Open library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
Key publications from Anthropic, OpenAI, Google DeepMind, Meta, DeepSeek, Qwen, Z.ai, Kimi and other labs, plus the most influential essays and blogs.
Google Research
Nested Learning argues that model architecture and training rules are levels of one learning system, a shift intended to help models learn new tasks without sacrificing old skills.
Qwen Team · Qwen
Qwen’s first open-source 7B and 14B Instruct checkpoints target 1M-token contexts, backed by an inference framework built for long inputs.
Lilian Weng
When a Transformer needs more context, less memory, or faster inference, which part of attention should change?
Sebastian Raschka, PhD · Sebastian Raschka
gpt-oss keeps the decoder-only Transformer but combines sparse expert routing, attention changes, and quantization to make large models more practical to run. The author’s benchmark comparisons are promising, but real-world reliability remains uncertain.
Simon Willison
Jev shifts an LLM from writing prose to returning typed decision scores. That suits classification, but its opaque numbers make evaluation and bias checks essential.
Andrej Karpathy
microgpt makes the GPT training-and-generation algorithm inspectable in 200 lines of dependency-free Python; its tiny scale is a feature for learning, not a model of production infrastructure.
Sebastian Raschka, PhD · Sebastian Raschka
The article’s useful map is about where each design saves work: cached state, attended positions, or full-attention layers.
Typesafe
TypeSafe’s thesis is that automating software calls for a different interface than chat: Jev returns typed, probabilistic decisions instead of free-form text.