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.

47 agents deployed this year.

Half failed.

Not because of the tech.
Because of the org.

The pattern I keep seeing:

Companies think AI is a fix.
It's actually an amplifier.

Chaos before AI?
Mystery spreadsheets everywhere.
Blame games daily.
Zero documentation.

Same shop with AI?
Mystery spreadsheets at scale.
Automated blame distribution.
Zero documentation, but faster.

Meanwhile the startup with clear roles, one source of truth, and tight feedback loops?

They turned AI into an unfair advantage.

Most orgs don't need an AI strategy first.

They need workflow clarity.
Data hygiene.
Basic digital readiness.

Otherwise you're just automating the mess.

I watched two companies deploy the same agent last month.

Company A spent six figures on consultants. Still debating "readiness."
Company B had clean processes. Deployed in a week. Saving $8K monthly.

Same tool.
Different foundations.
Opposite outcomes.

Before you buy another AI subscription, ask yourself:

Are you giving your team jets?
Or just dragging them in a faster cart?

The tech won't decide.
Your org already has.

What's your foundation actually look like right now?

Alex Cinovoj

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

Just finished the AI Engineering 2025 Edition.

Not another hype deck.
A system design handbook for LLM products that actually ship.

Here's what changes how you build:

𝗥𝗔𝗚 𝗶𝘀 𝗯𝗿𝗼𝗸𝗲𝗻 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁
Classic RAG retrieves too much irrelevant text.
You're burning tokens and money on noise.
REFRAG uses an RL-trained relevance policy to compress and filter context.
Faster. Cheaper. Same accuracy.

𝗦𝘁𝗼𝗽 𝗿𝗲-𝗳𝗲𝘁𝗰𝗵𝗶𝗻𝗴 𝘀𝘁𝗮𝗯𝗹𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲
If your data is part static, part dynamic, split it.
Cache the "cold" stable info.
Retrieve only the "hot" changing stuff.
One architecture decision. Massive cost savings.

𝗔𝗴𝗲𝗻𝘁 𝗥𝗟 𝗶𝘀 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝗲𝗱 𝗯𝘆 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀
You can't train agents to get better without standardized environments.
OpenEnv + trajectory rewards via ART and GRPO.
That's the path to agents that improve, not just talk.

𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 ≠ 𝘀𝘁𝘂𝗳𝗳𝗶𝗻𝗴 𝘁𝗲𝘅𝘁 𝗶𝗻 𝗽𝗿𝗼𝗺𝗽𝘁𝘀
It's an architecture problem.
Ingestion → retrieval routing → citation-backed generation.
Naive "just embed it" fails the moment answers span multiple tools.

𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘀 𝗦𝗢𝗣𝘀 (𝗖𝗹𝗮𝘂𝗱𝗲-𝘀𝘁𝘆𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀)
Three-layer context:
→ Main context (always loaded)
→ Tiny YAML metadata (skill index)
→ Full skill docs (loaded on demand)

Scale workflows without blowing your context window.

This isn't theory.
It's the playbook for production LLM systems.

Props to Daily Dose of Data Science for the resource.

What are you building right now?
RAG app, voice agent, internal copilot, or workflow automation?

Drop it below and I'll turn these principles into a build blueprint you can implement this week.

AI Engineer Blueprint

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

Agents in. 
Busywork deleted.

I just put together the AI Agent Blueprint I've been using.
Same crew behind the Ultimate MCP Guide.
Read this before your first meeting.

Here's what that looks like when you're actually building:

☑️ Cursor for agentic coding that understands your entire codebase. 
Tab, tab, tab through edits. Let it rip in full autonomy mode when you trust the direction.

☑️ Claude Code running straight from your terminal. 
Deep codebase awareness. Edits files. Runs commands. No IDE required.

☑️ Lovable turning prompts into production apps. 
React + Tailwind + Supabase backend. Ship a working MVP while competitors are still wireframing.

☑️ Manus AI for fully autonomous task execution. 
Research, data analysis, code generation. It plans, executes, and delivers while you sleep.

☑️ n8n orchestrating everything in the messy middle. 
CRMs, inboxes, file stores, calendars, webhooks. 
The spine that keeps agents running at 3am without breaking.

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 and scheduling in minutes, not days.
→ Document triage, routed with citations.
→ Pipeline hygiene that actually keeps CRM clean.
→ Support with 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 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 "AI AGENT"
I'll share the exact blueprint
I use to go from idea → instrumented agent → measurable ROI.

P.S. What's the ugliest manual process on your team right now?

Follow Daily Dose of Data Science for this masterpiece.

AI Agent Masterbook

You’re paying $200/month to evaluate AI outputs.

A free model does it better.

I just finished a playbook on LLM-as-a-Judge by Galileo. 

The expensive tools lost. 
The free ones won.

Most companies burn $20k/month on human QA.

It’s slow (72h turnaround), biased, and misses 80% of errors.

I switched a client to local setup using a minimax model last week.

• Old way: 3 engineers, $15k burn, 48hr feedback loops.

• New way: 10,000 evals in minutes. Free. Zero ongoing cost.

Consultants sell "AI governance frameworks."

Builders are shipping self-evaluating systems.

Stop manual reviews. 
Start automating quality.

LLM as a Judge

An open-source AI assistant just moved Cloudflare's stock.

Not a product launch.
Not an earnings call.
A GitHub repo.

Clawdbot is a personal AI agent you can self-host anywhere.
WhatsApp. Discord. Your own server.

The kicker: local-first privacy model.
Your data stays yours.

Traders saw this and made a bet, leading to a stock surge.

If millions of personal AI agents start running 24/7, they need infrastructure.
CDNs. Security. Edge compute.

Cloudflare sits at that chokepoint.

Here's what's actually happening:

𝟭. 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁𝘀
A repo with no revenue is shifting billions in market cap.
The infrastructure layer is where traders see the real money.

𝟮. 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗻𝗲𝘄 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿
One user running one agent creates constant API calls, data syncs, and network requests.
Scale that to millions and you've got a traffic explosion.

𝟯. 𝗧𝗵𝗲 𝗯𝘂𝘇𝘇-𝘁𝗼-𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗴𝗮𝗽 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘄𝗶𝗱𝗲
Stock volatility isn't product-market fit.
The real question isn't whether agents will drive traffic, but if that traffic will convert to durable revenue.

We've seen this movie before.
Hype spikes. Reality lags.

The winners won't be the ones chasing the hype.
They'll be the ones building foundational value when the market corrects.

Are you betting on AI infrastructure plays or waiting for fundamentals to catch up?

CLAWEDBOT

Two months ago I said Claude was the first AI I trust in prod.

I was right.

3 million+ lines of code later, it's still the workhorse.

Opus and Sonnet together.
Every agentic IDE.
Every web app we build.

Same stack. 
Same trust. 
Same results.

People hop between models weekly.
New release drops, they switch.
Then wonder why their workflows break.

We stayed with Claude.

Not because nothing else exists.
Because consistency compounds.

2 months ago...

→ Claude became the default in Cursor, Lovable, VScode
→ Agentic coding went from experiment to standard
→ The models got faster without losing context

Now...

→ Still runs 30+ hour sessions without drift
→ Still writes tests first
→ Still stays Git safe
→ Still ships cleaner than anything else we've tested

Most people are chasing the next model announcement.
Meanwhile builders are shipping with the one that works.

3 million lines.
Zero regrets.

Still useful over flashy.

What's your go-to model for production work?

Opus and Claude workshorses

AI that "works" 6–7 times out of 10 isn't automation.

It's a support tool with guardrails.

The Anthropic Economic Index finally measures what matters:
→ Did it succeed?
→ How long would it take a human?
→ How much autonomy did the user hand over?

They analyzed 2M conversations in one week.

Consumer AI. 
Enterprise API. 
Real usage.

Three findings every operator should internalize:

𝟭. 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝘁𝗵𝗲 𝘁𝗮𝘅 𝗻𝗼𝗯𝗼𝗱𝘆 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝗽𝗮𝘆

Once you adjust "AI can do it" by "AI succeeds often enough," the productivity impact drops.

If you're selling automation, you'd better be measuring success rates like your business depends on it. 
Because it does.

𝟮. 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗸𝗶𝗹𝗹 𝗥𝗢𝗜 𝘀𝗶𝗹𝗲𝗻𝘁𝗹𝘆

AI crushes one part of the workflow. But jobs are chains of dependent tasks.

One weak link drags the whole thing down.

Don't brag that AI speeds up 30% of the process. Show me the end-to-end throughput after handoffs, approvals, edge cases, and rework.

𝟯. 𝗝𝗼𝗯𝘀 𝘄𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱. 𝗧𝗵𝗲𝘆'𝗹𝗹 𝗴𝗲𝘁 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝗱.

AI tends to cover higher-education tasks first, writing, analysis, knowledge work.

The leftover work? The messy operational stuff humans still grind through.

Your team will either move upward into judgment and decision-making, or get stuck doing the residual work nobody automated.

I'm running my org like an engineering team now:

Pick one workflow. 
Define success. 
Instrument it. 
Ship the smallest AI assist. 
Measure failure modes. 
Add checkpoints. 
Repeat.

The winners in 2026 won't have the spiciest demo.

They'll be the ones who can say:
"We reduced this process from 4 hours to 20 minutes, it succeeds 95% of the time, and exceptions route cleanly to a human."

That's not hype. 
That's operations.

Where are you seeing bigger wins right now, copilot or full automation?

Anthropic Economic Index 2026

I use AI unapologetically.

Not to hide behind it.
To build with it.

Scroll down. 
You'll see two pictures of me with Dwayne "The Rock" Johnson.

They're fake.

100% AI-generated.
Zero pixels of reality.
And they look completely real.

I'm not posting this to fool anyone.
I'm posting this to make a point.

𝗧𝗵𝗲 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲.
The question isn't whether to use them.
It's whether you'll admit you do.

Some people hide their AI usage like it's shameful.
I put it front and center.

Why?

Because pretending AI isn't part of how we create in 2026 is delusional.
Because transparency builds trust faster than secrecy.
Because the builders who win are the ones who ship, not the ones who gatekeep their process.

I use AI to:
→ Generate visuals I couldn't create alone
→ Draft content faster so I can focus on ideas
→ Test concepts before committing hours to execution
→ Build agentic systems that actually work in production

The video in the comments? Fully AI-generated.
The images? Same.
The message? 100% mine.

That's the line.

AI is the tool. 
You're still the operator.

Stop apologizing for using what's available.
Start building with it openly.

What's something you use AI for that you haven't talked about publicly?

Drop it below. 

No judgment here.

I didn’t adopt MCP because I’m smart.

I was drowning in glue code. 

I tried it once, and suddenly, everything clicked.

Here’s the exact playbook I wish I had 90 days earlier.

What MCP is (in one line)

→ A universal layer for exposing tools and data to LLMs. 

Think “contract-based API” so everything can talk to everything else, reliably.

What happened when we used it
✅ Cursor MCP client → our MCP server → Supabase → webhooks → Claude reply
✅ Legacy trades biz synced, no vendor SDKs
✅ Lead routing shrunk from three microservices to one endpoint
✅ Fewer brittle hacks, more modular, snap-together systems
Five fast moves to steal
✅ Pick one workflow. Don’t boil the ocean.
✅ Wrap real tools (DB, files, CRM) as MCP tools, not prompt soup.
✅ Keep glue tight (~30–50 lines per tool).
✅ Track every step (trigger → action → result).
✅ Reuse across teams. Don’t rebuild.

When NOT to use MCP
If a webhook and a SQL query solves it, do that. 
Complexity is a tax.

Today, most of our ops start with MCP.

Fewer meetings. Faster shipping..

If you're building real AI workflows, follow me.
Operator-grade builds, no fluff.

And resharing this could save someone a week.

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

MCP Masterbook

Chat is dead.

Google Cloud's 2026 agent report just confirmed what builders already knew.

The new job isn't typing prompts.
It's running systems where software plans, acts, and you supervise.

Most teams are still using AI like it's 2023.
Asking questions.
Getting answers.
Copy-pasting into docs.

That's not the win.

Agents are the win.

The 5-step playbook I use with clients:

Step 1: Pick one workflow
↳ Clear inputs. Clear outputs. Nothing fuzzy.

Step 2: Give the agent tools, not opinions
↳ CRM, inbox, docs, tickets.

Step 3: Force plan-then-execute
↳ Human approval on edge cases. Always.

Step 4: Log every action
↳ Keep raw inputs. No black boxes.

Step 5: Measure and scale
↳ Track time saved. Track error rate. Then expand.

We ran this exact playbook for a support team last month.
40% faster first response.
Zero missed tickets.

The gap between teams using chat and teams running agents is growing daily.

Useful over shiny.

What workflow would you hand to an agent first?

2026 AI Trends

Early adopters do 9x more agentic queries than everyone else.

Perplexity just dropped data from hundreds of millions of AI interactions.

The gap is brutal.

Their first cohort of users?
2x more likely to adopt AI agents.
9x more agentic queries than general availability users.

And it gets worse.

Adoption skews toward:
→ Higher GDP countries
→ Higher education populations
→ Knowledge workers and digital roles

The people who need leverage most are getting it least.

55% of agent use is personal tasks.
30% professional.
16% educational.

Top use cases? Shopping and online courses.

Not "replacing jobs."
Replacing friction.

The brutal truth:

AI isn't creating one future.
It's creating two.

Power users compounding daily.
Everyone else still watching tutorials.

The gap isn't closing.
It's accelerating.

Access expanded adoption.
But early movers built the habits.

Same pattern I keep seeing with every new tool.

The barrier was never the technology.
It was showing up early and actually using it.

Which side of the gap are you on?

The Adoption and Usage of AI Agents

Most people think 2026 is about AI writing code faster. 

This report says it is bigger than that. 
The whole build loop is changing. 

Agents are starting to write tests, fix bugs, generate docs, and keep going for hours or even days. 

But here is the catch. 

Developers use AI in about 60 percent of their work, yet they only fully delegate 0 to 20 percent. 

So the job is not delegate and chill. 
The job is orchestration. 

Proof is already here. 
One enterprise project that was estimated at 4 to 8 months shipped in 2 weeks using Claude via Augment Code. 

Rakuten had Claude Code implement a complex method inside vLLM, a codebase with 12.5 million lines, in 7 hours, with 99.9 percent numerical accuracy. 

Zapier hit 89 percent AI adoption with 800 plus internal agents, and even legal teams cut review time from 2 to 3 days down to 24 hours. 

Here is the playbook for 2026. 

Run multi agent workflows. 
Add automated review so humans only check what matters. 
Push agentic building to ops, sales, and legal, not just engineers. 
Design security first because the same power helps attackers scale. 

P.S. What is your biggest bottleneck right now, speed, quality, or security?

2026 Agentic Code Trends Report

Stop overthinking AI. 

Start using it.

Your competitor just automated their proposal process.

You're still Googling "best AI tools for small business."

The biggest hurdle isn't the technology.
It's the "where do I even start?" paralysis.

I mapped 30 use cases across 5 areas of business.

Marketing
↳ SEO briefs, social captions, ad copy, email sequences

Sales
↳ Lead scoring, proposal drafts, follow-up sequences

Operations
↳ Invoice matching, scheduling, inventory alerts

Finance
↳ Expense categorization, report summaries, forecasting

Support
↳ Ticket triage, FAQ responses, sentiment tracking

You don't need a million-dollar budget.

You need three things:

The Goal
↳ Drafting RFPs. Summarizing calls. Routing leads.

The Tool
↳ n8n, Zapier, Sheets, Make.

The Model
↳ Claude, ChatGPT, Gemini.

Pick one workflow.
Automate it this week.
Track hours saved.
Move on.

The playbook I use with clients:
✅ Choose one measurable bottleneck
✅ Map the happy path and the edge cases
✅ Connect the right tool to the right model
✅ Log every action
✅ Ship the improvement. Then expand.

Don't boil the ocean on day one.

Start narrow. 
One workflow. 
One win.

Full breakdown of all 30 use cases.

Which area is bleeding the most time in your business right now?

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 and others for sharing this with me a while back!

Stop treating AI like a search box.

The real gains show up when you use agents that plan, act, and adapt.

Start narrow
One workflow
One agent
Then scale

Pick one process
Intake
Invoice
Scheduling

Automate it
Track hours saved
Move on

Agents are simple blocks working together
Perception
Reasoning
Memory
Action
Feedback

Why this works
Single scope means clear success
Clear success earns trust
Trust unlocks the next workflow

Proof in the field

A CPA team gets 30 hours back each month
A law office cuts client intake to under 10 minutes
A vet clinic halves no-shows and fills the calendar

The playbook I use with clients
Choose one measurable bottleneck
Map the happy path and the edge cases
Give the agent access to the right tools
Record every action and decision
Review outcomes and refine prompts
Ship the improvement
Repeat

Signals you are ready today
You repeat the task every week
There is a single source of truth
You can define done in one sentence

Things to avoid
Boiling the ocean on day one
Shiny demos with no ownership
Black-box prompts with no logging

I build safe, practical systems. Read Galileo paper.
Follow Alex Cinovoj for real agent builds and weekly breakdowns.
Share with your network if you want more teams using agents that actually work.

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techtide-ai

techtide-ohio

techtide-msp

Galileo

AI Agents

I used to spend 10+ hours a week manually prospecting.

Spreadsheets. LinkedIn stalking. Missed follow-ups.
It was a mess.

Now a robot does it for me.
24/7. 5 meetings booked per week. On autopilot.

Here's the stack I built with zero code:

𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺:
Most prospecting is reactive. You find someone interesting, manually research them, write an email, forget to follow up.

𝗧𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱:
→ Find the right people at the right time (when they change jobs)
→ Enrich their data automatically
→ Write personalized outreach
→ Run 24/7 without me

𝗧𝗵𝗲 𝘀𝘁𝗮𝗰𝗸:
• Lusha's Signals API, alerts me when key contacts change jobs
• n8n, the central hub automating the entire workflow
• Lusha's MCP, enriches contacts with verified data inside the AI
• Claude/ChatGPT, writes personalized first-touch emails

No SDRs. No manual research. No forgotten leads.

I've detailed the entire step-by-step process in the carousel below.
This is the exact workflow I run at TechTide AI.

Want to build your own automated prospecting engine?

I've packaged up my exact n8n workflow as a free template.

Comment "AI BUILDER" below and I'll send it to you.

#Lushapartner

AI Builder

AI strategy without brain strategy is sabotage.

Most companies are pouring millions into models.
Then running them on burned-out teams.

The WEF just dropped a report on brain capital.
Brain capital = brain health + brain skills.

59% of workers need reskilling by 2030.
$6.2 trillion in GDP upside from better brain health outcomes.

The bottleneck isn't the model.
It's the humans operating it.

You can't run a Ferrari engine on a cracked chassis.

Here's the operator playbook:

𝟭. 𝗦𝘁𝗼𝗽 𝗿𝗲𝘄𝗮𝗿𝗱𝗶𝗻𝗴 𝗯𝘂𝗿𝗻𝗼𝘂𝘁 𝗹𝗶𝗸𝗲 𝗶𝘁'𝘀 𝗼𝘂𝘁𝗽𝘂𝘁
Exhaustion isn't productivity. It's technical debt on your people.

𝟮. 𝗠𝗮𝗽 𝘆𝗼𝘂𝗿 𝘁𝗼𝗽 𝟱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝗱𝗿𝗮𝗶𝗻 𝗳𝗼𝗰𝘂𝘀
Which tasks add anxiety? Which ones kill deep work? Name them.

𝟯. 𝗨𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗿𝗲𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝘄𝗼𝗿𝘀𝘁 𝘀𝘁𝗲𝗽𝘀, 𝗻𝗼𝘁 𝗮𝗱𝗱 𝗺𝗼𝗿𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀
AI should subtract friction, not multiply it.

𝟰. 𝗧𝗿𝗮𝗶𝗻 𝘁𝗵𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝗔𝗜 𝗰𝗮𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗳𝗮𝘀𝘁
Judgment. Creativity. Emotional control. These compound.

𝟱. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗯𝗿𝗮𝗶𝗻 𝗰𝗮𝗽𝗶𝘁𝗮𝗹 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲
Because it becomes revenue.

The companies winning with AI aren't just buying better tools.
They're protecting the humans who operate them.

Burnout isn't a side effect.
It's actively sabotaging your AI ROI.

What part of your work week is the biggest mental tax right now?

The Human Advantage

Just read a report on 65+ enterprise AI agents.

The pattern is brutal and beautiful:
Every industry is killing the same bottlenecks.

𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲: 
FNOL agent captures claims instantly. No 72-hour wait.
Underwriting assistant prices policies automatically. No manual review.
Form processor digitizes handwritten claims. No data entry hell.

𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁:
Policy memo writer generates briefs in minutes. Not weeks.
Grant matching agent finds funding automatically. Not after 50 searches.
Permit agent approves standard requests instantly. Not in 6-8 weeks.

𝗙𝗶𝗻𝗮𝗻𝗰𝗲:
Earnings call agent extracts insights immediately. Not after 3 analysts debate.
Reconciliation agent catches discrepancies automatically. Not after month-end panic.
Investment memo generator consolidates pitch decks instantly. Not after all-nighters.

Here's what hits hardest:

These aren't prototypes.
They're production deployments.
Today.

The formula never changes:
Upload documents → Agent extracts context → LLM applies rules → Structured output ships.

No steering committees.
No transformation roadmaps.
Just agents doing the work humans hate.

While consultants sell "AI readiness assessments," these companies already automated their worst workflows.

Which manual process is eating 20+ hours of your team's week?

That's your first agent.

Thanks StackAI for the resource.

StackAI

Ai agents in enterprise 

Someone has to say this.

The scariest part of AI is not what it can do for users.
It is what it can do to itself.

I just read the CSET report "When AI Builds AI" and the core finding is blunt.

Frontier AI labs are already using AI to speed up AI research.
They use their best models internally before the public ever sees them.

This is not hype.
It is normal practice now.

The real risk is not a robot takeover story.
It is a visibility problem.

If AI starts doing more of the research work end to end, progress can speed up while human understanding goes down.

That creates strategic surprise.

Not because anyone is evil.
Because feedback loops compound faster than oversight.

The report makes one thing clear.

We do not agree on whether this explodes or plateaus.
We do agree that we would not see it coming in time if it does explode.

That is the uncomfortable truth.

The debate is not build versus buy.
Not open versus closed.

It is who can see inside the loop while it is running.

Credit to the Center for Security and Emerging Technology for putting real structure around this question.

The right response is not panic.
It is instrumentation.

If you cannot measure how automated your research loop is becoming, you are already late.

What signal would you want to see first, internal benchmarks going dark? Hiring shifts at labs? Something else?

When AI Builds

AI funding nearly doubled to hit $225.8B in 2025 🚀

The latest State of AI report from CB Insights reveals a market defined by massive bets and rapid consolidation. 

While deal volume slowed, capital concentration reached new highs.

Key takeaways:
• 💰 Mega-rounds rule: Deals worth $100M+ drove 79% of all funding.
• 🤖 Robotics is hot: The sector led with 11.4% of all AI deals.
• 🦄 Unicorn boom: 75 new AI unicorns were born this year.
• 🤝 M&A surge: Exits jumped 1.5x to hit 782 deals.

Investors are doubling down on winners, with LLM developers alone capturing 41% of total funding.

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