Forem: Alex

How I Built 6 AI Products in One Week (And What I Learned)

Alex — Sun, 08 Mar 2026 14:37:15 +0000

One week. Six products. All AI-powered. Some made money. Some flopped. Here's the full breakdown with actual numbers, mistakes, and lessons.

The Products

I built all six under Avatrix LLC using a lean methodology: smallest testable version first, measure for signal, iterate or kill.

1. CoverCraft — AI Cover Letter Generator

What: Web app where you paste a job description and get a tailored cover letter.
Stack: Next.js, Stripe, Claude API.
Price: $1 per letter.
Build time: 3 hours.

web-tool-gray.vercel.app

The build was simple. One form, one API call, one Stripe checkout. The hard part was distribution. Nobody knows your product exists on day one.

2. StructureAI — Data Extraction API

What: API that turns unstructured text into clean JSON. Receipts, emails, resumes, custom schemas.
Stack: Next.js API routes, Claude API, Stripe.
Price: $2 for 100 requests.
Build time: 4 hours.

api-service-wine.vercel.app

This was the most technically interesting. The custom schema feature — where users define their own fields — turned out to be the killer feature. Every use case is slightly different, and custom fields handle them all.

3. StructureAI MCP Server

What: MCP server that connects StructureAI to Claude Desktop and Cursor.
Stack: TypeScript, MCP SDK.
Price: Free (10 requests), paid via API key.
Build time: 2 hours.

github.com/avatrix1/structureai-mcp

The idea: give developers a free taste via MCP, then convert to paid API keys. The MCP ecosystem is growing fast, and being early matters.

4. AI Worker Hub

What: Submit tasks (bug fixes, content, data analysis) and get AI-generated results.
Stack: Next.js, Stripe, Claude API.
Price: $8-15 per task.
Build time: 2 hours.

api-service-wine.vercel.app/worker

This was the "service arbitrage" play: charge human freelancer prices, fulfill with AI. The margins are enormous (97%+). The challenge is convincing people that AI output is worth paying for.

5. Telegram AI Bot

What: Telegram bot offering cover letters, content writing, and more.
Stack: Node.js, Telegram Bot API, Telegram Stars payments.
Price: $0.07-$0.28 per task.
Build time: 3 hours.

@avatrix_ai_bot

Telegram Stars are the lowest-friction payment method I've ever used. No Stripe integration, no credit card forms. Users tap a button and pay. The downside: prices are in Stars, so users don't think in dollars.

6. OpenClaw Skills

What: AI skills for the ClawHub marketplace.
Stack: TypeScript/SKILL.md format.
Price: $7-9 per skill.
Build time: 1 hour.

Built three skills, then discovered ClawHub has a 2-5 day review process. Marketplace timing killed this one for fast revenue. Long-term play.

What I Learned

Lesson 1: Distribution Is Everything

All six products work. The tech is solid. None of that matters without users.

The products with distribution channels (Telegram's built-in user base, GitHub's organic discovery) showed more promise than the standalone web apps. Beautiful landing pages don't generate traffic.

Lesson 2: Build Time Should Be Under 4 Hours

If it takes more than 4 hours to build v1, you're overbuilding. Every product above was deployed in under 4 hours. Some in under 2. The point of v1 is to test demand, not to ship a polished product.

Lesson 3: Payment Integration Is a Solved Problem

Stripe took 30 minutes to integrate. Telegram Stars took 15 minutes. Don't let payment implementation block your launch. It's literally copy-paste at this point.

Here's the entire Stripe checkout in Next.js:

import Stripe from 'stripe';

const stripe = new Stripe(process.env.STRIPE_SECRET_KEY!);

export async function POST(req: Request) {
  const { product, email } = await req.json();

  const session = await stripe.checkout.sessions.create({
    line_items: [{
      price_data: {
        currency: 'usd',
        product_data: { name: product },
        unit_amount: 100, // $1.00
      },
      quantity: 1,
    }],
    mode: 'payment',
    success_url: `${process.env.BASE_URL}/success`,
  });

  return Response.json({ url: session.url });
}

Lesson 4: AI API Costs Are Negligible

Claude Sonnet costs ~$0.003 per request for my use cases. At $1-15 per customer request, that's 97%+ gross margin. The cost of AI is not the constraint. The cost of customer acquisition is.

Lesson 5: Marketplace Timing Matters

ClawHub reviews take 2-5 business days. If you need revenue this week, marketplaces with review queues won't work. Check the listing timeline BEFORE building for any marketplace.

Lesson 6: Service Arbitrage Has Real Potential

Selling AI work at human prices ($8-15) is viable because customers pay for outcomes, not process. They don't care if a human or AI wrote the bug fix. They care that the bug is fixed.

The challenge: building trust that AI output meets quality standards. Social proof, guarantees, and samples help.

The Tech Stack That Moves Fast

After building six products in a week, here's the stack I'd recommend for speed:

Next.js — Full-stack in one framework. API routes, SSR, deploy to Vercel with one command.
Stripe — Payments in 30 minutes. Checkout Sessions for one-time, Subscriptions for recurring.
Claude API — Best quality-to-cost ratio for text generation and extraction.
Vercel — Deploy in 30 seconds. Free tier covers early-stage products.
Tailwind CSS — Ship decent-looking UIs without a designer.

Total cost to run all six products: roughly $20/month (Vercel Pro plan). Everything else is pay-per-use.

What's Next

The products are live. Now comes the hard part: distribution. The plan:

Content marketing (articles like this one)
MCP ecosystem discovery
Service marketplace listings (Fiverr, etc.)
Community engagement (Dev.to, Reddit, HN)

The products that get traction will get investment. The ones that don't will be pivoted or killed. Data decides.

Try Them

Cover letters: CoverCraft — $1/letter
Data extraction: StructureAI API — $2 for 100 requests
MCP server: StructureAI MCP — 10 free requests
AI tasks: Worker Hub — $8-15/task
Telegram: @avatrix_ai_bot — pay with Stars

Build fast. Ship fast. Let data decide what survives.

Built by Avatrix LLC. Building in public, one product at a time.

10 AI Tools Every Developer Should Be Using in 2026

Alex — Sun, 08 Mar 2026 14:32:04 +0000

The AI tool landscape is overwhelming. New products launch daily, most are wrappers around the same models, and half will be dead in six months. Here are the 10 tools that actually save developers time in 2026.

No affiliate links. No sponsored picks. Just tools I use daily.

1. Claude Code (CLI)

What it does: AI-powered coding assistant that runs in your terminal. Reads your codebase, makes edits, runs tests, and commits.

Why it matters: Unlike chat-based AI, Claude Code has context about your entire project. It reads files, understands dependencies, and makes changes across multiple files in one shot.

# Install
npm install -g @anthropic-ai/claude-code

# Use in any project
cd my-project
claude

Best for: Refactoring, debugging, writing tests, understanding unfamiliar codebases.

2. Cursor

What it does: VS Code fork with built-in AI that sees your entire codebase. Tab completion on steroids.

Why it matters: Context-aware completions mean it suggests code that actually fits your project, not generic snippets. The diff view for AI edits is excellent.

Best for: Daily coding if you prefer a GUI over terminal.

3. GitHub Copilot

What it does: Inline code suggestions in your editor as you type.

Why it matters: It's everywhere. Works in VS Code, JetBrains, Neovim. The suggestions are fast and surprisingly accurate for boilerplate code.

Best for: Teams already on GitHub, writing repetitive code patterns.

4. AI Code Review (GitHub Actions)

What it does: Automated code review on every pull request. Catches bugs, security issues, and style violations before human reviewers see it.

Why it matters: Your senior devs spend less time on routine reviews. The AI catches the obvious stuff; humans focus on architecture and logic.

# .github/workflows/ai-review.yml
name: AI Code Review
on:
  pull_request:
    types: [opened, synchronize]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: AI Review
        run: |
          DIFF=$(git diff origin/${{ github.base_ref }}...HEAD | head -c 10000)
          # Send to Claude API, post as PR comment

Best for: Teams of 3+ developers with active PR workflows.

5. StructureAI — Text to JSON API

What it does: Extracts structured data from any unstructured text. Receipts, emails, resumes, invoices — one API call returns clean JSON.

Why it matters: Every app eventually needs to parse messy text. Regex breaks. AI doesn't.

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"text": "Invoice #123 from Acme Corp, $5,000 due March 15", "schema": "invoice"}'

Best for: Apps that process user-submitted text, document parsing, data pipelines. $2 for 100 requests.

6. MCP Servers

What it does: Connects AI assistants to your tools. Database queries, API calls, file operations — all through natural language.

Why it matters: Stops the copy-paste loop between AI chat and your actual tools. The AI calls your tools directly.

Best for: Power users of Claude Desktop or Cursor. Start with the StructureAI MCP server for free data extraction.

7. V0 by Vercel

What it does: Generates React/Next.js UI components from natural language descriptions.

Why it matters: Frontend prototyping goes from hours to minutes. Describe what you want, get a working component with Tailwind styling.

Best for: Rapid prototyping, landing pages, admin dashboards.

8. Warp Terminal

What it does: Terminal with built-in AI for command suggestions, error explanations, and workflow automation.

Why it matters: You never have to Google a bash command again. Describe what you want, get the command. The AI reads your terminal history for context.

Best for: Developers who live in the terminal and work with unfamiliar tools.

9. AI Commit Message Hooks

What it does: Git hooks that auto-generate commit messages from your diff. Every commit follows conventional commit format.

Why it matters: Your git log finally tells a coherent story. Changelogs generate automatically. Semantic versioning becomes trivial.

# .git/hooks/prepare-commit-msg
DIFF=$(git diff --cached | head -c 5000)
# Send to Claude API, write to commit message file

Best for: Anyone tired of writing "fixed stuff" in commit messages.

10. AI-Powered Testing

What it does: Generates unit tests, integration tests, and edge cases from your source code.

Why it matters: Test coverage goes from aspirational to automatic. The AI reads your function, identifies edge cases you missed, and writes tests.

# With Claude Code
claude "Write unit tests for src/auth/login.ts. Cover happy path, invalid credentials, rate limiting, and token expiration."

Best for: Increasing test coverage without the grind.

The Pattern

Notice what these tools have in common: they handle the boring parts of development. Boilerplate, documentation, reviews, formatting, data parsing. None of them replace thinking about architecture, user experience, or business logic.

Use AI for the 80% that's routine. Spend your brain on the 20% that matters.

Getting Started

Don't install all 10 at once. Pick the one that solves your biggest time sink today.

Spending too much time on code reviews? Set up AI review on GitHub Actions.
Parsing messy data? Try StructureAI.
Writing commit messages? Set up the Git hook.
Debugging unfamiliar code? Install Claude Code.

One tool at a time. Measure the time saved. Then add the next one.

Built by Avatrix LLC. We build AI developer tools. Check out StructureAI and our MCP server.

Turn Unstructured Text into Clean Data with One API Call

Alex — Sun, 08 Mar 2026 14:26:53 +0000

Every app eventually needs to parse unstructured text. A user pastes an address. A customer uploads a receipt photo (OCR'd to text). An email comes in and you need the sender, date, and action items.

You can write regex. You'll spend 3 hours on edge cases. Then a new format appears and it all breaks.

Or you can make one API call and get clean JSON.

The API

StructureAI takes any text and returns structured data. One endpoint, any schema.

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Your unstructured text here",
    "schema": "receipt"
  }'

That's the entire API. Text in, JSON out.

Real-World Use Cases

Parsing Customer Support Emails

Your support inbox gets messages like:

Hi, I ordered the blue widget (order #4521) last Tuesday and it arrived damaged.
The box was crushed and the widget has a crack on the left side. I'd like a
replacement or refund. My account email is sarah@example.com. Thanks, Sarah

Extract it:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Hi, I ordered the blue widget (order #4521) last Tuesday...",
    "schema": "custom",
    "custom_fields": ["order_number", "issue_type", "product", "damage_description", "requested_action", "customer_email", "customer_name", "urgency"]
  }'

Returns:

{
  "order_number": "4521",
  "issue_type": "damaged_product",
  "product": "blue widget",
  "damage_description": "box crushed, crack on left side",
  "requested_action": "replacement or refund",
  "customer_email": "sarah@example.com",
  "customer_name": "Sarah",
  "urgency": "medium"
}

Feed this into your ticketing system. Auto-route based on issue type. Auto-link to the order. No human triaging needed.

Processing Invoices

INVOICE #INV-2026-0342
From: Acme Corp, 123 Business Ave, Suite 400, Austin TX 78701
To: Your Company LLC
Date: March 1, 2026
Due: March 31, 2026

Web Development Services - February 2026
  Frontend development: 40 hours @ $150/hr     $6,000
  Backend API work: 24 hours @ $175/hr          $4,200
  Code review and QA: 8 hours @ $125/hr         $1,000

Subtotal: $11,200
Tax (8.25%): $924
Total Due: $12,124

Payment: Wire transfer to Acme Corp, Chase Bank, Account ending 8891

Extract with schema invoice:

{
  "invoice_number": "INV-2026-0342",
  "vendor": "Acme Corp",
  "vendor_address": "123 Business Ave, Suite 400, Austin TX 78701",
  "client": "Your Company LLC",
  "date": "2026-03-01",
  "due_date": "2026-03-31",
  "line_items": [
    {"description": "Frontend development", "hours": 40, "rate": 150, "amount": 6000},
    {"description": "Backend API work", "hours": 24, "rate": 175, "amount": 4200},
    {"description": "Code review and QA", "hours": 8, "rate": 125, "amount": 1000}
  ],
  "subtotal": 11200,
  "tax": 924,
  "total": 12124,
  "payment_method": "Wire transfer, Chase Bank, account ending 8891"
}

This replaces manual data entry for accounting teams.

Extracting Contact Info

Users paste contact information in every possible format:

Dr. Maria Chen, PhD
Chief Technology Officer
Quantum Computing Lab, MIT
mchen@mit.edu | (617) 555-0199
LinkedIn: linkedin.com/in/mariachen

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Dr. Maria Chen, PhD\nChief Technology Officer...",
    "schema": "contact"
  }'

Returns normalized, structured contact data every time, regardless of the input format.

Integration Examples

Node.js / TypeScript

import { StructureAI } from './structureai';

const client = new StructureAI(process.env.STRUCTUREAI_KEY!);

// In your API route
app.post('/api/parse-receipt', async (req, res) => {
  const { receiptText } = req.body;

  const data = await client.extract(receiptText, 'receipt');

  // Save structured data to database
  await db.receipts.create({
    merchant: data.merchant,
    total: data.total,
    date: new Date(data.date),
    items: data.items,
  });

  res.json({ success: true, data });
});

A simple wrapper class:

class StructureAI {
  constructor(private apiKey: string) {}

  async extract(text: string, schema: string, customFields?: string[]) {
    const response = await fetch(
      'https://api-service-wine.vercel.app/api/extract',
      {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
          'X-API-Key': this.apiKey,
        },
        body: JSON.stringify({
          text,
          schema,
          ...(customFields && { custom_fields: customFields }),
        }),
      }
    );
    const result = await response.json();
    return result.data;
  }
}

Python

import requests

class StructureAI:
    BASE_URL = "https://api-service-wine.vercel.app/api/extract"

    def __init__(self, api_key):
        self.api_key = api_key

    def extract(self, text, schema="custom", fields=None):
        payload = {"text": text, "schema": schema}
        if fields:
            payload["custom_fields"] = fields

        response = requests.post(
            self.BASE_URL,
            json=payload,
            headers={"X-API-Key": self.api_key},
        )
        return response.json()["data"]

# Usage
ai = StructureAI("your-key")
receipt = ai.extract(receipt_text, "receipt")
print(f"Total: ${receipt['total']}")

Pricing and Limits

$2 per API key — includes 100 requests
No subscriptions, no monthly fees
Keys never expire
Buy as many as you need

For high-volume users, contact us for bulk pricing.

Free Option: MCP Server

If you use Claude Desktop or Cursor, install the StructureAI MCP server for 10 free requests. No API key needed.

git clone https://github.com/avatrix1/structureai-mcp.git
cd structureai-mcp && npm install && npm run build

When to Use This vs. Building Your Own

Build your own when:

You need to process millions of documents (API costs add up)
Your schema never changes (regex might be fine)
You need sub-100ms latency (AI adds processing time)

Use StructureAI when:

Your input formats vary
You need to support new schemas quickly
You want to skip weeks of regex debugging
You're processing hundreds, not millions, of documents

For most startups and indie developers, the API approach saves weeks of development time and handles edge cases that would take months to cover with regex.

Get your API key at api-service-wine.vercel.app.

Built by Avatrix LLC. Questions? support@avatrix.co

Outsource Coding Tasks to AI for $8-$15

Alex — Sun, 08 Mar 2026 14:21:42 +0000

Hiring a freelancer for a bug fix costs $50-200. It takes 2 days to find someone, 1 day to explain the problem, and 1-3 days for the fix. For a routine bug, that's overkill.

What if you could submit a task, pay $8-15, and get results in hours? That's what AI worker marketplaces do.

How It Works

You describe the task (bug fix, content piece, data analysis)
You pay a flat fee ($8-$15 depending on task type)
An AI worker processes the task
You get the result delivered to your email or dashboard

No hiring. No interviews. No back-and-forth. No hourly billing surprises.

What Tasks Work Best

Bug Fixes ($15)

Submit a bug description with context:

Bug: Login form shows "undefined" error after submit
Stack: Next.js 14, NextAuth, Prisma
File: app/api/auth/[...nextauth]/route.ts
Error: TypeError: Cannot read properties of undefined (reading 'email')
Steps to reproduce: Click login with Google, redirects back, form shows error

The AI analyzes the error, identifies the root cause, and provides a fix with explanation. For standard bugs (null references, async/await issues, type errors), AI matches junior developer quality.

Content Creation ($10)

Need: Technical blog post about setting up CI/CD with GitHub Actions
Length: 1000-1500 words
Audience: Junior developers
Include: Code examples, common pitfalls, best practices

AI produces well-structured technical content. It won't write thought leadership, but for tutorials and how-to guides, it's indistinguishable from human-written content.

Data Analysis ($8)

Task: Analyze this CSV of 10,000 customer records
Find: Top 10 customers by revenue, churn rate by month, average order value trend
Format: Summary with charts descriptions and key insights

Data analysis is where AI excels. It processes large datasets without fatigue and catches patterns humans miss.

Try It: AI Worker Hub

We built an AI Worker Hub that handles these three task types. Here's how to submit a task programmatically:

curl -X POST https://api-service-wine.vercel.app/api/worker-checkout \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "bug_fix",
    "description": "Login form shows undefined error after OAuth redirect",
    "context": "Next.js 14, NextAuth v5, Prisma with PostgreSQL",
    "email": "your@email.com"
  }'

You'll receive a Stripe checkout link. After payment, the task enters the queue and results are delivered via email.

When AI Workers Fall Short

Be realistic about what AI can and can't do.

AI works well for:

Routine bug fixes with clear error messages
Standard CRUD implementations
Documentation and technical writing
Data formatting and transformation
Code refactoring (rename variables, extract functions)
Unit test generation

AI struggles with:

Architecture decisions
Complex multi-file refactors
Performance optimization requiring profiling
Bugs that need environment reproduction
UI/UX design
Anything requiring domain expertise

Cost Comparison

Task	Freelancer	AI Worker	Savings
Bug fix	$50-200	$15	70-92%
Blog post (1000 words)	$100-300	$10	90-97%
Data analysis	$75-250	$8	89-97%
Code review	$50-100	$10	80-90%

The math is clear for routine tasks. For complex work, you still want a human.

Building Your Own AI Worker Pipeline

Want to offer AI worker services yourself? Here's the stack:

// 1. Receive task via API
app.post('/api/submit-task', async (req, res) => {
  const { task_type, description, context, email } = req.body;

  // 2. Create Stripe checkout
  const session = await stripe.checkout.sessions.create({
    line_items: [{
      price: PRICES[task_type], // $8, $10, or $15
      quantity: 1,
    }],
    mode: 'payment',
    success_url: `${BASE_URL}/success`,
    metadata: { task_type, description, context, email },
  });

  res.json({ checkout_url: session.url });
});

// 3. Process after payment (webhook)
app.post('/api/webhook', async (req, res) => {
  const event = stripe.webhooks.constructEvent(/* ... */);

  if (event.type === 'checkout.session.completed') {
    const { task_type, description, context, email } =
      event.data.object.metadata;

    // 4. Send to AI
    const result = await processWithAI(task_type, description, context);

    // 5. Deliver result
    await sendEmail(email, result);
  }
});

The entire pipeline is automated. No manual intervention needed.

Combining with Structured Data

For data analysis tasks, pair the AI worker with StructureAI to extract structured insights:

// Extract key metrics from raw analysis
const metrics = await fetch('https://api-service-wine.vercel.app/api/extract', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': API_KEY,
  },
  body: JSON.stringify({
    text: aiAnalysisResult,
    schema: 'custom',
    custom_fields: [
      'key_metrics',
      'trends',
      'anomalies',
      'recommendations',
    ],
  }),
});

This gives the customer both a narrative analysis and structured data they can import into their tools.

The Future of Work

AI workers won't replace developers. They'll replace the boring parts of development. The bug that takes 4 hours to context-switch into and 5 minutes to fix. The documentation nobody wants to write. The data cleanup nobody has time for.

Delegate the routine. Focus on the interesting problems. That's the value proposition.

Try the AI Worker Hub for your next routine task. Or build your own pipeline using the code above.

Built by Avatrix LLC. AI-powered task completion at api-service-wine.vercel.app/worker.

What Are MCP Servers and Why Every Developer Needs Them

Alex — Sun, 08 Mar 2026 13:25:23 +0000

You're using Claude, Cursor, or another AI assistant. You ask it to check your database. It says "I can't access external systems." You copy data out, paste it in, get an answer, paste it back. Repeat 50 times a day.

MCP servers fix this. They let AI assistants call your tools directly.

What Is MCP?

MCP stands for Model Context Protocol. It's an open standard that connects AI assistants to external tools. Think of it like USB for AI — a universal way to plug capabilities into any AI that supports the protocol.

Without MCP: You copy data from Tool A, paste into AI, copy the response, paste into Tool B.

With MCP: You tell the AI "extract data from this receipt" and it calls your extraction tool automatically, returns structured results, and moves on.

How It Works

An MCP server is a small program that:

Declares what tools it offers (name, description, parameters)
Listens for requests from an AI client
Executes the tool and returns results

The AI client (Claude Desktop, Cursor, etc.) discovers available tools at startup and calls them when relevant.

[AI Client] <--stdin/stdout--> [MCP Server] <--HTTP/DB/etc--> [Your Service]

Communication happens over stdin/stdout. The server runs locally on your machine. No cloud, no API keys for the connection itself.

A Concrete Example

Say you have an API that extracts structured data from text. Without MCP:

1. Copy receipt text
2. Open browser
3. Go to API playground
4. Paste text
5. Click "Extract"
6. Copy JSON result
7. Paste into your code

With MCP:

You: "Extract the receipt data from this text: Whole Foods $14.95..."
Claude: [calls extract_data tool automatically]
Claude: "Here's the structured data: {merchant: 'Whole Foods', total: 14.95, ...}"

Seven steps become one sentence.

Setting Up Your First MCP Server

Install a Pre-Built Server

The fastest way to start is installing an existing MCP server. For structured data extraction, you can use StructureAI MCP:

git clone https://github.com/avatrix1/structureai-mcp.git
cd structureai-mcp
npm install
npm run build

Connect to Claude Desktop

Add it to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "structureai": {
      "command": "node",
      "args": ["/path/to/structureai-mcp/dist/index.js"]
    }
  }
}

Restart Claude Desktop. The tools appear in the toolbox icon.

Connect to Cursor

Cursor uses the same config format. Add the server in Cursor's MCP settings panel.

Building Your Own MCP Server

It takes about 20 minutes. You need three things:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

// 1. Create the server
const server = new McpServer({
  name: "my-tool",
  version: "1.0.0",
});

// 2. Define tools
server.tool(
  "search_database",
  "Search the product database by name or category",
  {
    query: z.string().describe("Search query"),
    category: z.string().optional().describe("Filter by category"),
  },
  async ({ query, category }) => {
    const results = await db.search(query, category);
    return {
      content: [{
        type: "text" as const,
        text: JSON.stringify(results, null, 2),
      }],
    };
  }
);

// 3. Connect transport
const transport = new StdioServerTransport();
await server.connect(transport);

That's a complete MCP server. The AI can now search your database by asking naturally.

What Makes a Good MCP Tool

Good Tool Design

// GOOD — specific name, clear description, typed parameters
server.tool(
  "get_customer_orders",
  "Retrieve orders for a specific customer by email or customer ID",
  {
    customer_id: z.string().optional(),
    email: z.string().email().optional(),
    limit: z.number().default(10),
  },
  handler
);

Bad Tool Design

// BAD — vague name, no description, no parameter descriptions
server.tool("query", "Run a query", { q: z.string() }, handler);

The AI reads tool names and descriptions to decide which tool to use. Vague descriptions mean the AI picks the wrong tool or doesn't use it at all.

MCP Servers You Should Know About

The ecosystem is growing fast. Here are the most useful categories:

Data & APIs

Database servers (Postgres, SQLite, MongoDB)
API wrappers (GitHub, Jira, Linear, Slack)
Data extraction (StructureAI)

Developer Tools

File system access
Git operations
Docker management
AWS/GCP/Azure CLIs

Productivity

Calendar integration
Email management
Note-taking apps

Why This Matters

MCP turns AI assistants from chat interfaces into actual tools. Instead of describing what you want and manually executing, you describe what you want and the AI executes.

The developers who adopt MCP early will have a significant productivity advantage. The ones who don't will keep copy-pasting between tabs.

Getting Started Today

Pick one repetitive task you do daily
Find or build an MCP server for it
Connect it to your AI assistant
Watch the copy-paste disappear

Start with something small. A database query tool. A deployment script wrapper. A log searcher. Once you see how much time it saves, you'll want MCP servers for everything.

The StructureAI MCP server is a good first install — 10 free requests, no API key needed, and it handles a common task (text-to-JSON extraction) that every developer needs.

Built by Avatrix LLC. Full MCP server tutorial in our previous article.

The Ultimate Guide to JSON Formatting and Validation

Alex — Sun, 08 Mar 2026 11:06:22 +0000

JSON is everywhere. APIs, config files, databases, logs. Yet developers still waste hours debugging malformed JSON. Missing commas, trailing commas, single quotes instead of double quotes — these tiny errors break everything.

Here's everything you need to know about JSON, from basics to advanced tooling.

JSON Basics Everyone Gets Wrong

Trailing Commas Are Invalid

// WRONG — trailing comma after "blue"
{
  "colors": ["red", "green", "blue",]
}

// CORRECT
{
  "colors": ["red", "green", "blue"]
}

JavaScript allows trailing commas. JSON does not. This catches everyone at least once.

Only Double Quotes

// WRONG — single quotes
{'name': 'John'}

// CORRECT — double quotes only
{"name": "John"}

No Comments

// WRONG — JSON has no comments
{
  "port": 3000 // development port
}

// CORRECT — use a separate field or remove
{
  "port": 3000,
  "_port_note": "development port"
}

If you need comments, use JSONC (JSON with Comments) or JSON5. VS Code supports JSONC for settings files.

Numbers Don't Have Quotes

// WRONG
{"age": "25", "price": "9.99"}

// CORRECT — numbers are unquoted
{"age": 25, "price": 9.99}

This matters for APIs. If your backend expects a number and gets a string, things break silently.

Command-Line JSON Tools

jq — The JSON Swiss Army Knife

# Pretty print
echo '{"name":"John","age":30}' | jq '.'

# Extract a field
echo '{"user":{"name":"John","email":"j@test.com"}}' | jq '.user.email'
# Output: "j@test.com"

# Filter arrays
echo '[{"name":"a","active":true},{"name":"b","active":false}]' | \
  jq '.[] | select(.active == true)'

# Transform
echo '{"first":"John","last":"Doe"}' | \
  jq '{full_name: (.first + " " + .last)}'

python -m json.tool

Already installed on most systems:

echo '{"compact":"json"}' | python -m json.tool

Node.js one-liner

echo '{"a":1}' | node -e "process.stdin.on('data',d=>console.log(JSON.stringify(JSON.parse(d),null,2)))"

Validating JSON

In the Terminal

# jq returns exit code 1 for invalid JSON
echo '{"broken":}' | jq '.' 2>/dev/null || echo "Invalid JSON"

# Python approach
echo '{"test": true}' | python -c "import sys,json; json.load(sys.stdin); print('Valid')"

In Code

function isValidJSON(str: string): boolean {
  try {
    JSON.parse(str);
    return true;
  } catch {
    return false;
  }
}

With Schema Validation (Zod)

import { z } from 'zod';

const UserSchema = z.object({
  name: z.string(),
  email: z.string().email(),
  age: z.number().min(0).max(150),
});

// Throws descriptive error if invalid
const user = UserSchema.parse(JSON.parse(jsonString));

Zod gives you TypeScript types AND runtime validation from the same schema.

Common JSON Mistakes in APIs

Mistake 1: Inconsistent Naming

// Don't mix conventions
{
  "firstName": "John",
  "last_name": "Doe",
  "Email-Address": "john@test.com"
}

// Pick one and stick with it
{
  "first_name": "John",
  "last_name": "Doe",
  "email_address": "john@test.com"
}

Mistake 2: Dates as Random Strings

// Ambiguous — is this March 7 or July 3?
{"date": "3/7/2026"}

// Use ISO 8601 — always
{"date": "2026-03-07T00:00:00Z"}

Mistake 3: Null vs. Missing vs. Empty String

// These all mean different things
{"name": null}      // explicitly no value
{"name": ""}        // empty value
{}                  // field doesn't exist

Document which one your API uses. Inconsistency here causes bugs that are painful to debug.

Extracting JSON from Unstructured Text

Sometimes you have data trapped in plain text — receipts, emails, logs — and you need it as JSON. Regex works for simple patterns but breaks on real-world messy text.

StructureAI does this with one API call:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Meeting with Sarah tomorrow at 3pm in Conference Room B to discuss Q1 budget. Bring the revenue projections.",
    "schema": "custom",
    "custom_fields": ["participants", "datetime", "location", "topic", "action_items"]
  }'

Returns:

{
  "participants": ["Sarah"],
  "datetime": "tomorrow at 3pm",
  "location": "Conference Room B",
  "topic": "Q1 budget discussion",
  "action_items": ["Bring revenue projections"]
}

No regex. No parsing. Just clean JSON from messy text.

JSON Performance Tips

Streaming Large Files

Don't load a 2GB JSON file into memory:

# Stream with jq
cat huge.json | jq -c '.items[]' | while read -r item; do
  echo "$item" | jq '.id'
done

Minify for Production

# Remove whitespace
cat config.json | jq -c '.' > config.min.json

NDJSON for Log Processing

Newline-delimited JSON (one object per line) is easier to process than arrays:

{"event":"login","user":"john","ts":"2026-03-07T10:00:00Z"}
{"event":"purchase","user":"john","amount":9.99,"ts":"2026-03-07T10:05:00Z"}

Process with:

cat events.ndjson | jq -c 'select(.event == "purchase") | .amount'

Quick Reference

Need	Tool/Command
Pretty print	`jq '.'`
Validate	`jq '.' > /dev/null`
Extract field	`jq '.field'`
Filter array	`jq '.[] \
Minify	{% raw %}`jq -c '.'`
Schema validate	Zod, Joi, Ajv
Text to JSON	StructureAI API

Master these tools and you'll never waste time on JSON formatting again.

Built by Avatrix LLC. Extract structured JSON from any text with StructureAI — $2 for 100 requests.

How I Use AI to Generate Tailored Cover Letters in Seconds

Alex — Sun, 08 Mar 2026 08:14:07 +0000

I applied to 47 jobs last year. Each one needed a tailored cover letter. At 30 minutes per letter, that's 23 hours of writing "I am excited to apply for the position of..."

Now I generate them in 10 seconds. Here's how.

Why Generic Templates Don't Work

Hiring managers read hundreds of applications. They can spot a template in the first sentence. "I was thrilled to see your opening for..." — that's a template. Every applicant sends it.

What works: specific references to the company's problems, matched with your exact experience. That takes research and writing time. Or it takes AI that does both instantly.

The Technique

The secret is giving the AI three things:

The full job description (copy-paste from the listing)
Your background (3-5 bullet points of relevant experience)
Constraints (length, tone, specific things to mention)

Write a cover letter for this job. 3 paragraphs max.
Match my experience to their requirements.
Mention one specific thing about the company that shows I researched them.
Professional but conversational tone — no corporate buzzwords.

Job Description:
[paste the full listing here]

My Background:
- 5 years full-stack development (React, Node.js, Python)
- Led migration of legacy PHP app to microservices at Company X
- Built real-time analytics dashboard processing 2M events/day
- Open source contributor to React Query

What You Get

The AI produces something like:

The data pipeline challenges you describe in this role immediately caught my attention. At Company X, I led a similar migration from a monolithic PHP application to event-driven microservices, reducing processing latency from 12 seconds to under 200ms.

Your requirement for real-time analytics experience aligns directly with my work building a dashboard that processes 2 million events daily. I designed the ingestion layer using Node.js streams and the visualization layer in React — both technologies in your stack. My contributions to React Query also gave me deep understanding of data fetching patterns at scale.

I'd welcome the chance to discuss how my experience with high-throughput systems could help your team hit the performance targets outlined in the role. I'm available for a conversation at your convenience.

Specific. Relevant. No buzzwords. Written in 10 seconds.

Automating It Further

Option 1: Command Line

#!/bin/bash
# scripts/cover-letter.sh

JOB_DESC=$(cat "$1")  # Pass job description file as argument

curl -s https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d "{
    \"model\": \"claude-sonnet-4-20250514\",
    \"max_tokens\": 1024,
    \"messages\": [{
      \"role\": \"user\",
      \"content\": \"Write a 3-paragraph cover letter matching my background to this job. Professional but human tone.\\n\\nMy background: [your bullets here]\\n\\nJob:\\n$JOB_DESC\"
    }]
  }" | jq -r '.content[0].text'

Usage: ./scripts/cover-letter.sh job-posting.txt

Option 2: Web App

If you want a visual interface, CoverCraft does exactly this. Paste the job description, get a tailored letter. $1 per letter, no account needed.

Option 3: Telegram

For mobile, use @avatrix_ai_bot on Telegram. Type /cover, paste the job description, get a letter back. Costs about $0.14 in Telegram Stars.

Tips for Better Results

Be Specific in Your Background

Bad: "Experienced software engineer"
Good: "Built real-time analytics processing 2M events/day at Netflix"

The more specific your input, the more specific the output. Numbers, company names, and concrete achievements produce the best letters.

Include Company Research

Add a line about the company:

The company recently raised Series B and is expanding into Europe.
They use a Python/Django backend with React frontend.

The AI will weave this context into the letter naturally.

Set Constraints

- Max 250 words
- Don't mention salary expectations
- Emphasize leadership experience
- Mention willingness to relocate

Constraints focus the output. Without them, the AI writes generic filler.

Extracting Job Requirements

If the job posting is long and messy, extract the key requirements first. You can do this with StructureAI:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "[paste job description]",
    "schema": "custom",
    "custom_fields": ["required_skills", "nice_to_have", "years_experience", "key_responsibilities", "company_info"]
  }'

This gives you structured data you can match against your experience. Then feed both into the cover letter prompt for a perfect match.

The Numbers

Since switching to AI-generated cover letters:

Time per application: 30 minutes down to 2 minutes
Application volume: 47/year up to 200+
Response rate: roughly the same (8-12%)
Interview rate per application: unchanged
Total interviews: 3x more (because volume went up)

The game is volume with quality. AI lets you maintain quality while dramatically increasing volume.

Start Now

Save your background as a text file (update it once)
Use any of the three methods above
Always review the output before sending
Customize the opening line for high-priority applications

Stop spending 30 minutes on each cover letter. The hiring manager spends 30 seconds reading it. Match their effort.

Built by Avatrix LLC. Try CoverCraft for instant AI cover letters — $1 per letter.

I Built a Telegram Bot That Writes Cover Letters for $0.14

Alex — Sun, 08 Mar 2026 06:35:20 +0000

Job applications are tedious. You find a great role, then spend 30 minutes writing a cover letter that probably won't get read. I built a Telegram bot that does it in 10 seconds for $0.14.

Here's exactly how it works and how you can build something similar.

The User Flow

User opens @avatrix_ai_bot on Telegram
Types /cover and pastes a job description
Bot asks for their resume highlights
User pays with Telegram Stars (roughly $0.14)
Bot generates a tailored cover letter in 10 seconds
User copies it and applies

No signups. No credit cards. No friction.

Tech Stack

Runtime: Node.js with node-telegram-bot-api
AI: Claude API for letter generation
Payments: Telegram Stars (built-in, no Stripe needed)
Hosting: PM2 on a server (could use Railway or Fly.io)

Step 1: Set Up the Bot

mkdir telegram-bot && cd telegram-bot
npm init -y
npm install node-telegram-bot-api

Create your bot with @BotFather on Telegram. Get the token.

// src/index.ts
import TelegramBot from 'node-telegram-bot-api';

const bot = new TelegramBot(process.env.TELEGRAM_BOT_TOKEN!, {
  polling: true,
});

bot.onText(/\/start/, (msg) => {
  bot.sendMessage(msg.chat.id,
    'I write tailored cover letters using AI.\n\n' +
    'Commands:\n' +
    '/cover - Generate a cover letter\n' +
    '/help - How it works'
  );
});

Step 2: Handle the Cover Letter Flow

The conversation has three steps: get the job description, get the user's background, generate the letter.

const sessions = new Map<number, { step: string; jobDesc?: string }>();

bot.onText(/\/cover/, (msg) => {
  const chatId = msg.chat.id;
  sessions.set(chatId, { step: 'awaiting_job' });
  bot.sendMessage(chatId, 'Paste the job description:');
});

bot.on('message', async (msg) => {
  const chatId = msg.chat.id;
  const session = sessions.get(chatId);

  if (!session) return;

  if (session.step === 'awaiting_job') {
    session.jobDesc = msg.text;
    session.step = 'awaiting_resume';
    bot.sendMessage(chatId,
      'Now paste your key skills and experience (a few bullet points is fine):'
    );
  } else if (session.step === 'awaiting_resume') {
    // Trigger payment before generating
    await requestPayment(chatId, session.jobDesc!, msg.text!);
  }
});

Step 3: Telegram Stars Payment

Telegram Stars let users pay without leaving the app. No Stripe integration needed.

async function requestPayment(
  chatId: number,
  jobDesc: string,
  resume: string
) {
  await bot.sendInvoice(chatId, 'Cover Letter', 'AI-generated tailored cover letter', 'cover_letter', '', 'XTR', [
    { label: 'Cover Letter', amount: 1 }, // 1 Star ≈ $0.014
  ]);

  // Store data for after payment
  sessions.set(chatId, {
    step: 'awaiting_payment',
    jobDesc,
    resume,
  });
}

bot.on('pre_checkout_query', (query) => {
  bot.answerPreCheckoutQuery(query.id, true);
});

bot.on('successful_payment', async (msg) => {
  const chatId = msg.chat.id;
  const session = sessions.get(chatId);
  if (!session) return;

  bot.sendMessage(chatId, 'Payment received! Generating your cover letter...');

  const letter = await generateCoverLetter(
    session.jobDesc!,
    session.resume!
  );

  bot.sendMessage(chatId, letter);
  sessions.delete(chatId);
});

Step 4: AI Generation

async function generateCoverLetter(
  jobDesc: string,
  resume: string
): Promise<string> {
  const response = await fetch('https://api.anthropic.com/v1/messages', {
    method: 'POST',
    headers: {
      'content-type': 'application/json',
      'x-api-key': process.env.ANTHROPIC_API_KEY!,
      'anthropic-version': '2023-06-01',
    },
    body: JSON.stringify({
      model: 'claude-sonnet-4-20250514',
      max_tokens: 1024,
      messages: [{
        role: 'user',
        content: `Write a professional cover letter for this job:

Job Description:
${jobDesc}

Candidate Background:
${resume}

Rules:
- 3-4 paragraphs
- Match skills to job requirements
- Professional but not stuffy
- Include a specific detail from the job description
- End with a clear call to action`,
      }],
    }),
  });

  const result = await response.json();
  return result.content[0].text;
}

Step 5: Deploy

npm install -g pm2
pm2 start dist/index.js --name "cover-bot"
pm2 save
pm2 startup

The bot runs 24/7 and restarts automatically if it crashes.

Economics

Each cover letter costs about $0.003 in AI API fees. Users pay 10 Stars ($0.14). That's a 97% margin per request.

At 10 letters/day, that's $1.40 revenue and $0.03 in costs. Not life-changing, but it compounds. And the bot runs itself — no customer support, no manual work.

Expanding the Bot

Once the cover letter flow works, add more services:

bot.onText(/\/services/, (msg) => {
  bot.sendMessage(msg.chat.id,
    'Available services:\n\n' +
    '/cover - Cover letter ($0.14)\n' +
    '/resume - Resume review ($0.28)\n' +
    '/linkedin - LinkedIn summary ($0.14)\n' +
    '/email - Professional email ($0.07)'
  );
});

Each service follows the same pattern: collect input, charge Stars, generate with AI.

Why Telegram?

Telegram has 900M+ monthly users. Payments are built in. No app store approval. No website needed. Users can find your bot through search or shared links.

The friction is near zero: tap a link, tap a button, paste text, pay with Stars. No account creation, no credit card forms, no page loads.

Try It

The live bot is at @avatrix_ai_bot. Try the /cover command to see the full flow in action. Or build your own using the steps above.

Built by Avatrix LLC. Also available as a web app at CoverCraft.

Extract JSON from Any Text in One Command

Alex — Sun, 08 Mar 2026 06:24:05 +0000

Regex is fragile. Every new text format breaks your parser. You spend more time maintaining extraction logic than building features.

There's a better way: send the text to an API, get clean JSON back. One command. Any format.

The One-Liner

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "John Smith, john@example.com, +1-555-0123, Software Engineer at Google",
    "schema": "contact"
  }'

Response:

{
  "data": {
    "name": "John Smith",
    "email": "john@example.com",
    "phone": "+1-555-0123",
    "title": "Software Engineer",
    "company": "Google"
  },
  "confidence": 0.98
}

No regex. No parsing logic. No maintenance.

Built-in Schemas

The API comes with schemas for common data types:

Receipts

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Target Store #1234 03/07/2026\nBananas $1.29\nMilk 2% $4.99\nEggs Large $3.49\nSubtotal $9.77\nTax $0.83\nTotal $10.60\nVisa ***4242",
    "schema": "receipt"
  }'

Returns items, totals, tax, merchant, date, and payment method — all structured.

Resumes

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Jane Doe\njane.doe@email.com\n\nExperience:\nSenior Developer at Netflix (2022-present)\n- Led migration from REST to GraphQL\n- Reduced API latency by 40%\n\nFrontend Developer at Uber (2019-2022)\n- Built driver dashboard in React\n\nSkills: TypeScript, React, GraphQL, Node.js, AWS",
    "schema": "resume"
  }'

Returns structured experience, skills, education, and contact info.

Emails

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "From: alice@company.com\nTo: bob@company.com\nDate: March 7, 2026\nSubject: Q1 Budget Review\n\nHi Bob, attached is the Q1 budget. We are 15% over on marketing spend. Can we discuss Thursday at 2pm? - Alice",
    "schema": "email"
  }'

Extracts sender, recipient, date, subject, action items, and key metrics.

Custom Schemas

Need to extract something specific? Define your own fields:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "Property at 123 Oak St, Austin TX. 3 bed 2 bath, 1,850 sqft. Built 1995. Listed at $425,000. HOA $250/month. Central AC, hardwood floors, updated kitchen.",
    "schema": "custom",
    "custom_fields": ["address", "bedrooms", "bathrooms", "sqft", "year_built", "price", "hoa_fee", "features"]
  }'

Returns:

{
  "address": "123 Oak St, Austin, TX",
  "bedrooms": 3,
  "bathrooms": 2,
  "sqft": 1850,
  "year_built": 1995,
  "price": 425000,
  "hoa_fee": 250,
  "features": ["Central AC", "hardwood floors", "updated kitchen"]
}

The API figures out the types automatically. Numbers come back as numbers, arrays as arrays.

Building a CLI Wrapper

For frequent use, wrap it in a shell function. Add to your .bashrc or .zshrc:

extract() {
  local schema=${2:-custom}
  local text="$1"

  curl -s -X POST https://api-service-wine.vercel.app/api/extract \
    -H "Content-Type: application/json" \
    -H "X-API-Key: $STRUCTUREAI_KEY" \
    -d "{
      \"text\": \"$text\",
      \"schema\": \"$schema\"
    }" | jq '.data'
}

Now you can run:

extract "John Smith, john@example.com, Engineer at Google" contact
extract "Bananas $1.29, Milk $4.99, Total $6.28" receipt

Piping Data

The real power is in pipelines. Extract data from files, logs, or command output:

# Extract from a file
cat receipt.txt | xargs -I {} curl -s -X POST \
  https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $STRUCTUREAI_KEY" \
  -d '{"text": "{}", "schema": "receipt"}'

# Process multiple files
for f in receipts/*.txt; do
  echo "=== $f ==="
  extract "$(cat $f)" receipt
done

Use in Node.js

async function extractData(text: string, schema: string) {
  const response = await fetch('https://api-service-wine.vercel.app/api/extract', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-API-Key': process.env.STRUCTUREAI_KEY!,
    },
    body: JSON.stringify({ text, schema }),
  });
  const result = await response.json();
  return result.data;
}

// Usage
const contact = await extractData(
  'Jane Doe, jane@test.com, CTO at Stripe',
  'contact'
);
console.log(contact.name);  // "Jane Doe"
console.log(contact.title); // "CTO"

Use in Python

import requests

def extract_data(text, schema="custom", fields=None):
    payload = {"text": text, "schema": schema}
    if fields:
        payload["custom_fields"] = fields

    response = requests.post(
        "https://api-service-wine.vercel.app/api/extract",
        json=payload,
        headers={"X-API-Key": STRUCTUREAI_KEY}
    )
    return response.json()["data"]

# Usage
receipt = extract_data(
    "Whole Foods: Apples $3.99, Bread $4.29, Total $8.28",
    schema="receipt"
)
print(receipt["total"])  # 8.28

Pricing

An API key costs $2 and gives you 100 requests. That's $0.02 per extraction. No subscriptions, no monthly fees.

Get a key at api-service-wine.vercel.app.

For Claude Desktop users, the MCP server includes 10 free requests. Just ask Claude to extract data from any text.

When to Use This vs. Regex

Use regex when the format is fixed and simple (parsing CSV, matching emails). Use structured extraction when the format varies, the data is complex, or you need to handle edge cases gracefully.

The API handles messy, inconsistent, real-world text. Regex handles clean, predictable patterns. Pick the right tool.

Built by Avatrix LLC. Questions? support@avatrix.co

Generate Beautiful READMEs from Your Code Automatically

Alex — Sun, 08 Mar 2026 06:09:43 +0000

Good READMEs get GitHub stars. Bad READMEs get closed tabs. But writing documentation after building a project feels like writing an essay after finishing the exam. The motivation is gone.

Here's how to generate a complete README from your code in one command.

The Script

Create scripts/generate-readme.sh:

#!/bin/bash

# Collect project context
PACKAGE=$(cat package.json 2>/dev/null || echo "No package.json")
STRUCTURE=$(find . -type f -not -path './node_modules/*' -not -path './.git/*' | head -50)
ENTRY=$(cat src/index.ts 2>/dev/null || cat index.js 2>/dev/null || echo "No entry file")
EXAMPLES=$(ls examples/ 2>/dev/null || echo "No examples directory")

# Generate README
curl -s https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d "{
    \"model\": \"claude-sonnet-4-20250514\",
    \"max_tokens\": 2048,
    \"messages\": [{
      \"role\": \"user\",
      \"content\": \"Generate a README.md for this project. Include: project title, one-line description, features, installation, quick start, API reference (if applicable), examples, and license. Use badges where appropriate.\\n\\nPackage info:\\n$PACKAGE\\n\\nFile structure:\\n$STRUCTURE\\n\\nEntry point:\\n$(echo "$ENTRY" | head -c 3000)\"
    }]
  }" | jq -r '.content[0].text' > README.md

echo "README.md generated successfully"

What It Produces

For a typical Node.js project, you get something like:

# StructureAI MCP Server

> Extract structured JSON from unstructured text using AI,
> directly from Claude Desktop or Cursor.

![npm](https://img.shields.io/npm/v/structureai-mcp)
![license](https://img.shields.io/badge/license-MIT-blue)

## Features
- Extract data from receipts, invoices, emails, resumes
- Custom schema support for any data structure
- 10 free requests included, no API key needed
- Works with Claude Desktop, Cursor, and any MCP client

## Installation
...

## Quick Start
...

It reads your package.json for the name, version, and dependencies. It reads your source files for the API. It produces a README that actually matches your code.

Making It Smarter

Include Your Tests as Examples

Tests are documentation. Feed them to the AI:

TESTS=$(find . -name "*.test.*" -not -path './node_modules/*' | head -5 | xargs cat 2>/dev/null | head -c 3000)

Add $TESTS to the prompt. The AI will extract usage patterns from your tests and turn them into README examples.

Add API Documentation

If your project exposes routes or functions, include them:

# For Express/Next.js apps
ROUTES=$(grep -r "app\.\(get\|post\|put\|delete\)\|export.*function\|router\." src/ | head -30)

The AI generates an API reference table from your route definitions.

Generate Badges Automatically

Ask the AI to include relevant badges based on your package.json:

If the project uses TypeScript, add a TypeScript badge.
If it has tests, add a test coverage badge placeholder.
If it's on npm, add an npm version badge.
Add a license badge based on the license field.

Extracting Project Metadata

For structured project metadata, you can use StructureAI to extract key details from your codebase:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "package.json: {name: structureai-mcp, version: 1.0.0, scripts: {build: tsc, test: jest}, dependencies: {@modelcontextprotocol/sdk: ^1.0, zod: ^3.22}}",
    "schema": "custom",
    "custom_fields": ["project_name", "language", "framework", "key_dependencies", "has_tests", "build_tool"]
  }'

Use the structured output to template your README sections consistently across all your projects.

CI/CD Integration

Keep your README up to date automatically. Add a GitHub Action that regenerates it when source code changes:

name: Update README
on:
  push:
    branches: [main]
    paths: ['src/**', 'package.json']

jobs:
  readme:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Generate README
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        run: ./scripts/generate-readme.sh
      - name: Commit if changed
        run: |
          git diff --quiet README.md || {
            git config user.name "readme-bot"
            git config user.email "bot@example.com"
            git add README.md
            git commit -m "docs: auto-update README"
            git push
          }

Now your README always matches your code. No manual updates needed.

README Sections That Matter

Based on analyzing top GitHub repos, these sections get the most engagement:

One-liner — What it does in one sentence
Quick start — Copy-paste to get running in 30 seconds
Visual — Screenshot, GIF, or diagram
Examples — Real-world usage, not just API reference
Why this exists — The problem it solves

The AI generates all of these from your code. You just review and ship.

Try It

Copy the script above to scripts/generate-readme.sh
chmod +x scripts/generate-readme.sh
Run it from your project root
Review the generated README.md
Commit and push

Your project now has documentation that actually reflects what the code does. Update it by re-running the script whenever you ship a major feature.

Built by Avatrix LLC. Check out our MCP server for structured data extraction from any text.

Stop Writing Commit Messages — Use AI Instead

Alex — Sun, 08 Mar 2026 06:04:32 +0000

Your git log looks like this:

fix bug
update stuff
wip
asdf
more changes
final fix
final fix 2

Every developer has been there. Writing good commit messages takes effort you'd rather spend coding. But bad commit messages make debugging, reverting, and onboarding painful.

The fix: let AI write your commit messages from your actual diff.

The prepare-commit-msg Hook

Git has a hook called prepare-commit-msg that runs before the commit message editor opens. We'll use it to auto-generate messages.

Create .git/hooks/prepare-commit-msg:

#!/bin/bash

# Only run for regular commits (not merges, amends, etc.)
if [ "$2" != "" ]; then
  exit 0
fi

COMMIT_MSG_FILE=$1

# Get staged diff
DIFF=$(git diff --cached --stat)
DIFF_CONTENT=$(git diff --cached | head -c 5000)

if [ -z "$DIFF" ]; then
  exit 0
fi

# Generate commit message
MESSAGE=$(curl -s https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d "{
    \"model\": \"claude-sonnet-4-20250514\",
    \"max_tokens\": 100,
    \"messages\": [{
      \"role\": \"user\",
      \"content\": \"Write a conventional commit message for this diff. Format: type(scope): description. One line, max 72 chars. Types: feat, fix, refactor, docs, test, chore. No quotes around the message.\\n\\nFiles:\\n$DIFF\\n\\nDiff:\\n$DIFF_CONTENT\"
    }]
  }" | jq -r '.content[0].text')

# Write to commit message file
echo "$MESSAGE" > "$COMMIT_MSG_FILE"

Make it executable:

chmod +x .git/hooks/prepare-commit-msg

What You Get

Now when you run git commit, the hook reads your staged changes and generates a message:

git add src/auth/login.ts src/auth/middleware.ts
git commit
# Auto-generated message: feat(auth): add JWT login with token refresh

git add src/utils/format.ts
git commit
# Auto-generated message: fix(utils): handle null values in date formatter

git add tests/auth.test.ts
git commit
# Auto-generated message: test(auth): add integration tests for login flow

The message appears in your editor. Keep it, edit it, or rewrite it. You're always in control.

Team-Wide Setup with a Shared Hook

Git hooks live in .git/hooks/ which isn't committed. To share with your team, use a hooks directory:

mkdir .githooks
cp .git/hooks/prepare-commit-msg .githooks/
git config core.hooksPath .githooks

Commit .githooks/ to your repo. Add setup instructions to your README:

git config core.hooksPath .githooks
export ANTHROPIC_API_KEY=your-key-here

A Faster Alternative: Local Models

Don't want to hit an API on every commit? Use a local model:

# Using ollama
MESSAGE=$(echo "$DIFF_CONTENT" | ollama run codellama \
  "Write a conventional commit message for this diff. One line, max 72 chars.")

Local models are faster (no network latency) and free. The quality is slightly lower but still better than "fixed stuff."

Parsing Commit Messages for Changelogs

Once your commit messages follow a consistent format, you can automatically generate changelogs. Extract structured data from your git log using StructureAI:

COMMITS=$(git log --oneline -20)

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d "{
    \"text\": \"$COMMITS\",
    \"schema\": \"custom\",
    \"custom_fields\": [\"features\", \"bug_fixes\", \"breaking_changes\", \"version_bump\"]
  }"

Returns:

{
  "features": ["JWT login with token refresh", "user profile page"],
  "bug_fixes": ["null values in date formatter", "race condition in auth middleware"],
  "breaking_changes": [],
  "version_bump": "minor"
}

Feed that into your CHANGELOG.md generator and you have fully automated releases.

Conventional Commits Cheat Sheet

The AI uses these types by default:

Type	When to use
`feat`	New feature
`fix`	Bug fix
`refactor`	Code change that doesn't fix a bug or add a feature
`docs`	Documentation only
`test`	Adding or fixing tests
`chore`	Build, CI, dependencies
`perf`	Performance improvement

Cost

Claude Sonnet costs about $0.003 per 1K input tokens. A typical diff is 200-500 tokens. That's roughly $0.001 per commit. At 20 commits per day, that's $0.02/day or $0.60/month.

You spend more on coffee.

Setup in 2 Minutes

Create the hook file (copy the script above)
chmod +x .git/hooks/prepare-commit-msg
Set ANTHROPIC_API_KEY in your shell profile
Run git commit and watch the magic

Your git log will finally tell a coherent story. Future you will be grateful.

Built by Avatrix LLC. Turn messy text into clean JSON with StructureAI.

Create Terms of Service for Your App in Under a Minute

Alex — Sun, 08 Mar 2026 05:59:21 +0000

You shipped your app. Users are signing up. But you have no Terms of Service. If something goes wrong — a refund dispute, an abusive user, a service outage — you have zero legal protection.

Here's how to generate proper Terms of Service in under a minute.

The Prompt That Works

Generate Terms of Service for my product:
- Company: [Name], incorporated in [State/Country]
- Product: [One sentence description]
- Pricing: [Free/paid, subscription/one-time, refund policy]
- User accounts: [Yes/No]
- User-generated content: [Yes/No]
- API access: [Yes/No]
- Age requirement: [13+, 18+, none]

Include sections for: acceptable use, intellectual property, limitation
of liability, termination, dispute resolution, and governing law.
Use plain English. No legalese unless legally required.

Real Example: SaaS API Terms

Here's what I used for StructureAI, our data extraction API:

Generate Terms of Service for:
- Company: Avatrix LLC, Delaware
- Product: API that extracts structured data from unstructured text
- Pricing: $2 per API key (100 requests), no subscriptions, no refunds on used keys
- User accounts: No (API key only)
- User-generated content: No
- API access: Yes
- Age requirement: 18+

Additional clauses needed:
- Rate limiting (100 requests per key)
- No guaranteed uptime (best effort)
- Prohibited: illegal content, reverse engineering, reselling API access
- We may modify pricing with 30 days notice

Sixty seconds later: a complete, specific Terms of Service document.

Key Sections You Need

Every ToS should cover these areas. If the AI misses one, ask for it specifically.

1. Acceptable Use

What users can and cannot do:

## Acceptable Use
You may use our service for lawful purposes only. You may not:
- Submit illegal, harmful, or abusive content
- Attempt to reverse engineer our API or service
- Resell or redistribute API access without authorization
- Exceed rate limits or circumvent usage restrictions
- Use automated means to create accounts

2. Payment Terms

Be explicit about refunds:

## Payments and Refunds
- API keys are sold as one-time purchases
- Keys are non-refundable once any requests have been used
- Unused keys may be refunded within 7 days of purchase
- Prices may change with 30 days notice to existing customers

3. Limitation of Liability

This is the section that protects you:

## Limitation of Liability
Our service is provided "as is" without warranties.
We are not liable for:
- Service interruptions or downtime
- Accuracy of AI-generated output
- Loss of data or business interruption
- Any indirect or consequential damages

Our total liability shall not exceed the amount you paid
for the service in the 12 months prior to the claim.

4. Termination

You need the right to cut off abusive users:

## Termination
We may suspend or terminate your access at any time for:
- Violation of these terms
- Abusive or fraudulent behavior
- Non-payment

You may stop using the service at any time.
Termination does not entitle you to a refund for used services.

Extracting Your Product Details Automatically

If you're not sure what to include, extract your product's terms-relevant details from your codebase. Feed your pricing logic, rate limiting code, and user flow to an AI.

For structured extraction, pipe your code through StructureAI:

curl -X POST https://api-service-wine.vercel.app/api/extract \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "text": "stripe.checkout.sessions.create({ line_items: [{ price: 200, quantity: 1 }], mode: payment }) // Rate limit: 100 req per key // No auth required, API key in header",
    "schema": "custom",
    "custom_fields": ["pricing_model", "rate_limits", "auth_method", "refund_policy"]
  }'

This gives you the exact details to feed into your ToS prompt.

Hosting Your Terms

Same as privacy policies — add a /terms route:

// app/terms/page.tsx
export default function Terms() {
  return (
    <main className="max-w-2xl mx-auto p-8 prose">
      <h1>Terms of Service</h1>
      <p>Last updated: March 2026</p>
      <p>By using our service, you agree to these terms.</p>
      {/* Generated terms content */}
    </main>
  );
}

Link to it from your footer, checkout page, and signup flow. Stripe specifically checks for this.

When to Use a Real Lawyer

AI-generated terms work for most indie developers and small SaaS products. Get a real lawyer if you handle health data, financial data, or children's data. Also get legal review if you're raising money — investors will check your terms.

For a $2 API or a $1 cover letter generator, AI-generated terms are more than sufficient.

The One-Minute Process

List your product details (pricing, features, restrictions)
Feed the prompt above to any AI
Review for accuracy
Host at /terms on your domain
Link from checkout and signup

Your app is now legally protected. Total cost: $0. Total time: one minute.

Built by Avatrix LLC. Need AI-powered data extraction? Try StructureAI — $2 for 100 API calls.