Open Source
AI Infrastructure
Ecosystem
A comprehensive suite of high-performance AI infrastructure libraries built on shared principles of static memory allocation, inline optimization, and predictable performance. From graph databases and LLM frameworks to multi-tier caching and networking - all designed for maximum efficiency and zero bloat.
Current Projects
Focused, interoperable pieces. All are pre‑release; expect rapid iteration and occasional breaking changes until initial stability milestones.
graph store
Static-memory, crash-safe graph/wal store with snapshots. Focus: durability + predictable footprint.
networking
High-performance, statically allocated HTTP/TCP framework with inline functions and connection pooling.
I/O operations
Zero-allocation I/O library with streaming, file operations, and network capabilities using static memory pools.
JSON processing
High-performance JSON parsing and manipulation with static memory allocation and streaming support.
embedding cache
Probabilistic + LRU-ish hybrid for hot embeddings & vector reuse. Focus: low latency + hit ratio.
function / inference
Planned lightweight function / model invocation layer to sit over cached vectors + store.
unified tool
Single CLI / runtime glue (build, introspect, admin). Will orchestrate DB, cache, functions.
workflow engine
High-performance workflow processing engine with static memory allocation and inline optimization.
Path To v0.x
Sequenced milestones. Feedback can reorder items—community input is welcome.
- NenDB WAL & snapshot hardening
- Crash-injection & recovery tests
- Initial release builds + installer
- Architecture docs
- Bench gating flag
- NenCache prototype (hot embedding LRU/seg)
- Read-path profiling
- Metrics / status schema versioning
- CLI UX polish
- NenFF design + minimal invoke layer
- Replication / log shipping design draft
- Secondary index exploration
- Stability & v0.1 tag
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