DEV Community: bao001 xiao

How to Build an AI Training Data Pipeline with the Web to Markdown/JSON API

bao001 xiao — Tue, 04 Aug 2026 06:52:23 +0000

Every AI engineer knows the bottleneck: getting clean, structured training data from the web. Whether you're fine-tuning an LLM, building a RAG application, or curating a domain-specific dataset, extracting web content into usable formats is a constant headache. You deal with messy HTML, JavaScript-rendered pages, rate limits, and boilerplate that pollutes your training corpus.

Enter the Web to Markdown/JSON API — a lightweight, no-nonsense service that converts any webpage into clean Markdown or structured JSON in a single API call. In this tutorial, I'll show you how to build a complete AI training data pipeline using it.

Why Your LLM Data Pipeline Needs This

When curating training data for language models, three things matter most:

Clean text — No HTML tags, no nav bars, no cookie banners. Just the content.
Consistent format — Every document in your pipeline should have the same structure.
Reproducibility — You need to re-scrape sources when content updates.

The Web to Markdown/JSON API gives you all three. It strips away the cruft, normalizes the output, and the deterministic endpoint means the same URL always produces the same structure.

The API at a Glance

Detail	Value
Endpoint	`POST https://web2md-api-production-d822.up.railway.app/extract`
Free Tier	50 requests/day
RapidAPI	Web to Markdown/JSON API

Request body:

{
  "url": "https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)",
  "format": "markdown",
  "max_length": 50000
}

Response (Markdown mode):

{
  "title": "Transformer (machine learning model)",
  "content": "# Transformer (machine learning model)\n\nThe **transformer** is a deep learning architecture...",
  "url": "https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)",
  "format": "markdown",
  "length": 28456
}

Three format options: markdown (headers, lists, code blocks intact), json (structured with sections), and text (plain, stripped-down).

Step 1: Building the Ingestion Pipeline

Let's build a Python pipeline that fetches web pages, stores them as clean Markdown, and prepares them for fine-tuning. First, install dependencies:

pip install requests tqdm datasets

Now, the core ingestion script:

import requests
import json
import time
from pathlib import Path
from typing import Optional

API_URL = "https://web2md-api-production-d822.up.railway.app/extract"

def extract_webpage(url: str, fmt: str = "markdown", max_length: int = 50000) -> Optional[dict]:
    """Extract a single webpage into clean Markdown or JSON."""
    resp = requests.post(
        API_URL,
        json={"url": url, "format": fmt, "max_length": max_length},
        timeout=30
    )
    if resp.status_code == 200:
        return resp.json()
    print(f"Failed {url}: {resp.status_code}")
    return None

def build_training_corpus(
    urls: list[str],
    output_dir: str = "./training_data",
    format: str = "markdown",
    delay: float = 1.0
) -> list[dict]:
    """Ingest a list of URLs and save as clean documents."""
    output_path = Path(output_dir)
    output_path.mkdir(parents=True, exist_ok=True)
    documents = []

    for i, url in enumerate(urls):
        print(f"[{i+1}/{len(urls)}] Extracting: {url[:80]}...")
        result = extract_webpage(url, fmt=format)

        if result and result.get("content"):
            doc = {
                "source": url,
                "title": result.get("title", ""),
                "content": result["content"],
                "length": result.get("length", 0)
            }
            documents.append(doc)

            # Save individual file
            safe_name = url.replace("https://", "").replace("/", "_")[:120]
            ext = "json" if format == "json" else "md"
            file_path = output_path / f"{i:04d}_{safe_name}.{ext}"
            file_path.write_text(result["content"], encoding="utf-8")

        time.sleep(delay)  # Respect rate limits

    # Save manifest
    manifest = {
        "total_documents": len(documents),
        "total_chars": sum(d["length"] for d in documents),
        "format": format,
        "sources": [d["source"] for d in documents]
    }
    (output_path / "manifest.json").write_text(json.dumps(manifest, indent=2))
    print(f"\nDone! {len(documents)} documents saved to {output_dir}/")
    return documents

# Example usage
if __name__ == "__main__":
    urls = [
        "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need",
        "https://arxiv.org/abs/1706.03762",
        "https://huggingface.co/blog/llama2",
        "https://lilianweng.github.io/posts/2023-01-27-the-transformer-family-v2/",
    ]
    build_training_corpus(urls)

Run it and watch your training_data/ directory fill with clean, ready-to-use Markdown files — no HTML soup in sight.

Step 2: Preparing for LLM Fine-Tuning

Now that we have clean documents, let's format them for fine-tuning with Hugging Face's datasets library:

from datasets import Dataset
import json
from pathlib import Path

def prepare_finetuning_dataset(
    input_dir: str = "./training_data",
    instruction_template: str = None
) -> Dataset:
    """Convert ingested Markdown files into a HuggingFace Dataset for fine-tuning."""
    if instruction_template is None:
        instruction_template = (
            "You are a knowledgeable AI assistant. Read the following document "
            "and answer questions about it accurately.\n\nDocument:\n{content}"
        )

    records = []
    for md_file in sorted(Path(input_dir).glob("*.md")):
        content = md_file.read_text(encoding="utf-8")
        records.append({
            "source": md_file.name,
            "content": content,
            "instruction": instruction_template.format(content=content),
            "length": len(content)
        })

    # Filter out very short or empty documents
    records = [r for r in records if r["length"] > 500]

    dataset = Dataset.from_list(records)
    return dataset

# Load and inspect
dataset = prepare_finetuning_dataset("./training_data")
print(f"Dataset size: {len(dataset)} documents")
print(f"Total tokens (approx): {sum(dataset['length']) // 4:,}")

# Push to HuggingFace Hub (uncomment when ready)
# dataset.push_to_hub("your-username/your-domain-corpus")

Step 3: Building a RAG Ingestion Pipeline

For Retrieval-Augmented Generation, the JSON format shines. Here's how to build a chunking pipeline:

import requests
import json
from langchain.text_splitter import RecursiveCharacterTextSplitter

def ingest_for_rag(urls: list[str], chunk_size: int = 1000) -> list[dict]:
    """Fetch webpages as JSON and split into RAG-ready chunks."""
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=200,
        separators=["\n\n", "\n", ".", " ", ""]
    )
    all_chunks = []

    for url in urls:
        resp = requests.post(
            "https://web2md-api-production-d822.up.railway.app/extract",
            json={"url": url, "format": "json", "max_length": 50000}
        )
        if resp.status_code != 200:
            continue

        data = resp.json()
        content = data["content"]

        # If JSON-format, content may be a dict with sections
        text = content if isinstance(content, str) else json.dumps(content)

        chunks = text_splitter.split_text(text)
        for chunk in chunks:
            all_chunks.append({
                "source": url,
                "title": data.get("title", ""),
                "chunk": chunk,
                "chunk_length": len(chunk)
            })

    return all_chunks

Each chunk is now ready to be embedded and stored in your vector database (Pinecone, Weaviate, ChromaDB, etc.).

Why Not Scrape It Yourself?

You could write your own scraper with BeautifulSoup and Trafilatura. But then you'd need to:

Handle JavaScript-rendered pages (headless browser overhead)
Deal with different site structures (no two websites use the same HTML)
Manage proxies and IP rotation for scale
Maintain extraction logic as sites change their markup

The API abstracts all of that. One endpoint. Predictable output. 50 free requests per day to test and prototype — more than enough for most experimentation.

Real-World Use Cases

Here's how developers are already using this API:

Use Case	Format	Why It Works
Domain-specific LLM fine-tuning	Markdown	Clean docs without boilerplate improve training quality
RAG knowledge bases	JSON	Structured sections make chunking smarter
Competitive intelligence	Text	Monitor competitor pages with diffable plain text
Academic research corpora	Markdown	ArXiv papers → readable training data
Website change monitoring	Text	Compare extracted text to detect content updates

Production Tips

Batch responsibly: The free tier gives you 50 requests/day. Space them out with time.sleep(1).
Validate URLs: Pre-check that URLs return 200 before sending them to the API.
Deduplicate: Hash your extracted content and skip near-duplicates (common across doc pages).
Store raw responses: Always save the full API response — you might want to re-chunk later.

def deduplicate_documents(documents: list[dict]) -> list[dict]:
    """Remove near-duplicate documents using content hashing."""
    import hashlib
    seen = set()
    unique = []
    for doc in documents:
        h = hashlib.md5(doc["content"][:1000].encode()).hexdigest()
        if h not in seen:
            seen.add(h)
            unique.append(doc)
    return unique

Getting Started

Head to the RapidAPI page and subscribe (free tier available).
Copy the endpoint: https://web2md-api-production-d822.up.railway.app/extract
Run the pipeline script above against your target URLs.
Build your dataset, embed your chunks, or feed it into your fine-tuning job.

The API handles the messy part of web data extraction so you can focus on what actually matters: training better models and shipping faster.

Got a cool use case? Drop a comment below — I'd love to see what datasets you're building!

How to Build an AI Training Data Pipeline with the Web to Markdown/JSON API

bao001 xiao — Mon, 27 Jul 2026 12:45:44 +0000

Introduction

Building high-quality training datasets for Large Language Models (LLMs) and other AI systems is one of the hardest unsolved problems in machine learning. The web is the largest corpus of human knowledge ever assembled — but turning raw HTML into clean, structured training data? That's where most pipelines break down.

Enter the Web to Markdown/JSON API: a zero-configuration service that converts any webpage into pristine Markdown or structured JSON in a single HTTP request. No headless browsers, no BeautifulSoup regex nightmares, no DOM traversal. Just clean content.

In this tutorial, you'll build a complete AI training data pipeline that:

Crawls a list of seed URLs and extracts clean text at scale
Deduplicates and normalizes content across sources
Outputs data in formats ready for fine-tuning (JSONL for OpenAI, Parquet for Hugging Face)
Respects rate limits and robots.txt

By the end, you'll have a reusable pipeline you can point at any domain to harvest clean training data.

The API at a Glance

Endpoint: POST https://web2md-api-production-d822.up.railway.app/extract

Request body:

{
  "url": "https://example.com",
  "format": "markdown",
  "max_length": 50000
}

Formats supported:

markdown — Clean, readable Markdown with headers, lists, and code blocks preserved
json — Structured JSON with title, content, and metadata separated
text — Plain text with all markup stripped

Free tier: 50 requests/day

RapidAPI: web-to-markdown-json-api

The json format is particularly valuable for training pipelines — it separates metadata from content, making it easy to filter, tag, and structure your dataset.

Step 1: Setting Up the Pipeline Skeleton

Create a project structure:

mkdir ai-data-pipeline && cd ai-data-pipeline
mkdir -p data/{raw,deduped,output}
touch pipeline.py config.py
pip install requests tqdm xxhash

Here's our configuration (config.py):

# config.py
import os

API_ENDPOINT = "https://web2md-api-production-d822.up.railway.app/extract"
RAPIDAPI_KEY = os.getenv("RAPIDAPI_KEY", "")
RAPIDAPI_HOST = "web-to-markdown-json-api.p.rapidapi.com"

# Pipeline settings
MAX_LENGTH = 50000          # Max chars per extraction
MIN_CONTENT_LENGTH = 200    # Skip pages with less content than this
RETRIES = 3                 # Retry on failure
REQUEST_DELAY = 1.5         # Seconds between requests (be polite!)
OUTPUT_FORMAT = "jsonl"     # jsonl or parquet

# Seed URLs — replace with your target domain list
SEED_URLS = [
    "https://docs.python.org/3/tutorial/",
    "https://realpython.com/",
    "https://www.freecodecamp.org/news/",
]

Step 2: The Core Extractor

This is where the Web to Markdown/JSON API shines. Instead of wrestling with HTML parsers, we get clean structured data in one call:

# pipeline.py
import json
import time
import hashlib
from pathlib import Path
from datetime import datetime

import requests
from tqdm import tqdm

import config


def extract_url(url: str, format: str = "json") -> dict | None:
    """
    Extract clean content from a URL using the Web to Markdown/JSON API.
    Returns parsed JSON or None on failure.
    """
    for attempt in range(config.RETRIES):
        try:
            # Direct endpoint (free tier, no API key needed for 50/day)
            resp = requests.post(
                config.API_ENDPOINT,
                json={
                    "url": url,
                    "format": format,
                    "max_length": config.MAX_LENGTH
                },
                timeout=30,
                headers={"Content-Type": "application/json"}
            )
            resp.raise_for_status()
            data = resp.json()

            # Enrich with pipeline metadata
            data["_pipeline"] = {
                "source_url": url,
                "extracted_at": datetime.utcnow().isoformat(),
                "content_hash": hashlib.sha256(
                    data.get("content", "").encode()
                ).hexdigest(),
                "content_length": len(data.get("content", ""))
            }
            return data

        except requests.exceptions.RequestException as e:
            if attempt < config.RETRIES - 1:
                wait = 2 ** attempt  # Exponential backoff
                print(f"  Retry {attempt+1}/{config.RETRIES} for {url}")
                time.sleep(wait)
            else:
                print(f"  Failed to extract {url}: {e}")
                return None


def run_extraction(urls: list[str], output_dir: str = "data/raw") -> int:
    """Extract content from URLs and save individual JSON files."""
    output_path = Path(output_dir)
    output_path.mkdir(parents=True, exist_ok=True)

    successful = 0

    for url in tqdm(urls, desc="Extracting"):
        data = extract_url(url, format="json")

        if data and data["_pipeline"]["content_length"] >= config.MIN_CONTENT_LENGTH:
            fname = f"{data['_pipeline']['content_hash'][:16]}.json"
            with open(output_path / fname, "w") as f:
                json.dump(data, f, ensure_ascii=False, indent=2)
            successful += 1

        time.sleep(config.REQUEST_DELAY)

    print(f"Extracted {successful}/{len(urls)} pages successfully")
    return successful


if __name__ == "__main__":
    run_extraction(config.SEED_URLS)

Run it:

python pipeline.py

Output:

Extracting: 100%|████████████| 3/3 [00:12<00:00,  4.12s/it]
Extracted 3/3 pages successfully

Each extracted page lands in data/raw/ as a clean JSON file with structured content and pipeline metadata.

Step 3: Deduplication and Quality Filtering

Real-world crawls produce duplicates. Two URLs might point to the same content, or pages might be near-identical boilerplate. Here's a dedup module using MinHash:

# dedup.py
import json
import xxhash
from pathlib import Path


def compute_minhash(text: str, shingle_size: int = 5, num_hashes: int = 128) -> list[int]:
    """Compute MinHash signature for near-duplicate detection."""
    words = text.lower().split()
    if len(words) < shingle_size:
        return [0] * num_hashes

    shingles = [
        " ".join(words[i:i+shingle_size]) 
        for i in range(len(words) - shingle_size + 1)
    ]

    signatures = []
    for seed in range(num_hashes):
        min_hash = min(
            xxhash.xxh32(shingle, seed=seed).intdigest() 
            for shingle in shingles
        )
        signatures.append(min_hash)

    return signatures


def estimate_jaccard(sig1: list[int], sig2: list[int]) -> float:
    """Estimate Jaccard similarity from two MinHash signatures."""
    matches = sum(1 for a, b in zip(sig1, sig2) if a == b)
    return matches / len(sig1)


def deduplicate(raw_dir: str = "data/raw", threshold: float = 0.85) -> list[Path]:
    """Remove near-duplicate documents. Returns paths to keep."""
    raw = Path(raw_dir)
    docs = []

    for fpath in sorted(raw.glob("*.json")):
        with open(fpath) as f:
            doc = json.load(f)
        text = doc.get("content", "")
        sig = compute_minhash(text)
        docs.append((fpath, doc, sig))

    keep = []
    discarded = 0

    for i, (fpath_i, doc_i, sig_i) in enumerate(docs):
        is_dup = False
        for _, _, sig_j in keep:
            if estimate_jaccard(sig_i, sig_j) >= threshold:
                is_dup = True
                break

        if is_dup:
            discarded += 1
        else:
            keep.append((fpath_i, doc_i, sig_i))

    print(f"Dedup: kept {len(keep)}, removed {discarded} near-duplicates")
    return [item[0] for item in keep]


if __name__ == "__main__":
    kept = deduplicate()
    print(f"Final dataset size: {len(kept)} documents")

Step 4: Exporting to Training Formats

Different fine-tuning frameworks expect different formats. Here's how to export for the two most common targets:

OpenAI Fine-Tuning (JSONL)

# export.py
import json
from pathlib import Path


def export_openai_jsonl(
    input_dir: str = "data/raw",
    output_file: str = "data/output/training_data.jsonl",
    system_prompt: str = "You are a helpful assistant."
):
    """Convert extracted documents to OpenAI chat-format JSONL."""
    output_path = Path(output_file)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    count = 0
    with open(output_path, "w") as out:
        for fpath in sorted(Path(input_dir).glob("*.json")):
            with open(fpath) as f:
                doc = json.load(f)

            title = doc.get("title", "Untitled")
            content = doc.get("content", "")

            if len(content) < 100:
                continue

            excerpt = content[:4000]

            record = {
                "messages": [
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": f"Explain this about '{title}':\n\n{excerpt}"},
                    {"role": "assistant", "content": f"Summary of '{title}':\n\n{excerpt}"}
                ]
            }

            out.write(json.dumps(record) + "\n")
            count += 1

    print(f"Exported {count} records to {output_file}")
    return count

Hugging Face Datasets (Parquet)

def export_parquet(input_dir: str = "data/raw", output_file: str = "data/output/dataset.parquet"):
    """Export extracted data as a Parquet file for Hugging Face datasets."""
    try:
        import pandas as pd
    except ImportError:
        print("Install pandas and pyarrow: pip install pandas pyarrow")
        return

    records = []
    for fpath in sorted(Path(input_dir).glob("*.json")):
        with open(fpath) as f:
            doc = json.load(f)

        records.append({
            "url": doc["_pipeline"]["source_url"],
            "title": doc.get("title", ""),
            "content": doc.get("content", ""),
            "content_hash": doc["_pipeline"]["content_hash"],
            "content_length": doc["_pipeline"]["content_length"],
            "extracted_at": doc["_pipeline"]["extracted_at"],
        })

    df = pd.DataFrame(records)
    df.to_parquet(output_file, index=False)
    print(f"Exported {len(df)} records to {output_file}")

Step 5: Scaling Up with Sitemaps

For real datasets, you need more than a handful of seed URLs. Here's a sitemap-powered crawler:

# crawler.py
import xml.etree.ElementTree as ET

import requests
from pipeline import extract_url


def fetch_sitemap_urls(sitemap_url: str) -> list[str]:
    """Parse a sitemap.xml and return all URLs."""
    resp = requests.get(sitemap_url, timeout=30)
    resp.raise_for_status()

    root = ET.fromstring(resp.content)
    ns = {"sm": "http://www.sitemaps.org/schemas/sitemap/0.9"}

    # Handle sitemap index files
    sitemap_refs = root.findall("sm:sitemap/sm:loc", ns)
    if sitemap_refs:
        all_urls = []
        for ref in sitemap_refs:
            all_urls.extend(fetch_sitemap_urls(ref.text))
        return all_urls

    # Regular sitemap
    urls = [loc.text for loc in root.findall("sm:url/sm:loc", ns)]
    return urls


if __name__ == "__main__":
    urls = fetch_sitemap_urls("https://docs.python.org/3/sitemap.xml")
    print(f"Found {len(urls)} URLs in sitemap")

Step 6: Full Pipeline Orchestration

Tie everything together with a main orchestration script:

# run_pipeline.py
#!/usr/bin/env python3
"""
Full AI training data pipeline using Web to Markdown/JSON API.

Usage:
    python run_pipeline.py --urls urls.txt --format jsonl
    python run_pipeline.py --sitemap https://docs.python.org/3/sitemap.xml
"""
import argparse
from pathlib import Path

from pipeline import run_extraction
from dedup import deduplicate
from export import export_openai_jsonl, export_parquet
from crawler import fetch_sitemap_urls


def main():
    parser = argparse.ArgumentParser(description="AI Training Data Pipeline")
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument("--urls", help="File with one URL per line")
    group.add_argument("--sitemap", help="Sitemap URL to crawl")
    parser.add_argument("--format", choices=["jsonl", "parquet"], default="jsonl")
    parser.add_argument("--max-pages", type=int, default=100)
    parser.add_argument("--output", default="data/output/training_data")
    args = parser.parse_args()

    # Step 1: Collect URLs
    if args.urls:
        with open(args.urls) as f:
            urls = [line.strip() for line in f if line.strip()]
    else:
        urls = fetch_sitemap_urls(args.sitemap)

    urls = urls[:args.max_pages]
    print(f"Collected {len(urls)} URLs")

    # Step 2: Extract content
    extracted = run_extraction(urls)
    if extracted == 0:
        print("No pages extracted. Exiting.")
        return

    # Step 3: Deduplicate
    kept = deduplicate()

    # Step 4: Export
    if args.format == "jsonl":
        export_openai_jsonl(output_file=f"{args.output}.jsonl")
    else:
        export_parquet(output_file=f"{args.output}.parquet")

    print(f"Pipeline complete! Output: {args.output}.{args.format}")


if __name__ == "__main__":
    main()

Run the full pipeline:

# From a URL list
python run_pipeline.py --urls seed_urls.txt --format jsonl --max-pages 50

# From a sitemap
python run_pipeline.py --sitemap https://docs.python.org/3/sitemap.xml --format parquet

Why This API Over Traditional Scraping?

Here is a quick comparison of approaches for AI training data extraction:

Approach	Setup Time	Maintainability	Content Quality	Cost
BeautifulSoup + requests	Hours per site	Brittle	Manual cleaning needed	Free
Playwright/Selenium	Hours	Moderate	Good but includes boilerplate	Free (compute)
Scrapy spiders	Hours per site	Better but needs XPath/CSS	Variable	Free
Web to Markdown/JSON API	Minutes	Excellent	Excellent — clean output	Free (50/day)

The API eliminates the entire HTML-to-clean-text pipeline, which is typically 60-70% of the engineering effort in building a training dataset from web content.

Best Practices

Respect robots.txt: Always check a site's robots.txt before crawling at scale.
Rate limit aggressively: The free tier gives you 50 requests/day. Space them out.
Filter aggressively: Not all web content is training-worthy. Set MIN_CONTENT_LENGTH high (500+ chars) and consider language detection.
Deduplicate ruthlessly: The web is full of mirrors and syndicated content. MinHash + content hashing catches most duplicates.
Version your datasets: Tag each export with a timestamp and source domain.
Use JSON format for pipelines: The json format separates metadata from content — essential for filtering, tagging, and structuring.

Going Further

Add language detection with fasttext or langdetect for multilingual datasets
Integrate with Hugging Face Datasets via datasets.Dataset.from_parquet()
Add quality scoring using textstat readability metrics or perplexity filtering
Schedule with cron/APScheduler for continuous dataset updates
Use RapidAPI integration for higher rate limits: web-to-markdown-json-api on RapidAPI

Conclusion

The Web to Markdown/JSON API turns the hardest part of AI data pipeline engineering — extracting clean content from the chaotic web — into a single API call. Combined with the deduplication and export modules we built above, you have a production-ready pipeline that can feed clean training data into any fine-tuning workflow.

API Endpoint: POST https://web2md-api-production-d822.up.railway.app/extract

Free Tier: 50 requests/day — more than enough to prototype and test your pipeline.

Try it out, build your dataset, and ship that fine-tuned model.

Questions or feedback? Drop a comment below!

How to Monitor Website Changes with the Web to Markdown/JSON API

bao001 xiao — Mon, 20 Jul 2026 12:11:23 +0000

Introduction

Keeping track of changes on websites—whether it's competitor pricing, job listings, government policy updates, or product release notes—is a common need for developers and businesses alike. But writing a full-blown web scraper from scratch for every site you want to monitor? That's brittle, time-consuming, and often breaks the moment the target site tweaks its layout.

Enter the Web to Markdown/JSON API: a simple, powerful API that converts any webpage into clean, structured Markdown or JSON in a single request. It handles all the messy HTML parsing, boilerplate stripping, and formatting so you can focus on what actually matters—detecting changes and acting on them.

In this tutorial, you'll build a website change monitor in Python that:

Fetches any web page as clean Markdown via the API
Compares snapshots over time to detect meaningful changes
Sends email alerts when something important shifts
Runs on a cron schedule for fully automated monitoring

Let's dive in.

The API at a Glance

Endpoint: POST https://web2md-api-production-d822.up.railway.app/extract

Request body:

{
  "url": "https://example.com",
  "format": "markdown",
  "max_length": 50000
}

Formats supported: markdown, json, text

Free tier: 50 requests per day

RapidAPI page: web-to-markdown-json-api

This API does one thing and does it well: give it a URL, and it returns the page's meaningful content stripped of navigation, ads, and scripting cruft. Exactly what you need for change detection—no BeautifulSoup or regex wrestling required.

Step 1: Fetching a Page Snapshot

Create a file called monitor.py. We'll start with a function that takes a URL and returns the clean Markdown content:

import requests

API_ENDPOINT = "https://web2md-api-production-d822.up.railway.app/extract"

def fetch_snapshot(url: str, format: str = "markdown", max_length: int = 50000) -> str:
    resp = requests.post(
        API_ENDPOINT,
        json={"url": url, "format": format, "max_length": max_length},
        timeout=30
    )
    resp.raise_for_status()
    data = resp.json()
    return data.get("markdown") or data.get("content", "")

# Quick test
if __name__ == "__main__":
    content = fetch_snapshot("https://example.com")
    print(content[:500])
    print(f"\n--- Total length: {len(content)} characters")

Run it and you'll see clean, readable Markdown instead of raw HTML—a huge win before we even start the monitoring logic.

Step 2: Storing and Comparing Snapshots

To detect changes, we need to store previous snapshots and compare them against new ones. We'll use a simple JSON file as our snapshot database:

import hashlib
import json
import os
from datetime import datetime

SNAPSHOT_FILE = "snapshots.json"

def load_snapshots() -> dict:
    if os.path.exists(SNAPSHOT_FILE):
        with open(SNAPSHOT_FILE) as f:
            return json.load(f)
    return {}

def save_snapshots(snapshots: dict):
    with open(SNAPSHOT_FILE, "w") as f:
        json.dump(snapshots, f, indent=2, default=str)

def compute_hash(content: str) -> str:
    return hashlib.sha256(content.encode("utf-8")).hexdigest()

def compare_snapshot(url: str, new_content: str):
    snapshots = load_snapshots()
    new_hash = compute_hash(new_content)

    if url not in snapshots:
        snapshots[url] = {
            "hash": new_hash,
            "timestamp": datetime.now().isoformat(),
            "length": len(new_content)
        }
        save_snapshots(snapshots)
        return None  # Baseline established

    old = snapshots[url]
    if old["hash"] == new_hash:
        return None  # No change

    length_delta = len(new_content) - old.get("length", 0)

    snapshots[url] = {
        "hash": new_hash,
        "timestamp": datetime.now().isoformat(),
        "length": len(new_content)
    }
    save_snapshots(snapshots)

    return {
        "url": url,
        "previous_checked": old["timestamp"],
        "length_delta": length_delta,
        "length_delta_pct": round(
            (length_delta / old["length"]) * 100, 1
        ) if old["length"] > 0 else 0
    }

This gives us hash-based equality checks (fast and reliable) plus a quick size-delta metric.

Step 3: Detecting Meaningful Changes

Not all changes matter. A page might change its timestamp, or a random ad insertion could shift content slightly. Let's add smart thresholds:

CHANGE_THRESHOLD = 0.02  # 2% content change before we alert

def detect_meaningful_change(url: str, new_content: str):
    diff = compare_snapshot(url, new_content)

    if diff is None:
        return None

    abs_pct = abs(diff["length_delta_pct"])
    if abs_pct < CHANGE_THRESHOLD * 100:
        print(f"Change too small ({abs_pct}%), skipping alert for {url}")
        return None

    return diff

Tune CHANGE_THRESHOLD per URL—higher for noisy pages, lower for pages where every word counts.

Step 4: Sending Email Alerts

When a meaningful change is detected, fire off an email using Python's built-in smtplib:

import smtplib
from email.mime.text import MIMEText

SMTP_HOST = "smtp.gmail.com"
SMTP_PORT = 587
SMTP_USER = "alerts@yourdomain.com"
SMTP_PASS = "your-app-password"
ALERT_EMAIL = "you@yourdomain.com"

def send_alert(change: dict):
    subject = f"Web Change Detected: {change['url']}"
    body = (
        f"A change was detected on a monitored page.\n\n"
        f"URL: {change['url']}\n"
        f"Last checked: {change['previous_checked']}\n"
        f"Size change: {change['length_delta']:+d} chars "
        f"({change['length_delta_pct']:+}%)\n\n"
        f"See: {change['url']}\n"
    )
    msg = MIMEText(body)
    msg["Subject"] = subject
    msg["From"] = SMTP_USER
    msg["To"] = ALERT_EMAIL

    with smtplib.SMTP(SMTP_HOST, SMTP_PORT) as server:
        server.starttls()
        server.login(SMTP_USER, SMTP_PASS)
        server.send_message(msg)

    print(f"Alert sent for {change['url']}")

Step 5: Putting It All Together

The main monitoring loop—feed it a list of URLs and it checks them all:

import time

MONITORED_URLS = [
    "https://example.com/pricing",
    "https://example.com/changelog",
    "https://example.com/jobs",
]

def run_monitor(urls: list[str]):
    print(
        f"Starting monitor for {len(urls)} URLs "
        f"at {datetime.now().isoformat()}"
    )

    for url in urls:
        try:
            print(f"Checking: {url}")
            content = fetch_snapshot(url)
            change = detect_meaningful_change(url, content)

            if change:
                print(
                    f"  -> CHANGE DETECTED! "
                    f"Size delta: {change['length_delta']:+d} chars"
                )
                # send_alert(change)  # Uncomment when SMTP configured
            else:
                print(f"  -> No meaningful change")

            time.sleep(1)  # Respect free tier limits

        except Exception as e:
            print(f"  -> ERROR for {url}: {e}")

    print("Monitor run complete.")

if __name__ == "__main__":
    run_monitor(MONITORED_URLS)

Step 6: Scheduling with Cron

Once working, schedule it to run daily:

# Every day at 8 AM
0 8 * * * cd /path/to/project && python3 monitor.py >> monitor.log 2>&1

# Every 6 hours for fast-moving pages
0 */6 * * * cd /path/to/project && python3 monitor.py >> monitor.log 2>&1

With the free tier (50 requests/day), monitor 40-45 URLs daily with a safety buffer.

Advanced Ideas

1. Diff Highlighting

Store full Markdown content and use difflib for human-readable diffs:

import difflib

def generate_diff(old: str, new: str) -> str:
    return "\n".join(difflib.unified_diff(
        old.splitlines(), new.splitlines(), lineterm=""
    ))

2. Per-URL Thresholds

Some pages change frequently, others rarely:

URL_CONFIG = {
    "https://example.com/news": {"threshold": 0.10},
    "https://example.com/terms": {"threshold": 0.01},
}

3. JSON Format for Structured Data

Switch format to "json" when monitoring structured fields:

resp = requests.post(
    API_ENDPOINT,
    json={
        "url": "https://example.com/product/123",
        "format": "json",
        "max_length": 20000
    }
)
# resp.json() returns structured data with title, content, metadata

4. LLM-Powered Summaries

Pipe the Markdown diff into an LLM and ask for a one-paragraph summary of what changed. Turns raw deltas into actionable intelligence.

Why This API Over DIY Scraping?

Aspect	Web to Markdown/JSON API	DIY Scraping
Setup time	1 API call	Hours of BeautifulSoup + custom parsing
Maintenance	Handled by the API	You fix it every site redesign
Content quality	Clean Markdown, boilerplate stripped	Raw HTML to parse yourself
Multiple formats	Markdown, JSON, plain text	You implement each
Rate limiting	Free tier included	You write retry logic

Wrapping Up

With under 100 lines of Python and the Web to Markdown/JSON API, you have a fully automated website change monitor that:

Fetches clean, readable content from any URL
Compares snapshots and detects meaningful changes
Alerts by email when something shifts
Runs on autopilot via cron

This pattern is production-ready for monitoring competitor pricing, government notices, job boards, documentation updates, and anything else on the web that matters.

Try It Now

Grab a key on RapidAPI or hit the endpoint directly:

curl -X POST https://web2md-api-production-d822.up.railway.app/extract \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com", "format": "markdown", "max_length": 50000}'

The first 50 requests each day are free. Go build something useful!

How to Extract Clean Content From Any URL in 3 Lines of Python

bao001 xiao — Fri, 17 Jul 2026 13:45:16 +0000

Have you ever needed to grab the actual content from a web page — without all the ads, nav bars, and JavaScript junk?

Whether you're building an AI training pipeline, a reading app, or a data scraper, extracting clean text from arbitrary URLs is a universal pain point.

Here's the fastest way I've found.

The Problem

Most websites are 70% boilerplate. Headers, footers, sidebars, cookie banners, popups. If you just use requests.get() and parse the HTML, you get garbage:

import requests
from bs4 import BeautifulSoup

resp = requests.get("https://example.com/article")
soup = BeautifulSoup(resp.text, "html.parser")
print(soup.get_text())
# Output: navigation menu sidebar footer ads copyright...

The Solution: One API Call

Instead of fighting with HTML parsing, use a dedicated content extraction API:

import requests

resp = requests.post("https://web2md-api-production-d822.up.railway.app/extract", json={
    "url": "https://www.python.org/doc/",
    "format": "markdown"
})
data = resp.json()
print(data["content"][:500])

That's it. Three lines. The API handles:

Fetching the page
Stripping all boilerplate (nav, ads, scripts)
Extracting the main article content
Converting to clean Markdown

What You Get Back

The API returns structured JSON:

{
  "success": true,
  "title": "Documentation — Python.org",
  "content": "# Markdown content here...",
  "description": "The official home of the Python Programming Language",
  "word_count": 1234,
  "response_time_ms": 850
}

Format Options

markdown (default) — perfect for LLM training data, note-taking, publishing
json — structured paragraphs, headings, and metadata
text — plain text when you just need the words

Pricing

It's cheap. Free tier handles 50 requests/day — enough for most side projects.

Free: 50 requests/day
Basic: 5,000/month — $4.99
Pro: 50,000/month — $19.99

Real Use Cases

AI Training Data — Feed clean web content into your LLM fine-tuning pipeline
Research — Download articles for offline reading and analysis
Content Monitoring — Track changes on competitor pages
Reading Apps — Convert any article to a clean reading view

Why Not Just Use BeautifulSoup?

You can, but:

Every site has different HTML structure
JavaScript-rendered content won't appear
You'll spend hours writing selectors for each site
Maintenance nightmare when sites redesign

A dedicated extraction API handles all of this with one endpoint. No selectors, no maintenance, no hassle.

This article was written with the help of AI. The API mentioned is a personal project — I built it because I needed it myself.

From Zero to $500/Month: My Side Hustle Journey in 2026

bao001 xiao — Sun, 07 Jun 2026 02:02:26 +0000

Last January, I was staring at my bank account with that familiar sinking feeling. Rent was due in five days, my day job covered the basics but left nothing for savings, and every "make money online" video I'd watched felt like a rehash of the same recycled advice. I decided to stop watching and start doing.

Twelve months and a lot of trial-and-error later, I'm not rich — but I'm consistently pulling in an extra $500–800 per month from side hustles I actually enjoy. No dropshipping gurus, no crypto rug pulls, no "DM me for the secret formula." Just real, repeatable work that anyone with a laptop can start today.

Here's exactly what worked, what didn't, and how you can skip the mistakes I made.

The Framework: Time vs. Leverage

Before diving into specific hustles, I had to confront a hard truth: trading time for money has a ceiling. You can only work so many hours. The side hustles that actually scaled for me shared one trait — they kept earning while I slept.

That meant building assets, not just doing gigs. A blog post you write once can bring traffic for years. A digital product you create once can sell indefinitely. Freelance writing? That's trading hours — useful for quick cash, but not the long game.

Hustle #1: Niche Blogging (The Slow Burn That Pays)

The first thing I did was spin up a WordPress site on a niche I actually cared about: budget home-office setups for remote workers. I grabbed a domain name for $12 and signed up for Hostinger's Business plan — their one-click WordPress installer meant I had a live site in under 15 minutes, and at less than $4/month, the barrier to entry was basically zero.

For three months, I wrote one article per week. No one read them. Month four, Google started noticing. By month six, I was getting 2,000 monthly visitors organically. I monetized with a mix of display ads (Ezoic) and a few carefully chosen affiliate links for standing desks and ergonomic chairs. That site now earns $200–300/month, almost entirely passive.

What I learned: Pick a niche narrow enough that you can actually rank, but broad enough that there's an audience. "Remote work ergonomics" beats "productivity tips" every time.

Hustle #2: Digital Templates (High Margin, Low Maintenance)

This one surprised me. I'm not a designer, but I noticed that small business owners in my network constantly struggled with the same things: client proposals, onboarding checklists, social media content calendars. So I built simple Notion and Google Sheets templates, packaged them nicely, and listed them on Gumroad.

Total upfront investment: one weekend of focused work. First month revenue: $87. Six months later: $250/month from the same templates, with zero additional effort beyond answering the occasional customer email.

What I learned: You don't need to create something revolutionary. Solve a specific, boring problem that people actively search for, and make the solution stupidly easy to use.

Hustle #3: Freelancing Platforms (Strategic, Not Desperate)

I'll be honest: I started on Upwork the wrong way, bidding on everything and racing to the bottom on price. That burned me out fast. The turning point came when I got selective — I only applied for jobs that paid at least $50/hour and required skills I was genuinely good at (technical writing and WordPress troubleshooting).

Having a professional portfolio site (built on Hostinger with their built-in website builder) was the single biggest factor in landing higher-paying clients. When a potential client Googles you and finds a clean, fast-loading site with case studies and testimonials, they're willing to pay 2–3x more than the platform average.

Freelancing now contributes $400–600/month on months I actively take projects, and I can dial it up or down depending on my bandwidth.

Hustle #4: Newsletter Curation (The One I Almost Skipped)

I almost didn't include this because "newsletters are saturated" is something I told myself for six months. Then I started one anyway — a weekly roundup of the best remote-work tools and tips I came across during the week. I spent more time reading and curating than writing, and the format was deliberately short (3-minute read).

I grew to 800 subscribers in four months by cross-posting summaries to Reddit and LinkedIn. ConvertKit's free tier handled everything. I haven't monetized it yet, but I've already had two brands reach out about sponsorship slots.

What Didn't Work (So You Can Skip It)

Print-on-demand: Too much time on designs for razor-thin margins. Maybe works for people with big social followings, but organic sales were nonexistent.
Survey sites: I tested Swagbucks and Survey Junkie for a month. Total earnings: $43. That's $1.43/hour. Not worth it.
Day trading: Treated it like a side hustle, lost $300 in two weeks. Lesson learned — this is a skill that takes years, not a quick income stream.

The Stack I Use Today

If you're starting from zero, here's the exact setup that got me to $500/month:

Tool	Purpose	Cost
Hostinger	Website hosting + domain	~$4/month
Gumroad	Digital product sales	Free (takes 10% cut)
ConvertKit	Email newsletter	Free up to 1,000 subs
Canva	Template design + graphics	Free
Notion	Content planning + templates	Free

Your First 30 Days

Don't try to do everything at once. Here's a realistic 30-day plan:

Week 1: Pick your niche. Buy a domain and set up hosting. Write your first blog post.
Week 2: Create one simple digital product (a checklist, template, or mini-guide). List it on Gumroad.
Week 3: Set up a freelance profile on one platform. Apply to exactly three jobs you're qualified for — no more.
Week 4: Evaluate. Which of these three channels showed the most promise? Double down on that one.

The people who succeed at side hustles aren't the ones who know the most. They're the ones who start before they feel ready, ship imperfect work, and keep showing up when no one's watching.

Disclosure: Some links in this article are affiliate links. If you sign up for Hostinger through my referral link, I may earn a commission at no extra cost to you. I only recommend tools I've personally used and genuinely believe in.

Web Development in 2026: The Only Beginner Roadmap You'll Need

bao001 xiao — Sat, 06 Jun 2026 02:03:29 +0000

Web Development in 2026: The Only Beginner Roadmap You'll Need

I watched a barista teach himself to code during his lunch breaks last year. Six months later, he landed his first freelance client building a restaurant website for $1,200. He didn't have a CS degree. He didn't attend a bootcamp. He just followed a roadmap and stayed consistent.

What struck me wasn't his talent — it was that the path into web development has never been more accessible. In 2026, the barrier is no longer "can I learn this?" It's "which direction should I go first?"

This guide cuts through the noise. No fluff, no 47-step plan, no "learn Rust before HTML" gatekeeping. Just the clearest route from zero to deployed.

Step 1: Decide What Kind of Developer You Want to Be

Before you touch a single line of code, make this decision:

Frontend Developer: You build what users see — layouts, buttons, animations. You live in HTML, CSS, and JavaScript.
Backend Developer: You build what users don't see — databases, server logic, APIs. You live in Python, Node.js, or Go.
Full-Stack Developer: You do both. Most beginners aim here eventually, but don't start with it.

My recommendation for 2026: start frontend. The feedback loop is instant — you change a color, you see a color. That immediate visual reward keeps you motivated through the hard parts.

Step 2: Grab a Domain and Hosting (Do This Today)

This is the step most beginners skip for months. Don't.

Sign up for a hosting plan, buy a domain name (something like yourname.dev), and install WordPress or a static site generator. Having a live website on day one — even if it's just your name and a photo — changes your psychology entirely. You stop being "someone learning to code" and start being "someone who builds websites."

I use Hostinger for this exact reason. Their plans start under $3/month and include a free domain for the first year, free SSL, and a one-click WordPress installer. Within 15 minutes, you have a real website on the internet. That momentum matters more than any tutorial.

Step 3: Learn the Core Stack (in This Exact Order)

3.1 HTML (1–2 weeks)

Structure. Headings, paragraphs, links, images, forms. That's it. Don't get fancy — learn how a web page is skeleton'd.

3.2 CSS (2–4 weeks)

Styling. Learn Flexbox and Grid first — those two layout systems solve 90% of real-world problems. Skip the 200-property memorization game. Just learn enough to make things look clean.

3.3 JavaScript (4–8 weeks)

This is where you'll spend most of your time. Focus on:

Variables, functions, arrays, objects
DOM manipulation (document.querySelector, event listeners)
Fetch API (making requests to servers)
Basic async/await

3.4 Git and GitHub (1 week)

Version control is non-negotiable in 2026. Learn git init, git add, git commit, git push. Create a GitHub account and push everything there. Future employers will look at your contribution graph.

Step 4: Build Three Projects Before You Move On

Tutorial hell is real. You watch 40 hours of courses and still can't build anything from scratch. The antidote: build first, watch second.

Here are three projects in order of difficulty:

Personal Portfolio – A single-page site with your bio, links, and a contact form. Host it on your Hostinger domain. Done in a weekend.
Weather Dashboard – Fetch data from a free weather API and display it beautifully. Teaches API calls, JSON, and error handling.
Simple Blog with CMS – Install WordPress through Hostinger's one-click installer, customize a theme, and publish 5 posts. This teaches you how real websites work in production — database connections, themes, plugins, and backups.

Each project should go on GitHub and your portfolio. By project #3, you'll have enough to apply for junior roles.

Step 5: Deploy Everything Publicly

Here's a rule I enforce with every student I mentor: nothing stays local. Every project goes live.

With Hostinger's shared hosting, you can host multiple websites on one plan. Build your portfolio on yourname.dev, put the weather app on a subdomain, and run the blog on another. Each deployed project is a line on your resume that a recruiter can actually click.

Tools That Accelerate Your Learning in 2026

VS Code — Free, fast, and the industry-standard editor. Install Prettier and ESLint extensions.
Chrome DevTools — Inspect any website, debug JavaScript, and test CSS changes live. Press Cmd+Option+I right now and start poking around.
MDN Web Docs — The only documentation you need. Forget W3Schools — MDN is written by Mozilla and is the authoritative reference.
Figma (free tier) — Design before you code. Even rough wireframes save hours of rewriting CSS.

Three Mistakes Beginners Make (and How to Avoid Them)

Mistake 1: "I'll start when I'm ready." You'll never feel ready. Buy the domain tonight, write HTML tomorrow, deploy it by Friday. Imperfect action beats perfect preparation every time.

Mistake 2: Jumping to frameworks too early. React, Vue, and Svelte are powerful, but they'll confuse you if you don't understand vanilla JavaScript first. Spend at least 8 weeks on the fundamentals before touching a framework.

Mistake 3: Building alone. Join communities — dev.to (you're here!), freeCodeCamp forums, and Discord servers. Share your ugly first projects. Get feedback. Teaching others what you just learned is the fastest way to solidify knowledge.

The 30-Day Challenge

If you're still reading and haven't started yet, here's your first action:

Today: Sign up for hosting at Hostinger and register a domain.
This week: Write your first HTML page and push it to GitHub.
By day 30: Have a live portfolio site with at least two projects on it.

Web development in 2026 is not about talent. It's about momentum. Once you have a live website, you'll never look at the internet the same way again.

Affiliate disclosure: Some links in this article are affiliate links. If you purchase Hostinger through my link, I may earn a commission at no extra cost to you. I only recommend tools I use myself and genuinely believe in.

10 Productivity Tools That Saved Me 20+ Hours a Week in 2026

bao001 xiao — Fri, 05 Jun 2026 03:49:16 +0000

Let's be real — 2026 is the year of information overload. Between Slack pings, email notifications, AI-generated news feeds, and the constant pressure to "do more with less," staying focused has never been harder.

Over the past few months, I've tested dozens of productivity tools. Most were garbage. But a handful genuinely changed how I work. Here are the 10 that actually saved me time — not added to the noise.

1. Notion — Your Second Brain

Notion has evolved far beyond a simple note-taking app. In 2026, it's a full-fledged workspace where I manage everything — from content calendars to affiliate marketing dashboards to personal finance tracking.

The key is building templates that automate recurring tasks. I have a "Daily Standup" template that auto-pulls my calendar, to-do list, and priority score into one page. It saves me at least 30 minutes every morning.

2. Linear — Project Management That Doesn't Get in the Way

If you're working on any kind of software or content project, Linear is a game-changer. Its keyboard-first design and ultra-fast UI mean you never wait for a page to load. Cycle tracking helps you focus on what matters this week, not everything forever.

3. Grammarly — AI-Powered Writing Assistant

Whether you're writing blog posts, emails, or social media captions, Grammarly's 2026 edition is scary good. It doesn't just fix typos — it rewrites sentences for clarity, adjusts tone for different audiences, and even suggests SEO-friendly alternatives.

4. Hostinger — Reliable Hosting for Your Projects

Here's an underrated productivity hack: stop fighting with slow hosting. If your website takes 4 seconds to load, that's 4 seconds of your life — multiplied by every visit. I moved all my blogs and side projects to Hostinger because their loading times are consistently under a second. When you're publishing content daily (like I do), a fast host means you spend less time debugging and more time creating.

Check out Hostinger's plans here →

5. Spark Mail — Inbox Zero, Actually

Spark's AI assistant reads your emails, categorizes them, and even drafts replies. I spend about 10 minutes per day on email now — down from over an hour. The "Send Later" and "Remind Me" features alone are worth the download.

6. Raycast — Spotlight on Steroids

If you're on macOS and still using Spotlight, stop. Raycast lets you search files, run calculations, control Spotify, open browser tabs, and even run custom scripts — all from one command bar. I've mapped it to Cmd+Space and never looked back.

7. Toggl Track — Time Audit Your Life

Before you can optimize your time, you need to know where it's going. Toggl Track runs in the background and logs which apps you use and for how long. I discovered I was spending 3+ hours a day in Slack doing low-value "quick replies." Now I batch Slack twice a day. That alone freed up 10 hours a week.

8. Hostinger's Built-in AI Website Builder

Another reason I recommend Hostinger? Their AI website builder generates a complete, professional site in under 5 minutes. For side projects and landing pages, this is a massive time saver. Instead of spending 2 days on HTML/CSS, you get a production-ready site before your coffee gets cold.

Start your site with Hostinger's AI builder →

9. OBS Studio — For Content Creators

Whether you're recording tutorials, streaming, or making short-form videos, OBS Studio is free, open-source, and incredibly powerful. Pair it with a decent microphone and you've got a professional studio setup for zero dollars.

10. RescueTime — The Accountability Buddy

RescueTime works quietly in the background and sends you a weekly report: "You spent X hours on productive work, Y hours on social media, Z hours in meetings." Seeing those numbers in black and white is surprisingly motivating. I cut my Twitter time from 2 hours/day to 30 minutes after the first week.

Bonus: Your Own Blog

The single best productivity decision I made in 2026 was starting a blog. Writing forces you to clarify your thinking, document what you learn, and build a library of resources you can reference later. And when you pair it with fast, affordable hosting, it becomes a low-maintenance asset.

Thinking about starting? Hostinger's shared hosting plans start at just a few bucks a month, and they include a free domain and SSL.

Final Thoughts

Productivity isn't about doing more — it's about doing less, better. The tools above helped me cut 20+ hours from my weekly workload by automating the boring stuff and eliminating distractions.

Try one or two this week. You don't need all 10. Just find the one that solves your biggest bottleneck and go from there.

This post contains affiliate links. If you make a purchase through these links, I may earn a commission at no extra cost to you.

10 Best AI Tools for Developers and Creators in 2026 (Tested & Reviewed)

bao001 xiao — Thu, 04 Jun 2026 07:57:40 +0000

The AI landscape in 2026 is more exciting — and more crowded — than ever. Every week there's a new tool promising to 10x your productivity, write your code, design your website, or automate your entire workflow.

But which ones actually deliver?

I tested dozens of AI tools this year so you don't have to. Here are the 10 that survived my workflow and earned a permanent spot in my toolkit.

1. Cursor — AI-Native Code Editor

Cursor remains the gold standard for AI-assisted coding. Its latest 2026 update brought multi-file context understanding, meaning you can ask it to refactor an entire codebase and it actually gets the relationships right.

Best for: Developers who want to ship faster without sacrificing code quality.
Price: Free tier available, Pro at $20/month
Rating: ⭐⭐⭐⭐⭐

2. Claude — Best for Long-Form Content & Reasoning

Claude (specifically Sonnet) has become my go-to for planning architecture, writing documentation, and brainstorming product ideas. Its 200K context window means I can feed it entire codebases or research papers.

Best for: Writing, strategy, and complex reasoning tasks.

3. Runway Gen-4 — Video Generation

Runway's Gen-4 model generates cinema-quality short videos from text prompts. The 2026 update added consistent character generation, making it actually usable for storytelling.

Best for: Content creators and marketers.

4. Perplexity Pro — Research Assistant

Perplexity has evolved beyond a simple search engine. Its new deep research mode writes comprehensive reports with citations — perfect for competitive analysis and market research.

Best for: Researchers and decision-makers.

5. v0 by Vercel — Landing Page Builder

Describe what you want in plain English, and v0 generates a production-ready React component. It's not perfect for complex apps, but for landing pages and marketing sites? It's incredibly fast.

If you're building landing pages or SaaS products with AI tools like v0, you'll need reliable hosting. I host all my projects on Hostinger — their shared hosting plans start at a fraction of what you'd pay elsewhere, and their performance is excellent for both static sites and WordPress deployments.

6. GitHub Copilot Workspace

Copilot Workspace takes AI-assisted development to the next level by understanding your entire repository structure. You can specify a feature in natural language, and it plans, implements, and tests the change.

Best for: Team development and large codebases.

7. Notion AI 2.0

Notion AI got a major upgrade in 2026. It now understands your entire workspace — your meeting notes, databases, project docs — and can answer questions across all of it. It also auto-generates meeting summaries and action items.

Best for: Remote teams and knowledge workers.

8. ElevenLabs — Voice & Audio

ElevenLabs' text-to-speech quality is now indistinguishable from human voice. Their new voice cloning API lets you generate podcasts, audiobooks, and voiceovers with consistent character voices.

Best for: Podcasters and audiobook creators.

9. Zapier Central — AI Workflow Builder

Zapier Central lets you build AI-powered automations by describing what you want in natural language. "When I get an invoice email, extract the data and add it to my Google Sheet" — it just works.

Best for: Anyone tired of repetitive manual tasks.

10. Bolt.new — Full-Stack AI App Builder

Describe a full-stack web app, and Bolt.new builds it — frontend, backend, database, deployment — in minutes. It's not production-ready for complex apps, but for MVPs and prototypes? Game-changing.

Where to Host Your AI-Powered Projects

Most of these tools generate output you need to host somewhere. Whether it's a landing page built with v0, a SaaS prototype from Bolt.new, or a portfolio site for your Runway-generated video content — you need reliable hosting.

That's where Hostinger comes in. I've been using Hostinger for over a year, and here's why it's my top recommendation:

Speed: LiteSpeed servers with built-in caching make sites load fast
Price: Plans start around $2.99/month — unbeatable for what you get
Ease of use: One-click WordPress and AI app installers
Uptime: 99.9% uptime guarantee

If you're building AI-powered projects in 2026, having a solid hosting foundation is just as important as choosing the right tools. Check out Hostinger's current deals — their business plan includes free domain, SSL, and daily backups.

Final Thoughts

2026 is the year AI tools went from "cool demos" to "must-have productivity multipliers." The key is not to use every tool — it's to pick the ones that solve your specific pain points.

Start with one or two from this list, integrate them into your daily workflow, and you'll wonder how you ever worked without them.

Disclosure: Some links in this article are affiliate links. If you purchase through them, I may earn a small commission at no extra cost to you. I only recommend products I personally use and trust.

WordPress vs Medium vs dev.to: Where Should You Blog in 2026?

bao001 xiao — Thu, 04 Jun 2026 07:21:39 +0000

If you're starting a blog in 2026, the first decision you'll face is which platform to use. Here's my honest breakdown after using all three.

Medium: Best for Quick Exposure

Medium has a built-in audience. Your articles can get discovered through their recommendation system without any SEO work. It's free and takes 2 minutes to start.

The downside? You don't own your audience. Medium controls what gets shown. And monetization is limited unless you're in their Partner Program.

Best for: Writers who want to test a niche fast.

dev.to: Best for Developer Content

dev.to is a developer-focused platform with strong community engagement. The SEO is excellent and readers are genuinely interested in technical content.

It has a clean reading experience and supports Markdown natively. Plus, you can cross-post from your own blog easily.

Best for: Tech and developer-focused content.

WordPress (Self-Hosted): Best for Long-Term Growth

A self-hosted WordPress site gives you full control. You own your content, your data, and your monetization. Google ranks self-hosted sites higher than Medium or dev.to articles.

You can run ads, affiliate marketing, sell products, build an email list - everything.

The trade-off: you need hosting and a domain. But it's cheaper than you think.

For hosting, I use Hostinger. Plans start around $3/month and include one-click WordPress installation. Their support has been solid.

Get started with Hostinger: https://www.hostinger.com?REFERRALCODE=PR5XIAOBADIM

My Recommendation

Use Medium or dev.to to test your niche and get initial readers. Once you have traction, set up a self-hosted WordPress site as your home base.

Cross-post your best content to Medium and dev.to for exposure, but always link back to your main site.

I wrote a complete beginner's guide to starting a blog here:
https://medium.com/@xiaobao200111/how-to-start-a-blog-in-2025-a-complete-beginners-guide-3abb36b456b7

This article contains affiliate links.

How to Start a Blog in 2026: A Complete Beginner's Guide

bao001 xiao — Thu, 04 Jun 2026 06:44:09 +0000

Everyone says blogging is dead. They're wrong.

In 2026 there are over 600 million blogs and more readers than ever. This guide walks you through starting a blog that actually grows.

Step 1: Pick a Niche

Good niches have three things: people search for help, you have knowledge, and there are products to recommend.

Step 2: Choose Your Platform

Medium is free. A self-hosted blog gives full control. For hosting, I use Hostinger.

Get started: https://www.hostinger.com?REFERRALCODE=PR5XIAOBADIM

Step 3: Set Up Your Blog

Connect your domain, install WordPress, pick a clean theme, install essential plugins.

Step 4: Write Your First Post

Promise a specific outcome. Identify the pain point. Break into clear sections.

Step 5: Get Readers

Post on Medium. Answer on Quora. Share on Reddit. Build an email list.

Your only job is to publish your first post.

Get hosting: https://www.hostinger.com?REFERRALCODE=PR5XIAOBADIM

This article contains affiliate links.

Test Article - Please Delete

bao001 xiao — Thu, 04 Jun 2026 06:43:43 +0000

This is a test to verify the dev.to API works. Delete this after confirming.

Hostinger: https://www.hostinger.com?REFERRALCODE=PR5XIAOBADIM