Bio: AI Engineer and founder of Atsentia. Previously: Microsoft, Google, YouTube. PhD in CS.
mojo-tokenizer converts AI output to readable text at 144 million tokens per second — 3.1x faster than the industry standard. 100% compatible, rigorously benchmarked.
InlineBuffer stores small data on the stack, provides zero-copy slice access via pointer arithmetic, and includes SIMD-accelerated operations. The missing piece for high-performance Mojo.
mojo-json achieves 6.5 GB/s throughput, outperforming the fastest Rust and C++ parsers by 52% on standard benchmarks. Empirical results from Apple M3 Ultra.
Introducing mojo-contrib: 26 pure Mojo libraries for authentication, resilience, observability, and more. Enterprise-grade infrastructure for the high-performance language.
Exploring the lower bounds of LLM quantization - from OpenAIs 4-bit GPT-OSS to research suggesting 3.6 bits per parameter may be the theoretical minimum.
Exploring ultra-efficient AI inference on wearable devices using MLX and Apple Silicon optimization.
Grokking implementations in Jax/Flax and Pytorch
