EverMind-AI/MSA

Trend Velocity Score: 0.00
Opportunity Score: 10.00
Confidence Score: 100.00
Language: <a class="text-blue-600 dark:text-blue-400 hover:underline" href="/languages/Python">Python
First Seen: 4 months ago

Worth installing(Strong adoption and recent activity)

Memory Sparse Attention - A scalable, end-to-end trainable latent-memory framework for 100M-token contexts.

Why this repo matters

Enables end-to-end trainable latent memory for 100M-token contexts with near-linear complexity.
Achieves massive context throughput on limited hardware via KV cache compression and memory parallelism.
Outperforms RAG and standard long-context models with less than nine percent degradation at scale.

3.5k stars+34 over 40 snapshots6/17/2026 – 9/6/2026

An agentic skills framework & software development methodology that works.

Skills for Real Engineers. Straight from my .agents directory.

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.

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