repoGitHubTrust 82 · PrimaryPublished 7d agoLive · 7d ago
jia-gao/leanctx
Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.
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) · 51%Would having a dedicated programming language specifically for LLMs be a viable solution? [D] →
- PossiblePossibly related (embedding) · 49%I developed my own quantized LLM from scratch, trained on 30B tokens, deploys in 60 MB [R] →
- PossiblePossibly related (embedding) · 47%Qwen 3.8 27B Overthinking, It has to be done, it has to be overthinking to punch Opus 4.6 →
Covers
newsWould having a dedicated programming language specifically for LLMs be a viable solution? [D]newsI developed my own quantized LLM from scratch, trained on 30B tokens, deploys in 60 MB [R]newsQwen 3.8 27B Overthinking, It has to be done, it has to be overthinking to punch Opus 4.6newsDoes telling an LLM to "be concise" actually save you money? We measured it across 9 models. Compressing the output can save you money and keep accuracy, compressing the input prompt does not. [R]
Related across the graph
newsQwen 3.8 27B Overthinking, It has to be done, it has to be overthinking to punch Opus 4.6newsWould having a dedicated programming language specifically for LLMs be a viable solution? [D]newsI developed my own quantized LLM from scratch, trained on 30B tokens, deploys in 60 MB [R]newsDoes telling an LLM to "be concise" actually save you money? We measured it across 9 models. Compressing the output can save you money and keep accuracy, compressing the input prompt does not. [R]