Alex Cinovoj

I didn’t start in tech.

I started on concrete floors.

Second shift.
Moving pallets.
Counting minutes until my legs stopped buzzing.

Then came an injury.
Not catastrophic, just enough to make me stop and ask:

“Is this really it?”

I had two choices:
Accept the ceiling.
Or find a new floor.

Per Scholas opened a door.
I walked through it with an A+ cert, a cheap laptop, 
and a chip on my shoulder that I’ve since replaced with systems.

Those early years?

Unsexy.
Priceless.

The Wendy's Company help desk.
Nights and weekends where the ticket queue never slept.
That’s where I learned “users” are just people with deadlines.
And uptime?

That’s a love language.

Then I moved up, systems engineer.
Racking gear.
Fighting flaky DHCP.
Mediating arguments between tools that barely spoke to each other.

Then Microsoft Azure Cloud hit.
And it clicked:

The fastest way to reduce pain wasn’t more servers.
It was fewer manual steps.

As cloud took off, I watched the same pattern everywhere:
Brilliant, overworked people trapped inside brittle processes.

ChatGPT came along.
Everyone chased chat bots.
I chased agents, systems that plan, act, and verify.

When I started posting about it, nobody cared.

Crickets.

So instead of bingeing more YouTube tutorials, I built a content machine.

Showed up every day.
Supported other creators, big and small.
Kept iterating.

And slowly, things started clicking.
Patterns showed up again.

Useful over shiny.
Metrics over vibes.
Substance over spectacle.

That became my filter for everything.

Late last year I was stuck in a dead end MSP, so I launched TechTide AI, 
helping small teams and digital agencies automate their way into the future.

It was ambitious.
Messy.
Worth every second.

Early summer came Automation Vibes.
Practical conversations about what actually moves the needle for operators.

And now, I split my world across three pillars.

Late summer I re-joined amazing team of old friends where, I keep systems running smoothly at StarSevenSix, helping mid-sized enterprises with IT, automation, and AI readiness.

Early founder mornings and weekends, are spent helping digital agencies grow and automate through TechTide AI and co-run Automation Vibes.

Me and my AI agents grow my personal brand on LinkedIn 24/7, while helping other C-level execs, VCs, and busy founders do the same.

Because the truth is, the leverage has shifted.
Your network is your operating system.

And if you’re not building in public, you’re invisible.

Check the comments, I’m sharing 12 of my favorite builders in October who are worth following.

As I celebrate 24k followers on LinkedIn this week, I just want to say thank you, to all the operators, friends, and LinkedIn family who’ve joined this journey, shared your insights, and helped me grow along the way.

Now, watch the 30-second clip of Perplexity Comet automatically accepting my 300+ LinkedIn requests from this week.

It’s wild what happens when systems meet storytelling.

Stop pretending the jargon makes sense.

Earlier this year I sat in a kickoff with smart people.

Great budget. 
Big goals.

Ten minutes in, we were lost.

Someone said “Let’s add RAG.”
Another said “We need orchestration.”

A third asked about memory, tools, and planning.
No one could explain them in plain English.

So the project drifted.
Meetings grew. 
Momentum died.

We built slides, not systems.

I went back to the shop floor.
I wrote every word I heard on a whiteboard.

LLMs. 
LRMs.
Agent.
ReAct. 
Tools.
State.
Swarm.
Debate.
Action.
Memory. 
Planning.
Handoffs.  
Evaluation.
Perception. 
Architecture.
Environment.
Multi-agents. 
Orchestration.
Knowledge base.  
Chain of thought.

Then I forced a rule.
No feature until we can teach the word.

Simple test. 
Sixth grade level.
If we stumble, we pause and learn.

That rule changed everything.
Teams aligned faster.

Tickets got smaller.
The agent shipped.

I turned the whiteboard into a one-screen glossary.

Twenty terms. 
Zero fluff.

Use it in your next meeting and watch the fog lift.

Save this.

Share with one teammate who owns automation.
Follow Alex for systems that ship, not slides.

Most “AI strategies” fail because teams mix up four different machines and expect one button to run them all.

Here is the clear map.

LLM workflow.
✔️ One prompt in. One answer out.
✔️Great for drafts and Q&A.
✔️No tools. No memory. No plan.

RPA.
✔️ Fixed clicks on a trigger.
✔️ Great for legacy screens.
✔️ Brittle when the UI moves.

AI agents.
✔️ You give a goal.
✔️ They plan tools and APIs.
✔️ They act in steps.
✔️ They keep short and long memory.
✔️ Great for fetch, transform, write, report.

Agentic AI.
✔️ An orchestrator plans the job.
✔️ It splits work. Research and execute.
✔️ It verifies, reworks, and compiles the result.
✔️ Great for complex, long running work with quality control.

Operator rules.
✔️ Start with one outcome.
✔️ Pick the right machine.
✔️ Log every action.
✔️ Set pass rate and time targets.
✔️ Add guardrails and an off switch.

If your team argues about tools, anchor on this model first.

Then choose the stack that fits the job, not the hype.

Save this for your next build.

Follow Alex for systems that ship and proof you can run today.

This diagram is simple by design.

Enterprise stacks add auth, data contracts, observability, extreme evals, retries, encryption, human review, and change control.

I don’t build chatbots. I ship agents that do the work.

Agents in, busywork out.

I just got my hands on the ultimate AI Agent Blueprint.
Same crew behind the Ultimate MCP Guide.
Read this before your first meeting.

Here’s what that looks like in the real world:

☑️ CrewAI agent crews that plan, reason, and delegate.
☑️ Patterns, no “assistant theater,” actual execution.
☑️ Custom Python tools exposed over MCP so agents can hit internal APIs, scrape, transform, and write back, secure, auditable, fast.
☑️ n8n orchestration to wire agents into the messy middle: CRMs, inboxes, file stores, calendars, payment rails, and webhooks.
☑️ Mastra + RAG to ground answers in your data, not vibes, citations, confidence, and retrieval that doesn’t hallucinate.
☑️ Microsoft Azure AI Foundry for model governance, evals, and safe rollouts, because “move fast” doesn’t mean “break prod.”
☑️ Salesforce Agentforce to act inside your pipeline, create tasks, update opps, triage cases, trigger flows.

Output isn’t a paragraph. It’s a changed record.
Guardrails + Observability.

Rate limits, validation, retries, and human-in-the-loop where it matters.
Every action is logged. Every step is testable. No black boxes, no magic.

Where this pays off 💰
→Intake, scheduling in minutes, not days.
→Document triage, routed with citations.
→Pipeline hygiene, agents that actually keep CRM clean.
→Support, faster first response and tighter SLAs.

Agents don’t sit next to the work. They are the workflow.

If your “AI” can’t click buttons, call tools, and update systems, you don’t have AI, you have a demo.

If you lead an SMB or a mid-market team and want an agent that clocks in on Day 1, I’ll show you what this looks like with your stack.

Comment “AGENT”
I’ll share the exact blueprint
I use it to go from idea → instrumented agent → measurable ROI.

P.S. What’s the ugliest 7-click process on your team right now?

Follow Daily Dose of Data Science for this masterpiece.

Everyone's talking about AI.

But China’s not just talking.

They’re executing a different strategy:

Power first. Then AI.

This isn’t about demo models or conference decks. It’s about real systems that run cities.

Here’s what they’re doing:
❌ Build the grid first
❌ Pool compute across cities
❌ Move workloads to where energy is cheap + green
❌ Treat data as infrastructure, not exhaust
❌ Run domain models in factories, banks, hospitals

The result?

Boring infrastructure.
That makes flashy AI actually work.

Useful beats shiny. Every time.

If you're building the AI that actually runs, not just demos,
Share this with one person who owns AI or automation.

And follow for more systems-level thinking.

Stop using n8n like it’s 2024.

Everyone still calls it “Zapier for devs.”

That’s not even close.

It’s the backbone for full-stack AI systems.

n8n runs the boring parts that make the flashy parts work.

Go to their site. You won’t see toy automations.

You’ll see production-grade systems like:

✅ AI onboarding agents
✅ Security ticket enrichment
✅ Natural language to API calls
✅ Review summaries → CRM
✅ Approval loops with humans in the middle
✅ Automated data cleanup before RAG
✅ Model evals wired to alerts
✅ Audit logs and retries you can trust
✅ Webhooks that don’t fail silently

And the kicker?

6,000+ on their site.
Another 6,000+ in my private vault.
That’s over 12,000 templates, just plug in and go.

I don’t use n8n for demos.

I use it to run real client operations:

Quotes → Cash
Intake → Scheduling
Triage → Resolution
Fetch → Transform → Write → Report

Useful > Shiny
Systems > Hacks

If you're still ignoring it, you're already behind.

Save this.
Comment "n8n", I will send the vault.

Send it to your most technical operator.
Follow Alex for more real-world automation stacks.

Most people are wasting their AI budget.

Not because they’re not buying enough tools.

Because they’re not using the ones they already have.

This week's Automation Vibes Sunday Edition dives into the rise of invisible AI, the quietly powerful features already baked into tools like Microsoft 365, Google Workspace, and Salesforce.

You don’t need another shiny AI startup.

You need to unlock the automation sitting right under your nose.

Here’s what’s inside:

✔️ How Microsoft’s Fall Copilot release changes the game for SMBs
✔️ Why LangChain raised $125M, and what it means for your tech stack
✔️ 5 ways to activate hidden AI features this week

→ Click the link and read the full Sunday Edition

The podcast returns in November.

Until then, what’s one invisible AI feature you’ve recently discovered?