AgenticSeek: Open-Source Local Manus

📖 TUTORIALS & CASE STUDIES

MCP: Building AI-Powered Microservices

Estimated read time: 12 min

Tim Berglund explains in this technical overview why MCP is transformative for building agentic AI systems. Going beyond desktop apps, MCP enables developers to create microservices that leverage real-time data streams and tool integration, making LLMs truly actionable in professional environments.

Structured CodeAgents for Reliable AI Tools

Estimated read time: 12 min

This research on Structured CodeAgents demonstrates how combining code-based actions with structured JSON outputs improves AI agent reliability and performance. By forcing agents to generate both thoughts and code in structured formats, developers can eliminate parsing errors and enhance reasoning capabilities, particularly beneficial when working with larger language models.

Building Autonomous AI Agents

Estimated read time: 25 min

Dive into this developer's guide to building autonomous AI agents that can plan, reason, and execute complex tasks. Learn how to leverage LLMs as reasoning engines, implement tool integration, and handle memory systems. Perfect for developers looking to create advanced AI applications that go beyond simple chatbots.

Understanding Vector Embeddings Visually

Estimated read time: 25 min

This visual guide explores vector embeddings, essential for RAG and semantic search applications. It covers embedding models like word2vec and OpenAI's text-embedding series, similarity metrics, vector compression techniques, and practical implementation considerations for vector search at scale.

Build AI Agents with Python

Estimated read time: 12 min

This tutorial demonstrates how to build a lightweight AI agent in Python using Hugging Face's Model Context Protocol (MCP) client. Developers can create agents that leverage LLMs for tool usage, web browsing, and image generation, with just 70 lines of code. The implementation includes streaming responses and flexible tool integration.

🧰 TOOLS

AgenticSeek: Open-Source Local Manus

Estimated read time: 16 min

AgenticSeek is a fully local, voice-enabled AI assistant that autonomously browses the web, writes and debugs code in multiple languages, and plans complex tasks while keeping all data on your device. This open-source alternative to Manus AI runs entirely on local hardware using models like Deepseek R1, ensuring complete privacy and zero cloud dependency.

32 MCP Servers for LLM Applications

Estimated read time: 10 min

This guide to MCP servers introduces Model Context Protocol servers that bridge LLMs with external systems. For developers building RAG frameworks or AI agents, these 32 servers provide connections to databases, search engines, APIs, and file systems—extending your LLM applications with real-time data and specialized capabilities.

Remote MCP Servers for AI Integration

Estimated read time: 4 min

Anthropic's documentation introduces remote MCP servers that enable AI integration with popular developer platforms. Companies like Asana, Atlassian, and Cloudflare now offer MCP servers, allowing developers to enhance their applications with AI-powered access to project management, collaboration, and infrastructure tools.

Mistral AI Launches Agents API

Estimated read time: 12 min

Mistral AI's new Agents API introduces a framework for building enterprise AI applications with built-in connectors for code execution, web search, and image generation. The system features persistent memory, agent orchestration, and MCP tools integration, enabling developers to create practical AI solutions with real-world applications.

Ollama's New Thinking Feature

Estimated read time: 8 min

Ollama's latest update introduces a "thinking" feature that lets developers control and visualize LLM reasoning processes. Available in DeepSeek R1 and Qwen 3 models, this capability can be toggled via CLI, API, or client libraries, enabling more transparent and controllable AI interactions in applications.

📰 NEWS & EDITORIALS

AI-First Development Should Augment Humans

Estimated read time: 10 min

Tim O'Reilly challenges the notion that "AI-first" means replacing workers with AI. In this analysis, he argues that successful AI integration requires augmenting human capabilities rather than eliminating them, drawing parallels to previous technological transitions and emphasizing how developers should approach AI-native application design.

Anthropic Open-Sources Model Interpretability Tools

Estimated read time: 4 min

Anthropic has released tools for tracing how language models make decisions through attribution graphs. Developers can now generate, visualize, and test hypotheses about model behavior using an interactive interface on Neuronpedia or the GitHub repository, enabling deeper understanding of popular open-weights models.