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GM readers 👋 This week’s issue is all about breaking out of the mold. Whether it’s Yann LeCun walking away from Meta to chase his own vision, Cursor rethinking how devs work with AI agents, or Marc Lou spinning a one-day build into $20K overnight—each story reflects the same trend: smart people are ditching constraints and building on their own terms.
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In this issue:
Why LeCun’s exit signals deeper cracks in Big Tech’s AI approach 👀
Cursor 2.0’s dual-mode workflow and lightning-fast model built just for code 👨💻
How one founder turned fake screenshot fatigue into a $20K+ revenue engine 🤫
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Tech News
TL;DR: Deep learning pioneer and Meta’s chief AI scientist, Yann LeCun, is reportedly planning to exit the company to launch his own AI startup. The move comes after internal restructuring placed him under Scale AI’s Alexandr Wang, signaling shifting dynamics inside Meta’s AI leadership.
Led Meta’s AI research since 2013; built FAIR into a global research force.
Resisted LLM hype; pushed alternative approaches to general intelligence.
Recently got layered under new AI head Alexandr Wang (ex-Scale AI).
Now raising for a new startup—outside Big Tech, closer to academic roots.
LeCun stepping out is part of a bigger shift—more top researchers are leaving big AI labs to chase ideas they can’t explore inside corporate walls. Meta’s all-in on LLMs and scale. LeCun wants to try something different. If he attracts the right people, this could fast-track new directions in AI that aren’t just more of the same.
Agentic Building
TL;DR: Cursor 2.0 isn’t just another AI coding update. It introduces a new dev workflow built around two modes (agent vs. editor) and a purpose-built LLM called Composer 1. It’s ultra-fast, supports running multiple agents in parallel, and actually fits how real developers work.
Dual modes let you code hands-on (editor) or delegate tasks entirely (agent), depending on scope and focus.
Composer 1 is Cursor’s own model, trained on user data, and 20–50x faster than general LLMs like GPT or Claude.
Parallel agents work in separate Git work trees, so you can spin up 3 versions of a feature and pick the best one.
Cursor 2.0 feels like a bridge between solo dev workflows and scalable agent teams—without context-switching or lag.
Most AI coding tools rely on you to be an expert prompter. Cursor 2.0 shifts that dynamic. It’s not just faster—it’s structured around how real developers build: plan, test, iterate. That makes it less like a gimmick and more like a serious power-up for indie hackers and engineering teams alike.
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Quick Builds
TL;DR: Marc Lou launched TrustMRR, a Stripe-powered revenue verification app, in under 24 hours—then made $20K the next day. By solving a real trust issue in startup circles (fake screenshots), and pairing it with scarcity-based monetization, the project hit $18K+ MRR within a week.
The idea came from a tweet about fake revenue claims; he shipped v1 with one button and a Stripe connect flow.
His launch tweet (quoting Peter Levels) got 3M views—driving instant demand for limited ad slots ($299 → $1,500/month).
Added features based on user feedback: verified badges, public directories, dark mode, even a gamified guessing game.
Verified users now include high-profile founders like Sahil from Gumroad, further boosting credibility.
This is a textbook case of indie execution: simple product, fast build, zero BS. What made it pop wasn’t just shipping fast—it was smart storytelling, social proof, and scarcity design. For builders, it’s proof that tiny bets with tight hooks can still outperform bloated startup playbooks.
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