mouaad
73 posts

mouaad retweetledi
mouaad retweetledi

After shipping 250+ apps and making $3M+ with AI, I built a course for @lovable apps—and I'm giving it away.
In the past two years, we cracked the code to building apps with AI. Refining our process as the tools got better.
I've distilled everything into this single document.
Comment "Build" and drop a follow. I'll DM it to you.
P.S. This will likely blow up, so give me some time to reply.
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I spent 65 hours creating a NEW Miro board which shows you exactly step by step how I made $15M with my digital products business
it includes case studies of 8+ accounts on X doing $100K/month profits each
( including full funnels )
Comment “Miro” and I’ll send it to you via DM
**must be following + retweet to receive

English

Hiring an AI-native developer intern (PMs who can vibe code are welcome).
• 90-day paid internship (might convert to full time)
• Build for global scale
• No token limits push real boundaries
Looking for builders who ship fast and think in systems.
Drop your GitHub or live project URLs in the replies and I’ll DM you.
English
mouaad retweetledi
mouaad retweetledi
mouaad retweetledi
mouaad retweetledi
mouaad retweetledi
mouaad retweetledi

Andrej Karpathy just dropped a project scoring every job in America on how likely an AI will replace it from 0-10
> Scraped all 342 occupations from the Bureau of Labor
> Fed each one to an LLM with a detailed scoring rubric
> Built an interactive treemap where rectangle size = number of jobs and color = how exposed that job is to AI
The key signal in his scoring: if the work product is fundamentally digital and the job can be done entirely from a home office, exposure is inherently high.
The scale:
0-1: Roofers, janitors
4-5: Nurses, retail, physicians
8-9: Software devs, paralegals, data analysts
10: Medical transcriptionists
Average across all 342 occupations: 5.3/10.
The entire pipeline is open source. BLS scraping, LLM scoring, the visualization. All of it. Much respect for the sensei this is scary and awesome
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