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A tweet states that only 7,000 humanoid robots were sold worldwide in 2025, with the author expressing bullish sentiment while noting that mass deployment has not yet arrived.
The author expresses deep pessimism about humanity's future with artificial intelligence, highlighting political leadership's failure to address the urgency of AI risks.
The article highlights the rapid progress in robotics over the past decade, with significant advances in the last two years, and speculates on future developments.
The author expresses awe at the rapid progress in AI, particularly generative AI, feeling that we're on the verge of transformative changes across industries, drawing parallels to past technological revolutions.
The article discusses and seeks insights on upcoming AI applications in biology, including labs, startups, and projects that could yield tangible results in areas like supplement optimization and bioactive compounds.
An analysis of the key factors that will differentiate production-ready AI agents from demo prototypes in the coming years, focusing on reliability, scalability, and real-world integration.
The author argues that current AI scaling methods, despite being the pinnacle of engineering, are woefully inefficient and will be viewed as primitive in hindsight, similar to how we now see 1960s mainframes.
A discussion on the next training paradigm for AI, covering research bets, grindability, RLVR, and a vision for 2027.
Analyzes whether NVIDIA remains the top choice for running large language models locally in 2026, considering competition and new hardware.
OpenAI publishes a position paper on AI progress and recommendations, discussing the rapid advancement of AI systems beyond the Turing test milestone, projections for discovery-making capabilities by 2026-2028, and their commitment to safety and alignment research as AI becomes more capable.