AI Agent with Infinite Context and Reasoning Steps

Today’s top AI Highlights:

1. AI agent with infinite reasoning steps, infinite context, infinite output

2. Microsoft’s releases opensource agentic web with human-in-the-loop

3. Microsoft will soon opensource GitHub Copilot in VS Code

4. Integrate Claude Code into your applications

5. This Runtime Layer lets AI see how its code is behaving in production

& so much more!

Read time: 3 mins

AI Tutorial

Building tools that truly understand your documents is hard. Most RAG implementations just retrieve similar text chunks without actually reasoning about them, leading to shallow responses. The real solution lies in creating a system that can process documents, search the web when needed, and deliver thoughtful analysis. Moreover, running the pipeline locally would reduce latency and ensure privacy and control over sensitive data.

In this tutorial, we'll build a powerful Local RAG Reasoning Agent that runs entirely on your own machine, with web search fallback when document knowledge is insufficient. You'll be able to choose between multiple state-of-the-art opensource models like Qwen 3, Gemma 3, and DeepSeek R1 to power your system.

This hybrid setup combines document processing, vector search, and web search capabilities to deliver thoughtful, context-aware responses without cloud dependencies.

We share hands-on tutorials like this every week, designed to help you stay ahead in the world of AI. If you're serious about leveling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

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Latest Developments

1000+ Reasoning Steps with 10M+ Token Context ♾️💭📝

Flowith has launched Agent Neo, a system capable of handling tasks with unlimited reasoning steps, context, and output in the cloud. This autonomous agent works 24/7, executing complex projects autonomously while you focus elsewhere. Neo doesn't work alone - it strategically breaks down objectives into subtasks and deploys multiple parallel agents across an infinite canvas, showing real-time progress as your website, game, or research project takes shape.

Neo supports over 10 million tokens of context and can handle anything from code and visuals to web scraping and writing. It's built for creators, marketers, developers, and basically everyone who wants real, scalable AI that actually works end-to-end.

Key Highlights:

1. Parallel Multi-Agent Architecture - Neo automatically splits complex tasks into smaller components and assigns them to multiple AI agents working simultaneously, dramatically accelerating completion time for large-scale projects while providing visual tracking on an infinite canvas.

2. Unlimited Processing Capacity - The system can execute over 1,000 reasoning steps while managing 10M+ token context windows, enabling it to tackle substantially larger and more complex projects than traditional AI agents with limitations. You can even give it your own knowledge base for more context.

3. Autonomous 24/7 Operation - Schedule tasks at your convenience and Neo continues working while you're offline, making it particularly valuable for lengthy processes like data analysis, content creation, or web development that would typically require continuous human oversight.

4. Performance Over Competitors - Agent Neo outperforms other frontier AI agents like Manus AI and even OpenAI Deep Research on the GAIA benchmark, a comprehensive eval for general-purpose AI agents.

We asked it to watch the OpenAI Codex release video of around 23 minutes and draft a blog with visuals, and it did pretty well! If tools like Manus AI and Genspark got you excited, Flowith Neo might just blow your mind. Watch it in action, you’ll feel a step closer to AGI.

Opensource Web Agent with Human Oversight 🌐🤖👀

Microsoft has released Magentic-UI, an opensource web agent with human-in-the-loop that helps automate complex browser tasks with user oversight. Unlike most autonomous agents, Magentic-UI doesn’t just act on its own. It lets users plan steps, approve key actions, and even take control mid-task. You can inspect and modify agent plans, pause execution, and save reusable workflows.

At its core, Magentic-UI is powered by AutoGen. It runs four AI agents: the Orchestrator (task planner and coordinator), WebSurfer (browser control), Coder (executes Python/shell code), and FileSurfer (reads and converts files). Each agent operates inside a Docker container, ensuring sandboxed and safe execution.

Key Highlights:

1. Human-in-the-loop - Magentic-UI introduces two concepts, Co-planning and Co-tasking. Before execution begins, Magentic-UI generates a clear step-by-step plan and lets the user modify it directly through a plan editor or by writing instructions in natural language. During execution, users can pause the task, inspect what the agent is doing in real-time, and even take over the browser.

2. Customizable safety layer with Action Guards - Magentic-UI asks for explicit user approval before taking any irreversible or sensitive actions, like submitting forms, closing tabs, or running shell commands. You can configure the guard level: require approval for only high-risk actions, or for every action if needed.

3. Plan learning and task memory - After a task is completed, Magentic-UI can save the full plan as a reusable workflow in a searchable gallery. These saved plans can be applied directly to repeat the same task (like checking flight prices weekly) or modified for similar tasks. This has cut planning time by 3x in early tests.

4. Gains on GAIA benchmark - On the GAIA benchmark, Magentic-UI showed up to 71% improvement in task completion rates with human feedback, even when only lightly guided by a user.

Quick Bites

GitHub has released a new Copilot coding agent that works independently on development tasks when assigned to GitHub issues or prompted through VS Code. The agent automatically creates a secure development environment using GitHub Actions, pushes changes to draft pull requests, and maintains detailed session logs for transparency.

It’s great for offloading routine tasks like bug fixes, feature implementation, and documentation updates. You can review its work, leave comments, and Copilot will automatically make updates based on your feedback. The coding agent is available to Copilot Enterprise and Pro+ customers.

VS Code is officially becoming an opensource AI editor. The team will open source the GitHub Copilot Chat extension under the MIT license and gradually fold its AI features into the core VS Code repo. This is huge — developers will now have full access to how Copilot works, can build their own AI assistants without reverse engineering, and debug or extend features with proper visibility. Also, Cursor and Windsurf are now going to face even more competition as developers innovate over it.

Anthropic has released the Claude Code SDK to programmatically integrate Claude Code into your applications. It enables running Claude Code as a subprocess, providing a way to build AI coding assistants and tools that leverage Claude’s capabilities. The SDK currently supports command-line usage. TypeScript and Python SDKs are coming soon.

You can now run 5,000+ LLMs from Hugging Face Hub locally on your Mac using MLX LM. Just hit “Use this model” on any supported model page, and it runs locally with Apple’s MLX backend; no setup, no fuss.

Tools of the Trade

1. Hud MCP Server: A Runtime Code Sensor built to make AI-generated code production-safe. It streams real-time production insights back to coding agents (like Cursor, Windsurf) to give them full visibility into how the code behaves in production.

2. Airweave: A tool that lets agents semantically search any app. It's MCP compatible and seamlessly connects any app, database, or API, to transform their contents into agent-ready knowledge.

3. FlowGram: A node-based flow building engine that helps developers quickly create workflows in either fixed layout or free connection layout modes. It provides a set of interaction best practices and is particularly suitable for visual workflows with clear inputs and outputs.

4. Awesome LLM Apps: Build awesome LLM apps with RAG, AI agents, MCP, and more to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos, and automate complex work.

Hot Takes

1. There's no moat in building models. The price of intelligence is quickly approaching $0.
   The moat is in wrappers (and specialized models, but that's a different tweet.) ~
   Santiago
   
   

2. vibe coding is dead

   its just called coding ~
   Ben Tossell

That’s all for today! See you tomorrow with more such AI-filled content.

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