Aadi @cacheMeeOutside

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@iyoushetwt Notepad++

@alex_prompter Claude

@basicprompts AI

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You can only optimize so much, but you can care forever. Efficiency has limits, devotion doesn't.

@Zephyr_hg IDEAS

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This video is literally 50 entrepreneurs giving you an MBA in 18 minutes:

The founder of Palantir shows how to think like a billionaire (even if you’re just starting out). Save this for later.

5 Most Dangerous Laws In The World: 1.

This research paper just broke my brain 🤯 Most people think multi-agent AI is just "multiple ChatGPTs in a room." That's like saying a surgical team is just "multiple people with knives." The real story is way deeper. These researchers cracked the code on what actually makes AI agents work together. And it's not what anyone expected. Task allocation is completely broken in current systems. We're basically throwing darts at a board. Give the math problem to whoever's free. Ask the creative agent to debug code. It's chaos disguised as intelligence. Real multi-agent systems need to understand specialization. Like, actually understand it. Not just "Agent 1 does X, Agent 2 does Y" but dynamic matching based on context, capability, and current workload. The memory problem is insane. Single agents just need to remember conversations. Multi-agent systems need five different types of memory: short-term, long-term, episodic (past collaborations), consensus (shared knowledge), and hierarchical access control. Most current systems are basically giving every agent amnesia between tasks. Context management is where everything breaks. Each agent needs to track three layers simultaneously: the big picture mission, their specific piece, and what everyone else is doing. Fail at any layer and the whole system becomes expensive nonsense. Game theory matters more than code. When agents debate or negotiate, you're not optimizing for "correctness." You're finding equilibrium states. The paper shows Stackelberg dynamics (leader-follower) work better than Nash equilibrium for most real tasks. Nobody talks about this stuff because it's not as sexy as "look, the robots are talking to each other." The blockchain applications they outline are wild. Agents that can negotiate smart contracts. Fraud detection systems where different agents specialize in different attack patterns. Consensus mechanisms that actually think. We're not building better chatbots. We're building the foundation for autonomous economic systems. The gap between current "multi-agent" demos and what this paper describes is massive. Most of what people call multi-agent is just single agents with fancy prompting. Real multi-agent systems will have specialized roles, shared memory architectures, and game-theoretic coordination. They'll be fundamentally different beasts. Teams solve problems no individual can handle. Same principle applies to AI.

📁 Matthew McConaughey says he wants a private LLM, fed only with his books, notes, journals, and aspirations, so he can ask it questions and get answers based solely on that information, without any outside influence.

I read through every single job posting on OpenAI's career page. Here's everything they're working on and who they compete with.

For nearly two decades, Box co-founder and CEO Aaron Levie (@levie) has been at the frontlines of how technology reshapes work—guiding the company through the rise of mobile, the cloud, and now the age of AI. In his fireside with YC's @dflieb at AI Startup School, Aaron reflects on adapting a company for the long term, staying relevant across multiple waves, and why he believes AI is the most transformative shift yet. 00:00 – AI hype vs. reality inside companies 01:00 – Early days: starting Box in 2005 03:30 – Pivoting from consumer storage to enterprise cloud 05:45 – Cloud transition lessons vs. AI today 07:10 – Why unstructured data is AI’s biggest opportunity 09:05 – Will AI replace jobs or create more work? 13:15 – Startups vs. incumbents in the AI era 15:20 – Finding the next “nouns and verbs” for startups 20:25 – Rethinking SaaS business models for AI agents 27:05 – Why companies won’t just build everything in-house 31:15 – Advice to young founders 35:40 – Fulfillment and meaning of life 36:40 – Enterprise design and craft in SaaS 39:00 – Competing with incumbents

It's not the prompt......it's the context youtu.be/zMM5zqesL1g?si…

How money works: 1. OpenAI signs $300B GPU deal with Oracle 2. Larry gains $100B (no GPUs shipped) 3. Larry invests in OpenAI’s $1T round 4. Sam uses $300B to pay Oracle 5. Oracle stock pumps again 6. Larry makes another $100B 7. Larry invests in OpenAI Flywheel go brrr.

I’ve never shared MRR before, but here’s the crazy story of how we built Cursor Directory, a 3-hour hack that now makes $35k/month. It started right before I moved to France. I had downloaded some videos for the flight, stumbled upon .cursorrules, and immediately called Viktor: “We need to build a website for these.” Three hours later, Cursor Directory was live. Within minutes, thousands of visitors landed on the site. While we focused on our main startup Midday, the directory kept exploding: - Covered by major tech YouTubers - Shared in meetups worldwide - Quickly hitting 150k unique visitors/month Then came MCPs We added a simple page to explore them. Traffic spiked to 200k/month. Since all traffic was organic, we tried ads. I posted here on X with a payment link. Minutes later - every slot was sold out. From there, we kept adding: - Job listings - Featured MCPs - Company profiles - User profiles - Rules generator And more ways for people to discover, share, and connect. That pushed us to 295k unique visitors/month. What started as a fun weekend hack has turned into something far bigger: - $35k+ MRR from ads & listings - $150k+ in profit (yes) - 55.5k registered users We could only dream of this outcome. Lesson? Sometimes the smallest hacks create the biggest waves. Cursor Directory has opened more doors than we could’ve imagined and it all started with a 3-hour flight.

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