Let AI do the heavy lifting for your Revenue.

Most sales teams still operate like it's 2019: manual prospecting, messy CRMs, and reps spending 80% of their time on tasks that don't make deals.Meanwhile, winning companies have built something different.They've built GTM engines. Systems where AI handles the research, the enrichment, the cleaning, the scoring, and the routing, so their team focuses on what actually builds pipeline: conversations.That's what I do.I'm the founder of TC9, where we design and build AI-powered GTM engines for B2B companies. We connect the dots between your data, your CRM, and automate the 80% that shouldn't need a human.But my work doesn't stop at execution.In my content, you'll find: → How AI is reshaping the way companies sell→ Which strategies are working right now (and which are already obsolete)→ The systems, tools, and frameworks behind modern revenue teamsIf you're a founder, VP of Sales, or revenue leader trying to figure out what the next era of GTM looks like: you're in the right place.

Jérémy Grandillon

OpenClaw hit 100,000 GitHub stars in 3 weeks.
OpenAI just hired its creator to lead their AI agent division.

Here's why every GTM team should be paying attention.

OpenClaw is an open-source AI agent that runs 24/7 on your machine. 

It connects to WhatsApp, Slack, your CRM, your email. 
Anything.

It doesn't just answer questions. It executes tasks autonomously while you sleep.

It has persistent memory, a built-in scheduler, and anything to use your entire tool stack.

ChatGPT waits for you to open a tab. 
OpenClaw checks what needs doing, and does it.

Now think about what an SDR actually does all day.

→ Research a prospect: 10 min
→ Log info into HubSpot: 5 min
→ Write a personalized email: 10 min
→ Follow up on stale deals: 15 min
→ Repeat 30x

For every hour of selling, nearly two hours go to admin, research, and data entry.

OpenClaw can already scrape a company's website, pull LinkedIn data, summarize recent news, draft personalized outreach, update your CRM, and schedule follow-ups. 

All from a single message.

This isn't replacing SDRs. It's removing the 60% of the job that never required a human.

The reps who adopt this will outperform entire teams. 
The ones who ignore it will spend their days doing work a $20/month agent handles better and faster.

Honest caveat though: it's early. 

Security researchers flagged real concerns. 
Exposed instances, prompt injection risks, etc. 

Not enterprise-ready (yet).
But the trajectory is obvious.

We're 6 to 12 months from sending one message to an AI agent: "Build me a campaign targeting Series B fintech companies in Europe." 

And it researches the companies, finds the contacts, writes the sequences, schedules the sends, monitors replies, and books meetings.

The full SDR workflow. Automated. End to end.

Sam Altman said it himself: "The future is going to be extremely multi-agent."

I'm considering installing OpenClaw, connecting it to a real GTM stack, and putting it to the test. 

Would you be interested in such an experiment? 
Let me know in the comments! 🦞

Can OpenClaw replace Sales Teams

Claude Code is becoming inevitable.

But 90% of sales and marketing teams haven't touched it yet.

I spent weeks testing resources, courses, and repos to find what actually matters for revenue teams.

Here's the full list:

(Save this post. You'll come back to it!)

📖 Official Anthropic Resources

→ Claude Code Docs & Installation (the starting point)
🔗 https://lnkd.in/erW_rUrD

→ Claude Code GitHub (open-source repo, plugins, community contributions)
🔗 https://lnkd.in/eNS_huU6

→ Claude Code Best Practices (CLAUDE.md setup, custom commands, multi-instance workflows)
🔗 https://lnkd.in/e2w8tCAe

→ Context Engineering for AI Agents (why context structure matters more than the model)
🔗 https://lnkd.in/es5HWkH7

→ MCP (Model Context Protocol) (connect Claude Code to your CRM, enrichment tools, and data sources)
🔗 https://lnkd.in/eWaxd7PQ

→ Claude Code in Action (Free, official Anthropic course). Context management, custom commands, MCP servers, planning modes.
🔗 https://lnkd.in/ejb4sn2n

→ Claude Use Cases for Marketing & Sales
🔗 https://lnkd.in/e_ZebcAc

🎧 Watch & Listen

→ AI Engineer: Barry Zhang and Mahesh Murag from Anthropic
🔗 https://lnkd.in/eZN9uuGi

→ Greg Isenberg + Ras Mic: "The Most CLEAR Explanation of Claude Code"
🔗 https://lnkd.in/eqGgVT9c

🛠️ Repos, Skills & Tools

→ Anthropic Official Skills (69K+ stars). Docx, pptx, xlsx, PDF creation, and more.
🔗 https://lnkd.in/eCpuP6HJ

→ Superpowers by obra (9K+ stars). Brainstorming, spec writing, TDD, subagent-driven development. Turns Claude Code into an autonomous engineering teammate.
🔗 https://lnkd.in/eZib-p7F

→ Claude Code Guide by Cranot (2,360+ stars). Most complete community reference. Auto-updates every 2 days.
🔗 https://lnkd.in/eQYiAVKu

→ Awesome Claude Code. The definitive community list. Slash commands, CLAUDE.md files, agents, skills, hooks, plugins.
🔗 https://lnkd.in/eXzWUxgw

→ Skills.sh. 55,000+ installable skills. Search marketing-relevant ones like copywriting (12K+ installs), SEO audit (17K+), social content (7K+).
🔗 https://skills.sh/

→ AI Templates (aitmpl.com). 30+ pre-built Claude Code agents, skills, commands, hooks, and MCP configs.
🔗 https://lnkd.in/eRgRPK_s

→ OneWave-AI Skills Collection. 100 production-ready skills for sales, business automation, content creation.
🔗 https://lnkd.in/eam8FR9k

I'm already using Claude Code to build custom tools and workflows for GTM.

The GTM teams that learn this now will have a 12-month head start.

What's the first GTM workflow you'd build with Claude Code?

Most salespeople send one email and move on.

Lemlist analyzed hundreds of thousands of outbound emails on their platform.

The pattern is clear: multi-touch sequences consistently outperform single sends.

Not by a little. By a lot.

Here's what's actually happening when you follow up:

→ Your prospect gets 100+ emails a day. 
One email is invisible. Three emails make you exist.

→ Timing is random. 
Your first email lands during a board prep. Your third lands when they're actively looking for a solution.

→ Following up isn't "being pushy." 
It's removing friction. Most people don't ignore you because they're not interested. They ignore you because they forgot.

The reps I see booking the most meetings all have one thing in common.

They follow up more than their competitors.

Not with "just checking in" emails. With new angles, new value, new reasons to reply.

The fear of being annoying costs more pipeline than persistence ever will.

What's your follow-up sequence look like right now? 2 emails? 5? More?

4 Reasons Why You Need Follow-Up Emails

Most sales teams are chasing the latest AI feature.
Meanwhile, fundamentals are broken.

No tool will save you if you can't answer three questions:

→ How am I relevant to my targets?
→ How do I reach out at the right time?
→ Is my technical setup delivering my messages?

Everything in outbound reduces to these three variables.

The rest is execution.

And execution goes wrong when you stack 5-6 tools and lose yourself in complexity.

Or, you can focus on one good tool that covers it all.

I’ve been using lemlist for almost 10 years, and I consider it one of the best tools in the outbound stack.

Here's what makes it work:

→ Built-in lead database. 
When I define my ICP, I build the list directly inside the tool. No exporting CSVs. No enrichment subscriptions. Just one workflow.

→ Signal detection. 
Job changes, website visitors, LinkedIn activity. You know when the timing is right before you hit send.

→ AI features that make sense.
Automatically cleaning names, classifying leads in relevant buckets, company latest news. No AI for the sake of it, only what you’d have done anyway. 

→ Multi-channel sequences. 
Email, LinkedIn, WhatsApp. Deliverability monitoring, warm-up included, all channels in one workflow.

Get the strategy right first. 
Then pick the tool that matches your resources.

After that, you'll have plenty of time to evaluate the next shiny feature the lead gen bubble is selling you.

What does your current outbound stack look like?

Stop overcomplicating outbound - Fix the basics

8 years of cold email. Many things changed.

But 3 principles never moved.

I've sent millions of cold emails since 2018.

Open rates went from easy to earn to nearly impossible if you skip the fundamentals.

Here's what still works, no matter what Gmail or Outlook throw at you:

→ Targeting
If you're emailing anyone outside your ICP, you're burning domains for nothing. Tight lists convert. Broad lists destroy your sender reputation.

→ Infrastructure 
SPF, DKIM, DMARC, domain rotation, inbox warming. In 2018, I set this up manually. Today, it's too complex and too costly to DIY. 

I use Mailpool for all my email infrastructure. Domains, inboxes, spam checks, Google Workspace, Microsoft 365, and SFTP. All handled. No going back.

→ Messaging 
Landing in the primary inbox is step one. Getting a reply is what keeps you there. Low volume. No spammy words. Relevant copy only. Replies signal to Gmail that you're a legit sender.

Most teams fail at cold email because they skip straight to copywriting.

Copy doesn't matter if your emails land in spam.
Deliverability doesn't matter if you're emailing the wrong people.

The order matters: targeting → infrastructure → message.

Get these 3 right, and cold email still outperforms every other outbound channel in cost per meeting.

And I highly recommend using a trustworthy provider like Mailpool to handle your infrastructure!

Which of these 3 is the hardest to get right for your team?

3 key principles after 8 years of cold email

I used to be "the cold email guy."

For years, that was my identity. Automating prospecting. Optimizing deliverability. Building sequences that actually get replies.

I was good at it. But at some point, I started feeling stuck.

I was solving the same problems over and over. Open rates. Reply rates. Infrastructure fixes. The tactics kept evolving, but the scope stayed small.

I wasn't thinking about revenue. I was thinking about replies.

And I kept asking myself: is this it?

The companies I admired were doing more. They had marketing, RevOps, sales, and data teams all working toward the same goal. Every piece of the revenue engine was in place.

But here's what I noticed: those pieces weren't connected. They were running in parallel, not as a system.

Then 2023 happened.

AI. Clay. 

AI-powered enrichment. Intelligent automation. Suddenly the tools existed to do something new.

Not just automate prospecting. But connect everything.

Signals feeding scoring. Scoring feeding routing. Routing feeding outbound, ads, and nurture. One system. One data layer. Every motion working together.

That's when it clicked.

The winning companies had all the pieces. What they didn't have yet was the glue.

AI + automation wasn't just the next chapter of outbound. It was the way to connect what the best companies were already doing separately.

So I made a choice.

I stopped positioning myself as an outbound specialist and started thinking like an engineer.

Not just "how do I send better emails?"
But "how do I build a revenue system where every piece compounds?"

→ I started mapping 100+ buying signals
→ I connected outbound, marekting, and CRM
→ I built scoring models that route leads automatically

Most importantly, I stopped thinking in campaigns and started thinking in systems.

Cold email is still in the playbook. But it's one play in a system of 75.

The real game is GTM Engineering. Building the glue that makes revenue engines work.

If you're feeling stuck in one skill, one channel, one identity, maybe it's time to zoom out.

The best version of your work might be bigger than what you're known for today.

What skill are you evolving beyond right now?

The SDR role isn't dying. 
It's being redesigned.

Humans doing robot work was never the play.

Sales is hard. I've lived it for years. 
But hard doesn't have to mean miserable.

And your growth doesn't have to suffer either.

The GTM Engine changes everything. 
I've seen it firsthand.

Here's what a day in the life of an SDR should look like in 2026:

𝟵:𝟬𝟬 𝗔𝗠 
❌ 12 open tabs. Manual scraping. Copy-paste chaos. 
✅ You log in. Warm leads are already waiting.

𝟭𝟬:𝟯𝟬 𝗔𝗠 
❌ LinkedIn + Google detective work. 
✅ Intent data is enriched, scored, and sorted for you.

𝟭:𝟬𝟬 𝗣𝗠 
❌ Staring at a blank screen, writing cold emails from zero. 
✅ Smart outreach is drafted. You refine. You don't rebuild.

𝟯:𝟬𝟬 𝗣𝗠 
❌ Manual follow-ups falling through the cracks. 
✅ Behavior-based triggers handle it. CRM stays clean.

𝟱:𝟯𝟬 𝗣𝗠 
❌ 40 touchpoints. 1 decent conversation. Soul-crushing. 
✅ Fewer hours. Better conversations. Real momentum.

Same title on the business card. 
Completely different reality behind it.

AI isn't here to replace SDRs. 
It's here to cut through the noise that was burning them out.

♻️ Reshare if this resonates. 
➕ Follow Jérémy Grandillon for more!

SDR Life Before VS After Automation

SDRs don't fail because of effort.
They fail because of infrastructure.

Most SDR teams spend 80% of their time on research, CRM updates, and admin.

The top-performing ones spend 80% on conversations and strategy.

Here's what separates them:

→ Lead research: 
Manual tab-switching vs. automated enrichment through Clay or Builtwith.

→ Personalization: 
Writing every email from scratch vs. AI-generated messaging based on intent signals.

→ CRM hygiene: 
Repetitive manual updates vs. auto-synced workflows in HubSpot or Salesforce.

→ Targeting: 
Static lists with zero context vs. signal-qualified prospects scored by job changes, funding rounds, tech installs.

→ Onboarding: 
Weeks of shadowing vs. prebuilt sequences and playbooks ready on day one.

The SDR role isn't dying.

But the SDR operating system is being completely rebuilt.

What's the one manual task your SDRs still waste the most time on?

I just made our entire GTM Engineering Playbook free. 20+ pages. 300+ operational assets. 

Everything we use to build signal-based revenue engines for B2B companies.

After 8+ years in Sales, Marketing, and RevOps, every framework, scoring model, database, and workflow we run lives inside this playbook.

We use it to:
→ Define TAMs and qualify accounts at scale 
→ Detect 1st, 2nd, and 3rd party buying signals
→ Score and route leads to the right channel
→ Launch outbound plays triggered by real-time signals
→ Build entire GTM motions from audit to full speed

102 signals. 75 plays. 9 workflows. 300+ ready-to-use assets.

Here’s what’s inside:

1️⃣ Signal Detection

102 intent signals mapped to source, strength, and response window
82 scraper triggers for automated signal capture via Apify and Phantombuster
3-layer signal taxonomy: 1st party, 2nd party, 3rd party

2️⃣ Lead Scoring & Routing

Dual-axis scoring framework: Account Score + Lead Score on a 1-10 scale
Lead Treatment Matrix that routes leads to 6 outreach channels automatically
6 AI qualification prompts with JSON scoring rubrics ready for Clay

3️⃣ Outbound Execution

75 ready-to-execute outbound plays organized by funnel stage and effort level
6 day-by-day cadence templates with message frameworks and benchmarks
Complete cold email infrastructure guide: domains, DNS, warm-up, deliverability

4️⃣ Automation & Implementation

9 end-to-end automation workflows with trigger logic and routing rules
90-day implementation roadmap: from discovery to fully operational in 12 weeks
ROI calculator with formulas, benchmarks, and leadership-ready reporting

I know, that's a lot. 

Every piece compounds. 
Your signals feed your scoring. 
Your scoring feeds your routing. 
Your routing feeds your outbound. 
Your analytics extract what converts and feed it all back.

And the good news is that we'll keep updating it as we experiment new plays.

(Claude Code is coming). 

This is the exact methodology we deliver to clients. Now it’s free.

→ Connect with me
→ Comment “GTM”

I’ll send it straight to your DMs.

The SDR role isn't disappearing. It's splitting in two.

One path leads to GTM Engineering.
The other leads to burnout.

Most SDR teams still run the old playbook.
Manual lead research. Static sequences. Activity metrics.
Hours spent on data entry instead of selling.

That model is collapsing.

The teams winning right now look completely different.

→ They use Clay to enrich leads from 10+ sources automatically.

→ CRM updates itself through HubSpot workflows and system triggers.

→ Outreach adapts in real time based on intent signals, not arbitrary cadences.

→ Follow-ups run on autopilot until human judgment is actually needed.

One GTM Engineer replaces the chaos of 7 disconnected tools with a single orchestrated system.

The shift isn't AI replacing SDRs.

It's AI removing everything that was never selling in the first place.

Lead research. Data entry. Sequence management. Activity logging.

That was 70% of an SDR's week. Now it's zero.

What's left is the work that actually moves pipeline.
Strategic conversations. Pattern recognition. Creative problem-solving.

The SDRs who adapt become the most valuable people on the revenue team.

The ones who don't get outpaced by a Clay table and 3 Zapier automations.

What's the first part of your outbound workflow you'd hand off to automation tomorrow?

Clay just launched Ads. 
And most people will miss why this matters.

Here's the short version: 

You can now build ad audiences directly from your CRM data and buying signals, then sync them to LinkedIn, Meta, and Google ads.

Same enrichment engine that powers your outbound. Now powering your paid media.

Let me break down the workflow:

→ Pull a high-intent list from your CRM
Not a generic audience. Your actual pipeline. Your actual target accounts.

→ Layer Clay's enrichment on top
Job changes, funding rounds, tech stack signals, website visits. The same data you already use for outbound.

→ Clay enriches contacts 
Emails and identifiers to maximize match rates. They're claiming up to 95% on LinkedIn and 65% on Meta. Way above standard CRM-to-ads sync.

→ Push those audiences directly to LinkedIn, Meta, or Google. One sync. No CSV exports. No manual uploads.

But here's where it gets interesting.

You can target the exact accounts your sales team is working. Not lookalikes. The actual people.

You can exclude customers, employees, and out-of-ICP contacts dynamically.

You can shift budget based on live intent signals. 
Tier 1 accounts on your pricing page: they see your ads first.

This is CRM-powered precision advertising built on the same engine that runs your outbound.

And this is exactly why I keep saying GTM Engineering is not just about sales automation.

The same data layer that powers your lead scoring, your sequences, your CRM hygiene now also powers your ad targeting. 

Same signals. Same enrichment. Same logic. Applied across channels.

When your sales team works 200 high-intent accounts and your marketing team runs ads to a completely different audience, you're burning money. 
Clay Ads makes those two motions run on the same data for the first time.

For teams that already have GTM automation in place, this is an immediate multiplier. You're extending your existing system into a new channel with zero additional data work.

For teams that don't... the gap just got wider.

Clay keeps shipping features that prove the same thesis: GTM is a system, not a set of disconnected tools.

What channel would you connect to your CRM data first if match rates were actually reliable?

If you're still sending cold emails from 2-3 inboxes in 2026, you're landing in spam.

Spam filters got brutal last year.

Blasting hundreds of emails from a single domain gets you flagged in days.

The fix is to scale horizontally.
More domains. More inboxes. Lower volume per inbox.

But setting this up manually through Gmail or Outlook is expensive, slow, and a technical mess.

You don't want to deal with SPF, DKIM, DMARC, and all the funky letters on your own.

That's why I use Mailpool for my clients and my own outbound.

Here's the setup in 3 steps:

→ Connect your domains (or buy them directly). 
DNS setup is guided. No more guessing which TXT record goes where.

→ Spin up inboxes across Gmail, Outlook, and SMTP. 
Mix providers for better deliverability and keep volume low per inbox. 
This is what actually keeps you out of spam.

→ Plug into your favorite outreach tool. 
Mailpool connects with most cold email platforms, so you don't rebuild your workflow from scratch.

The whole thing takes minutes, not days.

The difference between a clean infrastructure and a messy one is the difference between 40% open rates and landing in junk.

I've been in the cold email game for years, and I'm glad I found a provider I can trust. 

I definitely recommend checking out Mailpool for the best infrastructure!

Your CRM is quietly draining your pipeline.

Not because the tool is broken.
Because nobody owns what's inside it.

I've audited dozens of CRMs. The pattern is always the same.

→ 30-40 of contacts have no valid email.
Enrichment ran once, 18 months ago. Never again.

→ Lead routing is based on fields nobody updates.
Reps get assigned accounts they've never touched. Territories overlap. Deals fall through the cracks.

→ Duplicates everywhere.
Same company, 4 records, 3 owners, zero accountability.
The "source of truth" is a source of confusion.

→ Pipeline reviews built on stale data.
Forecasts are fiction. Leadership makes decisions based on numbers that were wrong weeks ago.

The real cost isn't inefficiency.
It's the deals your team never even sees.

A dirty CRM doesn't just slow you down. It makes automation impossible.

You can't build AI workflows, lead scoring, or signal-based outreach on top of garbage data.

The companies shipping real GTM automation right now all started the same way.

They fixed the foundation first.

What's the first thing you'd clean up in your CRM if you had a full week to dedicate to it?

Pushy selling is the fastest way to kill pipeline.

I've seen outbound teams burn through hundreds leads a month with zero results.

Not because the leads were bad.
Because every touchpoint screamed "BUY NOW."

Buyers don't respond to pressure. They respond to psychology.

Here's what actually moves deals forward:

→ Reciprocity 
Give before you ask. Share a relevant benchmark, a teardown, a custom insight. Our best-converting sequences all start with value, never a pitch.

→ Social proof 
"We helped 40+ B2B SaaS companies cut cost-per-meeting by 60%" hits harder than "we're the best at outbound." Peer wins beat promises every time.

→ Loss aversion 
Quantify the cost of doing nothing. "You're spending €18K/month on SDRs generating 12 meetings" is more powerful than any feature list.

→ Micro-commitments 
Don't ask for a 30-min call on the first touch. Ask for a reaction. A reply. A small yes that opens the door to a bigger one.

→ Authority
Credentials and case studies close more than objection-handling scripts. Be the advisor buyers trust, not the seller they avoid.

→ Emotional anchoring 
Start with their goal, not your product. Tie the solution to where they want to be before you address what's broken.

Most outbound fails because it leads with pressure instead of trust.

Trust, logic, and emotion close deals. 

Sequences built on psychology outperform brute-force volume every single time.

What's the one psychological trigger that works best in your outbound sequences?

Most sales teams are still hiring their way out of problems.

More SDRs. More seats. More fixed costs.
Then wondering why margins shrink and turnover spikes.

Here's what the shift actually looks like:

The legacy playbook:
→ 12-month contracts on tools nobody fully uses.
→ 15 SDRs grinding 200 calls/day against outdated lists.
→ 40% annual turnover. Constant ramp cycles.

The modern playbook:
→ 1-2 GTM Engineers replacing entire outbound teams.
→ Pay-as-you-go stack: Clay, enrichment APIs.
→ Automated lead scoring, routing, and sequencing.
→ Pipeline grows. Headcount doesn't.

The difference isn't "working smarter."
It's architectural.

One model scales with people.
The other scales with systems.

I've seen teams cut outbound costs by 60% while tripling qualified meetings. Not by optimizing scripts. By rebuilding the engine underneath.

The companies pulling ahead right now aren't the ones with the biggest sales floors.
They're the ones where a single GTM engineer runs what used to take a team of 10.

What's the first thing you'd replace with automation in your outbound stack?

The best automation in the world is worthless if your CRM is dirty.

You can have the best Clay workflow.
The best HubSpot scoring model.
The best outbound sequence.

If the data feeding it is wrong, the whole system loses its impact.

Same thing with AI.

A perfect prompt with garbage context gives you garbage output.
An agent connected to an unenriched CRM is an agent spinning its wheels.

The real lever isn't the tool.
It's the quality of what feeds it.

→ Data enriched in near real-time
→ Standardized fields
→ Duplicates eliminated
→ Routing built on reliable information

When all of that is clean, every layer you add (workflow, agent, scoring) runs at full power.

Otherwise, you're stacking tools on broken foundations.

What's the first field in your CRM you'd clean up?

Andrew Bolis

Influencer (700+ Brand Collabs) 🧠 AI & Marketing Consultant 📢 Former CMO 📩 DM for Influencer Partnerships ➡️ Follow for AI & business growth tips.

⭐ Brand Partnerships & Influencer Marketing: I partner with companies to promote their products to my 464k followers across LinkedIn, X/Twitter & Email Newsletter. I've previously partnered with Notion, HubSpot, Upwork, ClickUp, Miro, Semrush, Instantly, Clay, Lemlist & Gamma.

💬 DM me "COLLAB" to learn more!

---

⭐ AI & Marketing Consulting: 

I help companies leverage AI to optimize their marketing and sales. 

● Generate more qualified leads and sales opportunities
● Close more deals and shorten your sales cycle
● Grow your revenue while cutting costs

💬 DM me "AI" to learn more!

---

⭐ My Career Highlights: 
● $210M in attributed revenue from marketing campaigns
● 15 yrs of marketing leadership experience (Mngr/Dir/CMO)
● Drove revenue growth from Series A to E stages at startups
● Built & led global teams of up to 20 people across NA & EU
● Managed global expansions (NA, EU, LATAM) & acquisitions
● Established product as a Leader in Gartner Magic Quadrant
● Focus: Demand Gen, Digital Mktg, Content Mktg, Sales Dev
● Verticals: SaaS, MarTech, EdTech, Supply Chain & Logistics

Fractional CMO, Marketing Consultant & AI Strategist

I help companies leverage AI to optimize their marketing and sales. 

● Generate more qualified leads and sales opportunities
● Close more deals and shorten your sales cycle
● Grow your revenue while cutting costs

💬 DM me “AI” to learn more!

Visit my website (at top of my profile) for free resources.

- - -

My previous consulting gigs and contract work:

💼 Fractional CMO & Head of Marketing at The Virtual Hub (July 2023 - September 2023)

The Virtual Hub provides virtual assistants and offshore staffing services for businesses. It's a 350-employee company that's bootstrapped and profitable. 

💼 Marketing Strategy & Growth Consultant at Upshift (March 2023 - May 2023) 

Upshift connects people with businesses in need of extra staff. Upshift is trusted by 1600+ businesses across 25+ cities for staffing hourly workers. 

Upshift customers: Compass Group, American Airlines Arena, InterContinental Hotels Group, Six Flags, Victoria's Secret, Cedar Fair, Delaware North, Hard Rock Hotel & Casino, Hyatt, Kroger.

💼 Fractional CMO / VP of Marketing at Packback (February 2023 - April 2023)

Packback is an EdTech company that uses AI to help educators implement high-quality teaching methods at scale. 

Packback customers: Michigan State University, Maricopa Community Colleges, University of Illinois Urbana-Champaign, SUNY - The State University of New York, Ivy Tech Community College, FSCJ - Florida State College at Jacksonville, HACC, UNT - University of North Texas, Minnesota State, Miami Dade College.

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Start learning AI today to future-proof your career.

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Harvard University is offering its top courses 100% free.

No application. No fees. No prior skills required.

These 12 courses are free, online, and open to everyone:


1. Introduction to Computer Science

Learn foundational computer science concepts and programming in languages like C, Python, and JavaScript.

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2. Introduction to Artificial Intelligence with Python

Explore AI concepts including search algorithms, machine learning, and neural networks using Python.

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3. Web Programming with Python and JavaScript

Build web applications using Python, JavaScript, SQL, and frameworks like Django and React.

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4. Understanding Technology

Gain a broad understanding of how computers and the internet work, covering hardware, software, and security.

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5. Introduction to Game Development

Learn principles of 2D and 3D game development using languages like Lua and C#, and tools like Unity.

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6. Introduction to Cybersecurity

Understand the basics of cybersecurity, including securing systems, data, and software.
 
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7. Data Science: Machine Learning

Learn machine learning techniques to build predictive models and understand their applications.

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8. Data Science: Productivity Tools

Master tools like Git, Unix/Linux, and RStudio to enhance data science workflows.

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9. Leaders of Learning

Examine how people learn and how to lead learning environments effectively.

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10. Introduction to Data Science with Python

Apply Python programming to analyze data and solve real-world problems.

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11. Introduction to Programming with Scratch

Learn programming fundamentals using Scratch, a visual programming language.

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Understand relational databases and learn to use SQL for data management.

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Learn in-demand skills, earn certificates, and boost your career.

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All the AI buzzwords can be confusing. 

Here are simple definitions to understand AI. 

[ save 🔖 this post for later ]

Here are 28 AI terms even a kid can understand.

1. Artificial Intelligence – A computer brain that helps and learns.

2. Machine Learning – AI learns by studying examples.

3. Deep Learning – AI learns step by step in layers.

4. Neural Networks – AI parts work together to solve tasks.

5. Model Architecture – The setup for how AI learns.

6. Training Data – Info AI uses to get smarter.

7. Pretraining – AI reads first to get ready.

8. Fine-Tuning – Extra training after basics.

9. Reinforcement Learning – AI learns from rewards or mistakes.

10. Overfitting – AI memorizes too much and messes up.

11. Natural Language Processing – AI reads and talks like us.

12. Context Window – AI remembers just the recent words.

Find more terms in the infographic. 
AI doesn’t have to be complicated!

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