beginners → https://lnkd.in/gK8MiVfv
Advanced RAG Techniques → https://lnkd.in/g2ZHwZ3w
Production Agent Systems → https://lnkd.in/gkMZs-Ks
Made with ML → https://lnkd.in/gER7Stdw

I've shipped 50+ agents this year.
Built with these resources.
Not a single paid course.

Your competition is buying courses.
You're shipping systems.

That's the difference.

Follow Alex for resources that ship, not certificates that collect dust.

Save this before it disappears into your feed.
Share it with someone still paying for demos.

I shipped 27 AI agents last quarter.

23 failed in production.

The 4 that survived taught me what nobody's telling you about building agents that actually work.

The brutal truth...

Everyone's building demos.
Nobody's shipping systems.

I burned through $12K in compute learning this the hard way.

📊 Week 1: The Basics That Break You

Start small or die trying.
My first agent tried to do everything. It did nothing.

Text in → task out. That's it.
Pick 3 use cases. Master them. Then expand.

LLMs aren't magic. They're calculators with opinions.
Tokens = money. Context = memory. Cost = reality.

One model, know its limits.
GPT for reasoning. Claude for writing. Gemini for multimodal.
Stop model hopping. Ship with what works.

🎯 Week 2: The Prompts That Actually Ship

Prompts aren't conversations. They're contracts.

Role • Goal • Rules • Examples.
That's your entire prompt architecture.

Ask for JSON. Think like an API.
Natural language in, structured data out.

Wrap APIs into tools early.
Test everything. Log everything.
When it breaks at 3 AM, you'll thank yourself.

💡 Week 3: RAG Without the Tears

RAG matters when your data matters.
Not before.

Chunk. Embed. Retrieve.
Get this wrong, your agent hallucinates.

One golden query per use case.
Verify outputs with sources. Always.

I watched my agent cite documents that didn't exist.
Cost me a client. Never again.

🔄 Week 4: Agents That Don't Implode

Agents need structure:
Planner → Executor → Verifier.
Plan–Act–Check. Every time.

Ship tiny:
One job • One tool • Success rule • Timeout.

Complex agents are simple agents chained together.
Not Swiss Army knives that do nothing well.

🚨 Week 5: Production or Death

Workflows: DAGs, not spaghetti.
Least-privilege tools. Always.
Budgets + kill switches. Non-negotiable.

Memory isn't storage. It's strategy.
Short-term: Current task.
Long-term: What matters.
Expiry and resets: Mandatory.

My agent remembered everything.
Including test data.
In production.
$3K mistake.

📈 Week 6: The Evaluation That Matters

Golden tests: 10 perfect examples.
Pass rate: Track it religiously.
Failure types: Categorize and fix.
Human approvals: For anything that costs money.

Safety isn't optional:
Docker sandbox everything.
Sanitize all I/O.
Identity + audit trail + alerts.

One compromised agent can burn your entire stack.

🎯 Week 7: Ship and Iterate

Frameworks that actually work:
1 for complex flows.
2 for multi-agent systems.
3 for autonomous tasks.

But frameworks don't ship products.
You do.

The patterns that matter:

Build tiny: CLI or webhook first.
Tests + retries where safe.
Memory hygiene: expiry, resets.
Guardrails: budgets, kill switches.
Evaluation: Weekly. Religious.
Roles: Crystal clear.
Sources: Always cited.
Complexity: Always avoided.

Schemas clean.
Prompts tested.
Costs tracked.
Use cases proven.

That's how you build an agent in a week.
Not a demo. A system.

Save this roadmap.

Your competition is building chatbots.
You're shipping agents.

AI Agent Step by Step

I've been shipping with Lovable for 6 months. 2500+ commits.

Here's the workflow that saves me 180 credits per project.

New builders open Lovable and start typing.
I learned to plan first, then prompt.

The 0→1 Lovable Workflow that ships:

📊 Rule 1: Feed the Brain
Load custom knowledge first. Yesterday I uploaded my API docs before starting. Lovable remembered every endpoint without me explaining twice.

🎯 Rule 2: Plan Before You Prompt
Write a 1-page PRD. Last week this saved me from building the wrong auth flow for 2 hours.

💡 Rule 3: One Prompt, One Job
Break work into atomic chunks. When I tried "build me a dashboard with auth and payments," I got spaghetti. When I said "create login form," then "add validation," then "connect to Supabase", clean code every time.

🔄 Rule 4: Use Chat Mode
Plan, review, then build. Caught a database schema issue before generating 500 lines of wrong code.

🚨 Rule 5: Never Touch Production Data
Green/Red environments. Learned this when I accidentally deleted 400 user records testing a delete function.

📌 Rule 6: Pin Every Win
Hit pin after every working feature. Saved me Thursday when I broke auth trying to add OAuth.

🧠 Rule 7: Outsource Architecture
I sketch database schemas in Claude or ChatGPT first. Lovable builds faster when it knows the structure.

🔍 Rule 8: Research APIs First
Spent 3 days building Stripe integration. Their API changed. Now I verify endpoints work before coding.

🧹 Rule 9: Clean Weekly
Every Friday, 30 minutes refactoring. Keeps the codebase from becoming unmaintainable.

These patterns cut my Lovable credits from 500 to 180 per project.
Build time dropped from days to hours.

The workflow matters more than the prompts.

Save this if you're building on Lovable.
Comment "LOVABLE" for my top 50 Lovable prompts.

Follow Alex for systems that ship.

Which rule changed your Lovable game?

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