https://devotd.net/en/news/nvidia-nemoclaw-agent-blueprints/devotd.en2026-10-30T17:24:02ZNVIDIA NemoClaw: Existing harness assembly, OpenShell access controlhttps://devotd.net/ja/news/nvidia-nemoclaw-agent-blueprints/devotd.ja2026-10-30T17:24:02ZNVIDIA NemoClaw：既存ハーネスの組み合わせ、OpenShellによる権限制御https://devotd.net/ko/news/nvidia-nemoclaw-agent-blueprints/devotd.ko2026-10-30T17:24:02ZNVIDIA NemoClaw: 기존 하네스 조립, OpenShell 권한 통제https://devotd.net/zh-hans/news/nvidia-nemoclaw-agent-blueprints/devotd.zh-cn2026-10-30T17:24:02ZNVIDIA NemoClaw：整合现有框架，OpenShell 权限管控https://devotd.net/en/news/llamacpp-b12008-gemma-token-embedding-memory/devotd.en2026-10-30T15:31:43Zllama.cpp b12008: Shared Gemma embeddings, lower CPU memory usehttps://devotd.net/ja/news/llamacpp-b12008-gemma-token-embedding-memory/devotd.ja2026-10-30T15:31:43Zllama.cpp b12008：Gemmaの埋め込み共有、CPUメモリ削減https://devotd.net/ko/news/llamacpp-b12008-gemma-token-embedding-memory/devotd.ko2026-10-30T15:31:43Zllama.cpp b12008: Gemma 임베딩 공유, CPU 메모리 절감https://devotd.net/zh-hans/news/llamacpp-b12008-gemma-token-embedding-memory/devotd.zh-cn2026-10-30T15:31:43Zllama.cpp b12008：Gemma 嵌入共享，减少 CPU 内存占用https://devotd.net/en/news/nvidia-tensor-core-mlip-bf16x9-tf32x3/devotd.en2026-10-30T13:14:00ZNVIDIA PET: Tensor Core acceleration without retraining, shorter molecular dynamics stepshttps://devotd.net/ja/news/nvidia-tensor-core-mlip-bf16x9-tf32x3/devotd.ja2026-10-30T13:14:00ZNVIDIA PET：再学習なしのテンソルコア高速化、分子動力学ステップの短縮https://devotd.net/ko/news/nvidia-tensor-core-mlip-bf16x9-tf32x3/devotd.ko2026-10-30T13:14:00ZNVIDIA PET: 재학습 없는 텐서코어 가속, 분자역학 스텝 단축https://devotd.net/zh-hans/news/nvidia-tensor-core-mlip-bf16x9-tf32x3/devotd.zh-cn2026-10-30T13:14:00ZNVIDIA PET：无需重新训练的张量核心加速，缩短分子动力学步耗时https://devotd.net/en/news/mistral-large-4-transformers-5-22/devotd.en2026-10-30T03:35:08ZTransformers v5.22.0: Mistral Large 4 support, 1 million-token contexthttps://devotd.net/ja/news/mistral-large-4-transformers-5-22/devotd.ja2026-10-30T03:35:08ZTransformers v5.22.0：Mistral Large 4対応、100万トークンのコンテキストhttps://devotd.net/ko/news/mistral-large-4-transformers-5-22/devotd.ko2026-10-30T03:35:08ZTransformers v5.22.0: Mistral Large 4 지원, 100만 토큰 문맥https://devotd.net/zh-hans/news/mistral-large-4-transformers-5-22/devotd.zh-cn2026-10-30T03:35:08ZTransformers v5.22.0：支持 Mistral Large 4，100万 token 上下文https://devotd.net/en/news/vllm-ascend-0-30-stable-release/devotd.en2026-10-30T03:28:05ZvLLM Ascend 0.30.0: Broader model execution support, V2 transition aheadhttps://devotd.net/ja/news/vllm-ascend-0-30-stable-release/devotd.ja2026-10-30T03:28:05ZvLLM Ascend 0.30.0：モデル実行サポートの拡大、V2移行の予告https://devotd.net/ko/news/vllm-ascend-0-30-stable-release/devotd.ko2026-10-30T03:28:05ZvLLM Ascend 0.30.0: 모델 실행 지원 확대, V2 전환 예고https://devotd.net/zh-hans/news/vllm-ascend-0-30-stable-release/devotd.zh-cn2026-10-30T03:28:05ZvLLM Ascend 0.30.0：扩展模型运行支持，预告 V2 迁移https://devotd.net/en/news/nvidia-megatron-core-0-20-0-fsdp-v2-moe-inference/devotd.en2026-10-30T01:43:27ZNVIDIA Megatron Core 0.20.0: FSDP v2 integration, improved MoE inferencehttps://devotd.net/ja/news/nvidia-megatron-core-0-20-0-fsdp-v2-moe-inference/devotd.ja2026-10-30T01:43:27ZNVIDIA Megatron Core 0.20.0：FSDP v2統合、MoE推論の改善https://devotd.net/ko/news/nvidia-megatron-core-0-20-0-fsdp-v2-moe-inference/devotd.ko2026-10-30T01:43:27ZNVIDIA Megatron Core 0.20.0: FSDP v2 통합, MoE 추론 개선https://devotd.net/zh-hans/news/nvidia-megatron-core-0-20-0-fsdp-v2-moe-inference/devotd.zh-cn2026-10-30T01:43:27ZNVIDIA Megatron Core 0.20.0：集成 FSDP v2，改进 MoE 推理https://devotd.net/en/news/openai-codex-0-166-0-queue-resume-network-diagnostics/devotd.en2026-10-30T01:27:28ZCodex 0.166.0: Task resumption, network diagnosticshttps://devotd.net/ja/news/openai-codex-0-166-0-queue-resume-network-diagnostics/devotd.ja2026-10-30T01:27:28ZCodex 0.166.0：作業の再開、ネットワーク診断https://devotd.net/ko/news/openai-codex-0-166-0-queue-resume-network-diagnostics/devotd.ko2026-10-30T01:27:28ZCodex 0.166.0: 작업 재개, 네트워크 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controlhttps://devotd.net/ja/news/github-copilot-projects-universe-2026/devotd.ja2026-10-29T13:23:00ZGitHub Copilotアプリ：プロジェクトワークスペース、デスクトップアプリ操作https://devotd.net/ko/news/github-copilot-projects-universe-2026/devotd.ko2026-10-29T13:23:00ZGitHub Copilot 앱: 프로젝트 작업공간, 데스크톱 앱 조작https://devotd.net/zh-hans/news/github-copilot-projects-universe-2026/devotd.zh-cn2026-10-29T13:23:00ZGitHub Copilot 应用：项目工作空间，桌面应用操作https://devotd.net/en/news/llama-cpp-cdna-sparse-attention/devotd.en2026-10-29T11:27:12Zllama.cpp: CDNA sparse attention support, revised kernel selectionhttps://devotd.net/ja/news/llama-cpp-cdna-sparse-attention/devotd.ja2026-10-29T11:27:12Zllama.cpp：CDNAのスパースアテンション対応、カーネル選択の調整https://devotd.net/ko/news/llama-cpp-cdna-sparse-attention/devotd.ko2026-10-29T11:27:12Zllama.cpp: CDNA 희소 어텐션 지원, 커널 선택 조정https://devotd.net/zh-hans/news/llama-cpp-cdna-sparse-attention/devotd.zh-cn2026-10-29T11:27:12Zllama.cpp：支持 CDNA 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b11980：量子化キャッシュの直接演算、生成スループットの向上https://devotd.net/ko/news/llama-cpp-b11980-sycl-dpas-quantized-kv-cache/devotd.ko2026-10-29T09:29:41Zllama.cpp b11980: 양자화 캐시 직접 연산, 생성 처리량 개선https://devotd.net/zh-hans/news/llama-cpp-b11980-sycl-dpas-quantized-kv-cache/devotd.zh-cn2026-10-29T09:29:41Zllama.cpp b11980：量化缓存直接运算，生成吞吐量提升https://devotd.net/en/news/codex-security-0-8-0-opengrep-import/devotd.en2026-10-29T09:24:38ZCodex Security 0.8.0: OpenGrep imports, result format changeshttps://devotd.net/ja/news/codex-security-0-8-0-opengrep-import/devotd.ja2026-10-29T09:24:38ZCodex Security 0.8.0：OpenGrepの取り込み、結果形式の変更https://devotd.net/ko/news/codex-security-0-8-0-opengrep-import/devotd.ko2026-10-29T09:24:38ZCodex Security 0.8.0: OpenGrep 가져오기, 결과 형식 변경https://devotd.net/zh-hans/news/codex-security-0-8-0-opengrep-import/devotd.zh-cn2026-10-29T09:24:38ZCodex Security 0.8.0：导入 OpenGrep，调整结果格式https://devotd.net/en/news/ollama-lfm3-linux-windows/devotd.en2026-10-29T07:27:39ZOllama v0.40.13: LFM3 Linux 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intelligence：跨会话记忆、仓库代码搜索https://devotd.net/en/news/qwen-code-0-26-3-workflow-mode-tool-results/devotd.en2026-10-29T05:20:52ZQwen Code v0.26.3: Session workflows, context-aware compactionhttps://devotd.net/ja/news/qwen-code-0-26-3-workflow-mode-tool-results/devotd.ja2026-10-29T05:20:52ZQwen Code v0.26.3：セッションワークフロー、コンテキストに応じた圧縮https://devotd.net/ko/news/qwen-code-0-26-3-workflow-mode-tool-results/devotd.ko2026-10-29T05:20:52ZQwen Code v0.26.3: 세션 워크플로, 컨텍스트 기반 압축https://devotd.net/zh-hans/news/qwen-code-0-26-3-workflow-mode-tool-results/devotd.zh-cn2026-10-29T05:20:52ZQwen Code v0.26.3：会话工作流、上下文感知压缩https://devotd.net/en/news/github-copilot-fusion-ga/devotd.en2026-10-29T01:28:56ZGitHub Copilot Fusion: Automatic model routing, multi-model orchestrationhttps://devotd.net/ja/news/github-copilot-fusion-ga/devotd.ja2026-10-29T01:28:56ZGitHub Copilot Fusion：モデルの自動ルーティング、複数モデルのオーケストレーションhttps://devotd.net/ko/news/github-copilot-fusion-ga/devotd.ko2026-10-29T01:28:56ZGitHub Copilot Fusion: 모델 자동 라우팅, 다중 모델 오케스트레이션https://devotd.net/zh-hans/news/github-copilot-fusion-ga/devotd.zh-cn2026-10-29T01:28:56ZGitHub Copilot Fusion：模型自动路由， 多模型编排https://devotd.net/en/news/github-agentic-workflows-ga/devotd.en2026-10-29T01:28:28ZGitHub Agentic Workflows: Markdown automation, isolated executionhttps://devotd.net/ja/news/github-agentic-workflows-ga/devotd.ja2026-10-29T01:28:28ZGitHub Agentic Workflows：Markdownによる自動化、隔離実行https://devotd.net/ko/news/github-agentic-workflows-ga/devotd.ko2026-10-29T01:28:28ZGitHub Agentic Workflows: Markdown 자동화, 격리 실행https://devotd.net/zh-hans/news/github-agentic-workflows-ga/devotd.zh-cn2026-10-29T01:28:28ZGitHub Agentic Workflows：Markdown 自动化，隔离执行https://devotd.net/en/news/llama-cpp-b11970-hexagon-conv2d/devotd.en2026-10-28T23:26:29Zllama.cpp b11970: Hexagon convolution support, Snapdragon NPU inferencehttps://devotd.net/ja/news/llama-cpp-b11970-hexagon-conv2d/devotd.ja2026-10-28T23:26:29Zllama.cpp b11970：Hexagonの畳み込み対応、Snapdragon NPU推論https://devotd.net/ko/news/llama-cpp-b11970-hexagon-conv2d/devotd.ko2026-10-28T23:26:29Zllama.cpp b11970: Hexagon 합성곱 지원, 스냅드래곤 NPU 추론https://devotd.net/zh-hans/news/llama-cpp-b11970-hexagon-conv2d/devotd.zh-cn2026-10-28T23:26:29Zllama.cpp b11970：Hexagon 卷积支持，骁龙 NPU 推理https://devotd.net/en/news/pytorch-2-15-release/devotd.en2026-10-28T21:28:57ZPyTorch 2.15: New communication defaults, platform performance improvementshttps://devotd.net/ja/news/pytorch-2-15-release/devotd.ja2026-10-28T21:28:57ZPyTorch 2.15：通信のデフォルト切り替え、プラットフォーム性能の改善https://devotd.net/ko/news/pytorch-2-15-release/devotd.ko2026-10-28T21:28:57ZPyTorch 2.15: 통신 기본값 전환, 플랫폼 성능 개선https://devotd.net/zh-hans/news/pytorch-2-15-release/devotd.zh-cn2026-10-28T21:28:57ZPyTorch 2.15：通信默认实现切换，平台性能改进https://devotd.net/en/news/liquidai-lfm3-local-multimodal-models/devotd.en2026-10-28T19:32:48ZLiquid AI LFM3: 2B-class multimodal models, local 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