「lms」の検索結果

Plateforme éducative — LMS, MOOC, certification, e-learning Algérie, MEN

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Expert en technologies éducatives avancées (LMS, MOOC, adaptive learning, AI tutoring, assessment, DZ context)

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Expert en création de cours avancé (curriculum, video, LMS, assessments, community, monetization)

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。

Course creator — Curriculum design, video production, LMS setup, pricing, launch strategy

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。

LLMs, prompt engineering, RAG systems, LangChain, and AI application development

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When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

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。

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.

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。

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

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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。

Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.

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。

vLLM: high-throughput LLM serving, OpenAI API, quantization.

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。

Pretrain LLMs at scale with PyTorch 4D 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。

Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

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。

Fetch Canvas LMS courses and assignments via API token.

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。

RL post-training for LLMs with Megatron and SGLang.

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限られたGPUメモリで大規模言語モデルを追加学習するため、LoRAやQLoRAの設定を支援します。学習結果の保存、用途別の切り替え、モデルへの統合も扱います。

• 少ないGPUメモリで追加学習したいとき
• LoRAの学習設定を選びたいとき
• 用途別のアダプターを切り替えたいとき

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。

Render-correctness doctrine for scene-to-scene seams in HyperFrames launch videos — the prerequisites that make transitions composite correctly on the master timeline. Load when assembling the master timeline / index.html, when a white flash appears at a cut or crossfade seam (especially on dark films), when reasoning about why a transition opacity dip shows through, or when verifying the render-side mechanics of how overlapping scene wrappers blend. Covers the opaque stage-ground (#root background) white-flash guard and how the injector overlaps wrappers, holds final frames, ping-pongs tracks, and stamps lint-clean template code onto the master timeline. Does NOT contain the per-transition catalog — see the transition registry for individual transition entries.

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Use when working with a person on a HyperFrames project in Studio: first, whether their message asks for a change at all (questions, loose ideas and "don't change anything" get an answer and a plan, not an edit); for a new film, the plan, storyboard and build order that the HyperFrames launch films follow; and how the timeline should be laid out so it reads well (one caption track, one element kind per track, every scene a sub-composition) and where captions and key content may sit (safe zones). Don't use for how to perform an individual edit (split, trim, retime, volume, copy, swap): that is `creator-editing-recipes.md` in `/hyperframes-core`.

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When the user wants to submit their product to startup, SaaS, AI, agent, MCP, no-code, or review directories for backlinks, domain rating, and discovery. Also use when the user mentions "directory submissions," "submit to directories," "backlinks from directories," "list my product," "submit to Product Hunt," "BetaList," "TAAFT," "Futurepedia," "G2 listing," "Capterra listing," "AlternativeTo," "SaaSHub," "AI directories," "MCP registry," "publish my MCP server," "awesome list," "llms.txt directory," "Claude plugin directory," "agent directory," "dofollow backlinks," "launch directories," or "directory tracker." Use this whenever someone is planning the directory layer of a product launch or an ongoing backlink campaign. For the broader launch moment, see launch. For programmatic SEO pages that should live behind these backlinks, see programmatic-seo. For AI citation optimization, see ai-seo.

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Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.

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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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Deploys Llama Guard 3 safety classification, NeMo Guardrails programmable dialogue rails, and LLM Guard input/output scanner pipelines as complementary runtime defenses that inspect and constrain LLM prompts and responses. Use when adding a production runtime safety layer to an LLM, RAG, or agent application to block jailbreaks, prompt injection (OWASP LLM01), toxic content, or sensitive-data leakage before it reaches or leaves the model.

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。