repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
mlhher/late-cli
Stop degrading your model's reasoning. A minimal, zero-config AI coding agent. Enforced ephemeral subagents keep context pure. From tiny local models up to Sol, Fable and Kimi K3.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 48%I mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset) →
- PossiblePossibly related (embedding) · 48%Hypothetically speaking... →
- PossiblePossibly related (embedding) · 47%Liquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere' →
- PossiblePossibly related (embedding) · 47%Devs - you have 64gb of VRAM - which model do you use for coding? →
- PossiblePossibly related (embedding) · 47%Biggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed →
- PossiblePossibly related (embedding) · 46%Getting close to 100K context on 32GB VRAM with Qwen3.6-27 at Q8 →
Covers
newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsHypothetically speaking...newsLiquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'newsDevs - you have 64gb of VRAM - which model do you use for coding?newsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed
Covers (incoming)
Related across the graph
newsLiquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsGetting close to 100K context on 32GB VRAM with Qwen3.6-27 at Q8newsDevs - you have 64gb of VRAM - which model do you use for coding?newsHypothetically speaking...newsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed