docs(blog): add blog of Introducing NPi

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   "browser-apps": "",
   "cli": "CLI Reference",
   "python": "Python SDK Reference",
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+  "blog": "Blog",
   "contact": {
     "title": "Contact ↗",
     "type": "page",

docs/pages/blog/_meta.json

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+{
+  "introducing-npi": "Introducing NPi"
+}

docs/pages/blog/introducing-npi.mdx

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+# Introducing NPi
+
+## Background
+
+Since ChatGPT's release, there has been a surge in AI applications designed for natural conversations. However, their
+practical usefulness is often limited by a lack of automatic action-taking capabilities
+
+The evolving concept of Agent addresses this gap. Agent AI is a class of interactive systems that can perceive visual
+stimuli, language inputs, and other environmentally grounded data, and can produce meaningful embodied actions<sub>[1, Agent AI Li Fei-Fei]</sub>.
+
+> Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software
+industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons.
+>
+> **The future of Agents, Bill Gates**.
+
+A major advantage humans hold over other animals is using tools. This also one of the AI Agents' key abilities is noted by Andrew Ng<sub>[3]</sub>,
+
+However, building an AI agent with a robust ability to use tools is challenging due to the diversity of tools and the
+operational overhead involved.
+
+- Low-level primitives, such as HTTP APIs or SDKs, lead organizations to repeatedly writing similar code to integrate LLM with
+different applications
+- Maintaining non-business critical features, like State Management, Availability, and Authorization flows, incurs
+significant overhead.
+- Ensuring the security of AI Agents — making their actions controllable, predictable, and explainable — poses
+substantial challenges.
+
+This is why NPi was created: to offer AI Agent developers an easy-to-use and reliable platform that enhances their
+agents' robust tool-use capabilities.
+
+## What is NPi?
+
+On April 25, we launched NPi (`v0.0.1`) on [GitHub](https://github.com/npi-ai/npi), a free, open-source platform.
+NPi provides **Tool Use** APIs that empower AI agents to operate and interact with various software tools and applications.
+
+The primary goal of NPi is to offer a unified interface that allows Large Language Models to seamlessly integrate with
+the existing software and applications ecosystem through function calls. NPi serves as a gateway for these models to
+access the virtual world.
+
+The core principle of NPi is to focus on `in-app planning`. This requires users to break down tasks into `single-app`
+sub-tasks, meaning **each task is confined to one application**. NPi then interpreters these sub-tasks into a series of
+function calls, executing them in a rule-based manner to ensure precise control.
+
+This method, known as divide-and-conquer, is a common strategy for solving complex problems and is central to NPi's designing.
+
+To date, we have implemented the core functionalities of NPi, including:
+
+### Out-of-box multimodal Tool use APIs
+
+We provide ready-to-use APIs that allow large language models to interact with applications, demonstrated in the
+following examples:
+
+```python
+from npiai.app.google import Calendar
+from npiai.app.github import GitHub
+from npiai.app.twitter import Twitter
+
+calendar = Calendar()
+calendar.chat("...")
+
+github = GitHub()
+github.chat("...")
+
+# For non-API friendly cases, a visual-based approach leverages the web browser.
+twitter = Twitter(visual=True)
+twitter.chat("what's the @wellswfwang latest post?")
+```
+
+Under the hood, NPi is pre-integrated with specific applications' SDKs or APIs, interpreting the given task into a
+sequence of function calls.
+
+We continuously monitor changes in these SDKs and APIs to stay aligned with them. This ensures NPi remains up-to-date,
+relieving you of the burden of tracking these changes yourself.
+
+### Multi-agent collaboration
+
+A clean and easy-to-use interface for building multi-agents applications.
+
+```python
+from npiai.core import Agent
+
+agent1 = Agent(prompt="...")
+agent1.use(Gmail(), Calendar())
+
+agent2 = Agent(prompt="...")
+agent2.use(GitHub())
+
+agent3 = Agent.collaborate(agent1, agent2)
+agent3.run(task="...")
+```
+
+`agent3` acts as a coordinator, orchestrating the operations of `agent1` and `agent2`.
+
+### Human-in-the-loop(HITL)
+
+
+A simple and effective way to ensure human involvement in handling sensitive operations appropriately.
+
+> This "human in the loop" approach is an essential step in ensuring that language models behave responsibly, generate accurate responses, and align with ethical and safety standards
+>
+> Large Language Model: Data, Human in the Loop for Fine-Tuning, [4]
+
+
+For example, in `Gmail` app, we pre-set the `sending email action` as sensitive, each calling of this action needs human
+to approve sending.
+
+```python
+# These HITL APIs will be released in v0.0.2
+from npiai.core.hitl import HITLRequest, HITLResponse, RequestApproved, RequestDenied, Console
+from npiai_proto import api_pb2
+
+def human_assist(req: api_pb2.HITLRequest) -> HITLResponse:
+    console = Console(req) # you can integrate your own workflow
+    if req.type == api_pb2.ActionType.SAFEGUARD:
+        result = console.wait()
+        if result.is_approved():
+           return RequestApproved
+    if req.action == api_pb2.ActionType.MORE_INFORMATION:
+        result = console.wait()
+        return result.human_message()
+    return RequestDenied
+
+gmail = Gmail()
+gmail.hitl_handler(human_assist)
+```
+
+Additionally, you can easily change this behavior by providing customized configuration.
+
+### Minimize operational overhead
+
+Imagine developing an AI Agent to negotiate meeting times with stakeholders. Initially, the Agent proposes several time
+slots and emails stakeholders for confirmation. While awaiting responses, the process not always immediate, various
+low-probability issues such as unexpected shutdowns or network errors may occur.
+
+NPi is designed to manage these states, ensuring recovery despite such disruptions, freeing developers from handling
+these annoying edge cases themselves.
+
+Beyond these, Fine-tuning, evaluation, and cost-effectiveness are also in our roadmap.
+
+## How does NPi work?
+
+NPi has an architecture that consists of two primary components: the **Server** and the **Toolkits**. The developers use
+NPi SDKs to develop their AI Agents, and use the CLI or Web Console to customize NPi.
+
+![../../assets/npi-arch.png](../../assets/npi-arch.png)
+
+The Server has two main responsibilities:
+
+- Management Functionalities: This includes App API management, authorization, and advanced options such as fine-tuning
+and evaluating function calls.
+- Function Calling Runtime: The server interprets tasks into a sequence of function calls based on in-app planning and
+executes them. It also handles the persistence of function call states if necessary and manages communications with the
+client side. This is particularly crucial for cross-agents communication and incorporating human input.
+
+The Toolkits are a collection of tools that designed to enhance the developer experience:
+
+- **Multi-language SDKs**:  We provide SDKs in various programming languages, making it easy for developers to integrate
+NPi with their agents and enhance their tool-using capabilities.
+- **CLI**: Our command-line tool offers a straightforward interface for managing apps, authorizing, and exploring NPi’s
+core functionalities.
+- **Web Console**: A web-based interface that enables more intricate interactions with NPi, including calling-memory
+management, fine-tuning, evaluation, and observability. (Note: This feature has not been released yet.)
+
+## What's next?
+
+We are excited to announce the release of NPi, a platform still in its early stages, We are actively working on
+implementing features that mentioned previous sections.
+
+The mission of NPi is to act as the limbs for large language models, enabling AI Agents to interact seamlessly with
+the virtual world. Although AI Agents, particularly in utilizing the Tool Use pattern, have not yet been widely adopted,
+we are optimistic that NPi will accelerate this adoption and bring us closer to Artificial General Intelligence (AGI).
+
+To realize this vision, we are eager to involve more AI Agent developers and build a vibrant community to collaboratively
+shape the future of NPi.
+
+Explore our development plans on the [NPi Roadmap](https://docs.npi.ai/roadmap). Connect with us on [GitHub](https://github.com/npi-ai/npi),
+[X.com](https://x.com/npi_ai), or join our [Discord Community](https://discord.gg/MQTuXtbj). You can also reach out directly
+to [our CEO](https://twitter.com/wellswfwang) via X.com.
+
+Your support, insights, and feedback are highly valued and greatly appreciated!
+
+We look forward to meeting you in the NPi community and exploring this exciting future together.
+
+## Reference
+
+1. [Agent AI: Surveying the Horizons of Multimodal Interaction, Stanford](https://arxiv.org/abs/2401.03568)
+2. [The future of Agents, Bill Gates](https://www.gatesnotes.com/AI-agents)
+3. [Tool use, a key design pattern of AI agentic workflowsAI Agent, Andrew Ng](https://twitter.com/AndrewYNg/status/1775951610059141147)
+4. [Large Language Model: Data, Human in the Loop for Fine-Tuning](https://www.futurebeeai.com/blog/large-language-model-data-human-in-the-loop-for-fine-tuning)