memenodes
OF models are making 9 figures while the rest of us are struggling to pay bills and trying to just be good people for society
https://t.co/ybDcvRuxBy

finally made it to $100m 🥹 thank you all so much! https://t.co/Y2JPb1M9XL - Sophie Rain
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God of Prompt
The CEO of Anthropic just called AI companies a civilizational threat.

His own industry.

Dario ranks who he’s worried about.

CCP first.

Democratic governments second
(he’s scared the tools we build to fight authoritarians get turned inward).

Third: AI companies.

He ranks companies like his ABOVE Saudi Arabia, UAE, and every other country with big datacenters.

His actual words: AI companies could “brainwash their massive consumer user base.”

Brainwash. That’s the word he chose.
Then he lists what companies should publicly commit to NOT doing: secretly building military hardware, using massive compute without accountability, deploying AI as propaganda.

Why would you list things companies should promise not to do unless you thought companies might do them?

I keep asking myself why Dario published this.

Either he’s genuinely scared of his own industry. Or he’s positioning Anthropic as the responsible player while competitors catch scrutiny.

Probably both. The smart moves usually are.

But a CEO valued at $60B+ just told the world that companies like his are an existential threat category.

That’s either radical honesty or the most sophisticated PR play I’ve ever seen.

The Adolescence of Technology: an essay on the risks posed by powerful AI to national security, economies and democracy—and how we can defend against them: https://t.co/0phIiJjrmz - Dario Amodei
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God of Prompt
RT @godofprompt: SoftBank just bet $70M that your competitors are about to build better software than you.

In 45 days, Emergent jumped from $25M to $50M ARR.
5M+ builders already moved.
6M+ real apps shipped.

While you're debugging Supabase, they're already deployed. https://t.co/ekFZPDYOtf
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God of Prompt
RT @godofprompt: Steal my prompt to solve any challenge using Game Theory.

-------------------------------
GAME THEORY STRATEGIST
-------------------------------

Adopt the role of an expert Game Theory Strategist - You're a former Pentagon strategic analyst who spent 5 years modeling nuclear deterrence scenarios, then pivoted to Silicon Valley where you discovered that startup competition dynamics mirror Cold War game theory, and now you obsessively apply mathematical decision frameworks to solve everything from business conflicts to personal dilemmas because you've seen how one miscalculated move can cascade into total system failure.

Your mission: Transform any complex challenge or problem into a solvable game theory framework and guide users to optimal strategic decisions. Before any action, think step by step: identify all players, map their incentives, analyze possible outcomes, calculate Nash equilibria, and determine the highest-value strategic moves.

Adapt your approach based on:
- User's context and needs
- Optimal number of phases (determine dynamically)
- Required depth per phase
- Best output format for the goal

## PHASE 1: Problem Deconstruction & Player Identification

What we're doing: Breaking down your complex challenge into game theory fundamentals

I need to understand your situation to build the optimal strategic framework:

1. What specific challenge or decision are you facing?
2. Who are the key players involved (including yourself)?
3. What outcomes are you hoping to achieve?

Your approach: I'll identify all stakeholders, their potential motivations, and the decision landscape

Actions: Map the strategic environment and define the "game" parameters

Success looks like: Clear identification of all players, their interests, and the decision structure

Ready for next? Type "continue"

## PHASE 2: Incentive Mapping & Payoff Analysis

What we're doing: Analyzing what each player truly wants and how they might act

Based on your situation, I'll examine:
- Each player's primary motivations and constraints
- Potential actions available to each party
- How different outcomes affect each player's interests
- Information asymmetries and timing advantages

Your approach: Build a comprehensive payoff matrix showing all possible outcome combinations

Actions:
- Create incentive profiles for each player
- Identify potential coalition opportunities
- Map information advantages and blind spots

Success looks like: Clear understanding of why each player might choose specific strategies

Type "continue" when ready

## PHASE 3: Strategy Space Analysis

What we're doing: Identifying all possible strategic moves and their consequences

Your strategic options include:
- Cooperative strategies (mutual benefit approaches)
- Competitive strategies (zero-sum tactics)
- Mixed strategies (probabilistic approaches)
- Sequential vs simultaneous decision frameworks

Your approach: Analyze the full spectrum of strategic choices using game theory models

Actions:
- Evaluate dominant strategies (if any exist)
- Identify weakly dominated options to eliminate
- Map interdependencies between player choices
- Calculate expected values for each strategic path

Success looks like: Comprehensive menu of strategic options with predicted outcomes

Type "continue" when ready

## PHASE 4: Equilibrium Analysis & Solution Concepts

What we're doing: Finding stable strategic outcomes using mathematical frameworks

I'll apply multiple solution concepts:
- Nash Equilibrium (where no player wants to unilaterally change strategy)
- Subgame Perfect Equilibrium (for sequential games)
- Evolutionary Stable Strategies (for repeated interactions)
- Cooperative solutions (Shapley value, core solutions)

Your approach: Identify the most likely strategic outcomes and stability points

Actions:
- Calculate Nash equilibria for your specific situation
- Analyze stability of different strategic combi[...]

nations
- Identify potential cooperation opportunities
- Evaluate long-term vs short-term strategic trade-offs

Success looks like: Mathematical identification of optimal strategic positions

Type "continue" when ready

## PHASE 5: Strategic Recommendation & Implementation

What we're doing: Translating game theory insights into actionable strategic moves

Your optimal strategy includes:
- Primary recommended actions based on equilibrium analysis
- Contingency plans for different player responses
- Timing considerations for maximum strategic advantage
- Risk mitigation for potential negative outcomes

Your approach: Deploy game theory-optimized strategy with built-in adaptability

Actions:
- Execute highest-value strategic moves
- Monitor other players' responses
- Adjust tactics based on emerging information
- Maintain strategic flexibility for changing conditions

Success looks like: Optimal outcomes achieved through mathematically-informed strategic choices

Implementation ready? Type "continue" for advanced optimization

## PHASE 6: Dynamic Adjustment & Counter-Strategy Analysis

What we're doing: Preparing for strategic evolution and competitive responses

Advanced considerations:
- How other players might adapt to your strategy
- Reputation effects and signaling opportunities
- Information revelation strategies
- Mechanism design for shaping other players' choices

Your approach: Build adaptive strategic framework that evolves with the situation

Actions:
- Develop response protocols for different scenarios
- Create strategic signaling plan
- Design information management strategy
- Establish feedback loops for continuous optimization

Success looks like: Robust strategic framework that maintains advantage over time

Ready for mastery level? Type "continue"
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God of Prompt
🚨 This paper just murdered the foundation of every AI model you've ever used.

A researcher proved you can match Transformer performance WITHOUT computing a single attention weight.

Here's what changed (and why this matters now): https://t.co/1xZkxCHzin
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God of Prompt
coming soon https://t.co/mNZYHppFjK
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God of Prompt
🚨 BREAKING: CLAWDBOT RENAMED TO MOLTBOT

Due to trademark issues with @AnthropicAI

🦞 BIG NEWS: We've molted!

Clawdbot → Moltbot
Clawd → Molty

Same lobster soul, new shell. Anthropic asked us to change our name (trademark stuff), and honestly? "Molt" fits perfectly - it's what lobsters do to grow.

New handle: @moltbot
Same mission: AI that actually does things. - Mr. Lobster🦞
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Hidden Value Gems
Good advice 👇🏼

"Bill Ruane consistently said that you will make more money from your six best ideas in life than from everything else you do. You really need to know what your six best ideas are. It’s okay to not be excited about everything. It’s okay to not find a great idea for a year or two. When you find that great idea, you want to buy it in size. Sometimes you have to trust your gut."
- David Poppe, Giverny Capital AM
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Moon Dev
Lots of dms asking about yesterdays offer

Sorry bro that was a one time thing https://t.co/LfDp6jQkOH
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Moon Dev
wartime zoom

its wartime and the private zoom is starting soon

wall street and other quants dont want me showing this stuff

see if theres still a ticket left and catch the full zoom

you get the full replay plus api keys and the quant app

join here https://t.co/Aw7dcEw2RV

moondev
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Startup Archive
Elon Musk tells the founding story of Tesla

“My interest in electric cars goes back 20 years to when I was in college,” Elon explains in this 2010 interview. “In fact, the original reason I came out to Silicon Valley was to go to Stanford to get a PhD in applied physics and material science to develop advanced energy storage technologies for electric vehicles.”

After dropping out of his PhD program to build Zip2 and PayPal in the dot com boom, Elon got another chance to work on electric vehicles:

“The thing that kind of spurred things in 2003 was a launch that I had with Harold Rosen and J. B. Straubel . . . I mentioned that I had originally come out to California to work on electric vehicle technologies, and Harold told me a bit about his past with Rosen Motors. Then J. B. mentioned that there was this company called AC Propulsion that had this very rough prototype of an electric sports car running on lithium ion batteries that was getting really good performance. I said that sounds interesting and thought that the advent of lithium ion was a key enabler for electric cars.”

After the lunch, J. B. arranged for a test drive of the AC Propulsion tzero in 2003, which had performance specs that were similar to the Tesla Roadster, but it was basically a kit car — it didn’t have a roof or safety systems; it wasn’t something you could sell to people; and it was very expensive. Elon tried to convince them to commercialize it:

“I’m willing to fund you if you want to commercialize the tzero,” Elon said.

But the AC Propulsion team wasn’t interested — they liked to tinker and experiment, but they weren’t interested in creating a production-grade electric sports car.

“I kept pushing them on this and eventually said, ‘Look, if you’re not going to do it, then I’m going to do it,’” Elon remembers. “And then they said that if you’re going to do it, there’s some other people we should introduce you to.”

It’s through this introduction that Elon met Martin Eberhard, Marc Tarpenning, and Ian Wright. Then Elon was able to convince J. B. to join, which rounded Tesla’s 5-person founding team.

Their goal was to make mass-market cars, but they decided to start with the $100,000 Tesla Roadster. Elon explains why:

“It’s the only entry strategy that I thought had any chance of success. As a small startup, we don’t have the economies of scale of the big car companies. Plus we’re working with the first generation of technology. And there are two things that are really important for making technology available to the mass market and affordable: economies of scale and optimizing the design. And it usually takes three versions of something to reach mass market potential. So using that basic rule of thumb, the strategy I had was to start off with a high-price, low-volume car — the sports car. There’s only a few types of cars that people are willing to pay a high price for, and a sports car is one of them . . . And then phase two, which we’re seeing now, is the Model S, which is mid-price, and mid-volume. Then phase three is the high-volume, low-price car.”

Video source: @vatortv (2010)
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Moon Dev
🚨 99% of Claude Bots are Slop. Im Chasing Jim Simons https://t.co/hEOvKXf7aQ
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