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AIRadixArk says Miles runs reinforcement learning end to end on NVIDIA Vera Rubin, using SGLang rollouts, Megatron training and one container image. Agentic RL runs 64 concurrent sandboxes on the Vera CPU next to the GPUs. The linked SGLang post reports that Kimi K3 inference gained up to 20% faster FP8 MLA at batch 1 with 128K context, and a 5.9% end-to-end speedup from MoE tail fusion.

Why it matters: The post gives concrete speedup figures and a specific RL setup on early-access Rubin hardware, useful for engineers comparing inference and training stacks.

AIWorkBuddy's X post tags its update with #HTMLEditing and #CoWriting, but the post gives no details on what the features do. The post names no model, version, price, or release date.

AIWorkBuddy's X post links to its website, WorkBuddy.ai, and to a Discord community invitation. The post gives no product features, figures, or announcements.

AISGLang optimized attention, MoE, and speculative verification kernels for Kimi K3 NVFP4 on early-access NVIDIA Vera Rubin hardware. Reported gains include up to 20% faster FP8 MLA at batch 1 and 128K context, 20% faster KDA verification with bitwise-identical output, and a 5.9% end-to-end speedup from MoE tail fusion. Miles, from RadixArk, uses SGLang for rollouts in end-to-end RL training on Rubin, including agentic RL with 64 concurrent sandboxes on the Vera CPU.

AIInferact, working with NVIDIA, led performance tuning and integrated the MiniMax Sparse Attention kernel. The team uses the CUDA locality domain feature on Rubin GPUs so each SM reads MoE weights from its own locality domain, making MoE layers up to 1.2x faster in decode.

AIvLLM says users can try its Vera Rubin support today with the vllm/vllm-openai:cu134-nightly image, which runs DeepSeek, Kimi, GLM, and MiniMax models. The project thanked contributors and said it expects further improvements for vLLM on Vera Rubin.

AIvLLM now supports NVIDIA Vera Rubin NVL72, with daily container builds and support for models from DeepSeek, Moonshot AI, Z.ai, and MiniMax. In early AgentX benchmarks, vLLM running MiniMax M3 delivered up to 7.84x the throughput per GPU of GB200 NVL72 at matched interactivity. The post is an early look, and the team expects further gains from ongoing optimizations.

Why it matters: The post gives specific hardware specs, kernel configurations, and benchmark figures for running vLLM on Vera Rubin NVL72, useful for teams planning deployments.

AISGLang says restructured MLA decode kernels on Rubin, which fit a deeper pipeline in 327 KiB of shared memory, deliver a 16% speedup at batch 16 with 128K context and bit-identical output. The post also reports 20% faster full FP8 MLA at batch 1 and 20% faster KDA verify kernels after keeping weights in registers and reducing synchronization. It additionally covers fusing MoE finalization, the shared expert, 8-GPU all-reduce, and RMSNorm into one collective kernel.

AIMiles runs the full reinforcement learning loop on Nvidia Rubin, using SGLang for rollout, Megatron for training, and one container image. On a single 4-GPU tray, Qwen3-30B-A3B's GSM8K reward rises from about 45% to about 95% over 50 rollouts, matching the GB300 curve. The post also reports DeepSeek-V4-Flash end-to-end rollout and training, and Qwen3.5-35B-A3B agentic RL with 64 concurrent mini-SWE-agent sandboxes on SWE-bench Verified, where reward holds near 0.6 and median response length falls about 30%.

AISGLang says it worked with NVIDIA to optimize attention, MoE, and speculative verification kernels for Kimi K3 inference on early-access Rubin hardware. It reports up to 20% faster FP8 MLA at batch 1 with 128K context, 20% faster KDA verification with bitwise-identical output, and a 5.9% end-to-end speedup from MoE tail fusion that removes 276 kernel launches per decode step. The post also says SGLang powers Miles' end-to-end RL training on Rubin, including agentic RL with 64 concurrent sandboxes on the Vera CPU.

AIAngel Nwoha says he gave Muse AI a single instruction to find available domains for an emerging niche, bypassing slow manual searches on Namecheap. He posts a screenshot showing the agent working on the task, and calls it "love at first site."

AIA GitHub post announces that kernel kits for DeepSeek's TP=2 and TP=4 recipes are now live. The post links to a repository named deepseek-v4.1-tensorfold-tp2-2xgb10 and gives no further details on performance or features.

AITinker, an API for training and fine-tuning models, is cutting prices by up to 70% after engineering efficiency gains. The company says the savings are passed on to customers, and that buying more produces greater savings. GLM-5.3-Flash and DeepSeek-v4.1-Flash are also now available on Tinker for long-context work.

AIbig-arrow-on-the-screen (bigarrow) is a MIT-licensed macOS command-line tool and skill for Claude Code and Codex that draws arrows, boxes and text over any window. Clicks pass through, keyboard focus stays put, and each arrow removes itself after a set duration or when its agent process ends. The tool only points; it never clicks, types or captures the screen, and it requires no macOS permission to draw.

AIDatology's team tested its Curation Studio by generating large volumes of synthetic data on Modal, scaling to hundreds of B300 GPUs on demand. The post frames Curation Studio as a customizable data curation tool for teams training their own models.

AIDex Horthy (@dexhorthy) posted only the words "he cook" on X, with no further detail. The post is a short reaction and does not describe a product, release, or result on its own. Background from the quoted post by @0xblacklight describes a serverless background agent that created a GitHub pull request from an issue.

AISeismora Inc. is developing a control plane that routes AI workloads across devices, edge infrastructure and cloud providers, according to founder and CEO Vito Palermo. The company calls its approach "cognitive routing," choosing where work runs based on capability, cost, latency and policy constraints. Its intended customers are developers building agentic applications, not end consumers.

AILaunchBot is a Grok-based bot template for founders launching on X. It studies similar launches, reviews a founder's video, images, site, and copy, and tracks the launch post's engagement live.

AIRadixArk says Proximal is using Miles to train coding agents and calls it a flexible, scalable foundation for teams running their own training workloads. Proximal says its training framework is built on Miles, with runs on Modal's on-demand GPU clusters and serverless GPUs for inference. Its sandboxing infrastructure runs on Kubernetes and can handle millions of concurrent rollouts.

AILM Studio's Bionic 1.1.8 adds support for the /v1/systemone and /v1/decisions API endpoints. The update also lets users run Bionic alongside LM Studio.

AICline announces a new Desktop app, available at cline.bot/desktop. The post also gives the install command for the Cline CLI, npm i -g cline.

AIMagnific promotes Brand Kit in Magnific One for building campaigns. The post says Magnific Desktop is unlimited for Brand Kit users until October 15.