Sample edition — placeholder content demonstrating the digest format. Real curation starts once the pipeline is wired up.
<a class="digest-item-title" href="https://arxiv.org/abs/2402.15391" target="_blank" rel="noopener">Genie: Generative Interactive Environments</a>
<span class="digest-item-source">arXiv · DeepMind</span>TLDR A foundation model that turns a single image into an interactive, steerable world — an early step toward general-purpose world models.
Why read it The canonical recent reference for world models; a useful contrast to how “world models” are framed in telecom/6G literature.
<a class="digest-item-title" href="https://worldlabs.ai/" target="_blank" rel="noopener">World Labs</a>
<span class="digest-item-source">World Labs</span>TLDR Fei-Fei Li’s “spatial intelligence” lab — building generative models that produce rich, interactive 3D worlds.
Why read it A signal of where world models are heading commercially — beyond text into spatial and physical simulation.
<a class="digest-item-title" href="https://transformer-circuits.pub/2023/monosemantic-features/index.html" target="_blank" rel="noopener">Towards Monosemanticity: Decomposing Language Models Into Concepts</a>
<span class="digest-item-source">Anthropic</span>TLDR Uses sparse autoencoders to decompose LLM activations into interpretable, mostly monosemantic features — the paper that kicked off the modern interpretability wave.
Why read it Foundational for mechanistic interpretability; the feature-decomposition lens maps well onto asking “what is the network actually doing” in slice management.
<a class="digest-item-title" href="https://github.com/ggml-org/llama.cpp" target="_blank" rel="noopener">llama.cpp</a>
<span class="digest-item-source">GitHub</span>TLDR A minimal C/C++ runtime for LLM inference on CPUs and consumer hardware — the engine behind most local inference stacks.
Why read it The practical foundation for local/edge inference experiments; skim the README and quantization docs to ground edge-inference thinking.