Sponsored LinkedIn post
A dedicated post to introduce your brand to my audience.
Founder @ ROAS PIG
Companies pay Yaron to write a post about their product on LinkedIn.
This page is where they book it. Below: Yaron's audience, recent posts and price. Naano, the platform behind this page, handles the brief, your approval of the post and the payment.
€500per post, fixed price
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Building AI Agents and Automations at ROASPIG
"We lost 3.2K Yesterday" I told my wife. We were spending 5K-6K daily on Facebook ads. Back in the days It seemed like a lot, Today... as I've spent more than 30M on ads I understand there are just different levels to the game. And for some people spending 5k is just their remarketing budget. Anyway, Spending 6k per day on ads, And suddenly the Shopify checkout broke. "But how can that be? did you contact Shopify? Aren't they supposed to take care of this?" She asked. "Well, yeah... I contacted them, they said they are sorry, but there's not much they can do" That was the moment it hit me, You can't trust a platform to take care of you. And you must have systems in place if you want to scale. The day after, I created an alerting monitor. It would calculate our conversion rates in the last 3hours, make sure it wasn't abnormal. It would ping the wesbite every few minutes And if something was wrong it would send me an email and call my phone and say "The checkout is broken". And this was on-top of the Facebook automated 'Stop loss' rules I've set. That incident taught me a crucial lesson, Or better say it reminded me what I already knew. Extreme Ownership. Create backups and systems. Automate whatever you can, and track like a madman. --- These are the things I believe in... And I've been implementing these principles in all companies I've worked with. 9 Figures DTC companies, Pre Seed startups and even working with soloprenuers. If you're interested in setting up automations, Optimizing your ads and growing your business, Feel free to reach out :)
My most engaged audience
34% Founders
From 41 people who reacted to or commented on my posts
Public LinkedIn data · Sep 28, 2026
Just got back from a party with 550 n8n builders in Berlin, and I walked out grateful. I got invited as one of n8n's top template contributors, which honestly still feels a little surreal. The people made it. Some of the most generous, genuinely sharp folks I've met in ages. A few things I can't stop thinking about. The way people build is changing. Barely anyone drags and drops now. They talk to their tools, and n8n quietly runs the whole thing in production. Nearly everyone was on Claude Code. I was the odd one out still using OpenCode, haha. The use cases are refreshingly practical. Inbox triage. Lead scoring. Research agents. No hype, just real value shipping every day. This crowd solves real problems. And the biggest opportunity I saw? Evals. Almost nobody is building them yet, which means the door is wide open for whoever moves first. One more. When the talk turned to scraping tools, most people reach for the popular names. My pick is Bright Data, and there's a specific reason I go there first. Wrote the full breakdown, and the whole thing is in the video too.
The best ad I made this month began as a stranger's 1am meltdown on Reddit. I didn't write the angle. I went and found it. Most of us write ad copy from the inside out. We picture the customer, guess what they feel, and hope we're close. So I flipped it. Now an agent reads a few hundred real complaints before a single line of copy gets written. It pulls the threads where people are venting, clusters the phrases that keep repeating, and surfaces the ones with the most heat behind them. One search for advertisers frustrated with Meta kept turning up the same idea. "I've been burning money on Meta ads like an idiot." "I just shut down 27 of my campaigns." The angle was sitting right there: wasted spend. Everything after that was fast. The scraping runs through the Bright Data MCP, which is what lets the agent read enough of the internet fast enough for this to be worth doing. Speed isn't the point though. Starting from real words instead of a blank page is. I broke down the full workflow, and where it gets risky, in this short article
A better model can make your AI product worse. Sounds backwards, I know. But it happens when your evals reward the wrong thing. The new model sounds cleaner. The answers are more confident. The demo looks better. But production gets messier: Unsupported claims. Higher cost. More stale evidence. More users trusting answers that should have escalated. The issue is not always the model. It is the judgment layer. A serious AI product needs evals that check: 1. Deterministic stuff Schema, URLs, required fields, citations, latency, empty outputs, tool-call success. 2. Grounding Is each claim supported by the source data, or did the model infer too much? 3. Semantic quality Relevance, tone, uncertainty handling, instruction-following, usefulness. 4. Fresh evidence Customer feedback, support tickets, user corrections, product analytics, and sometimes public web data. The loop should be: Collect evidence. Generate output. Judge against evidence. Find the failure mode. Add it to evals. Improve the system. Run it again. The model generates the answer. The eval loop builds the company judgment. That is the part I think most AI teams are underbuilding.
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Based on 41 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 41 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 41 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 41 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
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Yaron Been is a B2B LinkedIn creator based in Israel with 23,644 followers, posting about AI and Marketing. On Naano, one sponsored LinkedIn post by Yaron Been costs €500, a fixed price per post.
The Naano team casts the creators, writes the briefs, reviews every draft with you and reports results per creator. You approve the shortlist and every draft.