Claude vs GPT-Codex: the AI ​​trends that are changing AI Engineering (and validating OpenClaw) #44

Оригинальный автор：Risorse Artificiali

Первоисточник：https://www.youtube.com/embed/UwzkzXjyIvg

In this episode of Artificial Resources – Notes and insights from the world of AI, we analyze the main trends in artificial intelligence that are redefining the way we build software, agents and complex systems. Let's start with a concrete comparison between Claude and GPT-Codex, two increasingly central approaches in the world of AI Engineering, to understand how models, workflows and methods of use are evolving. One of the key themes is the role of tokens, which are increasingly crucial in complex tasks: from context management to the efficiency of parallel sessions. We also discuss how AI models are developing self-research capabilities, and what this really means in practical terms, between hype and reality. We also address: * the differences between the main AI models (Claude vs GPT-Codex) * the economic and operational value of tokens in the labor market * strategies for efficient use in development workflows * security, validation and responsibility in the use of AI * new releases and innovations (GLM, Minimax, Meta, OpenAI) * the integration of AI into real products (like Google Maps) An episode that combines practical experience and strategic vision, to understand where AI Engineering is really going and why these trends are already validating new models like OpenClaw. ---------------------------------------- ⏱️ CHAPTERS 00:00 Introduction and happiness with tokens 03:19 Reflections on addiction and concentration 07:33 Comparison between AI models and tokens 12:10 Personality of AI models and subscriptions 13:10 Value of tokens in the labor market 19:45 Efficient use of tokens and parallel sessions 23:12 Communication and transparency problems 23:40 Evolution of tokens AI models: OpenAI vs Meta 25:17 Performance and innovation in AI models 26:50 New releases: GLM and Minimax 28:30 Improvement strategies in AI models 30:54 Auto research and strategy development 33:14 Practical applications of new AI technologies 36:37 Criticisms and skepticisms on auto research 40:10 The future of self-improvement 41:49 Startups, market and competition 43:38 Acquisitions and innovations 45:42 OpenAI, Nvidia: hype and criticism 48:15 Permissions and security in AI 50:42 Model validation 53:07 Risks and responsibilities 54:08 Reasoning and understanding in models 55:52 Parallel between AI and humans 57:44 Graphical representation and understanding 01:01:32 Cloud Code and interfaces 01:05:25 AI in Google Maps 01:12:15 Extras and fun moments ---------------------------------------- 🔗 RESOURCES CITED * Claude (Anthropic): https://www.anthropic.com * GPT-4 (OpenAI): https://openai.com/research/gpt-4 * Google Maps AI features: https://blog.google/products/maps * Meta Avocado & Mango: https://www.meta.com/research/avocado-mango * Auto Research (Karpathy): https://github.com/carpati/auto-research ---------------------------------------- 🎧 FOLLOW US Spotify → https://open.spotify.com/show/16dTKEEtKkIzhr1JJNMmSF YouTube → https://www.youtube.com/channel/UCYQgzIby7QHkXBonTWk-2Fg 🌐 https://risorseartificiali.com