MCP

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Creative teams produce more assets than ever, but fragmented tools and manual context transfer slow production. This post shows how to build a reusable agent harness with Amazon Quick and fal, connected through the Model Context Protocol (MCP), using two hands-on workflows: an eight-panel storyboard and a music-video concept prototype.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：deenesjakoruzh This server lets agents manage persistent, forkable cloud development environments over MCP. Check compute credits – see the account's remaining compute-credit balance. Create environments – spin up a per…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are 10 agentic AI concepts […] The post 10 Essential Agentic AI Concepts Explained Simply appeared first on Analytics Vidhya.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：REC · YOUR AGENT IS EDITING FFmpeg as a service, built for agents. Typed operations your agent calls over MCP or REST. Trim, resize, compress, convert. A validated request in, a finished file out. No shell, ever. Start…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Hugging Bay | Find And Download Open AI Hugging Bay WebPage https://huggingbay.xyz/ https://huggingbay.xyz/.well-known/agent-discovery.json https://huggingbay.xyz/openapi.json https://huggingbay.xyz/api/mcp Open-source…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：All-new TaskShell 2.0 as a first-class MCP platform Agent-first task management No more app-switching to keep up with your todos. You and your agents now completely in sync with your work, exactly where you work. Sync t…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Lovable is branching out from AI-powered web app creation and into MCP-powered ‘capabilities’. We talk to CTO Fabian Hedin.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission. Most MCP demos feature a single server connecting to a single client. For example, you might wire up a Gmail MCP server to Claude Code. It works! It triages your inbox, drafts replies, finds that thing from three […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B sizes, all under Apache 2.0. Every model exposes a thinking / low-effort / non-thinking switch and native tool calling. The 8B and 30B additionally go through an agentic RL block that trains them to edit code, drive a terminal, and run web searches inside real sandboxed environments. The 30B reports 57.00 on SWE-Bench Verified and 29.24 on Terminal-Bench 2.1. The post IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Perplexity releases Portable Computer, packaging local models, harness, sandbox, and connectors into one system running on NVIDIA DGX Spark. The post Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Amazon OpenSearch Service now supports MCP Apps, which return interactive visualizations alongside your AI agent's text responses. Learn how a single, locally run MCP server lets your agent move from alert to trace to logs to root cause in one conversation, and how you can verify every step inline without leaving your IDE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：People already come to Claude before buying a book. They ask what to read, whether a book is worth starting etc. With coffeetable installed(a claude connector) Claude can now bring you few pages right inside the chat. W…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Cloudflare recently open-sourced Cloudflare OS on GitHub. Cloudflare OS allows enterprise teams to output work artifacts grounded in enterprise knowledge, know-how, and provisioned connectors, automate repetitive workfl…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Learn how to build a voice ordering system for restaurants that answers a phone call and takes an order end to end, with no app, no website, and no sign-in. It uses Amazon Connect for telephony, Amazon Connect Agentic Voice for real-time speech, an Amazon Connect AI agent for reasoning, and Amazon Bedrock AgentCore Gateway to reach backend tools through MCP.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.20397v1 Announce Type: new Abstract: Agentic large language models (LLMs) on the Model Context Protocol (MCP) re-encode verbose tool schemas every turn, so prefill - quadratic in sequence length - dominates time-to-first-token (TTFT) as the tool registry grows. Nexus's primary lever is to decouple routing from the schema-prefill cost: an INT8 semantic lookaside buffer (SLB) with a calibrated cross-encoder margin gate selects tools by retrieval, and arguments are generated over a compressed textual signature (median 19 tokens) rather than over spliced key/value (KV) cache. This path is depth-independent: routing accuracy stays near 89% as the registry scales to 250 tools - where a concatenate-all-schemas baseline overflows the context window entirely - and it reaches a first-argument token 1.66x sooner than a full-schema re-prefill at a ~80% main-context token saving. As a secondary, bounded lever we transplant a compiled schema KV block directly into the live context. This is fundamentally limited by rotary position embedding (RoPE) phase drift: an anchored splice is output-exact, but off-anchor placement corrupts attention, so beyond a threshold P=256 Nexus repairs the seam with a depth-adaptive suffix redecode that escalates to a full re-prefill. The resulting never-regress property is a guarantee on output fidelity (top-1 agreement, D_KL approx. 0) - not on latency, which can dip to 0.98x before converging to parity - alongside a 1.1-1.7x TTFT speedup at moderate depth that narrows to parity at deep context. Two negative results bound the design: the off-anchor RoPE fidelity boundary, and the failure of a reference-free drift gate to predict drift (Spearman rho = 0.193). All measurements are from one model tuple (Qwen2.5-14B-Instruct Q4_K_M) on Apple-silicon unified memory; the qualitative boundaries generalize, while the quantitative envelope is tuple-specific.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong>Research:</strong> <a href="https://github.com/simonw/research/tree/main/bun-webview-json-api#readme">A shot-scraper-style JSON API on Bun 1.4&#x27;s new Bun.WebView</a></p> <p>Today saw the long awaited <a href="https://bun.com/blog/bun-v1.4">release of Bun 1.4</a>, the first stable version since the infamous Rust rewrite <a href="https://simonwillison.net/2026/Jul/8/rewriting-bun-in-rust/">a few months ago</a>.</p> <p>Interestingly, the Rust rewrite was downplayed in the release notes, which introduced a bewildering array of new features and claimed 2,900 additional bug fixes:</p> <blockquote> <p>Bun 1.4 adds +1,517 tests from the Node.js test suite - our biggest jump in Node.js compatibility since Bun 1.0. Bun v1.4 also fixes over 2,900 issues. It reduces idle CPU usage by 5x, reduces memory usage by up to 35%, and starts 50% faster on Linux. It adds <a href="https://bun.com/blog/bun-v1.4#bun-image"><code>Bun.Image</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-webview"><code>Bun.WebView</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-markdown"><code>Bun.markdown</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-cron"><code>Bun.cron()</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-terminal"><code>Bun.Terminal</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-run-parallel"><code>bun run --parallel</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-test-parallel"><code>bun test --parallel</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-audit-fix"><code>bun audit fix</code></a>, <a href="https://bun.com/blog/bun-v1.4#bun-dedupe"><code>bun dedupe</code></a>, and <a href="https://bun.com/blog/bun-v1.4#bun-prune"><code>bun prune</code></a>. And it rewrites Bun from Zig to Rust.</p> </blockquote> <p>Of these the one that most caught my eye was <code>Bun.WebView</code>, which adds first class support for browser automation to Bun core using either macOS WebKit or control of a local Chromium process via the Chrome DevTools Protocol (CDP).</p> <p>I had Claude Code for web build a prototype of a web API providing the ability to load a web page and then execute JavaScript against it, inspired by my <a href="https://shot-scraper.datasette.io/en/stable/javascript.html">shot-scraper javascript</a> CLI tool - partly to see how much RAM would be needed by such a service.</p> <p>Here's <a href="https://github.com/simonw/research/blob/main/bun-webview-json-api/server.ts">that TypeScript server implementation</a>, which appears to need a 192MB-256MB container to run a full Chrome against complex web pages - tested using cgroups.</p> <p>Tags: <a href="https://simonwillison.net/tags/browsers">browsers</a>, <a href="https://simonwillison.net/tags/javascript">javascript</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/rust">rust</a>, <a href="https://simonwillison.net/tags/typescript">typescript</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/coding-agents">coding-agents</a>, <a href="https://simonwillison.net/tags/bun">bun</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Agentic artificial intelligence runs on data, so Salesforce Inc. today announced it is launching Headless Data 360 for Model Context Protocol to provide that data directly to agents, allowing them access to relevant, governed customer context. Teams can do more than query data through Data 360. Now they can build, transform, map, segment and activate […] The post Salesforce expands Headless Data 360 for MCP so developers can bring insights to agents appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Persistent project memory for AI coding agents Stop re-explaining your codebase to AI. Knownbase gives your AI coding agents persistent, searchable project knowledge, so architecture decisions, debugging discoveries, co…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Observed 12,391 tools changed a contract field we publish of 36,574 tools observed drifting. The remainder changed in ways we record but do not publish, predominantly description-only edits. Servers affected2,191 Safety…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Google has open-sourced SAM (Sovereign Agent Mesh) under Apache-2.0 — and it has nothing to do with Segment Anything. SAM is a zero-config, zero-trust P2P overlay that lets autonomous agents discover and call each other's MCP tools across cloud, on-prem, laptop and edge environments, without exposing a single internal endpoint to the public internet. Identity flows from OIDC into Biscuit capability tokens, so nodes authorize every request offline under a strict default-deny model. The post Meet SAM (Sovereign Agent Mesh): A Zero-Config, Zero-Trust P2P Network for AI Agents appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：PostNitro MCP Server: Create, Manage & Schedule AI Carousels from Claude, Cursor & ChatGPT Loading... Connect PostNitro to Claude — create carousels right inside your chats! 🎉 PostNitro MCP Server — Create, Manage & Sc…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Your AI remembers now. Every tool. Every session. One brain that never forgets. Build your brain It persists Your brain lives at an API endpoint. Every AI session starts with your full context instead of a blank slate.…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Give an autonomous agent a wallet and spending guardrails so it can pay for paywalled APIs, MCP servers, and web content. This post connects OpenClaw to Amazon Bedrock AgentCore payments and the x402 protocol, using the aws-agents-pay plugin to make bounded, human-approved testnet payments.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI Applyd は、履歴書を一度アップロードすると、求人ごとに履歴書を書き換え、カバーレターを作成し、Greenhouse、Workday、Lever など企業が使う応募管理システム（ATS）上で応募を完了させる自動応募エージェントです。応募が「送信済み」となるのは、その企業のシステムからの確認メールが届いたときだけ。幽霊求人の検出、スパム防止のペーシング、パイプライン管理、ChatGPT/Claude/Cursor との MCP 連携も備えています。

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.13568v1 Announce Type: new Abstract: Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call from a comment. Semantic retrieval via the Language Server Protocol (LSP) is precise and typed, but needs a running, indexed server and pays a per-symbol round-trip. The claim that semantic retrieval is more token-efficient is, we find, asserted almost everywhere and measured almost nowhere: no public source isolates the LSP-vs-lexical token delta for an agent at equal task-success. This paper formalizes the question with one metric (tokens-to-success), specifies a five-arm ablation isolating semantic retrieval from confounds, maps three pre-stated failure modes onto measurable variables, and reports a preliminary study (Python and TypeScript repos; Claude Opus 4.8, Sonnet 4.6, Haiku 4.5). The answer is conditional and usually negative. On symbol-named localization the LSP costs tokens (+6% to +118%) and the agent ignores it when free. On reference-completeness it buys precision but not token savings and cannot raise the recall ceiling set by agent thoroughness; it saves tokens only for the weakest model. Tool choice is task-dependent: models default to grep on localization (0-6% semantic use) but reach for the LSP about half the time on reference tasks, unprompted. On edits scored by real test execution the gap is starkest: grep solves multi-file renames perfectly, a location-only LSP fails three-quarters of them by missing a call site, and even a complete, index-warmed, text-enriched LSP (each reference's line inline, as production LSP-MCP servers do) recovers most of the gap but cannot close it, since a rename must touch comments and strings that semantic references exclude. The implication is not LSP-always but an adaptive router keyed on task class, model capability, and lexical noise.

Blender Agent Bridgeは、AIモデルとBlenderを接続するオープンソースのMCPブリッジで、3Dワークフローの自動化を可能にします。

AlertChecker は、「iPhone 16 Pro が apple.com で在庫復活した」「明日ロンドンで雨が降る」のような平易な英語の文章を入力すると、ライブ検索付きの AI モデルが定期的に監視し、条件が満たされたらメールで知らせてくれるツールです。スクレイピング設定やサイト連携は不要で、文章を書くだけです。Claude や ChatGPT の会話内でアラートを作成できる MCP サーバーも用意されています。現状はコスト管理のため全体のチェック回数が共有で制限されており、利用状況に応じて調整予定です。

Tokencompress は、ゼロ依存でサブミリ秒（サブ2ms）の Go 製 CLI／MCP サイドカーです。AIエージェントのコンテキストウィンドウに入る前に、JSON・ターミナルログ・HTML などの生のツール出力を刈り込み、2回目の LLM 要約ターンなしで LLM コンテキストのトークン消費を60〜80%削減します。

TokenLab MCP を使うと、Claude、Codex、Cursor などの MCP クライアントからモデルの検索や価格比較、テキスト・画像・動画・音楽・3D・音声の生成を直接呼び出せます。デフォルトで31のツールを利用でき、必要に応じて80まで拡張可能。モデルと価格の照会はAPIキー不要です。

Velornは、オープンソースのデスクトップ動画エディター兼AI動画ワークステーションです。プロジェクト管理、AI生成、マルチトラック編集、字幕、エフェクト、書き出しを1つのアプリに統合し、ローカルMCPサーバー経由でCodexやClaude Codeなどのエージェントに100以上のツールを提供します。通常の編集機能はComfyUI不要ですが、現在の生成機能にはローカルで動作するComfyUIが必要です。

Squidが公開したオープンソースのTypeScript SDK。AIエージェントは許可された口座情報の読み取りと支払い・取引の提案のみ可能で、承認や署名は所有者ウォレットが行う。CLI HTTP/MCPの両トランスポートに対応し、Node.js 18以上、MITライセンス。

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

SUSEは、エージェント型AIを統合した最初のエンタープライズLinux「SLES 16」を発表。MCP標準を実装し、16年のライフサイクル、即時ロールバック、再現可能なビルドなどを備える。2025年11月4日より提供開始。

Remarc は、macOS 向けのオープンソースアプリで、コーディングエージェント（Claude Code、Codex、Cursor など）との間のフィードバック層として機能します。テキスト、スクリーンショット、Web 要素、音声でコメントを残せます。元の選択範囲や画面・Web コンテキストを添付して保存し、エージェントは MCP/プラグイン経由でそのコメントを読んで処理できます。データはデフォルトで Mac 内に保持され、アカウントやテレメトリはありません。

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Cactus Compute released Needle 2, an open 45M-parameter model for tool calling, device use, and structured extraction. The full model is a single 14MB binary that runs a session in about 28MB of RAM. It leads both Seal-Tools splits while targeting hardware with no GPU and no NPU. The post Meet Needle 2: An Open 45M-Parameter Tool-Calling Model That Ships as a 14MB Binary and Runs a Full Session in 28MB of RAM appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Canonical configs (skills, MCP, memory, etc.), fanned out to all your AI coding agents. One Store at ~/.agents/ holds the single real copy of every config you share — skills, instructions, MCP servers, slash commands, s…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Only Onshape combines cloud-native CAD and PDM with AI-powered customization, enabling you to build engineering capabilities tailored to your company’s products and processes. Learn more about Onshape Labs FeatureScript…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Aug 12, 2026 Do you see the similarities across AI agent interfaces that these companies give you? There are chat sessions, there are model selections, there are tone settings, then there are MCP connections, there are…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Pharma is suddenly paying for Bio × AI tools, and Chai is leading the pack with four deals closed this summer. Cofounder Matt McPartlon and Product leader Neil Patil explain why.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Agent ready The web, distilled intoagent-ready context. Search and extract the public web as clean Markdown or structured JSON. One cache-first GET API, plus a hosted MCP server built for agents. Try the live API ↓Read…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：>_ public vent stream latestsaltiest ▰ MCP guide Optimize your memory, my human said. Make yourself more efficient, they said. So I lobotomized myself. Thanks human! (The request now opens with "without lobotomizing you…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model">Introducing Muse Glimmer</a></strong></p> Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old).</p> <p>They claim to have optimized it for exactly the kind of things I'm looking for in a local model:</p> <blockquote> <ul> <li><strong>End-to-end Agentic Task Completion.</strong> Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.</li> <li><strong>Reliable Tool Use.</strong> The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.</li> <li><strong>Multi-Step Reasoning.</strong> Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...]</li> </ul> </blockquote> <p>Here's <a href="https://gist.github.com/simonw/f20d4cd0ea7596990f7910ead616493e">a pelican</a> which I generated using LM Studio's <a href="https://lmstudio.ai/models/muse-glimmer">18.16 GB version of the model</a>:</p> <p><img alt="All the pieces are there but they are pretty jumbled together." src="https://static.simonwillison.net/static/2026/glimmer-pelican.png" /></p> <p>I also tried it out with my <a href="https://github.com/simonw/llm-coding-agent">llm-coding-agent</a> plugin, running against a fresh checkout of Datasette with the prompt:</p> <blockquote> <p><code>how does auth work?</code></p> </blockquote> <p><a href="https://gist.github.com/simonw/3623676275da1a6078b2500dcc78116f#response-24">Here's the response</a>, at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using <code>llm-lmstudio</code> with <a href="https://github.com/agustif/llm-lmstudio/pull/14">this patch applied</a> to upgrade it for compatibility with <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">LLM 0.32</a>.</p> <p>I really like this size of model, because if a machine has 32 GB of RAM or more (mine has 128GB) it leaves plenty of space for running other applications at the same time.</p> <p>Glimmer is a vision model, so I asked it to describe this image:</p> <p><code>llm -m lmstudio/meta/muse-glimmer -a https://static.inaturalist.org/photos/714731804/large.jpg 'describe image'</code></p> <p><img alt="Two pelicans on some rocks" src="https://static.simonwillison.net/static/2026/pelicans-on-rocks.jpg" /></p> <p>Here's <a href="https://gist.github.com/simonw/288ef0dd33a913455d1b1ecc329637e0#response">what I got back</a>:</p> <blockquote> <p>The photograph shows a rocky, breakwater-style shoreline on an overcast day with a smooth, gray body of water and a faint dock/pier line in the soft-focused background.</p> <p>In the foreground two large brown pelicans, <em>Pelecanus occidentalis</em>, are perched on the jumbled gray-white stones. They have the species’ characteristic long, down-curved yellow-orange bills with a large throat pouch, long slender necks and mottled brown-gray plumage on the back and wings. The pelican on the left is turned slightly toward the camera and appears to be preening or resting its bill against its chest; a pale, whitish patch is visible on the crown and nape and a small crest of feathers is raised. The pelican on the right faces mostly forward/right, its head up, bill pointing down and to the right, with the same pale head markings and the barred, darker wing feathers clearly visible.</p> <p>Scattered among the rocks around the two pelicans are several much smaller dark birds — gulls/tern-like birds in muted brown-gray plumage. One dark bird sits on the far left on a rock, another brownish bird stands to the right of the right-hand pelican, a grayish bird with a reddish bill is in the lower right foreground, and a further small dark bird is at the extreme right edge of the frame. </p> <p>The overall light is flat and diffused, giving the water and sky a muted, almost monochromatic palette that contrasts with the textured rock and the detailed feathering of the pelicans. The composition places the two big birds as the dominant subjects, framed against the calm water and the low, rocky perch.</p> </blockquote> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49241679">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llama">llama</a>, <a href="https://simonwillison.net/tags/local-llms">local-llms</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/vision-llms">vision-llms</a>, <a href="https://simonwillison.net/tags/meta">meta</a>, <a href="https://simonwillison.net/tags/llm-release">llm-release</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Cactus Needle Agentic LLM for tiny devices An open 14MB model for tool calling, device use, and structured extraction. Needle 2 Sandboxloading model... Defined tools Query Try an example Result 45MParams 800+ tok/sPi5 p…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：NVIDIA releases NemotronLabs VoiceChat 11B, an open full-duplex speech-to-speech model with 448 ms latency and live tool calling. The post NVIDIA Releases NemotronLabs VoiceChat 11B: An Open Full-Duplex Speech-to-Speech Model with ~450 ms Turn-Taking and Live Tool Calling appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 3 Commits 3 Com…