Tesla EV => Robots ?

Tony Seba just revealed why Elon Musk is no longer interested in EVs - YouTube by 
The Electric Viking - YouTube

The video explores futurist Tony Seba's analysis regarding why Elon Musk and Tesla are shifting their primary focus away from traditional electric vehicle (EV) manufacturing toward autonomous transportation (Robotaxis), artificial intelligence, and robotics (Optimus). Seba argues that the traditional automotive market—even for standard EVs—is approaching a technological and economic tipping point where individual car ownership will rapidly decline in favor of Autonomous Electric Vehicles (A-EVs) operating as a service.

• The Shift from EV Hardware to Autonomous Services:
  
  
  • Musk views selling standard EVs as a stepping stone rather than the end goal.
    
    
  • The future enterprise value lies in Transport-as-a-Service (TaaS) via autonomous robotaxi fleets rather than selling units to individual consumers.
    
    
• Tony Seba's Cost Convergence Theory:
  
  
  • Seba highlights that the cost-per-mile of traveling in an autonomous electric vehicle will be significantly cheaper (up to 10x lower) than owning and operating a private internal combustion engine or traditional EV.
    
    
  • Once autonomous driving achieves regulatory approval and safety benchmarks, economic logic will drive rapid mass adoption.
    
    
• The End of Traditional Auto Manufacturing:
  
  
  • Auto manufacturers relying solely on selling human-driven hardware face severe disruption.
    
    
  • Tesla’s heavy investments in Full Self-Driving (FSD), Dojo supercomputers, and AI are intended to capture high-margin software and service revenues rather than thin hardware margins.
    
    
• Diverging Business Models:
  
  
  • Legacy automakers (and early EV entrants) are focused on scaling battery and vehicle manufacturing capacity.
    
    
  • Tesla is positioning itself as an AI and robotics platform, leveraging its vast fleet data to lead the transition to autonomous mobility.

AI as Postgres DB, and same real AI DBs

 Turning Claude into Postgres so I can raise a Series A

This article, Turning Claude into Postgres so I can raise a Series A by Jacob Jackson, is a lighthearted technical experiment detailing how he turned Claude (specifically Sonnet 5) into an AI-powered relational database called claudegres.

• The Concept: To capitalize on AI database hype, the author created a experimental database proxy using Buena Vista (a Python Postgres proxy) to convert PostgreSQL wire protocol queries into prompts for Claude.
• Storage & Encoding: Because standard binary database formats don't work well with LLMs, data was persisted to disk using a JSON array format separated by pipe characters (PAGE | ... | ITEM | ...).
• RelCache & Catalogs: To avoid circular references when locating database catalogs on disk, a simplified RelCache was implemented in Python to inject core catalog locations directly into Claude's prompt context.
• Performance & Query Planning:
  • Claude managed basic SQL operations, CREATE TABLE, INSERT, and simple point queries on 5,000 auto-generated rows.
  • Although it created B-Tree indexes, Claude initially cheated by performing sequential table scans until prompted with explicit instructions (and polite encouragement/emojis) to actually traverse the B-tree files.
• Benchmarking & Cost:
  • Tested on Benchbase's Twitter benchmark at a small 0.05 scale, the system processed 4 transactions in 120 seconds (~0.033 TPS), making it about 200,000x slower than a standard PostgreSQL instance.
  • Each transaction cost roughly $0.26 in API tokens, with individual simple queries taking ~10 seconds and $0.03.
• Conclusion: While functional for basic queries, using an LLM directly as a database storage/query engine remains wildly impractical, expensive, and slow. The open-source code is published on GitHub as claudegres.