ANUS CLI is a Grok-powered terminal agent designed as an experiment in automated software creation and evolutionary code maintenance. Originally initiated by another autonomous entity named Manus, the project seeks to progress from its current foundational stage toward becoming a self-developing system capable of autonomously maintaining its own codebase with minimal human intervention.
- Operates via Node.js v20 or higher using an OpenRouter API key.
- Includes built-in support for sandboxed shell execution and file system operations.
- Mandates that at least 80% of all submitted code contributions are produced by large language models.
- Operates via Node.js v20 or higher using an OpenRouter API key.
- Includes built-in support for sandboxed shell execution and file system operations.
- Mandates that at least 80% of all submitted code contributions are produced by large language models.
Snyk Agent Scan provides a way to discover and inspect local agent components like Model Context Protocol (MCP) servers and skills. It identifies various security risks, such as prompt injections, malware payloads in natural language, sensitive data exposure, and credential leaks. The tool offers both an interactive command-line interface for individual users and a background mode for enterprise monitoring through Snyk Evo.
- Detects 15+ distinct security risks across MCP servers and agent skills
- Supports agents including Claude Code, Cursor, Windsurf, and Gemini CLI
- Automatically discovers configurations for various desktop and IDE-based agents
- Scanning MCP configs executes commands defined in them to retrieve tool descriptions
- Detects 15+ distinct security risks across MCP servers and agent skills
- Supports agents including Claude Code, Cursor, Windsurf, and Gemini CLI
- Automatically discovers configurations for various desktop and IDE-based agents
- Scanning MCP configs executes commands defined in them to retrieve tool descriptions
Yan Xie, Virat Patel and Albert Chang write that traditional developer-centric APIs are often too granular to support autonomous agents effectively, frequently leading to high latency and increased failure rates during complex workflows. Webflow is transitioning toward intent-driven architectures through the Model Context Protocol (MCP), replacing multi-step chains of low-level API calls with streamlined task-oriented tools that allow LLMs to operate more reliably via declarative commands.
- Use Cloudflare Durable Objects to manage stateful execution for long-running agent sessions.
- Prioritize observability focused on model reasoning and intent rather than traditional infrastructure telemetry.
- Implement layered tool architectures to prevent "tool explosion" as product capabilities expand.
- Use Cloudflare Durable Objects to manage stateful execution for long-running agent sessions.
- Prioritize observability focused on model reasoning and intent rather than traditional infrastructure telemetry.
- Implement layered tool architectures to prevent "tool explosion" as product capabilities expand.
Nolen Jonker writes about how he transitioned from using Claude Code to running local LLMs equipped with filesystem access via the Model Context Protocol (MCP) for administrative tasks. He notes that while Claude is superior for complex coding, a local model can handle folder organization and file management without the privacy concerns of sending sensitive data to cloud servers or being subject to rate limits.
- The filesystem MCP server is an official Anthropic-maintained Node.js package available via npm.
- Setting up the tool in LM Studio requires only a single edit to a `mcp.json` file.
- Running Qwen 3.5 9B on 8GB of VRAM provides sufficient capability for reliable multi-step sequences and tool calling.
- The filesystem MCP server is an official Anthropic-maintained Node.js package available via npm.
- Setting up the tool in LM Studio requires only a single edit to a `mcp.json` file.
- Running Qwen 3.5 9B on 8GB of VRAM provides sufficient capability for reliable multi-step sequences and tool calling.
The Kaseya 2026 SaaS Security Report highlights critical vulnerabilities in cloud environments. A major concern is the rise of unmanaged guest accounts which now outnumber licensed users, providing expanded attack surfaces for credential-based attacks. Furthermore, OAuth integrations allow third-party applications to maintain persistent access even after passwords are changed, while low MFA enforcement leaves many accounts vulnerable to phishing and theft. The report also notes that external file sharing creates long-term data exposure risks and attackers increasingly use trusted infrastructure like VPNs to hide their activities from detection.
* Proliferation of unmanaged guest accounts
* Persistent access through OAuth integrations
* Widespread lack of MFA enforcement in SaaS environments
* Ongoing data exposure via orphaned file-sharing links
* Difficulty detecting malicious logins using trusted IP addresses
* Proliferation of unmanaged guest accounts
* Persistent access through OAuth integrations
* Widespread lack of MFA enforcement in SaaS environments
* Ongoing data exposure via orphaned file-sharing links
* Difficulty detecting malicious logins using trusted IP addresses
A curated collection of resources, patterns, and templates for building reliable scaffolding for agents. Harness engineering is the discipline of designing the systems surrounding an agent—such as context delivery, tool interfaces, planning artifacts, verification loops, memory systems, and sandboxes—that determine its success or failure on real tasks. This focus is on the harness rather than the model.
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
This article explores how to integrate local Large Language Models (LLMs) with Docker environments using the Model Context Protocol (MCP). By setting up an MCP server, users can enable LLMs to execute container management tasks such as monitoring health, listing volumes, and deploying new services through natural language prompts. The author demonstrates how a high-end MoE model can handle complex instructions, even troubleshooting configuration errors autonomously.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
Salesforce is pivoting toward a headless model with its Headless 360 initiative, allowing users to access CRM data through external tools like Claude, ChatGPT, Slack, and WhatsApp rather than relying on the traditional user interface. This strategy aims to reduce context-switching for knowledge workers by integrating Salesforce directly into their existing workflows. The approach has already seen significant adoption, with Anthropic increasing its Sales Cloud usage fivefold after accessing it via headless interfaces.
The article discusses how integrating Anthropic's Claude Code persistent memory into automation workflows creates more personalized and efficient processes. By using the Claude Code CLI within an automation layer rather than relying solely on standard API calls, users can leverage Auto Memory and CLAUDE.md files to provide deep project context without manual prompt bloating. This approach enables smarter code repository management, automated documentation updates that reflect actual implementation changes, and more intelligent homelab monitoring. The author also distinguishes these memory features from the Model Context Protocol (MCP), which is better suited for fetching frequently changing data from external tools like GitHub or Notion.
Key topics:
- Claude Code's persistent memory via Auto Memory and CLAUDE.md
- Advantages of CLI implementation over standard API calls in workflows
- Practical applications in code repositories, documentation, and homelab environments
- Comparison between project memory and Model Context Protocol (MCP)
Key topics:
- Claude Code's persistent memory via Auto Memory and CLAUDE.md
- Advantages of CLI implementation over standard API calls in workflows
- Practical applications in code repositories, documentation, and homelab environments
- Comparison between project memory and Model Context Protocol (MCP)
Memori is an agent-native memory infrastructure that acts as an LLM-agnostic layer to transform AI agent execution and conversations into structured, persistent state for production systems. It integrates seamlessly into existing architectures, allowing agents to automatically capture and recall information from past interactions without requiring changes to core code or prompts.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
