AI Coding2023
A statically typed, parallel programming language that prevents AI-introduced bugs by making correctness claims (laws) require machine-checked proofs. Compiles to native code and targets CPU/GPU parallelism with a fast proof checker.
AI Video2026
Detects and restores mosaic-censored regions in videos and images on the GPU while leaving the rest of the frame untouched. Offers a GUI and CLI, two primary restoration models (free BasicVSR++; LTX for NVIDIA supporters), optional secondary upscalers (UNet 4x / RTX Super-Res / Topaz TVAI), and a live preview — requires modern NVIDIA or experimental AMD GPUs.
AI Infra2026
Runs autonomous AI agents in isolated containers with declarative YAML policies that enforce filesystem, network, process, and inference controls. Provides credential providers, inference routing, hot-reloadable network/policy updates, and formal verification of policy changes for auditable agent execution.
AI Audio2026
Zero-shot text-to-speech across 600+ languages with short-reference voice cloning and attribute-based voice design. Uses a diffusion-style discrete non-autoregressive architecture mapping text to multi-codebook acoustic tokens, supports phoneme/pinyin control and very low RTF.
AI Infra2026
Streams routed experts from disk and treats storage, RAM and VRAM as a single inference hierarchy so MoE models (hundreds of billions to trillions of parameters) can run on consumer or heterogeneous hardware; single-file C engines with zero runtime deps and optional Vulkan/CUDA/Metal backends, speed governed by placement and disk I/O.
AI Agent Papers2026
Turns past discovery traces into replayable simulators so alternative exploration policies can be evaluated offline ('dreaming'), enabling fast, low-cost meta-level policy improvement for agent-driven discovery across coding, optimization, and GPU-kernel tasks.
AI Model2026
A 27B-class language model packaged in GGUF with end-to-end ternary weights for on-device or single-GPU llama.cpp inference; reduces FP16 footprint to ~5.9–7.2 GB while retaining ~98% of baseline performance and supporting up to 262K tokens.
MLOps2026
Provides a programming model and distributed runtime that preserves parent–child lineage and ordered variable-cardinality expansions for 1→M→1 dataflow pipelines, enabling completion-driven cross-input GPU batching and ordered gathers for foundation-model data preparation. Demonstrates multi-GPU scaling speedups and lower end-to-end time versus Ray Data and Daft.
AI Model2026
Open-weight 309B Mixture-of-Experts causal LLM with 15.5B active parameters and a native 1M-token context for coding and AI R&D. Combines Sliding-Window Attention and DeepSeek Sparse Attention (no full-attention layers), supports FP8 inference; weights under MIT license.
AI Audio2026
Transcribes English speech into punctuated, capitalized text — a 164 MB quantized ASR model that averages 5.21% WER across seven Open ASR Leaderboard sets. Optimized for on-device and CPU/GPU inference, with fast runtimes on Apple M5 and Docker/GPU support.
AI Model2026
Converts Turkish text to 48 kHz speech offline using a compact 5.6M DiT acoustic model plus a 3M vocoder (8.6M parameters, ~34 MB). Streams audio with very low latency (~3.86 ms first audio on an RTX 4090), supports fast batching and runs under an Apache‑2.0 license.
AI Model2026
Returns calibrated probabilities for yes/no and multi-choice decisions using a two-stage System 1 (fast classifier) and System 2 (Gemma‑4 reasoning) pipeline; this NVFP4 release quantizes the 3,840 routed MoE experts to 4-bit so the model fits ≈17–18 GB of GPU memory while other weights remain bf16.