Zohe Mustafa

I Grow Your User Base w/ AI, Automation + Vibe Coding. CMO Head of Marketing / Growth Hacking / GTM. AI Generalist, AI Strategy | No-Code Workflows - Development, SaaS Founder. AI Agents, Adverts, Avatars, & Automations.

𝗜’m a restless builder who thrives on creating, testing, and scaling ideas. From Growth Hakka to JournoPitch to AICRM, I treat every venture as a living organism, feeding it, tweaking it, and seeing how it grows. Over nearly three decades I’ve worked with global enterprises like Virgin Mobile, Nokia, and Accenture as well as ambitious startups, always with the same goal: turning creativity and systems thinking into growth.𝗪𝗵𝗮𝘁 𝘀𝗲𝘁𝘀 𝗺𝗲 𝗮𝗽𝗮𝗿𝘁 is how I connect dots others overlook. One hour I’m mapping a SaaS GTM strategy; then Vibe Coding an app, then next, mentoring students on how to turn complex analytics into stories. I dive into the granular, dashboards, frameworks, AI prompts, while never losing sight of the ecosystems and futures they serve.𝗜 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝗲 𝗶𝗻 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗔𝗜 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 to transform go-to-market strategies, SaaS scalability, and customer acquisition. As founder of Growth Hakka, I help businesses rethink traditional approaches and build systems that are smart, scalable, and repeatable. I’ve launched ventures like JournoPitch and AICRM my first Vibe Coding projects, designed to compress tomorrow’s innovation into tools companies can use today.𝗣𝗹𝗮𝘆 𝗶𝘀 𝘄𝗼𝘃𝗲𝗻 𝗶𝗻𝘁𝗼 𝗺𝘆 𝘄𝗼𝗿𝗸. I’ve gamified complex ideas through frameworks because storytelling sticks where spreadsheets don’t. Beneath the creativity is discipline: structure, clarity, and measurable outcomes that drive results at scale.𝗘𝗮𝗿𝗹𝘆 𝘀𝗶𝗴𝗵𝘁. I’ve had a track record of spotting shifts before they hit. In 2012 I predicted voice would become the dominant input before Siri or Alexa mattered. In 2017 I concepted Autherbot, anticipating AI copywriting tools years before they went mainstream. Today I’m asking bigger questions: how will AI-human coexistence reshape marketing, PR, and business?𝗪𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲𝘀 𝗺𝗲 is connecting people, ideas, and technologies. My work lives at the junction of innovation and execution, with one purpose: to equip businesses and individuals with the tools, insights, and strategies to thrive in an AI-powered future.𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸: diagnose bottlenecks, design experiments, automate for scale, measure what matters. I build repeatable growth loops in content, distribution, data, and iteration. I favour simple systems, blending no-code with code and treating AI as a co-pilot.𝗜𝗺𝗽𝗮𝗰𝘁: faster pipeline velocity, better conversion, lower CAC. With enterprises I’ve delivered multimillion-pound outcomes; with startups I help sharpen positioning and ship GTM playbooks.

Head of Marketing,  Head of Digital Marketing, CMO, CGO, Marketing Automation

Growth Hakka is a growth hacking consultancy based in London, UK. Worked on campaigns for these industry verticals:

SAAS providers, Telecoms, Logistics, News & Media, Hospitality, Technology, FinTech, Home & Garden, Social Networks, Health and Wellbeing & E-commerce stores.

I am a no-code, growth and digital marketing, and automation specialist

I join these companies and provide marketign leadership. I can slot in as aChief Growth Officer or Chief Marketing Officer, Head of Marketing or Head of Digital Marketing manage or build their existing teams. I provide leadership, vision, plan, execution strategy, and mentoring.

My raison d'etre revolves around the principle of growth hacking. What is growth hacking? The term originated in Silicon Valley and in its essence it’s the idea that an entrepreneur or organisation can take a non-traditional approach to increasing the growth rate/adoption of their product by “hacking” something together specifically for growth purposes. Its about achieving an objective rather than following a prescribed process. Its about the intersection between Marketing, Product, Technology, Data and even HR, Finance. 

Services whilst always focused on Growth, either as a managed service or strategic input include any relevant combinations of the below:

Affiliate Marketing
A/B Testing - Product and Marketing
Analytics, Dashboards 
Conversion Rate Optimisation, 
Content Marketing
CRM
Developer Marketing
Digital: Branding, Consumer Profiling
Digital Marketing & Merchandising
Digital Strategy
Digital Media Planning & Buying across Search, Display, Mobile & Social Media
E-Commerce
Email Marketing
Gamification Strategy
Influencer Marketing
Journalist Marketing
Marketing Automation 
Mobile Marketing & Advertising
Multi-Channel Strategy
Multi Screen Campaign Strategy
Platform / Tool Selection
PR Digital Distribution
Programmatic Advertising & Marketing
SEM & SEO
Social Media Automation, Care, Enterprise, Management, Marketing & Monitoring

One AI lives in your terminal.
The other AI lives in your life.
That’s the fastest way to understand
Claude Code vs Clawd .bot.

Claude Code is a CLI tool built for developers.
It reads your codebase, edits files, runs tests, and commits to Git, all from the terminal.

Clawd .bot is a self-hosted personal assistant.
It works inside WhatsApp, Telegram, Slack and more, automating emails, calendars, system tasks and daily routines.

Same AI power underneath.
Very different execution layers.

Claude Code is optimised for software engineering workflows.
Clawd .bot is designed to act as an always-on personal agent.

Different surfaces.
Different scope.
Different outcomes.

𝗠𝗼𝘀𝘁 𝗕𝟮𝗕 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘀𝘁𝗶𝗹𝗹 𝘀𝘁𝗼𝗽𝘀 𝗮𝘁
𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗻𝗱 𝗳𝗼𝗿𝗺 𝗳𝗶𝗹𝗹𝘀.

Pipeline decisions need more than that.

In B2B, the hard part isn’t
getting people to visit.

It’s understanding
who is showing intent
when it matters
and why deals move or stall.

GA4 quietly crossed an important line.

It’s no longer just telling you
what pages were viewed.

It can now surface
buyer signals
account intent
and early pipeline risk
without living inside reports.

This carousel is about using GA4 as
behavioural intelligence
that sales, marketing, and RevOps
can actually act on.

If your GA4 setup still ends at
traffic and conversions,
you’re leaving signal on the table.

Where do you still lose visibility
between 𝘢𝘯𝘰𝘯𝘺𝘮𝘰𝘶𝘴 𝘷𝘪𝘴𝘪𝘵
𝘢𝘯𝘥 𝘲𝘶𝘢𝘭𝘪𝘧𝘪𝘦𝘥 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦?

GA4 B2B

Most dashboards tell you 𝘸𝘩𝘢𝘵 happened.

This is about asking why
and what next.

My previous GA4 carousel was about
getting answers quickly.

This one goes a step further.

Analytics stops being
a reporting function
and starts acting like a strategist.

These questions are for decisions.

Budget shifts.
Retention plays.
Content bets.
Growth priorities.

𝗬𝗼𝘂’𝗿𝗲 𝗻𝗼𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝗻𝗴 𝗚𝗔𝟰 𝗵𝗲𝗿𝗲.
𝗬𝗼𝘂’𝗿𝗲 𝗶𝗻𝘁𝗲𝗿𝗿𝗼𝗴𝗮𝘁𝗶𝗻𝗴 𝗶𝘁.

And the interesting part is not
that GA4 can answer these.

𝗜𝘁’𝘀 𝘁𝗵𝗮𝘁 𝗺𝗼𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗻𝗼𝘄 𝗰𝗮𝗻 𝗮𝘀𝗸 𝘁𝗵𝗲𝗺.

That changes who gets to make calls.

Which question would change
your next decision the fastest?

GA4 Advanced Diagnostics

𝗔𝗜 𝘀𝗲𝗮𝗿𝗰𝗵 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗿𝗲𝗮𝗱 𝘆𝗼𝘂𝗿 𝘄𝗲𝗯𝘀𝗶𝘁𝗲.
𝗜𝘁 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝘀 𝗶𝘁.

That sounds subtle.
It isn’t.

Most content strategies still assume
AI works like Google did.

𝘗𝘢𝘨𝘦𝘴. 𝘒𝘦𝘺𝘸𝘰𝘳𝘥𝘴. 𝘙𝘢𝘯𝘬𝘪𝘯𝘨𝘴.
𝘏𝘦𝘢𝘥𝘪𝘯𝘨𝘴. 𝘉𝘢𝘤𝘬𝘭𝘪𝘯𝘬𝘴. 𝘔𝘦𝘵𝘢𝘥𝘢𝘵𝘢.

But large language models don’t crawl.
They synthesise.

They don’t ask:
“Is this keyword present?”

They ask:
“Is this answer reliable, specific, and reusable?”

That’s why beautifully written content
can still be invisible in AI search.

And why vague PR coverage can actually hurt you.

If AI can’t clearly understand:
  • who you are
  • what you do
  • why you’re credible

it fills in the gaps.

Often incorrectly.

This infographic breaks down what AI search engines actually read
and why 𝗣𝗥 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 are now the same system.

The brands winning next aren’t gaming algorithms.

They’re designing for interpretation.

Clear for humans.
𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗳𝗼𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀.

I’m 𝗿𝗶𝗱𝗶𝗰𝘂𝗹𝗼𝘂𝘀𝗹𝘆 excited about 𝗔𝘃𝗲𝗻𝗴𝗲𝗿𝘀: 𝗗𝗼𝗼𝗺𝘀𝗱𝗮𝘆.

Which is probably not age-appropriate behaviour
for a grown man.
But here we are.

I’ve been obsessed with Marvel and comics since I was a kid.
So much so that I once owned and ran a comic shop
IRL and online.

When I was younger, I wanted to draw comics for Marvel.
Turns out I can’t draw to save my life.

Plan B was writing comics.
My dad (a doctor, with plans) shut that down immediately.
Also… writing for Marvel is wildly competitive
and mostly US-based.

Fast-forward a few decades.

AI opens a door I didn’t expect to open again.

Especially after the recent announcement
that 𝗗𝗶𝘀𝗻𝗲𝘆 and 𝗢𝗽𝗲𝗻𝗔𝗜 have reached a landmark agreement,
bringing characters from Disney, Marvel, Pixar and Star Wars
into Sora for fan-inspired short-form video.

That moment genuinely made me stop and think.

Because suddenly, the distance between
𝘧𝘢𝘯 and 𝘤𝘳𝘦𝘢𝘵𝘰𝘳 feels… smaller.

With generative AI, I can now:
• write the stories
• create comic panels
• animate scenes
• build cinematic sequences
• even place myself into those worlds

And yes… I’ve done that.
To the point where younger family members are genuinely confused
about how “𝗨𝗻𝗰𝗹𝗲 𝗭𝗼” ended up in a Marvel movie.

The realism of AI video still blows my mind.

Which brings me to this…

𝗦𝗨𝗕𝗝𝗘𝗖𝗧: 𝗖𝗔𝗦𝗧𝗜𝗡𝗚 𝗦𝗨𝗕𝗠𝗜𝗦𝗦𝗜𝗢𝗡 –
𝗧𝗵𝗲 “𝗘𝘃𝗲𝗿𝘆-𝗠𝗮𝗻” 𝗩𝗮𝗿𝗶𝗮𝗻𝘁 𝗳𝗼𝗿 𝗔𝘃𝗲𝗻𝗴𝗲𝗿𝘀: 𝗦𝗲𝗰𝗿𝗲𝘁 𝗪𝗮𝗿𝘀

Dear Marvel Studios Team,

With the Multiverse Saga concluding,
you’re facing an impossible task:
variants, legacy characters, and somehow topping Endgame.

I’d like to help.

𝗜 𝗮𝗺 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳... 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲.

Statistically speaking, there must be a timeline
where Tony Stark, Steve Rogers and Logan
all share my face.

Attached: 👉 My 𝗔𝗜-𝗠𝗲 𝗔𝘂𝗱𝗶𝘁𝗶𝗼𝗻 𝗥𝗲𝗲𝗹
(AI is currently better at acting than I am.
The voice is actually me.)

𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀:
• 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: a long-overdue South Asian male Avenger
• 𝗠𝗲𝘁𝗵𝗼𝗱 𝗔𝗰𝘁𝗶𝗻𝗴:  genuine confusion at all times
• 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲: one guy, many variants (Downey Jr is pricey)

I am ready for vibranium, adamantium,
or a very tight spandex suit.

Excelsior,
Zohe Mustafa
(The Variant You Didn’t Know You Needed)

This isn’t about replacing creativity.
It’s about unlocking versions of it
some of us never got to explore.

AI didn’t make me a Marvel creator.
But it let me 𝗽𝗹𝗮𝘆 𝗶𝗻 𝘁𝗵𝗲 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗲
I’ve loved my entire life.

And honestly?
That feels kind of… inevitable.

Whaddaya reckon LinkedIn, 
have I got a shot?

P.S. AI lipsync tools have revealed
I may have a slight lisp.

Thank you for reading this far. 𝗬𝗼𝘂 𝗧𝗿𝗼𝗼𝗽𝗲𝗿 :-)

𝗗𝗮𝘆 𝟭9 𝗕𝘂𝗶𝗹𝗱 𝗟𝗼𝗴

𝗡𝗼 𝗼𝗻𝗲 𝗿𝗲𝗮𝗹𝗹𝘆 𝘄𝗮𝗿𝗻𝘀 𝘆𝗼𝘂 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗽𝗮𝗿𝘁.

Once you ship an app and people start signing up, you’re no longer just building, you’re now running support too. And the moment someone pays you, that responsibility becomes very real.

JournoPitch is now at the “real users” stage, which means real questions, real confusion, and real expectations showing up in your product.

I knew early on I didn’t want the old support model.

Support inboxes, ticket systems, long email threads, and constant context switching felt like the fastest way to drain energy as a solo builder.

𝘌𝘮𝘢𝘪𝘭-𝘧𝘪𝘳𝘴𝘵 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘫𝘶𝘴𝘵 𝘧𝘦𝘭𝘵 𝘣𝘢𝘤𝘬𝘸𝘢𝘳𝘥𝘴.

I looked at what I was already using on my Growth Hakka site and realised the answer was hiding in plain sight.

I’ve been using 𝗝𝗼𝘁𝗳𝗼𝗿𝗺 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 for a while now. They book meetings, answer pricing questions, and handle surprisingly nuanced queries even when they’re not trained perfectly.

That’s when it clicked.

Why not do the same inside JournoPitch?

So I did.

I added a customer support AI agent directly into the product. From start to finish, 𝗶𝘁 𝘁𝗼𝗼𝗸 𝘂𝗻𝗱𝗲𝗿 𝘁𝘄𝗲𝗻𝘁𝘆 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. No dev sprint, no inbox setup, no extra tooling.

The best part? 𝗜𝘁’𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗳𝗿𝗲𝗲, which matters a lot when you’re scaling carefully.

Users can type to it or just talk. Voice support, no typing required. And if I want to later, the same agent can connect to 𝗜𝗻𝘀𝘁𝗮𝗴𝗿𝗮𝗺 𝗗𝗠𝘀, 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽, 𝗠𝗲𝘀𝘀𝗲𝗻𝗴𝗲𝗿, 𝗦𝗠𝗦, 𝗮 𝗽𝗵𝗼𝗻𝗲 𝗻𝘂𝗺𝗯𝗲𝗿, 𝗼𝗿 𝗚𝗺𝗮𝗶𝗹.

All optional. All there.

This is the kind of leverage solo builders actually need. Not more features. Less drag.

Build with energy.
Not syntax.

AI Customer Support Agent

𝗜 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝗲𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲
𝗮𝗻𝗱 𝗶𝘁 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗯𝗿𝗼𝗸𝗲 𝗺𝘆 𝗯𝗿𝗮𝗶𝗻.

𝘐𝘯 𝘢 𝘨𝘰𝘰𝘥 𝘸𝘢𝘺.

I have spent a lot of time trying to build AI agents. I tried

  • Make.
  • n8n.
  • Gumloop.
  • Relevance.
  • Zapier.
  • Taskade.
  • Pipedream.
  • Kortix.
  • Lindy.

I have not tried Replit to build agents yet.

Some worked.
Some sort of worked. Most became fragile science projects.

The more powerful the agent, the more time it demanded.

  • Nodes.
  • Memory.
  • Triggers.
  • APIs.
  • Webhooks.
  • Mental overhead.

The plan was never ambitious.

Just summarise my last 7 weeks of blog posts
and turn them into a newsletter. Just so I could decide which platform was easier. 

In the end I solved it with an automation
not an agent.

Then I recently installed Claude Code.

I asked it a hypothetical question
about building a course creator agent.

45 minutes later
it was done.

Two PDF courses created. End to end. No ceremony.

What really surprised me was how it handled beginners.

Not tools. Not jargon. Just conversation.

I mapped that experience into a carousel
because this feels like a shift or sumfink.

From building systems
to having a conversation with one.

Claude Code AI Agent

𝗜’𝘃𝗲 𝗯𝗲𝗲𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗲𝗱𝗱𝗶𝘁 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗺𝘆 𝗣𝗥 𝘁𝗲𝗰𝗵 𝗮𝗽𝗽.

I’ve just added AI sentiment analysis on posts and top comments.

I think it’s working.

Redditors don’t hold back.
At all. 😅

Which is… actually the point.

When sentiment turns, it doesn’t start in a press release or a headline.
It starts in comments.
Language.
Tone.
Frustration.

So instead of just labelling things 𝘱𝘰𝘴𝘪𝘵𝘪𝘷𝘦 / 𝘯𝘦𝘶𝘵𝘳𝘢𝘭 / 𝘯𝘦𝘨𝘢𝘵𝘪𝘷𝘦, I’ve been wiring this into something more practical:

When there’s a 𝘯𝘦𝘨𝘢𝘵𝘪𝘷𝘦 𝘴𝘱𝘪𝘬𝘦, you can now trigger a 𝗖𝗿𝗶𝘀𝗶𝘀 𝗣𝗮𝗰𝗸 that pulls directly from recent mentions and produces things like:
– a clear incident summary
– red flags to watch
– a holding statement
– journalist Q&A
– internal and external response guidance

Not to panic.
But to think clearly while everyone else is reacting.

This screenshot made me laugh…
but it also reminded me why unfiltered platforms like Reddit are often the earliest warning system brands have.

I'm quite chuffed with this feature tbh :-D

𝗦𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘀𝗶𝗴𝗻𝗮𝗹 𝗶𝘀𝗻’𝘁 𝗽𝗼𝗹𝗶𝘀𝗵𝗲𝗱.
𝗜𝘁’𝘀 𝗯𝗿𝘂𝘁𝗮𝗹𝗹𝘆 𝗵𝗼𝗻𝗲𝘀𝘁.

𝗗𝗼𝗲𝘀 𝗔𝗜 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆?

After visibility comes classification.
After classification comes the real question:

Does AI traffic behave differently?

Once you bucket AI tools into a proper channel
(I call mine AI Referrals),
you can finally compare like-for-like.

Not vibes.
Not anecdotes.
Actual behaviour.

Here’s what matters when analysing AI traffic.

First: reset expectations.

AI traffic is usually:
• lower volume
• narrower entry points
• more purposeful

So judging it on raw volume misses the point.

What you should look at instead:

• Conversion rate vs Organic
• Time to conversion
• Depth after entrance

In many cases, AI behaves closer to:
high-intent referral traffic
than traditional search.

But this is still early signal.

Low volume.
Directional insight.
Not something to declare victory over yet.

I'd be very curious to see if 𝗔𝗡𝗬𝗢𝗡𝗘 is getting high volumes.

This carousel breaks down:
• how to compare AI traffic properly
• which metrics actually tell you something
• where people misread the data

𝘝𝘪𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 → 𝘊𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 → 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯.

That sequence matters.

AI Signals Converrsion

😆 Happy Sunday! I know how Jon feels 😆

𝗕𝘂𝗶𝗹𝗱 𝗟𝗼𝗴 𝗗𝗮𝘆 𝟮𝟮 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗝𝗨𝗦𝗧 𝗮𝗯𝗼𝘂𝘁 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗽𝗮𝗴𝗲𝘀.
𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗼𝗽𝘁𝗶𝗼𝗻𝘀.

One of the quiet superpowers of vibe coding
is how fast you can create variation.

Not pixels.
Not layouts.
But messaging and positioning.

With AI-assisted builds, I can spin up:

5 versions of the same landing page

Each one anchored to a different buyer psychology

Each one testing a different “why they say no”

  • Revenue logic.
  • Objections.
  • ICP clarity.
  • Language theft.
  • Fluff removal.

None of these pages are “𝘵𝘩𝘦 𝘰𝘯𝘦”.

𝗧𝗵𝗲𝘆’𝗿𝗲 𝗽𝗿𝗼𝗯𝗲𝘀.

Instead of arguing about copy in Slack,
I let real users vote with behaviour.

  • Clicks.
  • Scroll depth.
  • Conversion rate.
  • Sales conversations.

Vibe coding turns landing pages into experiments,
not artefacts you emotionally attach to.

Build fast.
Test ruthlessly.
Delete without guilt.

That’s how you find the 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗳𝗼𝗿𝗺𝘂𝗹𝗮
without pretending you knew it upfront.

How to Vibe-Code & A/B Test Your Way Winning Landing Page

𝗧𝗵𝗲 𝗨𝗞 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴𝗹𝘆 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗽𝗿𝗲𝘁𝘁𝘆 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝘁𝗵𝗶𝘀 𝗺𝗼𝗿𝗻𝗶𝗻𝗴.

A large set of free AI courses covering:
• Civil Service capability
• Ethics and governance
• Business and workforce upskilling
• Technical, creative, and academic pathways

What’s interesting isn’t just the volume.
It’s the intent.

This isn’t “𝘭𝘦𝘢𝘳𝘯 𝘱𝘳𝘰𝘮𝘱𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘳𝘢𝘤𝘬 𝘰𝘯”.
It’s a deliberate attempt to 𝗽𝗮𝗶𝗿 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆.

You can see the policy shape clearly:
• Skills first
• Ethics embedded, not bolted on
• Public and private sectors moving together

Whether you work in government, business, education, or just want to future-proof your skills there’s a real pathway here.

I turned the full list into a visual breakdown because a lot of this will otherwise get missed.

Worth a proper look.

UK Gov Free Ai Courses

𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁.
𝗜𝘁 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝘆𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴.

Give two teams the same data.
Give them the same AI.
They’ll still make very different decisions.

Not because the tools differ.
Because the framing does.

More dashboards don’t fix confusion.
More data doesn’t create clarity.
AI just turns the volume up on whatever intent is already there.

That’s why some teams get:
- conflicting metrics
- faster confusion
- activity without direction

And others get:
- fewer metrics
- clearer decisions
- action they trust

The leverage isn’t the model.
𝗜𝘁’𝘀 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻.

AI is an amplifier.

𝗕𝘂𝗶𝗹𝗱 𝗟𝗼𝗴 𝗗𝗮𝘆 𝟮𝟬 JournoPitch, my PR tech app has quietly crossed an interesting line.
𝗜 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗶𝘁 𝘁𝗼 𝗵𝗲𝗹𝗽 𝗽𝗲𝗼𝗽𝗹𝗲 𝘀𝗲𝗻𝗱 𝗯𝗲𝘁𝘁𝗲𝗿 PR 𝗽𝗶𝘁𝗰𝗵𝗲𝘀.

But after wiring together 𝗔𝗘𝗢, 𝗻𝗲𝘄𝘀𝗿𝗼𝗼𝗺, 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, 𝗥𝗲𝗱𝗱𝗶𝘁 𝘀𝗶𝗴𝗻𝗮𝗹𝘀, 𝗰𝗿𝗶𝘀𝗶𝘀 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲, 𝗼𝘂𝘁𝗿𝗲𝗮𝗰𝗵 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴, 𝘀𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 and now 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝗿𝘀… I realised something:

PR isn’t a single action.
It’s a loop.

Listen.
Decide.
Create.
Send.
Learn.
Respond.

Most tools optimise one step and ignore the rest.

So instead of adding “more AI generators”, I’ve been focused on 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘴𝘶𝘱𝘱𝘰𝘳𝘵:

  • When not to pitch
  • How to respond under pressure
  • Why an influencer or Journalist might care
  • What evidence actually matters

That’s led to features like:

  • Reddit-first influencer discovery (with proof)
  • Crisis packs that respect legal guardrails
  • Outreach that won’t destroy your sender reputation
  • AEO & Clarity Score
  • Newsroom publishing and sharing [great for AEO]

I'm very happy with the AEO score which basically means any content you produce be it an AEO explainer page or press release is 𝗻𝗼𝘁 𝘀𝗶𝗻𝗴𝗹𝗲 𝘂𝘀𝗲 𝗮𝗻𝗱 𝗰𝗮𝗻 𝗯𝗲 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘄𝘀𝗿𝗼𝗼𝗺. 

Next up is something I’m excited about:
A collaboration planner that helps you decide how to approach influencers, based on what they’ve actually said, not a generic pitch.

Still building.
Still shipping.
Still learning where AI helps and where humans should stay in the loop.

Vibe Coded Capabilities for JournoPitch PR Tech App

𝗛𝗼𝘄 𝗜 𝘁𝘂𝗿𝗻𝗲𝗱 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝘁𝗼 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗿𝗼𝗮𝗱𝗺𝗮𝗽
(𝗮𝗻𝗱 𝘄𝗵𝘆 𝗜 𝗵𝗮𝗱 𝘁𝗼)

Early versions of JournoPitch were… fine.

v1 had one feature. It got zero registrations. 

v2 had five features. It got a handful of users.

It wasn’t a distribution problem, more a value problem.

At that point I had a choice:
romanticise the original idea or confront the data.

I chose the second.

In 𝗧𝗵𝗲 𝗟𝗲𝗮𝗻 𝗦𝘁𝗮𝗿𝘁𝘂𝗽, a pivot isn’t random change.
It’s a structured course correction when learning tells you the current path isn’t working.

So instead of adding “cool” features or guessing what users might want, I did something very deliberate:

I stopped looking at competitors as rivals
and started treating them as 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘢𝘳𝘵𝘦𝘧𝘢𝘤𝘵𝘴.

Whitepapers. Lead magnets. Guides. Calendars. Feature pages.

Not to copy them but to understand what problems they’re orbiting.

Why?

Because competitor content is rarely marketing fluff.
It’s usually a lagging signal of:
– where customers are stuck
– what workflows are broken
– what value props keep failing in practice

My competitors are large software companies, in the PR tech game for years!

That insight became the input to a new pivot.

This time the hypothesis wasn’t:
“People need this feature”

It was: “𝘗𝘦𝘰𝘱𝘭𝘦 𝘢𝘳𝘦 𝘴𝘵𝘳𝘶𝘨𝘨𝘭𝘪𝘯𝘨 𝘢𝘵 𝘵𝘩𝘦𝘴𝘦 𝘱𝘰𝘪𝘯𝘵𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸 𝘸𝘩𝘢𝘵 𝘸𝘰𝘶𝘭𝘥 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘳𝘦𝘮𝘰𝘷𝘦 𝘧𝘳𝘪𝘤𝘵𝘪𝘰𝘯?”

That’s how the current JournoPitch roadmap emerged:
– not from ideation sessions
– not from feature envy

But from systematically:

stripping claims down to jobs, mapping the real workflow

finding where strategy collapses, turning those gaps into feature hypotheses

It’s still a pivot.
But it’s a learning-led pivot, not a panic one.

Competitor-Research-Product-Roadmap

𝗧𝗵𝗲 𝗹𝗶𝗲 𝘄𝗲’𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝘁𝗼𝗹𝗱 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗔𝗜.

AI itself isn’t expensive.

In fact, if you have a decent laptop or a gaming PC, you can run powerful, useful models right there on your desk.

𝘓𝘰𝘤𝘢𝘭. 𝘗𝘳𝘪𝘷𝘢𝘵𝘦. 𝘍𝘳𝘦𝘦.

So why is the narrative so different?

Why are we being told we need gigawatts of new power? Why do we need to override planning laws? Why do we need data centres that drink millions of gallons of water? Why do we need eye watering Billions of pounds in investment? Why do we need to mine more?
 
𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗯𝗲𝗶𝗻𝗴 𝗯𝘂𝗶𝗹𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗔𝗜 𝗶𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲.

It is a strategy of 𝗳𝗼𝗿𝗰𝗲𝗱 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻.

OpenAI and Google 𝘄𝗼𝗻’𝘁 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂 that you don’t need the cloud. It doesn't fit the subscription model.

They are building a moat made of copper, silicon, and land.

But here is the friction.

I know most people don't have the skills to spin up a local instance of Llama 3. It’s technical. It’s fiddly.

But imagine a different world.

𝗧𝗿𝗲𝗮𝘁 𝗔𝗜 𝗹𝗶𝗸𝗲 𝗮 𝗽𝘂𝗯𝗹𝗶𝗰 𝘂𝘁𝗶𝗹𝗶𝘁𝘆. 𝗟𝗶𝗸𝗲 𝘄𝗮𝘁𝗲𝗿 𝗼𝗿 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆.

Remember when BT used to send an engineer to your house to set up the phone line? They still come round to connect T'internet.

Why not a government technician? They come round, install a secure server, and hook you up.

Democratised intelligence. No massive grid drain. 𝗡𝗼 𝗱𝗮𝘁𝗮 𝗵𝗮𝗿𝘃𝗲𝘀𝘁𝗶𝗻𝗴. Coz you know are being harvested right?

I say this, but I’m part of the problem.

I could install it locally. I have the hardware. But I haven't.

Why? Time constraints. And frankly, the cloud models are "good enough."

𝗖𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗿𝗮𝗽.

We are trading privacy for convenience. We are trading the environment for speed.

We are accepting the ugly monstrosities of data centres in our countryside.

We are accepting the "gazillion" gigawatts.

And for what, exactly?

So we can make action figures of ourselves to share on LinkedIn?

It makes you wonder what the real agenda is.

We have the technology to distribute power. But we are choosing to centralise it.

𝗔𝗿𝗲 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝘁𝗼𝗼𝗹 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲? 𝗢𝗿 𝗮 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 𝗳𝗼𝗿 𝗮 𝗳𝗲𝘄?

𝗦𝗲𝗮𝗿𝗰𝗵 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗿𝗲𝘄𝗮𝗿𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻.

𝗡𝗼𝘄 𝗶𝘁 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲.

Not keyword density.
Not clever hacks.
Not tactical SEO theatre.

Confidence.

AI search engines don’t ask
“Who used the right words?”

They ask
“Who sounds like they know what they’re talking about?”

That changes a lot me thinks.

Authority is no longer inferred from backlinks alone.
It’s inferred from consistency.
Clarity.
Repetition of belief across surfaces.

Websites.
Posts.
Articles.
Tools.
Products.

If you say one thing on LinkedIn,
another on your site,
and something else in interviews,

you don’t look nuanced.
You look unsure.

Humans might tolerate that.
Machines don’t.

Confidence compounds when:
- Your ideas repeat
- Your language stabilises
- Your point of view doesn’t wobble like a Womble of Wimbledon

This is why some people “accidentally” dominate AI answers.
They’re not better optimisers.
They’re clearer thinkers.

SEO was about being found.
AI search is about being trusted.

Different game.
Different winners.

Audit where your authority actually comes from.

𝗧𝗿𝘂𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗦𝗘𝗢.

In this age of 𝗔𝗜𝘀𝗽𝗹𝗮𝗶𝗻𝗶𝗻𝗴 where AI gives a hugillion ideas faster than you can say 𝗠𝘅𝘆𝘇𝗽𝘁𝗹𝗸, more info than you asked for... 𝗠𝗼𝗺𝗲𝗻𝘁𝘀 𝗼𝗳 𝗦𝗼𝗹𝗶𝘁𝘂𝗱𝗲 are where my best ideas come from.

I’m lucky to live by fields and forests,
and I take my dog brother, Odin, for walks there a lot.

I don’t know if it’s the peace and quiet,
or the crackle of leaves beneath my feet,

but I’m always struck by waves
of inspiration and ideas.

AI can never give me those moments.

No matter how I prompt it.
No matter the model number.

Of course, me being me,
I’m never far from technology.

I never forget my ideas
because I either send myself a Slack DM
or a WhatsApp voice note.

Which then goes into a Make automation
that feeds it into AI
to give me those hugillion ideas I 
mentioned,
waiting in my inbox.

But this time, 𝘐 𝘢𝘴𝘬𝘦𝘥 𝘧𝘰𝘳 𝘵𝘩𝘦𝘮.

The circle of AI.
Complete. lol.

Happy Sunday.

𝗬𝗼𝘂 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝟰
𝘁𝗼 𝘂𝘀𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝟰.

That sentence still feels wrong but it’s now true!

I’ve spent years learning to exploit GA4.

And years watching smart marketers
pretend they understood it.

Charts everywhere. Confidence nowhere.

Now Google quietly did the most 𝘥𝘪𝘴𝘳𝘶𝘱𝘵𝘪𝘷𝘦 thing possible.

They added an 𝗔𝗜 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗔𝗱𝘃𝗶𝘀𝗼𝗿.

You don’t need to know:
𝗲𝘃𝗲𝗻𝘁𝘀
𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀
𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻𝘀
or where anything lives

You just ask questions.

In plain English.

Like you would ask an analyst.
Or a colleague.
Or yourself at 11pm
when revenue suddenly dips.

GA4 has moved from a tool you learn
to a system you talk to.

I’ve put together a carousel of 𝟮𝟬 𝗽𝗿𝗼𝗺𝗽𝘁𝘀.

One question per slide.
𝗚𝗿𝗼𝘂𝗽𝗲𝗱 𝗯𝘆 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗻𝗲𝗲𝗱𝘀.

Not “GA4 expert mode”.
Just
“𝘵𝘦𝘭𝘭 𝘮𝘦 𝘸𝘩𝘢𝘵’𝘴 𝘨𝘰𝘪𝘯𝘨 𝘰𝘯”.

If you’ve avoided GA4 until now
this is your on-ramp.

If you already use GA4
this is how you go faster.

Which of these would you ask first? Or what would you add?

Know any good prompts? Pls share below.

Lets build a GA4 Git!

20 Prompts GA4 Analytics Advisor

𝗜 𝗱𝗶𝗱𝗻’𝘁 𝗱𝗶𝘃𝗲𝗿𝘀𝗶𝗳𝘆 𝗺𝘆 𝗶𝗻𝗰𝗼𝗺𝗲 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗔𝗜.

I did it because of 𝗶𝗻𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.

One minute I had a job. Other times… I didn’t.

That pattern shows up early in your career
and it never really leaves you.

So a long time ago, I made a call:

Always have side projects.
𝘈𝘭𝘸𝘢𝘺𝘴 𝘩𝘢𝘷𝘦 𝘰𝘱𝘵𝘪𝘰𝘯𝘴.
Always have something building in the background.

AI just accelerated a decision
I’d already made.

Recently, a big AI creator, Sabrina Ramonov shared a
10-step “𝗔𝗜 𝘄𝗲𝗮𝗹𝘁𝗵 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁.”

Instead of reposting it,
I did something more uncomfortable.

I mapped it against my actual income last year.
Real numbers.
No vibes.

I felt scattered.

Speaking. Affiliates. Agency work.
Apps. 
Random experiments that refused to die.

It felt messy. Unfocused. Hard to explain.
My family keep telling me off for it.

Once I mapped it properly, something clicked.

I’m already hitting 8 out of the 10.

Public speaking & workshops
→ biggest driver

Affiliate income
→ quietly compounding

Consulting + automation
→ via Growth Hakka

Courses
→ small, but real

Vibe-coded products
→ JournoPitch + AICRM

Brand collaborations
→ occasional but aligned

The real insight
The gap wasn’t ambition.

It was scalability shape.

The two things I’m not doing yet?

• Faceless AI content
• A paid AI community

Everything else?
Built. Tested. Earning, unevenly.
Also many not earning.

And no, before anyone asks:

Am I making millions?
𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲𝗹𝘆 𝗻𝗼𝘁.

Am I anywhere near Sabrina Ramonov’s level?
𝗡𝗼𝘁 𝗲𝘃𝗲𝗻 𝗰𝗹𝗼𝘀𝗲.

But I am having fun trying.

And more importantly,
I’ve built something that doesn’t disappear
the moment one income stream dries up.

Messy? Yes.
Diversified? Also yes.
Still going? Definitely.

𝗧𝗵𝗮𝘁 𝘄𝗮𝘀 𝗮𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗽𝗼𝗶𝗻𝘁.

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Zohe Mustafa's Linkedin Analytics

Zohe Mustafa

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.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Zohe Mustafa's Linkedin Analytics

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