import { useState } from 'react'
import { motion, AnimatePresence } from 'framer-motion'
import { useInView } from 'react-intersection-observer'
import { FiBookOpen, FiClock, FiArrowRight, FiTrendingUp, FiX, FiArrowLeft, FiTag } from 'react-icons/fi'

const BLOGS = [
  {
    emoji: '🔌',
    tag: 'MCP Deep Dive',
    tagColor: '#6366f1',
    title: 'Understanding MCP: Model Context Protocol Explained',
    excerpt: 'MCP (Model Context Protocol) is Anthropic\'s open standard that bridges AI assistants to external data, tools, and APIs. This deep-dive covers how MCP\'s client-server architecture enables stateful context management, JSON-RPC transport, and how LLMs like Claude interact with real-world systems through structured tool calls.',
    readTime: '6 min read',
    date: 'Apr 2025',
    topics: ['MCP', 'Anthropic', 'LLM', 'Protocols'],
    featured: true,
    content: `
## What is MCP?

**Model Context Protocol (MCP)** is Anthropic's open standard that bridges AI assistants to external data, tools, and APIs. It defines how AI models can securely and reliably connect to the context they need — whether that's a database, a file system, a web API, or any other data source.

Think of MCP as the **USB standard for AI** — a universal connector that lets any AI model plug into any data source without custom integration code.

## The Problem MCP Solves

Before MCP, every AI integration was a one-off engineering effort:
- Custom code to connect Claude or GPT to your database
- No standard for how tools should expose capabilities
- Context was siloed — each AI app had to reinvent the wheel
- Security and trust boundaries were unclear

MCP standardizes all of this with a well-defined protocol.

## Core Architecture

MCP uses a **client-server architecture**:

\`\`\`
Claude (Client) ←→ MCP Protocol ←→ MCP Server ←→ Your Data/Tools
\`\`\`

**MCP Servers** expose:
- **Tools** — functions that AI can call (e.g., query_database, send_email)
- **Resources** — data that AI can read (e.g., files, database records)
- **Prompts** — reusable prompt templates for common workflows

**MCP Clients** (like Claude Desktop) connect to servers and make these capabilities available to the LLM.

## Transport: JSON-RPC

MCP uses **JSON-RPC 2.0** as its transport protocol. Messages are structured as:

\`\`\`json
{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "query_database",
    "arguments": {
      "query": "SELECT * FROM users LIMIT 10"
    }
  },
  "id": 1
}
\`\`\`

This makes MCP language-agnostic — any language that can speak JSON-RPC can implement an MCP server.

## Stateful Context Management

One of MCP's key innovations is **stateful context**. Unlike simple one-shot API calls:
- Sessions persist across multiple tool calls
- Context accumulates as the conversation progresses
- The AI can make multi-step decisions using context from previous steps

## Security Model

MCP has a clear security boundary: **the user is always in control**. Servers declare what they can do, users grant permission, and clients enforce boundaries. No server can access resources it hasn't been explicitly granted permission to use.

## Key Takeaways

1. MCP is the **standard for AI-tool integration** — use it instead of custom integrations
2. The client-server model separates concerns cleanly
3. JSON-RPC transport makes it easy to implement in any language
4. Stateful sessions enable complex multi-step AI workflows
5. Built-in security model protects user data

MCP is rapidly becoming the de-facto standard for connecting AI to the real world. Understanding it positions you at the cutting edge of AI engineering.
    `
  },
  {
    emoji: '🛠️',
    tag: 'MCP Tools',
    tagColor: '#8b5cf6',
    title: 'Building Your First MCP Server: Tools, Resources & Prompts',
    excerpt: 'A hands-on guide to building production-grade MCP servers using the Python SDK. Learn how to define Tools, expose Resources, and create reusable Prompt templates — making your AI agent capable of reading files, querying APIs, and executing custom logic through a standardized protocol.',
    readTime: '9 min read',
    date: 'Apr 2025',
    topics: ['MCP Server', 'Python SDK', 'Tools', 'Resources'],
    featured: true,
    content: `
## Getting Started with MCP Python SDK

The fastest way to build an MCP server is with the **official Python SDK**. Let's build a complete server from scratch.

## Installation

\`\`\`bash
pip install mcp
\`\`\`

## Your First MCP Server

\`\`\`python
from mcp.server import Server
from mcp.server.models import InitializationOptions
import mcp.types as types

# Initialize the server
server = Server("my-first-mcp-server")

@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
    return [
        types.Tool(
            name="get_weather",
            description="Get current weather for a city",
            inputSchema={
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"}
                },
                "required": ["city"]
            }
        )
    ]

@server.call_tool()
async def handle_call_tool(name: str, arguments: dict):
    if name == "get_weather":
        city = arguments["city"]
        # Call your weather API here
        return [types.TextContent(type="text", text=f"Weather in {city}: Sunny, 25°C")]
\`\`\`

## Defining Resources

Resources let the AI read data from your system:

\`\`\`python
@server.list_resources()
async def handle_list_resources() -> list[types.Resource]:
    return [
        types.Resource(
            uri="file:///data/users.json",
            name="User Database",
            description="Current user records",
            mimeType="application/json"
        )
    ]

@server.read_resource()
async def handle_read_resource(uri: str) -> str:
    if uri == "file:///data/users.json":
        with open("data/users.json") as f:
            return f.read()
\`\`\`

## Creating Prompt Templates

Prompts are reusable templates for common AI workflows:

\`\`\`python
@server.list_prompts()
async def handle_list_prompts() -> list[types.Prompt]:
    return [
        types.Prompt(
            name="analyze-data",
            description="Analyze a dataset and provide insights",
            arguments=[
                types.PromptArgument(
                    name="dataset_name",
                    description="Name of the dataset to analyze",
                    required=True
                )
            ]
        )
    ]

@server.get_prompt()
async def handle_get_prompt(name: str, arguments: dict):
    if name == "analyze-data":
        return types.GetPromptResult(
            messages=[
                types.PromptMessage(
                    role="user",
                    content=types.TextContent(
                        type="text",
                        text=f"Please analyze the {arguments['dataset_name']} dataset..."
                    )
                )
            ]
        )
\`\`\`

## Running Your Server

\`\`\`python
import asyncio
from mcp.server.stdio import stdio_server

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream, InitializationOptions(
            server_name="my-first-mcp-server",
            server_version="0.1.0"
        ))

asyncio.run(main())
\`\`\`

## Best Practices

1. **Descriptive tool names** — AI uses names to decide which tool to call
2. **Clear input schemas** — Be explicit about required vs optional parameters
3. **Error handling** — Always return meaningful error messages
4. **Idempotency** — Tools should be safe to call multiple times
5. **Rate limiting** — Protect your backend from AI-driven overload

You now have everything you need to build production-ready MCP servers. The possibilities are endless.
    `
  },
  {
    emoji: '🤖',
    tag: 'MCP Automation',
    tagColor: '#06b6d4',
    title: 'Claude + MCP: Orchestrating Multi-Step AI Automations',
    excerpt: 'Claude\'s native MCP integration unlocks a new paradigm in AI automation — where a single LLM can read emails, query databases, call APIs, and write files in a coordinated, multi-step flow. This post explores architectural patterns for building reliable MCP orchestration pipelines with tool chaining, error recovery, and state persistence.',
    readTime: '10 min read',
    date: 'Mar 2025',
    topics: ['Claude', 'MCP', 'Orchestration', 'Agents'],
    featured: true,
    content: `
## The Power of Claude + MCP

When Claude is connected to an MCP server, it transforms from a conversational AI into an **autonomous agent** capable of taking real-world actions. This opens up a new class of AI automations.

## A Real-World Example: Email Triage Agent

Imagine an agent that:
1. Reads your inbox via Gmail MCP server
2. Queries your CRM for sender context
3. Classifies email priority using Claude
4. Drafts responses for routine queries
5. Creates calendar events for meeting requests
6. Writes summaries to your Notion workspace

All of this runs automatically, orchestrated by a single Claude session.

## Tool Chaining Patterns

### Sequential Chaining
Each tool call's output feeds into the next:

\`\`\`
Read Email → Extract Info → Query CRM → Draft Response → Send
\`\`\`

### Parallel Execution
Multiple tools called simultaneously:

\`\`\`
Read Email → [Query CRM | Search Knowledge Base | Check Calendar] → Synthesize → Respond
\`\`\`

### Conditional Branching
Claude decides which path based on tool results:

\`\`\`
Read Email → Classify Intent → 
  if "meeting request": Check Calendar → Propose Slots
  if "support query": Search Docs → Draft Solution
  if "sales lead": Score Lead → Route to CRM
\`\`\`

## Error Recovery Architecture

Production MCP orchestrations must handle failures gracefully:

\`\`\`python
# In your MCP server, return structured errors
@server.call_tool()
async def handle_tool(name: str, arguments: dict):
    try:
        result = await execute_action(name, arguments)
        return [types.TextContent(type="text", text=result)]
    except RateLimitError:
        return [types.TextContent(
            type="text", 
            text="ERROR: Rate limited. Please retry in 60 seconds."
        )]
    except PermissionError:
        return [types.TextContent(
            type="text",
            text="ERROR: Insufficient permissions for this action."
        )]
\`\`\`

Claude is trained to handle these errors intelligently — it will retry, use fallbacks, or ask for clarification rather than silently failing.

## State Persistence

For long-running automations, persist state between sessions:

\`\`\`python
import json
from pathlib import Path

class StateManager:
    def __init__(self, session_id: str):
        self.path = Path(f"sessions/{session_id}.json")
    
    def save(self, state: dict):
        self.path.write_text(json.dumps(state))
    
    def load(self) -> dict:
        if self.path.exists():
            return json.loads(self.path.read_text())
        return {}

# Expose as MCP resource
@server.read_resource()
async def read_session_state(uri: str):
    session_id = extract_session_id(uri)
    manager = StateManager(session_id)
    return json.dumps(manager.load())
\`\`\`

## Monitoring & Observability

Always instrument your MCP orchestrations:

1. **Log every tool call** with inputs, outputs, and latency
2. **Track token usage** — multi-step pipelines can be expensive
3. **Alert on repeated errors** — usually indicates a broken tool
4. **Human-in-the-loop checkpoints** for high-stakes actions

## Key Architectural Principles

1. **Start simple** — build linear pipelines before adding branches
2. **Fail fast** — return clear errors rather than partial results
3. **Idempotency matters** — Claude may retry failed steps
4. **Budget tokens wisely** — complex orchestrations use many tokens
5. **Test with edge cases** — AI behavior can be surprising

MCP orchestration with Claude represents a fundamental shift in how we build software. Actions that required complex workflow engines now emerge naturally from a well-prompted AI with the right tools.
    `
  },
  {
    emoji: '⚙️',
    tag: 'AI Workflows',
    tagColor: '#f59e0b',
    title: 'n8n + LLM: Building Visual AI Automation Workflows',
    excerpt: 'n8n\'s node-based workflow engine pairs powerfully with LLM APIs and MCP to build production-ready AI automations without complex code. This guide covers integrating OpenAI, Claude, and MCP tool nodes in n8n to automate document processing, customer support triaging, and smart notification pipelines.',
    readTime: '8 min read',
    date: 'Mar 2025',
    topics: ['n8n', 'LLM', 'Automation', 'No-Code'],
    featured: false,
    content: `
## Why n8n + LLM is a Game Changer

n8n is an open-source workflow automation platform that lets you visually build complex pipelines. When combined with LLM APIs, it becomes a powerful tool for building AI automations without writing complex orchestration code.

## Setting Up Your First AI Workflow

### Step 1: Install n8n
\`\`\`bash
npm install -g n8n
n8n start
\`\`\`

### Step 2: Connect Your LLM
In n8n, add an **OpenAI** or **Anthropic** node and configure your API key. n8n handles authentication, retry logic, and rate limiting automatically.

## Document Processing Pipeline

A common use case: automatically process incoming documents:

\`\`\`
Webhook Trigger
    ↓
Extract Text (PDF/DOCX node)
    ↓
OpenAI Node (Summarize + Extract Key Points)
    ↓
Condition Node (Check document type)
    ↓
├── Contract: Notion Database (save summary)
├── Invoice: Google Sheets (extract amounts)
└── Report: Slack (send to team channel)
\`\`\`

## Customer Support Triage

Route support tickets intelligently:

\`\`\`
Email Trigger (Gmail node)
    ↓
Claude Node: "Classify this support ticket: [ticket]. 
Categories: billing, technical, account, general"
    ↓
Switch Node (based on classification)
    ↓
├── billing: Create Stripe customer record + assign billing team
├── technical: Search knowledge base + create Jira ticket
└── general: Draft response + send via Gmail
\`\`\`

## Smart Notification System

Use LLMs to decide what's worth notifying about:

\`\`\`javascript
// In n8n Code Node
const events = $input.all();
const summaryPrompt = \`
You have \${events.length} new events. 
Identify only the CRITICAL ones that need immediate attention.
Events: \${JSON.stringify(events)}
Return JSON: { critical: [], can_wait: [] }
\`;
// Pass to OpenAI node, then route based on criticality
\`\`\`

## Best Practices for n8n + LLM

1. **Use structured outputs** — Ask LLMs to return JSON for reliable parsing
2. **Add error workflows** — n8n supports error handler workflows
3. **Cache LLM responses** — Use Redis nodes to avoid repeated API calls
4. **Rate limit your webhooks** — Prevent accidental runaway costs
5. **Test with mock data** — n8n's built-in test mode is invaluable

## Cost Optimization Tips

- Use GPT-3.5 or Claude Haiku for classification tasks (cheap, fast)
- Reserve GPT-4 or Claude Sonnet for complex reasoning
- Set max_tokens limits to avoid unexpected large outputs
- Batch similar requests when possible

n8n democratizes AI automation — you can build in hours what would take weeks to code from scratch.
    `
  },
  {
    emoji: '🕸️',
    tag: 'AI Automation',
    tagColor: '#8b5cf6',
    title: 'Building AI Automation Workflows with LangGraph',
    excerpt: 'LangGraph brings graph-based state machines to LLM applications, enabling complex branching, loops, and multi-step automation. Learn how to design production-ready agentic workflows that handle real-world complexity — including conditional routing, human-in-the-loop nodes, and persistent memory across sessions.',
    readTime: '8 min read',
    date: 'Feb 2025',
    topics: ['LangGraph', 'Automation', 'Agents', 'Python'],
    featured: false,
    content: `
## What is LangGraph?

LangGraph is a library from LangChain that enables building **stateful, multi-step LLM applications** using graph-based state machines. It's designed for agentic workflows that require branching, looping, and complex decision-making.

## Core Concepts

### State
Every LangGraph workflow has a **state** — a typed dictionary that flows through the graph:

\`\`\`python
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph

class AgentState(TypedDict):
    messages: list[str]
    current_step: str
    data: dict
    error: str | None
\`\`\`

### Nodes
Nodes are Python functions that transform the state:

\`\`\`python
def call_llm(state: AgentState) -> AgentState:
    response = llm.invoke(state["messages"])
    return {"messages": state["messages"] + [response]}

def execute_tool(state: AgentState) -> AgentState:
    tool_result = run_tool(state["current_step"])
    return {"data": tool_result}
\`\`\`

### Edges
Edges connect nodes and can be conditional:

\`\`\`python
def route_decision(state: AgentState) -> str:
    if state.get("error"):
        return "handle_error"
    elif state["current_step"] == "done":
        return "end"
    else:
        return "continue"
\`\`\`

## Building a Research Agent

\`\`\`python
from langgraph.graph import StateGraph, END

# Build the graph
workflow = StateGraph(AgentState)

# Add nodes
workflow.add_node("plan", plan_research)
workflow.add_node("search", search_web)
workflow.add_node("synthesize", synthesize_results)
workflow.add_node("write_report", write_final_report)

# Add edges
workflow.set_entry_point("plan")
workflow.add_edge("plan", "search")
workflow.add_conditional_edges(
    "search",
    lambda s: "synthesize" if len(s["data"]) >= 3 else "search",
    {"synthesize": "synthesize", "search": "search"}
)
workflow.add_edge("synthesize", "write_report")
workflow.add_edge("write_report", END)

# Compile and run
app = workflow.compile()
result = app.invoke({"messages": ["Research quantum computing trends"]})
\`\`\`

## Human-in-the-Loop

LangGraph supports pausing for human approval:

\`\`\`python
from langgraph.checkpoint.sqlite import SqliteSaver

# Add checkpointer for persistence
memory = SqliteSaver.from_conn_string(":memory:")
app = workflow.compile(checkpointer=memory, interrupt_before=["execute_action"])

# Run until interruption
config = {"configurable": {"thread_id": "session-1"}}
result = app.invoke(initial_state, config=config)

# Human reviews and approves
human_approval = input("Approve action? (y/n): ")
if human_approval == "y":
    # Resume from checkpoint
    result = app.invoke(None, config=config)
\`\`\`

## Persistent Memory

\`\`\`python
# State persists across sessions with the same thread_id
config = {"configurable": {"thread_id": "user-123"}}

# First session
app.invoke({"messages": ["Remember: I prefer Python"]}, config=config)

# Days later, same thread_id
app.invoke({"messages": ["What language should I use?"]}, config=config)
# Agent remembers the preference!
\`\`\`

## When to Use LangGraph

✅ Complex multi-step workflows with branching logic
✅ Agents that need to loop until a condition is met
✅ Human-in-the-loop approval workflows
✅ Long-running tasks requiring state persistence

❌ Simple single-turn Q&A
❌ Straightforward sequential pipelines (use LangChain chains instead)
❌ When you need real-time streaming primarily

LangGraph is the right tool when your AI workflow needs to "think, act, observe, repeat" with production-grade reliability.
    `
  },
  {
    emoji: '🤝',
    tag: 'Multi-Agent',
    tagColor: '#06b6d4',
    title: 'AutoGen vs LangGraph: Which Multi-Agent Framework to Use?',
    excerpt: 'A practical comparison of Microsoft AutoGen and LangGraph for multi-agent development. We examine developer experience, orchestration flexibility, memory management, and when to choose each framework for production AI systems that require collaborative agent teams.',
    readTime: '7 min read',
    date: 'Jan 2025',
    topics: ['AutoGen', 'LangGraph', 'Agents', 'Architecture'],
    featured: false,
    content: `
## The Multi-Agent Framework Landscape

Both **Microsoft AutoGen** and **LangGraph** are popular frameworks for building multi-agent AI systems, but they take fundamentally different approaches. Choosing the wrong one can cost you weeks of refactoring.

## AutoGen at a Glance

AutoGen models agents as **conversational participants** that talk to each other:

\`\`\`python
import autogen

# Define agents
assistant = autogen.AssistantAgent(
    name="Assistant",
    llm_config={"model": "gpt-4"},
    system_message="You are a helpful AI assistant."
)

critic = autogen.AssistantAgent(
    name="Critic",
    llm_config={"model": "gpt-4"},
    system_message="You review and improve the assistant's work."
)

user_proxy = autogen.UserProxyAgent(
    name="User",
    human_input_mode="TERMINATE",
    code_execution_config={"work_dir": "coding"}
)

# Start the conversation
user_proxy.initiate_chat(
    assistant,
    message="Write a Python script to analyze sales data"
)
\`\`\`

**AutoGen strengths:**
- Natural conversation flow between agents
- Built-in code execution sandbox
- Easy to set up group chats
- Great for iterative refinement tasks

**AutoGen weaknesses:**
- Less control over execution flow
- Difficult to implement precise branching
- State management is conversation history (unstructured)
- Can be unpredictable in production

## LangGraph at a Glance

LangGraph models agents as **graph nodes** with explicit state transitions:

\`\`\`python
from langgraph.graph import StateGraph

# Explicit, controllable state
class State(TypedDict):
    task: str
    draft: str
    feedback: str
    approved: bool

workflow = StateGraph(State)
workflow.add_node("write", write_agent)
workflow.add_node("review", review_agent)
workflow.add_node("revise", revise_agent)

# Deterministic routing
workflow.add_conditional_edges(
    "review",
    lambda s: "approve" if s["approved"] else "revise"
)
\`\`\`

**LangGraph strengths:**
- Precise control over agent orchestration
- Typed state management
- Excellent for complex branching workflows
- Production-ready with checkpointing
- Human-in-the-loop support

**LangGraph weaknesses:**
- More boilerplate code
- Steeper learning curve
- Verbose for simple use cases

## Side-by-Side Comparison

| Feature | AutoGen | LangGraph |
|---------|---------|-----------|
| Learning Curve | Low | Medium |
| Control | Low | High |
| State Management | Conversation history | Typed graph state |
| Branching | Implicit | Explicit |
| Code Execution | Built-in | Manual |
| Production Readiness | Medium | High |
| Human-in-the-Loop | Basic | Advanced |
| Debugging | Hard | Easy |

## Decision Framework

**Choose AutoGen when:**
- You're prototyping or exploring
- The task is conversational by nature (debate, review, brainstorm)
- You want agents to execute code iteratively
- Speed of development > control

**Choose LangGraph when:**
- You're building production systems
- The workflow has clear, defined steps
- You need reliable branching and routing
- State management and observability matter
- You need human approval at specific steps

## The Hybrid Approach

Many teams use both: **AutoGen for rapid prototyping** to understand the problem, then **LangGraph for production implementation** with proper state management and reliability.

My recommendation: learn LangGraph first if you're building anything that needs to run reliably in production.
    `
  },
  {
    emoji: '⚡',
    tag: 'Agentic AI',
    tagColor: '#10b981',
    title: 'Agentic AI Design Patterns: ReAct, Plan-and-Execute & Beyond',
    excerpt: 'From simple ReAct loops to sophisticated Plan-and-Execute architectures, this post dissects the core design patterns powering today\'s agentic AI systems. Understand when to use each pattern, their failure modes, and how MCP tool use changes the agent reliability equation.',
    readTime: '11 min read',
    date: 'Dec 2024',
    topics: ['ReAct', 'Planning', 'Agents', 'Design Patterns'],
    featured: false,
    content: `
## The Agent Design Pattern Landscape

Agentic AI is maturing rapidly, and with it, a set of proven design patterns has emerged. Understanding these patterns is crucial for building reliable, efficient AI agents.

## Pattern 1: ReAct (Reason + Act)

The simplest and most widely used pattern. The agent alternates between reasoning about what to do and taking actions.

\`\`\`
Thought: I need to find the current price of NVIDIA stock
Action: search_web("NVIDIA stock price today")
Observation: NVIDIA (NVDA) is trading at $875.40

Thought: Now I can answer the user's question
Action: respond("NVIDIA is currently trading at $875.40")
\`\`\`

**When to use ReAct:**
- Simple, linear tasks
- When each step depends on the previous result
- Limited number of tool calls needed

**ReAct failure modes:**
- Gets stuck in loops when tools fail
- "Hallucinates" tool outputs
- Poor at multi-horizon planning

## Pattern 2: Plan-and-Execute

Separate planning from execution for better control:

\`\`\`python
# Phase 1: Planning
plan = llm.plan("""
Create a market analysis report for Tesla:
1. Get current stock price
2. Fetch last 3 earnings reports
3. Search for recent news (last 7 days)
4. Analyze sentiment from news
5. Synthesize into executive summary
""")

# Phase 2: Execution
for step in plan.steps:
    result = executor.run(step)
    plan.update_with_result(step, result)

# Phase 3: Synthesis
report = llm.synthesize(plan.all_results())
\`\`\`

**Advantages over ReAct:**
- Complete picture before starting → better decisions
- Can parallelize independent steps
- Easier to audit and debug
- Fail-fast on impossible plans

**When to use Plan-and-Execute:**
- Complex, multi-step research tasks
- When steps can be parallelized
- When you need auditability

## Pattern 3: Self-Reflection

The agent reviews and critiques its own outputs:

\`\`\`python
def reflexion_loop(task, max_iterations=3):
    response = agent.execute(task)
    
    for i in range(max_iterations):
        critique = agent.reflect(f"""
        Task: {task}
        Response: {response}
        
        What's wrong or incomplete? How can this be improved?
        """)
        
        if critique.score >= 0.9:
            break
            
        response = agent.revise(response, critique.feedback)
    
    return response
\`\`\`

This dramatically improves output quality for writing, code generation, and analysis tasks.

## Pattern 4: Multi-Agent Orchestration

Different specialized agents handle different domains:

\`\`\`
Orchestrator Agent
├── Research Agent (web search, document reading)
├── Analysis Agent (data processing, calculations)
├── Writing Agent (report generation)
└── Review Agent (quality check, fact verification)
\`\`\`

**Key principle:** Each agent has a **single responsibility** and communicates through structured interfaces.

## Pattern 5: Human-in-the-Loop (HITL)

Insert human checkpoints at critical decision points:

\`\`\`python
# Pause before irreversible actions
@checkpoint("before_send_email")
def send_email(recipient, content):
    # Human reviews here
    pass

@checkpoint("before_database_write")  
def update_customer_record(id, data):
    # Human approves changes
    pass
\`\`\`

HITL is **essential** for high-stakes agents in production.

## The MCP Effect on Agent Reliability

MCP significantly improves agent reliability because:

1. **Typed interfaces** — tools have schemas, reducing hallucinated arguments
2. **Clear error messages** — agents can recover from failures intelligently
3. **Resource isolation** — agents can't accidentally access unauthorized data
4. **Observability** — every tool call is logged for debugging

## Choosing Your Pattern

| Scenario | Recommended Pattern |
|----------|---------------------|
| Simple Q&A with tools | ReAct |
| Complex research report | Plan-and-Execute |
| Code generation | ReAct + Self-Reflection |
| Collaborative content creation | Multi-Agent |
| High-stakes business automation | Plan-and-Execute + HITL |

The best agents combine multiple patterns. Start with ReAct, add reflection when quality matters, add planning when complexity grows, and always add HITL when stakes are high.
    `
  },
  {
    emoji: '📚',
    tag: 'RAG',
    tagColor: '#ec4899',
    title: 'RAG vs Fine-Tuning: Making the Right Choice for Your LLM App',
    excerpt: 'Retrieval-Augmented Generation and fine-tuning both improve LLM performance, but solve different problems. This guide breaks down cost tradeoffs, latency implications, vector database selection, and when each approach delivers the best results for production AI applications.',
    readTime: '5 min read',
    date: 'Nov 2024',
    topics: ['RAG', 'Fine-Tuning', 'LLM', 'Architecture'],
    featured: false,
    content: `
## The Core Question

Every AI engineer eventually faces this decision: **should I use RAG or fine-tune my model?**

The answer depends entirely on what problem you're actually solving.

## What RAG Does

RAG (Retrieval-Augmented Generation) gives the LLM access to **external, dynamic knowledge** at inference time:

\`\`\`python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA

# Build knowledge base
vectorstore = Chroma.from_documents(
    documents=your_docs,
    embedding=OpenAIEmbeddings()
)

# Query with retrieval
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(k=4)
)

answer = qa_chain.run("What is our refund policy?")
\`\`\`

**RAG solves:**
- Knowledge that changes frequently
- Proprietary knowledge the base model doesn't have
- Need to cite sources
- Knowledge that's too large to fit in context

## What Fine-Tuning Does

Fine-tuning adjusts the model's **weights** to change its behavior, style, or knowledge:

\`\`\`python
# Fine-tuning with OpenAI
from openai import OpenAI
client = OpenAI()

# Upload training data
training_file = client.files.create(
    file=open("training_data.jsonl", "rb"),
    purpose="fine-tune"
)

# Start fine-tuning job
job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-3.5-turbo"
)
\`\`\`

**Fine-tuning solves:**
- Consistent tone/style (e.g., always respond formally)
- Domain-specific formats (e.g., medical coding)
- Specialized reasoning patterns
- Reducing hallucinations in a specific domain

## Cost Comparison

| Approach | Upfront Cost | Per-Query Cost | Update Cost |
|----------|-------------|----------------|-------------|
| RAG | Vector DB setup | Embeddings + retrieval | Low (just add docs) |
| Fine-tuning | Training compute ($$$) | Inference only | High (retrain) |
| RAG + Fine-tune | Both | Both | Medium |

For most startups: **RAG is 10x cheaper** to iterate on.

## Latency Comparison

| Approach | Added Latency |
|----------|---------------|
| RAG | +100-500ms (retrieval) |
| Fine-tuning | None (or marginal) |

If sub-100ms response times matter, fine-tuning wins.

## Vector Database Selection

For RAG, choosing the right vector DB matters:

| Database | Best For | Hosted? |
|----------|----------|---------|
| Pinecone | Production, scale | Yes |
| Weaviate | Complex queries | Both |
| Chroma | Development, small scale | Self-hosted |
| pgvector | Already on Postgres | Self-hosted |
| Qdrant | Performance-critical | Both |

**My recommendation:** Start with Chroma locally, move to Pinecone or Qdrant in production.

## The Decision Framework

**Use RAG when:**
✅ Your knowledge changes (weekly/daily/hourly)
✅ You need to cite sources
✅ Knowledge base > 100K tokens
✅ You have limited budget for training

**Use Fine-tuning when:**
✅ You need a very specific output format or style
✅ The model repeatedly fails at a task even with good prompts
✅ Latency is critical
✅ You have high, consistent query volume (reduces per-query cost)

**Use Both when:**
✅ You need consistent style AND up-to-date knowledge
✅ Building a domain-specific assistant (e.g., legal, medical)

## The Emerging Approach: RAG + Long Context

With 1M+ token context windows (Gemini 1.5), you can often skip retrieval entirely and just stuff your entire knowledge base into the context. Simpler architecture, but expensive at scale.

**My final recommendation:** Start with RAG. It's faster to build, cheaper to iterate, and solves 80% of "my LLM doesn't know about X" problems.
    `
  },
]

export default function Blog() {
  const [ref, inView] = useInView({ triggerOnce: true, threshold: 0.05 })
  const [selectedBlog, setSelectedBlog] = useState(null)

  const featured = BLOGS.filter(b => b.featured)
  const rest = BLOGS.filter(b => !b.featured)

  return (
    <>
      <section id="blog" className="section">
        <div className="orb orb-purple" style={{ width: 350, height: 350, bottom: '5%', right: '-5%', position: 'absolute', opacity: 0.3 }} />
        <div className="container" ref={ref}>
          <motion.div className="section-header"
            initial={{ opacity: 0, y: 24 }} animate={inView ? { opacity: 1, y: 0 } : {}} transition={{ duration: 0.55 }}>
            <div className="section-tag"><FiBookOpen size={12} /> Blog</div>
            <h2 className="section-title">Thoughts &amp; Insights</h2>
            <p className="section-subtitle">Deep research on MCP, AI automation workflows, agentic systems, and the future of intelligent AI engineering.</p>
          </motion.div>

          {/* Featured row */}
          <motion.div
            initial={{ opacity: 0 }} animate={inView ? { opacity: 1 } : {}} transition={{ duration: 0.4, delay: 0.1 }}
            style={{ marginBottom: '1.5rem' }}
          >
            <div style={{ display: 'flex', alignItems: 'center', gap: 8, marginBottom: '0.9rem' }}>
              <FiTrendingUp size={13} style={{ color: '#6366f1' }} />
              <span style={{ fontSize: '0.72rem', fontWeight: 700, letterSpacing: '0.1em', textTransform: 'uppercase', color: 'var(--text-accent)' }}>
                Featured Research
              </span>
            </div>
            <div className="blog-featured-grid">
              {featured.map((blog, i) => (
                <BlogCard key={blog.title} blog={blog} i={i} inView={inView} large onRead={() => setSelectedBlog(blog)} />
              ))}
            </div>
          </motion.div>

          {/* Divider */}
          <div style={{ height: 1, background: 'var(--border-subtle)', marginBottom: '1.5rem', opacity: 0.5 }} />

          {/* All articles grid */}
          <div style={{ display: 'flex', alignItems: 'center', gap: 8, marginBottom: '0.9rem' }}>
            <span style={{ fontSize: '0.72rem', fontWeight: 700, letterSpacing: '0.1em', textTransform: 'uppercase', color: 'var(--text-muted)' }}>
              More Articles
            </span>
          </div>
          <div className="blog-rest-grid">
            {rest.map((blog, i) => (
              <BlogCard key={blog.title} blog={blog} i={i + featured.length} inView={inView} onRead={() => setSelectedBlog(blog)} />
            ))}
          </div>
        </div>
      </section>

      {/* Read Modal */}
      <AnimatePresence>
        {selectedBlog && (
          <BlogReadModal blog={selectedBlog} onClose={() => setSelectedBlog(null)} />
        )}
      </AnimatePresence>
    </>
  )
}

function BlogCard({ blog, i, inView, large, onRead }) {
  return (
    <motion.article
      className="glass-card blog-card-item"
      style={{ padding: large ? '1.75rem' : '1.5rem', display: 'flex', flexDirection: 'column', gap: '0.85rem', cursor: 'pointer', position: 'relative', overflow: 'hidden' }}
      initial={{ opacity: 0, y: 36 }}
      animate={inView ? { opacity: 1, y: 0 } : {}}
      transition={{ duration: 0.55, delay: i * 0.08, ease: [0.22, 1, 0.36, 1] }}
      whileHover={{ y: -6, borderColor: blog.tagColor + '50' }}
      onClick={onRead}
    >
      {/* Subtle top accent line */}
      <div style={{ position: 'absolute', top: 0, left: 0, right: 0, height: 2, background: `linear-gradient(90deg, ${blog.tagColor}60, transparent)` }} />

      {/* Header */}
      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
        <span style={{ fontSize: large ? '2rem' : '1.8rem' }}>{blog.emoji}</span>
        <span style={{
          padding: '3px 10px', borderRadius: 999, fontSize: '0.68rem', fontWeight: 700,
          background: `${blog.tagColor}15`, border: `1px solid ${blog.tagColor}30`,
          color: blog.tagColor, letterSpacing: '0.05em',
        }}>{blog.tag}</span>
      </div>

      {/* Title */}
      <h3 style={{ fontSize: large ? '1.05rem' : '0.97rem', fontWeight: 700, color: 'var(--text-primary)', lineHeight: 1.45 }}>
        {blog.title}
      </h3>

      {/* Excerpt */}
      <p style={{ fontSize: '0.82rem', color: 'var(--text-secondary)', lineHeight: 1.75, flexGrow: 1 }}>
        {blog.excerpt}
      </p>

      {/* Topic chips */}
      <div style={{ display: 'flex', flexWrap: 'wrap', gap: 5 }}>
        {blog.topics.map(t => (
          <span key={t} className="tech-tag" style={{
            fontSize: '0.65rem', background: `${blog.tagColor}10`,
            borderColor: `${blog.tagColor}20`, color: blog.tagColor,
          }}>{t}</span>
        ))}
      </div>

      {/* Footer */}
      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', paddingTop: '0.5rem', borderTop: '1px solid var(--border-subtle)' }}>
        <div style={{ display: 'flex', gap: 12 }}>
          <span style={{ fontSize: '0.75rem', color: 'var(--text-muted)' }}>{blog.date}</span>
          <span style={{ display: 'flex', alignItems: 'center', gap: 4, fontSize: '0.75rem', color: 'var(--text-muted)' }}>
            <FiClock size={11} /> {blog.readTime}
          </span>
        </div>
        <motion.button
          style={{
            background: 'none', border: 'none', cursor: 'pointer',
            display: 'flex', alignItems: 'center', gap: 4,
            fontSize: '0.78rem', fontWeight: 600, color: blog.tagColor,
          }}
          whileHover={{ gap: 8 }}
          onClick={(e) => { e.stopPropagation(); onRead(); }}
        >
          Read <FiArrowRight size={12} />
        </motion.button>
      </div>
    </motion.article>
  )
}

function BlogReadModal({ blog, onClose }) {
  // Parse markdown-like content to simple HTML rendering
  const renderContent = (content) => {
    const lines = content.trim().split('\n')
    const elements = []
    let inCodeBlock = false
    let codeLines = []
    let codeLang = ''
    let inTable = false
    let tableRows = []

    const flushTable = () => {
      if (tableRows.length > 0) {
        const header = tableRows[0]
        const body = tableRows.slice(2)
        elements.push(
          <div key={`table-${elements.length}`} className="blog-table-wrap">
            <table className="blog-table">
              <thead>
                <tr>
                  {header.split('|').filter(c => c.trim()).map((cell, ci) => (
                    <th key={ci}>{cell.trim()}</th>
                  ))}
                </tr>
              </thead>
              <tbody>
                {body.map((row, ri) => (
                  <tr key={ri}>
                    {row.split('|').filter(c => c.trim()).map((cell, ci) => (
                      <td key={ci}>{cell.trim()}</td>
                    ))}
                  </tr>
                ))}
              </tbody>
            </table>
          </div>
        )
        tableRows = []
        inTable = false
      }
    }

    lines.forEach((line, idx) => {
      if (line.startsWith('```')) {
        if (inCodeBlock) {
          elements.push(
            <div key={`code-${idx}`} className="blog-code-block">
              <div className="blog-code-lang">{codeLang || 'code'}</div>
              <pre><code>{codeLines.join('\n')}</code></pre>
            </div>
          )
          codeLines = []
          codeLang = ''
          inCodeBlock = false
        } else {
          if (inTable) flushTable()
          codeLang = line.slice(3).trim()
          inCodeBlock = true
        }
        return
      }

      if (inCodeBlock) {
        codeLines.push(line)
        return
      }

      // Table detection
      if (line.includes('|') && line.trim().startsWith('|')) {
        inTable = true
        tableRows.push(line)
        return
      } else if (inTable) {
        flushTable()
      }

      if (!line.trim()) {
        elements.push(<div key={`br-${idx}`} style={{ height: '0.5rem' }} />)
        return
      }

      if (line.startsWith('## ')) {
        elements.push(<h2 key={idx} className="blog-modal-h2">{line.slice(3)}</h2>)
      } else if (line.startsWith('### ')) {
        elements.push(<h3 key={idx} className="blog-modal-h3">{line.slice(4)}</h3>)
      } else if (line.startsWith('**') && line.endsWith('**') && line.length > 4) {
        elements.push(<p key={idx} className="blog-modal-bold">{line.slice(2, -2)}</p>)
      } else if (line.startsWith('- ') || line.startsWith('✅') || line.startsWith('❌')) {
        const text = line.startsWith('- ') ? line.slice(2) : line
        // Parse inline bold
        const parts = text.split(/(\*\*[^*]+\*\*)/)
        elements.push(
          <div key={idx} className="blog-modal-li">
            <span className="blog-modal-bullet">›</span>
            <span>
              {parts.map((p, pi) =>
                p.startsWith('**') && p.endsWith('**')
                  ? <strong key={pi}>{p.slice(2, -2)}</strong>
                  : p
              )}
            </span>
          </div>
        )
      } else if (/^\d+\. /.test(line)) {
        const num = line.match(/^(\d+)\. /)[1]
        const text = line.replace(/^\d+\. /, '')
        const parts = text.split(/(\*\*[^*]+\*\*)/)
        elements.push(
          <div key={idx} className="blog-modal-li">
            <span className="blog-modal-num">{num}</span>
            <span>
              {parts.map((p, pi) =>
                p.startsWith('**') && p.endsWith('**')
                  ? <strong key={pi}>{p.slice(2, -2)}</strong>
                  : p
              )}
            </span>
          </div>
        )
      } else {
        // Normal paragraph with inline bold parsing
        const parts = line.split(/(\*\*[^*]+\*\*)/)
        elements.push(
          <p key={idx} className="blog-modal-p">
            {parts.map((p, pi) =>
              p.startsWith('**') && p.endsWith('**')
                ? <strong key={pi}>{p.slice(2, -2)}</strong>
                : p
            )}
          </p>
        )
      }
    })

    if (inTable) flushTable()

    return elements
  }

  return (
    <motion.div
      className="blog-modal-overlay"
      initial={{ opacity: 0 }}
      animate={{ opacity: 1 }}
      exit={{ opacity: 0 }}
      transition={{ duration: 0.2 }}
      onClick={onClose}
    >
      <motion.div
        className="blog-modal-container"
        initial={{ opacity: 0, y: 40, scale: 0.97 }}
        animate={{ opacity: 1, y: 0, scale: 1 }}
        exit={{ opacity: 0, y: 40, scale: 0.97 }}
        transition={{ duration: 0.3, ease: [0.22, 1, 0.36, 1] }}
        onClick={e => e.stopPropagation()}
      >
        {/* Modal Header */}
        <div className="blog-modal-header">
          <button className="blog-modal-back" onClick={onClose}>
            <FiArrowLeft size={16} />
            <span>Back</span>
          </button>
          <button className="blog-modal-close" onClick={onClose} aria-label="Close">
            <FiX size={18} />
          </button>
        </div>

        {/* Article Header */}
        <div className="blog-modal-article-header">
          <div style={{ display: 'flex', alignItems: 'center', gap: 12, flexWrap: 'wrap', marginBottom: '1rem' }}>
            <span style={{ fontSize: '2.5rem' }}>{blog.emoji}</span>
            <span style={{
              padding: '4px 14px', borderRadius: 999, fontSize: '0.72rem', fontWeight: 700,
              background: `${blog.tagColor}15`, border: `1px solid ${blog.tagColor}30`,
              color: blog.tagColor, display: 'flex', alignItems: 'center', gap: 6,
            }}>
              <FiTag size={10} /> {blog.tag}
            </span>
          </div>
          <h1 className="blog-modal-title">{blog.title}</h1>
          <div className="blog-modal-meta">
            <span>{blog.date}</span>
            <span className="blog-modal-meta-dot">·</span>
            <FiClock size={12} />
            <span>{blog.readTime}</span>
          </div>
          <div style={{ display: 'flex', flexWrap: 'wrap', gap: 6, marginTop: '1rem' }}>
            {blog.topics.map(t => (
              <span key={t} className="tech-tag" style={{
                fontSize: '0.68rem', background: `${blog.tagColor}10`,
                borderColor: `${blog.tagColor}20`, color: blog.tagColor,
              }}>{t}</span>
            ))}
          </div>
        </div>

        {/* Divider */}
        <div style={{ height: 1, background: 'var(--border-subtle)', margin: '0 2rem 1.5rem' }} />

        {/* Content */}
        <div className="blog-modal-content">
          <div className="blog-modal-body">
            {renderContent(blog.content)}
          </div>
        </div>

        {/* Footer */}
        <div className="blog-modal-footer">
          <button className="blog-modal-close-btn" onClick={onClose}>
            <FiArrowLeft size={14} /> Back to Articles
          </button>
        </div>
      </motion.div>
    </motion.div>
  )
}