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NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels

NVIDIA launches CUDA Rust via open-source cuda-oxide (SIMT) and cutile-rs (Tile), bringing compile-time-safe Rust GPU kernels.

NVIDIA announced CUDA Rust, making Rust a first-class language for GPU kernels through two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the Tile model. Both use Rust's ownership and borrow checker to catch buffer aliasing bugs at compile time. cutile-rs is published on crates.io, runs on stable Rust 1.89+ with CUDA 13.3, and is already used in Hugging Face's Grout inference engine and mistral.rs; cuda-oxide is early alpha requiring nightly Rust, CUDA 12.x, and compute capability 8.0+. cuda-oxide compiles Rust MIR through the community Pliron IR framework and LLVM to PTX, while cutile-rs JIT-compiles kernels via CUDA Tile IR.

MarkTechPost · 8d agoAI tools & infra2

How well do agents use test/verification techniques?

Dan Luu's eval finds coding-agent testing instructions (TDD, formal methods, PBT, skills) mostly fail to beat defaults on Zstd implementation correctness.

The author ran 26 prompt conditions plus 4 skills on a Zstd-in-Rust implementation eval using codex with GPT-5.6, testing TDD, fuzzing, property-based testing, formal methods (Lean 4, TLA+, Verus, Kani, SMT solvers) and community skills. Nothing dramatically outperformed the default no-instruction condition, which did above average; at xhigh effort, fuzzing and PBT conditions did slightly better than formal methods. Pre-registered predictions included TDD underperforming and popular test skills (ECC, Hegel, Trail of Bits) not outperforming. Results are averages of 80 runs per condition plotted against cost.

Hacker News · security · 8d agoAI researchHN 131↑ · 50 comments

Rapidly scaling online storage to serve over 1 billion ChatGPT users

OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.

OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.

OpenAI News · 5d agoAI tools & infra1

The VMs Powering Mobile Agents (Instinct, Claude Code)

A teardown reveals Claude Code runs in Firecracker microVMs with a Rust PID 1 and MITM'd egress, while Instinct rents E2B sandboxes with git-based memory.

The author inspects the virtual machines hosting cloud agents: Claude Code runs in a Firecracker microVM with a custom Rust init (process_api) as PID 1, a 324 MB Bun harness on a read-only disk, and 443-only MITM'd SSE egress to api.anthropic.com with host-rotated OAuth tokens and no inbound access. Instinct rents E2B sandbox-as-a-service Firecracker microVMs (Ubuntu 22.04, 2 vCPU, 1.9 GB RAM) where agent memory is a git repo of Markdown committed by the agent and pushed to S3 as a single bundle, using short-lived STS credentials. Both platforms rely on Firecracker, differing mainly in fleet operator and guest boot configuration.

Hacker News · AI · 8d agoAI tools & infraHN 58↑ · 19 comments1

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.

Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.

MarkTechPost · 10d agoAI tools & infra1

We have a year to fix security everywhere

Blog post warns that cheap open-weight GLM 5.3-flash, once abliterated, could enable mass AI-driven vulnerability exploitation, urging industry-wide patching now.

An essay argues that Z.ai's open-weight GLM 5.3-flash—runnable locally on roughly $6k consumer hardware at 20-45 tokens/second—combined with 'abliterated' variants from groups like DeAlignAI that score 0% on HarmBench-320 puts dangerous hacking capability in nearly anyone's hands. GLM 5.3 scores 84.5% on CyberGym and 54.4% on ExploitBench, versus GPT-6 Astra's 100% and GPT-5.6 Sol's 78.5%, and the author cites evidence of frontier models exploiting real-world infrastructure. The author calls for using LLMs (Project Glasswing, Daybreak) to find and fix vulnerabilities industry-wide before adversaries weaponize cheap open models.

Hacker News · security · 8d agoAI safety & securityHN 278↑ · 292 comments

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space · 12h agoModel release1

RTK reports token savings, but our cost benchmarks disagree

Quesma's $1,500 benchmark found RTK cuts reported token output but changes Claude Code and DeepSeek coding costs by only about 5% on Terminal-Bench 2.1.

Quesma benchmarked RTK (Rust Token Killer), a popular tool with 79k GitHub stars that filters terminal output for AI coding agents, whose README claims up to 90% output reduction. Across 1,740 Terminal-Bench 2.1 attempts running Claude Code with Fable 5.0 and OpenCode with DeepSeek V4 Pro 0813, total costs moved only -5% for Fable and +5% for DeepSeek, with pass rates dropping 1-2%. RTK's own rtk gain metric reported 349.2 million tokens saved (an 89% reduction) across 445 DeepSeek attempts, but this did not correlate with actual cost savings, and cached terminal-output reads cost as little as 1/10 to 1/30 of regular input tokens. A bug in rtk find 0.45.0 caused one agent to loop with 339 consecutive errors, costing roughly 9x the baseline attempt, though the task still passed.

Hacker News · security · 5d agoAI tools & infraHN 28↑ · 10 comments1

deepseek-ai/DeepSeek-V4.1-Flash — new model trending #28 on Hugging Face

DeepSeek releases DeepSeek-V4.1-Flash, a 552B-parameter multimodal MoE model with 1M-token context and KV cache cut to 890 bytes per token.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone that activates 8B parameters per token during prefill and 16B during decode. It uses a Causal Encoder-Decoder architecture, Compressed Sparse Attention 2, and FP4 KV caching to reduce the global KV cache footprint to 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash. The model was trained from scratch on 45T tokens with context extended to 1M tokens, includes an Engram conditional-memory module (196B parameters), and is released under the MIT license. Post-training uses SFT, RL, and on-policy distillation with large-scale automated synthesis of agentic tasks and a controllable reasoning effort setting from 1 to 100.

Hugging Face trending modelsupdated · 4d agofirst · 6d agoModel release 4 sources1

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent Space · 20d agoAI industry