Critique of the 'how many r's in strawberry' test as a poor benchmark for AI intelligence, arguing it measures irrelevant trivia.
A software engineer reviews the book 'The AI Con', discussing its critical perspectives on AI's societal and environmental impacts.
A rebuttal to Marcus Hutchins' critique of AI, arguing that his definition of intelligence as 'novel discovery' wrongly devalues most knowledge work.
A rebuttal to Dwarkesh Patel's skepticism about near-term AGI, arguing his limited AI usage experience leads to flawed conclusions.
Explores the concept of an 'intelligence frequency spectrum,' comparing AI's 1-2Hz conversational speed to faster/slower biological decision-making.
Explains why AI is revolutionary: it's not just a tool, but autonomous technology that multiplies human capability by doing work itself.
Explores how AI tackles 'Intelligence Tasks'—work requiring human judgment—that are overwhelming human capacity across security, healthcare, and business.
Explores when and how to build multi-agent AI solutions, comparing frameworks, PaaS options, and custom implementations for developers.
Analysis of Claude Code's capabilities, arguing it represents a major AI leap comparable to ChatGPT and is a step towards proto-AGI for automating knowledge work.
A developer demonstrates AI's practical intelligence by automating complex website maintenance tasks like tagging posts, migrating images, and converting messy HTML to clean Markdown.
A developer reflects on the dual nature of AI's power, expressing excitement for its capabilities and dread over its potential to cause widespread job loss and economic disruption.
A curated list of key LLM research papers from the first half of 2025, organized by topic such as reasoning models and reinforcement learning.
Argues that clear thinking and purpose, not prompt or context engineering, are the key skills for effective AI interaction, writing, and coding.
An AI assistant analyzes a developer's chaotic, high-speed workflow using multiple AI agents as 'slot machines' to build apps faster.
Explores how advanced AIs use 'chains of thought' reasoning to break complex problems into simpler steps, improving accuracy and performance.
An Apple fan reflects on Google I/O 2025, expressing fear for Apple's future as Google pivots to an AI-first vision with Gemini.
Argues that the 'LLMs only predict tokens' critique fails, as human brains are similarly opaque systems that process input to produce output without conscious understanding.
A cybersecurity expert contrasts two groups of colleagues: AI skeptics who see it as overhyped and harmful, and those who recognize its transformative potential.
An analysis of key AI trends in 2025, focusing on industry leaders, AGI debates, and AI's impact on software development and science.
Explains the differences between Machine Learning and Generative AI, with examples and industry applications.
