Author Archives: Rick's Cafe AI
The Legend of Zelda: AI Movie Trailer! | Made by VideoMax AI & Midjourney
The Chinese Room Experiment— AI’s Meaning Problem
“The question is not whether machines can think, but whether men can.” — Joseph Weizenbaum (creator of ELIZA, first chatbot)
Imagine you’re in a locked room. You don’t speak a word of Chinese, but you have an enormous instruction manual written in English. Through a slot in the door, native Chinese speakers pass you questions written in Chinese characters. You consult your manual, it tells you: “When you see these symbols, write down those symbols in response.” You follow the rules perfectly, sliding beautifully composed Chinese answers back through the slot. To everyone outside, you appear fluent. But here’s the thing: you don’t understand a single word.
This is the Chinese Room, philosopher John Searle’s 1980 thought experiment that has ‘haunted’ artificial intelligence ever since. Today’s models produce increasingly sophisticated text, writing poetry, debugging code and also teach complex concepts. The uncomfortable question, then, is whether any of this counts as understanding; or are we just being impressed by extremely elaborate rule-following. — Read More
How Autonomous Vehicles Learn to Reason With NVIDIA Alpamayo
The AI revolution is here. Will the economy survive the transition?
Michael Burry called the subprime mortgage crisis when everyone else was buying in. Now he’s watching trillions pour into AI infrastructure, and he’s skeptical. Jack Clark is the co-founder of Anthropic, one of the leading AI labs racing to build the future. Dwarkesh Patel has interviewed everyone from Mark Zuckerberg to Tyler Cowen about where this is all headed. We put them in a Google doc with Patrick McKenzie moderating and asked: Is AI the real deal, or are we watching a historic misallocation of capital unfold in real time? — Read More
Use multiple models
The meta for getting the most out of AI in 2026.
… [I]t doesn’t feel like I could get away with just using one of these models without taking a substantial haircut in capabilities. This is a very strong endorsement for the notion of AI being jagged — i.e. with very strong capabilities spread out unevenly — while also being a bit of an unusual way to need to use a product. Each model is jagged in its own way. Through 2023, 2024, and the earlier days of modern AI, it quite often felt like there was always just one winning model and keeping up was easier. Today, it takes a lot of work and fiddling to make sure you’re not missing out on capabilities. — Read More
When Google Locked the Door, Three MIT Students Picked the Lock
Agent-native Architectures
Software agents work reliably now. Claude Code demonstrated that a large language model (LLM) with access to bash and file tools, operating in a loop until an objective is achieved, can accomplish complex multi-step tasks autonomously.
The surprising discovery: A really good coding agent is actually a really good general-purpose agent. The same architecture that lets Claude Code refactor a codebase can let an agent organize your files, manage your reading list, or automate your workflows.
The Claude Code software development kit (SDK) makes this accessible. You can build applications where features aren’t code you write—they’re outcomes you describe, achieved by an agent with tools, operating in a loop until the outcome is reached.
This opens up a new field: software that works the way Claude Code works, applied to categories far beyond coding. — Read More
The AI Learned to Think on Its Own. Nobody Taught It How.
[In]January 2025, a Chinese startup that most Western engineers had never heard of publishes a research paper that shocks the AI world.
The claim: they trained a reasoning model as capable as OpenAI’s best, for a fraction of the cost. The method? They removed humans from the training loop entirely. No “reward model” (an auxiliary model that learns to predict what humans would prefer). No thousands of annotators paid to rate responses. Just a single signal: the answer is correct, or it isn’t. — Read More
AI & Humans: Making the Relationship Work
Leaders of many organizations are urging their teams to adopt agentic AI to improve efficiency, but are finding it hard to achieve any benefit. Managers attempting to add AI agents to existing human teams may find that bots fail to faithfully follow their instructions, return pointless or obvious results or burn precious time and resources spinning on tasks that older, simpler systems could have accomplished just as well.
The technical innovators getting the most out of AI are finding that the technology can be remarkably human in its behavior. And the more groups of AI agents are given tasks that require cooperation and collaboration, the more those human-like dynamics emerge.
Our research suggests that, because of how directly they seem to apply to hybrid teams of human and digital workers, the most effective leaders in the coming years may still be those who excel at understanding the timeworn principles of human management.
We have spent years studying the risks and opportunities for organizations adopting AI. Our 2025 book, Rewiring Democracy, examines lessons from AI adoption in government institutions and civil society worldwide. In it, we identify where the technology has made the biggest impact and where it fails to make a difference. Today, we see many of the organizations we’ve studied taking another shot at AI adoption—this time, with agentic tools. While generative AI generates, agentic AI acts and achieves goals such as automating supply chain processes, making data-driven investment decisions or managing complex project workflows. The cutting edge of AI development research is starting to reveal what works best in this new paradigm. — Read More
Rick's Cafe AI 