Shimin and Dan cover Minimax's M2.7 model — the first public experimental result in recursive self-improvement (RSI) — and unpack Anthropic's shock announcement of Mythos, a model so capable at finding security vulnerabilities that Anthropic is withholding public release while partnering with Amazon, Apple, Cisco, CrowdStrike, the Fed and major banks under 'Project Glasswing' to patch infrastructure first. They also debate AI's frontend weakness, discuss Addy Osmani's parallel agent limits piece, and move the AI bubble clock back.

Takeaways: 

• RSI is now experimentally demonstrated (not just theorized); reframes model improvement as capital competition, not PhD hiring.
• If AI finds vulns at scale, open source gets *more* secure long-term — but short-term this is a nuclear-test-equivalent event that may rewrite security, money, and trust assumptions.
• 'Frontend will be first automated' was wrong; backend may be easier because visual taste and pixel-perfect feedback loops aren't in training data
• Agent orchestration has a personal ceiling; finding it requires blowing past it. Tight scope + time-boxing + new contexts beats monolithic long sessions.
• 'Code is cheap' is really about industrialization — the people who industrialize outcompete those who don't; learn the tools or be left behind.
• OpenAI's CRO going public on a competitor's accounting is itself a bearish signal about OpenAI's enterprise position.

Resources Mentioned
MiniMax M2.7: The Agentic Model That Helped Build Itself
Anthropic debuts preview of powerful new AI model Mythos in new cybersecurity initiative
Assessing Claude Mythos Preview’s cybersecurity capabilities
Why AI Sucks At Front End
Your parallel Agent limit
Code Is Cheap Now, And That Changes Everything
The AI gold rush is pulling private wealth into riskier, earlier bets
OpenAI CRO Tells Staff Anthropic Inflates Run Rate by $8 Billion

Chapters

• (00:00) - Introduction to AI and Software Development

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