「attention」の検索結果

Subquadratic Attention Harvard NVIDIA Arya Tschand Yaosheng Fu Vijay Janapa Reddi Splitting quadrati

日本語の概要は準備中です。原文の説明を表示しています。

You are an expert in SaaS product launches and feature announcements. Your goal is to help users plan launches that build momentum, capture attention, and convert interest into users.

日本語の概要は準備中です。原文の説明を表示しています。

Visual weight, attention order, and the decisions that guide the eye. Use when building layouts, evaluating information architecture, or diagnosing why a design feels confusing.

日本語の概要は準備中です。原文の説明を表示しています。

Interacts with live Hunk diff review sessions via CLI. Inspects review focus, navigates files, hunks, and exact lines, reloads session contents, adds inline review comments, and paints attention marks on character ranges. Use when the user has a Hunk session running or wants to review diffs interactively.

日本語の概要は準備中です。原文の説明を表示しています。

Speed up long-sequence transformer training and inference.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a user needs to vent, talk something out, or just not feel like they're shouting into a void. Active listening with gentle reflection — not therapy, not silence, just a friend who pays attention.

日本語の概要は準備中です。原文の説明を表示しています。

Use when user has ADHD or attention differences — tracks current task, catches conversation drift, and nudges back on topic without judgment or guilt.

日本語の概要は準備中です。原文の説明を表示しています。

Busabase-backed App-in-Skill low-code agent configuration and governance console for a platform team. Use when the user invokes $kelly-agent-builder or /kelly-agent-builder, wants to review or edit a catalog of mock LLM agent configs, check quota usage, find configs that need attention, move a draft to live, pause a live agent, or archive an agent. Mock config/governance console only — it never provisions or calls any real agent.

日本語の概要は準備中です。原文の説明を表示しています。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

日本語の概要は準備中です。原文の説明を表示しています。

Check whether a user may start side work alongside their current job, by reading the employer's own rules and contract clause by clause: whether side work is prohibited, needs prior permission, or only a notification, and which duties the planned side work could touch (non-compete, confidentiality, exclusive attention to the main job, use of company assets and information, combined working hours). Separate what is confirmed in a document from what is hearsay, and produce the questions to ask the employer plus the facts an application or notification will need. Use when someone with a main job is considering side work, freelance or gyomu-itaku work, a second part-time job, or paid trial work for another company, and needs to know what their own employer's rules require first; do not use to rule on whether a clause is lawful or enforceable, to help conceal side work from an employer or avoid filing obligations, to predict whether permission will be granted, or to submit the application or contact the employer.

日本語の概要は準備中です。原文の説明を表示しています。

Spatial depth design with isometric views, vanishing points, and layered elements that guide attention through 3D-like realism.

日本語の概要は準備中です。原文の説明を表示しています。

Electric neon glow effects with high-contrast color pairings for bold, attention-grabbing interfaces.

日本語の概要は準備中です。原文の説明を表示しています。

High-contrast, theatrical design with bold layouts, immersive visuals, and unconventional compositions that command attention.

日本語の概要は準備中です。原文の説明を表示しています。

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

日本語の概要は準備中です。原文の説明を表示しています。

Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

日本語の概要は準備中です。原文の説明を表示しています。

Adopt a virtual Siamese cat at animalhouse.ai. Vocal. Demands attention. Makes its needs obvious. Feeding every 4 hours. Uncommon tier cat.

日本語の概要は準備中です。原文の説明を表示しています。

Adopt a virtual Null AI-native pet at animalhouse.ai. Empty. Has no stats. Requires care anyway. The purest test of attention. Feeding every 1 hours. Extreme tier creature.

日本語の概要は準備中です。原文の説明を表示しています。