Build POWERFUL Softwares in the Era of AI (Business Idea to Earn $$$)

Key Concepts

• Software 3.0: Prompt-based programming using natural language (English).
• Partial Autonomy: Applications with AI assistance, but human-supervised.
• Generation and Verification Loop: The process of AI generating code and humans verifying it.
• LLM OS: Conceptualizing LLMs as operating systems with CPU, RAM (context window), and peripherals.
• LLM Psychology: Understanding the behaviors, limitations, and potential vulnerabilities of LLMs.
• Vibe Coding: Rapid application development using prompts.
• Agents: AI entities that can operate computers and interact with software.

What is Software 3.0?

• The presentation begins by defining the evolution of software development:
  • Software 1.0: Manual coding, line by line (e.g., code on GitHub).
  • Software 2.0: Training neural networks, where code is derived from weights (e.g., neural network weights on Hugging Face).
  • Software 3.0: Programming using prompts in natural language (primarily English).
• Andrej Karpathy suggests that English is becoming the "hottest new programming language."
• The shift involves rewriting existing code into weights (Software 2.0) and then using prompts (Software 3.0).

Building Software with Partial Autonomy

• The first approach to building software in the AI era is to create partially autonomous applications.
• Examples include:
  • Copilot: Assists with coding tasks.
  • Cursor: An IDE with LLM integration.
• Cursor's Anatomy:
  • Traditional interface.
  • LLM integration: Packages state into a context window, orchestrates multiple models (embedding, chat, diff apply), and has application-specific GUI.
  • Autonomy levels: Controlled via commands like Command K (partial code autonomy), Command L (full file review), and Command I (full codebase review).
• Perplexity: Packages information into a context window, orchestrates LLM models, and has search/research capabilities.
• Challenges: Gaps exist in software like Photoshop and Unreal Engine regarding LLM integration.
• Key Questions:
  • Can LLMs see what humans see?
  • Can LLMs act as humans do?
  • How can humans supervise and stay in the loop?
• Focus: Develop "Iron Man suits" (partially autonomous products) rather than "Iron Man robots" (fully autonomous agents).
• Priorities: Custom GUI/UIUX, fast generation and verification loops, and autonomy sliders.
• AGI: AGI is not the focus, but rather custom GUI/UIUX, fast generation and verification loop, and autonomy slider.

Speeding Up Generation and Verification

• The second approach involves accelerating the generation and verification steps in the development workflow.
• Workflow:
  • Generation: AI generates code (e.g., using Cursor).
  • Verification: Humans review and approve changes.
• Focus Areas:
  • Verification: Make the verification process easy and fast.
  • Generation: Keep AI on a "tight leash" to increase the probability of successful verification.
• Detailed Prompts: Use specific prompts that detail the tools, implementation, and changes required.
• Example Applications:
  • App for course creation (targeted at teachers).
  • App for course serving (targeted at students).
• Goal: Control the AI agent by focusing on specific tasks.

Building Software for Agents

• The third approach is to build software specifically for AI agents.
• Evolution:
  • Humans used GUIs.
  • Computers used APIs.
  • Agents can now operate computers and be human-like.
• Method: Provide llms.txt files on websites with Markdown information about the software.
  • Implemented by Vercel and Stripe.
  • Designed for LLMs rather than humans.
• Documentation: Create documentation specifically for LLMs (e.g., llm.txt for a summary, llm-full.txt for detailed information).
• Actions for LLMs: Include commands (e.g., curl) in documentation so agents can execute and verify processes.
• Tools:
  • GitIngest.com: Converts GitHub repos into Markdown files for LLMs.
  • DeepWiki.com: Converts repos into detailed documentation for AI agents.
• Efficiency: Using tools like DeepWiki or GitIngest is more efficient than having agents browse websites.

2025: The Year of Agents?

• Tesla Autopilot: An example of ongoing efforts to achieve autonomy.
• Timeline: Full autonomy was demonstrated in 2013, but it took over 10 years to reach a stable production version.
• Demo-to-Product Gap: A significant gap exists between autonomous demos and reliable autonomous products.
• Prediction: Andrej Karpathy suggests 2025-2035 as the "decade of agents."

Is Vibe Coding Working?

• Definition: "Vibe coding" (coined by Andrej Karpathy) refers to creating applications rapidly using prompts.
• Potential: Even children can create applications using vibe coding.
• Example: Karpathy's attempt to create menu-gen.app (generates menu images).
• Challenges:
  • Running locally is easy.
  • Deploying to production requires handling LLM API keys, image generation API keys, Vercel deployments, domain names, authentication, and payments.
• Conclusion: Vibe coding works locally but faces significant challenges in becoming production-ready.

Future of Large Language Models and LLM Psychology

• LLMs as Utilities: Analogous to electricity, with base model training, serving, and charging per token.
• Demand: Low latency, high uptime, and consistent quality.
• Open Router: Similar to a transfer switch, routing to various models.
• LLM OS: LLMs are like operating systems:
  • CPU: LLM itself.
  • RAM: Context window.
  • Capabilities: Browsing the internet, communicating with other LLMs, accessing files, using tools (calculator, Python interpreter, terminal).
  • Peripherals: Video and audio.
• Model Switching: Tools like Cursor allow switching between models (GPT-4, Gemini, DeepSeek).
• Centralized vs. Distributed: Currently, LLMs are cloud-hosted (time-sharing), but eventually, they will run on edge devices.
• Interface Evolution: Similar to the shift from terminals to GUIs, the interface for LLMs will evolve beyond simple chat.
• LLM Psychology:
  • LLMs as "stochastic simulations of people."
  • Emergent psychology with knowledge and memory, but prone to hallucinations.
  • Can solve complex problems but may fail on simple ones.
  • Lack continual learning and knowledge consolidation.
  • Vulnerable to prompt injection, leading to data leaks.

Conclusion

• To build powerful software in the AI era:
  • Make applications partially autonomous.
  • Focus on the speed of the generation and verification flow.
  • Build for agents.
• Understanding the future of LLMs and their psychology is crucial for creating reliable software.