arXiv:2609.09503v1 Announce Type: new Abstract: Rapid bespoke commissioning of the Cognitive Digital Twin (CDT) is a major challenge in reconfigurable manufacturing. Traditional digital twin (DT) construction methods primarily focus on geometric reconstruction, often neglecting the deep semantic integration and functional interoperability necessary for autonomous reasoning. This paper proposes an agent-based, AI-driven workflow to automate end-to-end CDT debugging. The system utilises LangGraph as a multi-agent orchestration engine to achieve dual-path synthesis: the semantic path extracts technical specifications from unstructured documents using Retrieval Augmented Generation (RAG), while the functional path autonomously discovers and binds to real-time industrial telemetry data using M…
Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.
Meta has introduced Muse, a personal AI agent that takes actions rather than just answering questions. It can send emails, book travel, negotiate bills, keep pursuing long-term goals after you close the app, and pause only when approval is needed. Each user's agent runs in an isolated Muse Secure VM, while an independent Sentinel agent governs network egress and connector actions. Muse is rolling out in the US, and its underlying model Muse Spark 1.3 is now available to developers.
In this technical post, Databricks explains how they used OpenTelemetry tracing in Unity Gateway and natural language queries with Genie One to identify and fix seven minor bugs in MCP tool servers that were causing an estimated $499K/year in wasted tokens and 12,000 engineering hours per year (totaling $1.2M in lost productivity). The fixes were implemented in one hour. The post also highlights the importance of designing tools that handle LLM's guessing behavior gracefully.
datasette-mcp 0.2 is the first non-alpha release of the MCP plugin for Datasette. Key changes: execute_sql now returns rows as an array of objects (previously array of arrays), and it now depends on mcp>=2.1.1.
This is part of our series on how we're AI-pilling Sierra. In our last post, we wrote about Pinecone, an internal agent we built to make employees more effective. One of the biggest challenges in building Pinecone was m…
TrackMCP is positioned as Google Analytics for MCP servers, offering usage analytics for Model Context Protocol servers. It is currently live on Product Hunt with discussion and links.
In this post, you will learn how to deploy and host your MCP server in AgentCore Runtime and integrate it with Amazon Quick, along with the prerequisites. With this pattern, you promote reusability and avoid duplication of AI tools, so clients can reuse commonly used tools and agents exposed through an MCP server instead of authoring them from scratch again. Your customers get a way to use your product inside Amazon Quick (chat agents and workflows) without building custom connectors for every use case.
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Your agent reads EDGAR. It cannot read DART. Your model knows NVIDIA. It guesses about SK Hynix. Ask it about both. “Find KOSDAQ names above RS 90 that pass the trend template.” Ask Claude and it screens every Korean li…
The Silent Bug: How an AI Agent Can Quietly Blank Out Your Excel Formulas 30 August 2026 · mcpexcelai-agentsfsopenxmldsl Here’s a bug that doesn’t look like a bug. Ask an AI agent to add one line to an exist…
Anthropic has opened a research preview of the Model Hardware Standard (MHS), a shared driver specification that lets AI agents discover and safely operate physical devices. Instrument integration that normally takes weeks or months drops to hours: Carnegie Mellon went from raw equipment to a finished dose-response curve in eight, and QuEra's laser relock improved from 58% to 99.3% across 700 trials. MHS is model-agnostic, reachable over MCP, and enforces safety limits in the driver rather than the prompt. The post Anthropic Opens a Research Preview of the Model Hardware Standard (MHS): A Shared Specification for AI Agents to Safely Operate Physical Devices appeared first on MarkTechPost.
MCPSTRICT TYPESCRIPTZERO-CONFIG Persistent, Project-Local Memory for AI Coding Agents Coding agents forget decisions between sessions. OpenContext MCP exposes a lightweight Model Context Protocol server that enables AI…
Rob May Aug 29, 2026 Today we’re making our Neurometric tool calling SLM available on TrustedRouter. It does one thing: it turns intent into valid, schema-bound tool calls. It has its own pipeline and harness tuned for…
AI-assisted development Use Cursor, Claude, Copilot, or any coding agent to build on Lumify — with MCP tools, machine-readable docs, and copy-paste prompts that prevent hallucinated endpoints. API key Sign in, or get an…
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.
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 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.
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…
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…
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…
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 […]
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.
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.
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.
In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for...
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…
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…
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.
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-arg…
Research: A shot-scraper-style JSON API on Bun 1.4's new Bun.WebView Today saw the long awaited release of Bun 1.4, the first stable version since the infamous Rust rewrite a few months ago. 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: 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 Bun.Image, Bun.WebView, Bun.markdown, Bun.cron(), Bun.Terminal, bun run --parallel, bun test --parallel, bun audit fix, bun dedupe, and bun prune. And it rewrites Bun from Zig to Rust. O…
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.
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…
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…
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.
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…
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.…
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 Applyd is an automated job-application agent: upload one resume, and it rewrites it for each role, writes a cover letter, and fills out and submits the application on the employer's own ATS (Greenhouse, Workday, Lever, etc.). An application is marked 'sent' only when the employer's system confirms receipt, not when the submit button is clicked. It also flags ghost jobs, paces submission volume, tracks your pipeline, and integrates with ChatGPT, Claude, and Cursor via MCP.
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…
AlertChecker lets you enter an everyday English statement like "the iPhone 16 Pro is back in stock at apple.com" or "it's going to rain in London tomorrow," and it uses AI models with live web search to monitor the condition periodically. You get an email when it becomes true. No scraping rules or site integrations are needed—just plain English. The project also includes an MCP server so you can create alerts by asking in a conversation with Claude or ChatGPT. Check quotas are currently shared and rate-limited until usage patterns are better understood.
Tokencompress is a zero-dependency, sub-millisecond Go CLI and MCP sidecar that prunes raw tool outputs (JSON, terminal logs, HTML) before they enter your AI agent's context window, cutting LLM context token consumption by 60% to 80% without a second LLM summarization turn.
TokenLab MCP lets AI assistants like Claude, Codex, and Cursor look up models, compare pricing, and call text, image, video, music, 3D, and audio generation tools. It ships with 31 common tools by default and can expand to 80; model and pricing queries work without an API key.
Velorn is an open-source desktop video editor and AI video workstation that combines project-based editing, AI generation, captions, effects, and export, plus a local MCP server exposing 100+ tools for coding agents such as Codex, Claude Code, and Cursor. Editing features work without ComfyUI, while all current generation workflows require a local ComfyUI instance.