As Rocks May Think

kept by eddie feb 05

Coding agents combined with reasoning LLMs have become automated scientists, enabling a golden age where all computer science problems appear tractable through vast inference compute.

On-Device LLMs: State of the Union, 2026 – Vikas Chandra – AI Research @ Meta
Billion-parameter LLMs now run in real time on phones due to advances in model compression, quantization, and efficient architectures, not just faster chips.
also on DeepSeek-R1, #ai-research
What the hell happened with AGI timelines in 2025?
Expert AGI timelines contracted then expanded in 2025 as reasoning model gains proved costly to scale, though steady progress and growing revenue undercut radical pessimism.
also on #reasoning-models
Thinking through how pretraining vs RL learn
Reinforcement learning provides far fewer bits per FLOP than pretraining until models achieve high pass rates, limiting RLVR's ability to learn new capabilities.
also on AlphaGo, DeepSeek
2025 letter | Zhengdong
Zhengdong Wang argues that compute scaling—underpinned by Moore's Law and empirical trends—has driven AI progress more reliably than human innovation, despite repeated underestimation.
also on AlphaGo, #ai-research
400 Bad Request
AGI capable of most cognitive work could arrive by 2028–2034, but deployment will lag capability due to verification bottlenecks, uneven automation, and institutional friction.
also on DeepSeek, #ai-research, Claude, #automation, #software-engineering