AI is becoming a core part of modern application development, and .NET offers a practical, incremental way to get started. In this session, you'll learn how to build intelligent .NET applications by adding useful AI capabilities—well beyond the chatbot—using https://msft.it/63329agwPD and .NET’s unified AI abstractions. We'll start with the essentials: integrating large language models, adding features like summarization, classification, and semantic search, and working with both cloud-hosted and local models in a maintainable, production-ready way. You'll see concrete patterns for introducing AI into existing applications with minimal friction. You'll also learn how to ensure quality and governance as you integrate AI into your enterprise. From there, we'll look at how these same foundations scale into more advanced, agentic solutions. You'll get an introduction to agentic development and see how the Microsoft Agent Framework enables applications to reason, plan, and take action using tools and workflows. Whether you're just beginning with AI in .NET or preparing for more autonomous, agent-driven systems, this session provides a clear roadmap from practical AI features to agentic architectures. Connect with .NET: Blog: https://msft.it/63321agwP1 Twitter: https://msft.it/63323agwPH TikTok: https://msft.it/63324agwPy Mastodon: https://msft.it/63325agwPJ LinkedIn: https://msft.it/63326agwPK Facebook: https://msft.it/63327agwPz Docs: https://msft.it/63328agwPM Forums: https://msft.it/63329agwP3 🙋‍♀️Q&A: https://msft.it/63320agwPO 👨‍🎓Microsoft Learn: https://msft.it/63322agwPu #dotnet

600+ verified reviews show search and summarization dominate AI adoption, while agentic AI lags behind vendor hype.

本記事はAIを利用して作成した技術解説・実装例です。掲載するコードや手順は一次情報を基に構成していますが、筆者による実機での動作確認は行っていません。環境やバージョンによって動作が異なる場合があります。 NVIDIA VSS Blueprint 3.3によるビジュアルAIエージェント構築と最適化の仕組み 、NVIDIA Metropolis Blueprint for Video Search and Summarization (VSS) 3.3を用いて、ビジュアルAIエージェントの構築コストと運用コストを削減する手法を整理します。開発時の構成自動化から、実行時のビジュアル言語モデル（VLM）処理の効率化まで、公式情報に基づく技術的なポイントを安全かつ実用的に解説します。 VSS Blueprint 3.3の概要と背景 生産規模のビジュアルAIエージェントは、映像の取り込み、ストリーム処理、イベント検知、検索、要約、レポート作成といった複数のワークフローを組み合わせる必要があります。これまでは、これらを統合する際の開発コスト、運用時のGPU負荷やトークン消費、そしてPoCから本番移行する際の変更コストが課題となっていました。 VSS 3.3では、以下の2つのアプローチでこれらのコストを削減します。 Build Vision Agentスキル（vss-build-vision-ai）による迅速なアプリケーション構成 Adaptive Efficient Video Sampling (EVS)による実行時のVLM処理効率化 flowchart TD A["自然言語プロンプト"] --> B["Build Vision Agentスキル"] B --> C["4つの開発者プロファイル選択"] C --> D["最小差分（Delta）の計算"] D --> E["共通インフラの統合・再利用"] E --> F["self-contained build 生成"] F --> G["Adaptive EVS による実行時最適化"] G --> H["VLM トークン削減 & ストリーム増加"] …

Get free transcripts and subtitles for YouTube videos online, then utilize ChatGPT and Claude for video summarization. Enhance learning efficiency at no cost! YouTube summary with NoteGPT and No Login...

Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces this history but adds latency and provides no explicit bound on information loss. We propose LAM, a Lossy Agent Memory…

Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces this history but adds latency and provides no explicit bound on information loss. We propose LAM, a Lossy Agent Memory…

Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetil...

Personalization requires language models to capture user-specific knowledge from a growing user history. Existing context-based approaches incur increasing inference costs as user history accumulates and rely on separate retrieval or summarization stages, while parametric-based approaches often req…

Anthropic PBC today announced the launch of Claude Sonnet 5.5, the most capable mid-tier model in the company’s AI family, designed for everyday tasks and a clear upgrade over the previous generation, running over 30% faster at a lower cost. Sonnet operates as the workhorse of Anthropic’s Claude family of models for everyday use, and Sonnet 5.5 is well trained for tasks such as document generation, summarization, spreadsheets and fixing coding bugs. The company added that it also has a “strong eye for design,” allowing it to add polish to user interfaces and it can follow templates to create slides that need minimal editing. This release, which follows the Sept. 22 launch of Opus 5.5, the flagship model in the family, incorporates clearer writing styles. Although the company said the new Sonnet 5.5 writes more clearly and in a more human-like way, it also includes invisible text watermarking. When the company announced the new watermarking technology, it said it would not affect text quality or overall readability, but would increase the likelihood that AI-generated text could be detected. Anthropic added this capability to comply with global regulations, including the European Union’s AI Act. Like Opus 5.5, Sonnet 5.5’s writing and...

At Luna’s prices, routine extraction and summarization can be pushed toward a much cheaper tier. Sol targets recurring coding and agent work at Sonnet-class token pricing. Astra remains available for the hardest jobs.