Scaling Hierarchical Networks
guybedo
53 points
2 comments
October 28, 2026
Related Discussions
Found 5 related stories in 111.1ms across 10,874 title embeddings via pgvector HNSW
- The Emergent Symbolic Structure of Artificial Neural Networks schmuhblaster · 36 pts · September 02, 2026 · 47% similar
- AI Model Atlas – visualizing populations of ML models as interconnected 3D graph bj-rn · 57 pts · August 14, 2026 · 46% similar
- The Hierarchy of Money gwgundersen · 129 pts · September 20, 2026 · 46% similar
- Cerebras Deploys Trillion-Parameter AI Models gmays · 77 pts · October 25, 2026 · 45% similar
- Scaling and benchmarking a critical message bus using a new indexing strategy eatonphil · 35 pts · October 08, 2026 · 43% similar
Discussion Highlights (2 comments)
vatsachak
Oh man, that haskell quicksort tokenization has got me feeling great
augment_me
Its cool, but it goes against the whole optimization that NLP has gone through with BPE and redundancy/repetition in large data in general. Put this on a coding task or English and you will underperform against tokenizer that exploit multi-char patterns and can decode much faster. Given where things are going with spec decode (I've seen 32 token spec-decode pop up in the frontier models), you will need a very domain-specific use for this to be useful imo like massive tabular data, but at that point you already have everything structured, and might as well use an LLM with polars/sql/arrow.