#Trend

AIYi Tay posted that enjoying the work is the secret sauce to AGI, responding to Logan Kilpatrick's remark that having fun is his competitive advantage. The post offers no technical details, figures, or products.

AIAndrej Karpathy's year-in-review lists the LLM paradigm changes he found most notable in 2025. He highlights Reinforcement Learning from Verifiable Rewards (RLVR), which drove most capability gains as labs ran longer RL training, and describes LLM intelligence as jagged, strong in verifiable domains and weak elsewhere. He also covers Cursor-style LLM apps, Claude Code running on the user's computer, vibe coding, and the case for a visual LLM GUI.

AIChatGPT's weekly users in Latin America have nearly tripled year over year. The post credits ChatGPT Go with making it easier for more people in the region to use the service.

AIARC Prize reports that the top Kaggle entry reached 24% on the ARC-AGI-2 private dataset at $0.20 per task, and that all winning solutions and papers are open source. The top verified commercial model, Opus 4.5 (Thinking, 64k), scored 37.6% at $2.20 per task, while a Poetiq refinement on Gemini 3 Pro reached 54% at $30 per task. The author argues that refinement loops are the main driver of 2025 progress, and says ARC-AGI-3 is planned for early 2026.

Why it matters: The post links 2025 competition results to a broader argument about refinement loops, showing how benchmark outcomes are being read as evidence of AI reasoning progress.

AIQuoc Le announced that Google's Gemini team is recruiting for a new Singapore team focused on advanced reasoning and LLM reinforcement learning. The post invites exceptional engineers and researchers to join the team, which is led by Yi Tay and reports into Quoc Le's Mountain View group.

AIIlya Sutskever says scaling current AI methods will keep producing improvements and will not stall. He adds that something important will still be missing.

AIReflection says it will be at NeurIPS in San Diego next week, with its team staffing a booth and a panel on open AI ecosystems. The panel features speakers from Reflection, Meta, AI2, Berkeley's Ray, and SGLang, and will discuss what building open ecosystems requires, lessons from China, and the meaning of transparency and sovereignty.

AIKarpathy argues that verifiability, not specifiability, is the most predictive feature for AI automation, since verifiable tasks can be optimized directly or through reinforcement learning. He says a task is suited to this approach when the environment is resettable, efficient, and rewardable. This explains the jagged frontier of LLM progress, with verifiable domains like math and code advancing rapidly while creative and strategic tasks lag behind.

AIChip Huyen argues that many AI product failures stem from user experience, data quality, and organizational structure rather than the AI itself. She cites a chatbot whose traction improved after adding pre-populated questions and a voice option for users whose hands were busy, and a lead scoring model that was broken because marketing wasn't asking the right questions. She also notes that senior engineers gain the most from AI coding while resisting it more, and recommends building small tools for daily frustrations to solve the "idea crisis."

AIA hiring manager told Chip Huyen that a software engineering candidate who has not experimented with vibe coding is a red flag. Huyen posted the remark on X and asked for readers' thoughts.

AIGeoffrey Hinton recorded a podcast with Jon Stewart, whom he describes as a longtime hero, focused on explaining how AI works. Hinton says Stewart was especially eager to understand the underlying mechanics.