Von is an open-source, non-autoregressive system for fast decision-making that evaluates discrete criteria in sub-25ms without token-by-token text generation. It achieves 91.23% accuracy on multi-hop reasoning benchmarks and 71.5% macro accuracy on expanded task suites, with calibrated probabilistic outputs and native support for NVIDIA CUDA, AMD ROCm, and Apple Silicon.
A robotics startup's vision-guided robot lost track of moving targets due to Python's GIL preventing true parallelism and causing unpredictable timing in the control loop. The system was rewritten in Rust, which provides true parallelism, memory safety without garbage collection, and compiled performance, resulting in the robot successfully tracking targets and cutting end-to-end latency roughly in half.
Celeris is developing faster large language models by designing for speed from inception, focusing on diffusion-based approaches that enable parallel token generation. The company combines sequential and parallel decoding within hybrid architectures, leveraging research showing diffusion models can achieve 27.6× throughput improvements and better reasoning capabilities than autoregressive models.
mold is a high-performance Unix linker rewritten in Rust that links 4.9x faster than LLVM lld and 1.9x faster than wild, created by lld's original developer. Written in Rust and in production use since 2021, mold supports multiple architectures and achieves its speed through pervasive parallelism and efficient data structures.
Bend is a programming language designed for the post-AGI era that combines C-speed performance with GPU parallelism and Lean-style proof checking. It enables developers to specify immutable laws (LAWS.bend) that AI agents must satisfy when writing code, ensuring bugs cannot be merged by making violations mathematically impossible to prove.