Other○Pass
Csrct – The Bottleneck Is the Strategy
Long-form geopolitical essay that belongs on a blog, not a software showcase.
MADEinPARIS
101mo ago
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GitHub Repository
High-throughput long-context LLMs. Scaling context via RandNLA and massive vocab capacity through MAXIS Loss and Fisher-SVD.
28 starsPython
MaximusLLM, Breaking transformer's O(N^2) and O(V) scaling bottlenecks
by yousef_g·Mar 13, 2026·1 point·0 comments
AI Analysis
●●SolidBig BrainBold Bet
Claims 17.5x training speedup with Matryoshka embeddings for native RAG.
Strengths
- •MAXIS Loss compresses embeddings to 64 dimensions for faster vector search
- •RandNLA Attention enables constant-time throughput regardless of sequence length
- •Papers on SSRN and HuggingFace models available for independent verification
Weaknesses
- •Only 2 GitHub stars means limited community validation and adoption
- •Bold 17.5x speedup claims need independent reproduction by other researchers
Category
Target Audience
ML researchers and engineers training long-context models
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Mamba · Linear Attention · RetNet
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