CEO @Fomow, Make sexy your unsexy business

I love people, I love life and I love new technologies. I interact with different and exciting people all day long, while trying to improve their business with the solutions I can offer them.

As a businessman, I quickly found success because I love what I do.
Workaholic, curious and pragmatic, I always contribute to the improvement of processes in other departments such as sales administration, marketing or IT.

I am good at sales, business networking and orchestration, and moderately good at everything else and outperform when I get curious about a subject. That's why I always work with experts to ensure the success of my projects or my clients' projects.

📩 youneskhouakhi@webyn.ai / younes@fomow.co

Younes Khouakhi

Othmane Khadri

We help B2B tech SaaS startups build an acquisition funnel that generates leads and not just traffic | Generated over $800,000 in pipeline opportunities for clients | Founder Earleads

(scaling Earleads from $1M → $10M ARR in public)

If you’re reading this, you probably looked into AI SDRs or cold emailing agencies but something felt off "where's the human touch here?..." i feel you, what we do is completely different.

At Earleads we build and manage AI native go to market systems for B2B companies. AI native means we've designed them to maximize both human taste and machine's efficiency. You get best of both worlds without losing credibility in your market. 

What we do is dead simple: 

1) We get your ICP attention with social engineering content 
2) We monitor all signals shown by your ICP to engage with them when it makes sense 
3) We use targeted ADS to drive more qualified traffic toward your offers

We've worked with over 40 B2B companies helping them generate over $15 million in pipeline. We've booked over 1,500 qualified conversations and drove > 20,000 free trials accounts to our clients. 

If you want to learn more about our systems, DM me.

And if you want to follow the crazy road to $10M in ARR, i am documenting the back end of the journey on a weekly newsletter here: https://taap.it/YlAB6v

Head of Staff

I just built the playbook for GTM engineers who want to make the jump from the Clay era to the Claude Code era. It's free and you can access it right now.

Clay defined the last era of GTM engineering. The next one just started and most GTM engineers are going to miss the shift.

Clicking buttons on a UI is done.

You can now both automate execution and orchestration.

Claude Code can read your ICP, scans for buying signals, scores relevance, picks the angle, and drafts the approach. The part that used to take you 20 minutes per account now takes seconds.

GTM engineering is splitting into 2 roles:

1) Operators: They know the tools. They run playbooks someone else designed. Every junior SDR will learn this stack in 6 months.

2) Engineers: They build the systems. They encode their GTM judgment into skills that chain together and run autonomously. Signal detection feeds scoring feeds outreach feeds reporting. No manual steps.

The operators get commoditized. 

The engineers become irreplaceable.

So i packaged everything into an open-source playbook:

- 10 installable Claude Code skills: ICP building, signal detection, lead scoring, outreach sequencing, competitive battlecards, meeting prep, CRM hygiene, weekly reporting, and agent architecture planning

- 100+ curated GTM resources: every repo, every open-source release, every new MCP server dropping in the GTM space. Compiled and organized

- Ready-to-use templates: ICP frameworks, signal taxonomies, scoring rubrics, outreach sequences, enrichment waterfall patterns

- A full guide on the new GTM stack: what changed, what tools matter now, and how the pieces fit together

The skills chain together. 

Your ICP feeds your signal scanner. Your signals feed your lead scorer. Your scores feed your outreach sequences. Your outreach results feed your weekly report. 

The whole system compounds.

This is the shift. From running tools to building systems. From prompts to skills. From operator to engineer.

Free, open source, installable in 30 seconds.

Comment "GTM" below and i'll send you the link.

(we must be connected before)

Claude code for go-to-market → follow along

The sales industry is broken for so many reasons. Your sales team is stressed ASF but you can do something  about it: 

I didn't plan to film this. I was just listening to a depressed SDR vent. Then he said something so brutal I asked if I could hit record:

Think about 2 people for a second.

Person A (Jack) is 25. Got told this role would build character and launch a career in sales. Wakes up at 6 AM to rehearse a pitch for people who never asked to hear it. 

Makes 80 calls a day. Gets hung up on 77 times. His manager tells him those 3 conversations mean he's "getting close."

He's not getting close to anything.

Person B is a VP of Ops at a saas company. Genuinely looking for a solution to a problem she's been dealing with for months. 

She posted about it on LinkedIn this morning. 

She's in a buying cycle right now.

Jack calls Person B at 2 PM with a script that opens with "is this a good time?"

She hangs up. He logs it as "not interested." His manager tells him to call the next one.

They needed each other but the system made them enemies.

This is what kills me. 
The demand exists. 
The supply exists. 

But the delivery mechanism is so broken it turns potential buyers into people who block your number.

I built Earleads to fix the delivery mechanism: 

1- You build brand awareness so Person B already recognizes your name. 

2- You monitor signals so you know Person B is actively looking right now. 

3- You reach out with context not a script. By the time you call she picks up because she's curious not cornered.

Person A stops hating their job. 

Person B gets the solution she needed.

Sales could be healthy if done properly.

Don’t let this be your team story, book your free go to market audit: https://lnkd.in/eWwqvCdV

Andrej Karpathy (ex-Tesla AI, OpenAI founding team) just open-sourced autoresearch. This is a game changer if you're using Claude Code:

You point an AI agent at a real LLM training setup, give it an instruction file and go to sleep.

The agent modifies the architecture autonomously by running a 5-minute training loop on a single GPU. 

It checks validation loss, decides what to keep and what to discard based on YOUR instructions and keep looping all night.

You wake up to a full experiment log and an optimized model.

How to make hyper intelligence even more...intelligent? 

What's fascinating here is running ML experiments used to require a PhD team and a compute cluster. 

Now it's a repo you can clone rn.

Let me know if you would like to receive the github repo i'll DM you the link.

This is literally the key to make your agents better, more specific, and obviously spend less tokens per task :).

taplio

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Othmane Khadri

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