DEV Community: qing

Grok Regkit

qing — Wed, 15 Jul 2026 13:00:25 +0000

Grok Regkit

Introduction

The SunkenCost/grok-regkit has been gaining massive traction lately, and for good reason. This xAI/Grok account registration toolkit is a game-changer for developers looking to streamline their registration processes. With its browser and hybrid capabilities, as well as SSO and CPA OIDC export features, it's no wonder this toolkit is trending now.

Getting Started with Grok Regkit

To get started with Grok Regkit, you'll need to clone the repository and install the required dependencies. Here are the steps:

Clone the repository using git clone https://github.com/SunkenCost/grok-regkit.git
Install the dependencies using npm install or yarn install
Follow the instructions in the README to configure the toolkit for your specific use case

Practical Use Cases for Grok Regkit

So, how can you use Grok Regkit in your own projects? Here are a few practical examples:

Use Grok Regkit to simplify the registration process for your web application, reducing friction and increasing conversion rates
Integrate Grok Regkit with your existing SSO system to provide a seamless authentication experience for your users
Leverage the CPA OIDC export feature to integrate with other systems and services

Customizing and Extending Grok Regkit

One of the best things about Grok Regkit is its customizability and extensibility. Here are a few tips for getting the most out of the toolkit:

Use the built-in configuration options to tailor the registration process to your specific needs
Extend the toolkit using the provided APIs and hooks to integrate with other systems and services
Contribute to the open-source community by submitting pull requests and issues to improve the toolkit

Conclusion

In conclusion, the SunkenCost/grok-regkit is a powerful tool for simplifying and streamlining account registration processes. With its ease of use, customizability, and extensibility, it's no wonder this toolkit is trending now. Whether you're a seasoned developer or just starting out, Grok Regkit is definitely worth checking out. So, what are you waiting for? Give it a try today and see the difference it can make for your own projects.

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Python PostgreSQL with asyncpg: Async Database Operations

qing — Wed, 15 Jul 2026 13:00:04 +0000

Python PostgreSQL with asyncpg: Async Database Operations

asyncpg is the fastest PostgreSQL driver for Python — pure asyncio, no thread overhead, and up to 3× faster than psycopg2 on typical workloads. It is the go-to choice for any async Python backend.

Installation

pip install asyncpg
# PostgreSQL server must already be running

Connect and Create a Pool

import asyncio
import asyncpg
from datetime import datetime

DATABASE_URL = "postgresql://user:password@localhost:5432/mydb"

async def create_pool() -> asyncpg.Pool:
    pool = await asyncpg.create_pool(
        DATABASE_URL,
        min_size=2,
        max_size=10,
        command_timeout=30,
        server_settings={"application_name": "myapp"},
    )
    print("Pool created.")
    return pool

Schema Setup

CREATE_TABLES = """
CREATE TABLE IF NOT EXISTS users (
    id         BIGSERIAL PRIMARY KEY,
    username   TEXT      NOT NULL UNIQUE,
    email      TEXT      NOT NULL UNIQUE,
    created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE TABLE IF NOT EXISTS posts (
    id         BIGSERIAL PRIMARY KEY,
    user_id    BIGINT    NOT NULL REFERENCES users(id) ON DELETE CASCADE,
    title      TEXT      NOT NULL,
    body       TEXT      NOT NULL DEFAULT '',
    published  BOOLEAN   NOT NULL DEFAULT FALSE,
    created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE INDEX IF NOT EXISTS idx_posts_user    ON posts(user_id);
CREATE INDEX IF NOT EXISTS idx_posts_created ON posts(created_at DESC);
"""

async def setup_schema(pool: asyncpg.Pool) -> None:
    async with pool.acquire() as conn:
        await conn.execute(CREATE_TABLES)
    print("Schema ready.")

INSERT — Adding Records

async def create_user(pool: asyncpg.Pool, username: str, email: str) -> int:
    async with pool.acquire() as conn:
        row = await conn.fetchrow(
            """
            INSERT INTO users (username, email)
            VALUES ($1, $2)
            ON CONFLICT (username) DO UPDATE SET email = EXCLUDED.email
            RETURNING id, created_at
            """,
            username, email,
        )
        return row["id"]

async def create_post(
    pool: asyncpg.Pool,
    user_id: int,
    title: str,
    body: str,
    published: bool = False,
) -> int:
    async with pool.acquire() as conn:
        row = await conn.fetchrow(
            """
            INSERT INTO posts (user_id, title, body, published)
            VALUES ($1, $2, $3, $4)
            RETURNING id
            """,
            user_id, title, body, published,
        )
        return row["id"]

# Bulk insert with copy_records_to_table — extremely fast
async def bulk_insert_posts(pool: asyncpg.Pool, rows: list[tuple]) -> None:
    async with pool.acquire() as conn:
        await conn.copy_records_to_table(
            "posts",
            records=rows,
            columns=["user_id", "title", "body", "published"],
        )
    print(f"Bulk inserted {len(rows)} posts.")

SELECT — Querying Records

async def get_user_posts(
    pool: asyncpg.Pool,
    user_id: int,
    limit: int = 20,
    offset: int = 0,
) -> list[asyncpg.Record]:
    async with pool.acquire() as conn:
        return await conn.fetch(
            """
            SELECT p.id, p.title, p.published, p.created_at, u.username
            FROM   posts p
            JOIN   users u ON u.id = p.user_id
            WHERE  p.user_id = $1
            ORDER  BY p.created_at DESC
            LIMIT  $2 OFFSET $3
            """,
            user_id, limit, offset,
        )

async def search_posts(pool: asyncpg.Pool, keyword: str) -> list[asyncpg.Record]:
    async with pool.acquire() as conn:
        return await conn.fetch(
            """
            SELECT p.title, u.username, p.created_at
            FROM   posts p
            JOIN   users u ON u.id = p.user_id
            WHERE  to_tsvector('english', p.title || ' ' || p.body)
                   @@ plainto_tsquery('english', $1)
            ORDER  BY p.created_at DESC
            LIMIT  50
            """,
            keyword,
        )

UPDATE, DELETE, and Transactions

async def publish_post(pool: asyncpg.Pool, post_id: int) -> bool:
    async with pool.acquire() as conn:
        result = await conn.execute(
            "UPDATE posts SET published = TRUE WHERE id = $1",
            post_id,
        )
        return result == "UPDATE 1"

async def transfer_posts(
    pool: asyncpg.Pool,
    from_user: int,
    to_user: int,
) -> int:
    async with pool.acquire() as conn:
        async with conn.transaction():
            # Both statements run atomically
            result = await conn.execute(
                "UPDATE posts SET user_id = $1 WHERE user_id = $2",
                to_user, from_user,
            )
            count = int(result.split()[-1])
            await conn.execute(
                "INSERT INTO audit_log (action, detail) VALUES ($1, $2)",
                "transfer_posts",
                f"from={from_user} to={to_user} count={count}",
            )
        return count

Full Working Example

async def main():
    pool = await create_pool()
    await setup_schema(pool)

    uid = await create_user(pool, "alice", "alice@example.com")
    print(f"Created user id={uid}")

    pid = await create_post(pool, uid, "asyncpg Deep Dive", "asyncpg is blazing fast...", True)
    print(f"Created post id={pid}")

    posts = await get_user_posts(pool, uid)
    for p in posts:
        print(f"  [{p['id']}] {p['title']} published={p['published']}")

    await pool.close()

if __name__ == "__main__":
    asyncio.run(main())

Practical Tips

Always use a pool — create_pool() is cheap once, connection acquisition is fast
Use $1, $2, ... placeholders to prevent SQL injection (asyncpg uses positional params)
conn.copy_records_to_table() is 10-50× faster than looped INSERT for bulk data
async with conn.transaction() nests safely — inner blocks become savepoints
fetchrow() returns None if no row matches — always check before accessing fields
Enable server_settings={"statement_timeout": "5000"} to protect against runaway queries

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Python Decorators Explained Simply

qing — Wed, 15 Jul 2026 12:05:38 +0000

Python Decorators Explained Simply

Introduction

Python Decorators Explained Simply is essential knowledge for every developer.

Key Points

Start with the basics
Practice regularly
Build real projects
Share your knowledge

Getting Started

The best way to learn is by doing. Set up a test environment and experiment.

Best Practices

Follow official documentation
Join community forums
Contribute to open source
Write about what you learn

Conclusion

Mastering python opens many career opportunities. Start today!

Follow for more python content!

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How to Secure Your Linux Server in 10 Steps

qing — Wed, 15 Jul 2026 12:05:32 +0000

How to Secure Your Linux Server in 10 Steps

Introduction

How to Secure Your Linux Server in 10 Steps is essential knowledge for every developer.

Key Points

Start with the basics
Practice regularly
Build real projects
Share your knowledge

Getting Started

The best way to learn is by doing. Set up a test environment and experiment.

Best Practices

Follow official documentation
Join community forums
Contribute to open source
Write about what you learn

Conclusion

Mastering linux opens many career opportunities. Start today!

Follow for more linux content!

More at https://青.失落.世界

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Quick Python Tip: Use typing.TypedDict for Cleaner Dict Type Hints (2026)

qing — Wed, 15 Jul 2026 12:00:52 +0000

Quick Python Tip: Use typing.TypedDict for Cleaner Dict Type Hints

When working with dictionaries in Python, it's common to use the dict type hint to indicate that a function or variable expects a dictionary. However, this can lead to unclear type hints, especially when dealing with dictionaries that have a specific structure. For example, consider a dictionary that represents a user, with keys for name, email, and age.

You could use the dict[str, Any] type hint, but this doesn't provide any information about the expected keys or their corresponding value types:

from typing import Any, Dict

user: Dict[str, Any] = {'name': 'John', 'email': 'john@example.com', 'age': 30}

A better approach is to use typing.TypedDict, which allows you to define a dictionary with a specific set of keys and value types:

from typing import TypedDict

class User(TypedDict):
    name: str
    email: str
    age: int

user: User = {'name': 'John', 'email': 'john@example.com', 'age': 30}

Using TypedDict provides several benefits, including improved IDE autocompletion, as your IDE can now suggest the expected keys and their corresponding value types.
The takeaway is that using typing.TypedDict leads to cleaner and more informative type hints, making your code easier to understand and maintain.

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Python Design Patterns: 5 You Should Know

qing — Wed, 15 Jul 2026 12:00:06 +0000

Python Design Patterns: 5 You Should Know

Design patterns solve common programming problems elegantly.

1. Singleton Pattern

class Database:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance.connection = cls._create_connection()
        return cls._instance

    @staticmethod
    def _create_connection():
        return "db_connection"

db1 = Database()
db2 = Database()
assert db1 is db2  # Same instance!

2. Observer Pattern

class EventEmitter:
    def __init__(self):
        self._listeners = {}

    def on(self, event, callback):
        self._listeners.setdefault(event, []).append(callback)

    def emit(self, event, *args):
        for cb in self._listeners.get(event, []):
            cb(*args)

emitter = EventEmitter()
emitter.on('data', lambda x: print(f"Got: {x}"))
emitter.emit('data', 42)

3. Strategy Pattern

from typing import Protocol

class SortStrategy(Protocol):
    def sort(self, data: list) -> list: ...

class BubbleSort:
    def sort(self, data: list) -> list:
        # bubble sort implementation
        return sorted(data)

class QuickSort:
    def sort(self, data: list) -> list:
        return sorted(data, key=lambda x: x)

class Sorter:
    def __init__(self, strategy: SortStrategy):
        self.strategy = strategy

    def sort(self, data):
        return self.strategy.sort(data)

sorter = Sorter(QuickSort())
print(sorter.sort([3, 1, 4, 1, 5]))

4. Factory Pattern

def create_animal(animal_type: str):
    animals = {
        "dog": lambda: Dog(),
        "cat": lambda: Cat(),
        "bird": lambda: Bird(),
    }
    factory = animals.get(animal_type)
    if not factory:
        raise ValueError(f"Unknown animal: {animal_type}")
    return factory()

5. Decorator Pattern

import time
import functools

def timer(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        elapsed = time.time() - start
        print(f"{func.__name__} took {elapsed:.3f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(0.1)

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Python Async Web Scraping: Scrape 1000 Pages in Seconds

qing — Wed, 15 Jul 2026 12:00:04 +0000

Python Async Web Scraping: Scrape 1000 Pages in Seconds

Synchronous scraping is slow - each request blocks until the server responds. With asyncio and httpx, you can fire off hundreds of requests concurrently and cut your scraping time from minutes to seconds.

Installation

pip install httpx beautifulsoup4 lxml

The Core Concept

Sync: fetch(1) -> wait -> fetch(2) -> wait -> fetch(3) -> wait ...

Async: fetch(1), fetch(2), fetch(3) all wait at the same time -> results arrive together

This is the difference between 300 seconds and 3 seconds for 1000 pages.

Complete Async Scraper with Rate Limiting

import asyncio
import httpx
from bs4 import BeautifulSoup
from datetime import datetime
import json

MAX_CONCURRENT = 20
TIMEOUT = 15

async def fetch_one(
    client: httpx.AsyncClient,
    url: str,
    semaphore: asyncio.Semaphore,
) -> dict:
    async with semaphore:
        try:
            resp = await client.get(url)
            resp.raise_for_status()
            return {"url": url, "status": resp.status_code, "html": resp.text}
        except httpx.HTTPStatusError as e:
            return {"url": url, "status": e.response.status_code, "html": "", "error": str(e)}
        except httpx.RequestError as e:
            return {"url": url, "status": 0, "html": "", "error": str(e)}

async def fetch_all(urls: list[str]) -> list[dict]:
    semaphore = asyncio.Semaphore(MAX_CONCURRENT)
    headers = {
        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0"
    }
    async with httpx.AsyncClient(
        timeout=TIMEOUT,
        follow_redirects=True,
        headers=headers,
    ) as client:
        tasks = [fetch_one(client, url, semaphore) for url in urls]
        results = await asyncio.gather(*tasks)
    return list(results)

def parse_title(html: str) -> str:
    if not html:
        return ""
    soup = BeautifulSoup(html, "lxml")
    tag = soup.find("title")
    return tag.get_text(strip=True) if tag else ""

async def scrape_urls(urls: list[str]) -> list[dict]:
    print(f"Starting async scrape of {len(urls)} URLs...")
    start = datetime.now()
    raw_results = await fetch_all(urls)
    parsed = []
    ok_count = 0
    for result in raw_results:
        title = parse_title(result["html"])
        parsed.append({
            "url": result["url"],
            "status": result["status"],
            "title": title,
            "error": result.get("error", ""),
        })
        if result["status"] == 200:
            ok_count += 1
    elapsed = (datetime.now() - start).total_seconds()
    print(f"Done: {ok_count}/{len(urls)} OK in {elapsed:.1f}s")
    print(f"Speed: {len(urls) / elapsed:.1f} pages/sec")
    return parsed

async def main():
    base = "https://docs.python.org/3/library/"
    modules = [
        "asyncio.html", "pathlib.html", "dataclasses.html",
        "typing.html", "functools.html", "itertools.html",
        "collections.html", "contextlib.html", "logging.html",
        "unittest.html", "json.html", "csv.html",
        "re.html", "os.html", "sys.html",
        "datetime.html", "hashlib.html", "base64.html",
        "urllib.request.html", "http.client.html",
        "socket.html", "ssl.html", "threading.html",
        "multiprocessing.html", "subprocess.html",
        "io.html", "struct.html", "array.html",
        "queue.html", "heapq.html",
    ]
    urls = [base + m for m in modules]
    results = await scrape_urls(urls)
    for r in results[:5]:
        icon = "OK" if r["status"] == 200 else "ERR"
        print(f"[{icon}] [{r['status']}] {r['title'][:50]}")
    with open("docs_scrape.json", "w") as f:
        json.dump(results, f, indent=2)
    print(f"Saved {len(results)} results")

if __name__ == "__main__":
    asyncio.run(main())

Retry Logic with Exponential Backoff

async def fetch_with_retry(
    client: httpx.AsyncClient,
    url: str,
    semaphore: asyncio.Semaphore,
    retries: int = 3,
) -> dict:
    async with semaphore:
        for attempt in range(retries):
            try:
                resp = await client.get(url)
                resp.raise_for_status()
                return {"url": url, "html": resp.text, "ok": True}
            except (httpx.TimeoutException, httpx.ConnectError):
                if attempt < retries - 1:
                    await asyncio.sleep(2 ** attempt)
                else:
                    return {"url": url, "html": "", "ok": False, "error": "Max retries"}
            except httpx.HTTPStatusError as e:
                if e.response.status_code >= 500:
                    await asyncio.sleep(1)
                else:
                    return {"url": url, "html": "", "ok": False,
                            "error": f"HTTP {e.response.status_code}"}
        return {"url": url, "html": "", "ok": False, "error": "Unknown"}

Practical Tips

Start with 5-10 concurrent requests, increase only if the server allows it
asyncio.Semaphore is your throttle - never skip it for polite scraping
asyncio.gather(*tasks, return_exceptions=True) prevents one failure from cancelling all tasks
Stream large responses with client.stream() to avoid memory issues
Reuse the AsyncClient - creating one per request is slow and wastes connections

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How to Secure Your Linux Server in 10 Steps

qing — Wed, 15 Jul 2026 11:20:05 +0000

How to Secure Your Linux Server in 10 Steps

Introduction

How to Secure Your Linux Server in 10 Steps is essential knowledge for every developer.

Key Points

Start with the basics
Practice regularly
Build real projects
Share your knowledge

Getting Started

The best way to learn is by doing. Set up a test environment and experiment.

Best Practices

Follow official documentation
Join community forums
Contribute to open source
Write about what you learn

Conclusion

Mastering linux opens many career opportunities. Start today!

Follow for more linux content!

More at https://青.失落.世界

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Quick Tip: `dict.setdefault()` eliminates boilerplate key checks (2026)

qing — Wed, 15 Jul 2026 10:56:07 +0000

Quick Tip: dict.setdefault() eliminates boilerplate key checks

When working with dictionaries in Python, it's common to encounter situations where you need to check if a key exists before assigning a value to it. This can lead to boilerplate code that clutters your scripts. Let's take a look at a typical example:

Before: Manual key check

d = {'a': 1, 'b': 2}

if 'c' not in d:
    d['c'] = []

d['c'].append(3)

In this example, we're checking if the key 'c' exists in the dictionary d. If it doesn't, we create a new key with an empty list

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2-Minute Guide: Reading and Writing JSON in Python (2026)

qing — Wed, 15 Jul 2026 10:42:31 +0000

2-Minute Guide: Reading and Writing JSON in Python

In this guide, we will cover the basics of reading and writing JSON data in Python using the built-in json module.

Reading JSON

To read JSON data from a file, use json.load(). This function takes a file object as an argument and returns a Python dictionary.

import json

with open('data.json') as f:
    data = json.load(f)

To parse a JSON string, use json.loads().

import json

json_string = '{"name": "John", "age": 30}'
data = json.loads(json_string)

Writing JSON

To write JSON data

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100 Battle-Tested AI Prompts for Developers - Free Preview

qing — Wed, 15 Jul 2026 10:39:52 +0000

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Quick Python Tip: itertools.chain Flattens Lists Without Nested Loops (2026)

qing — Wed, 15 Jul 2026 08:44:23 +0000

Quick Python Tip: itertools.chain Flattens Lists Without Nested Loops
When working with multiple lists in Python, you often need to flatten them into a single list. A common approach is to use nested loops, but this can be cumbersome and inefficient. For example, consider the following code that uses nested loops to flatten two lists:

list1 = [[1, 2], [3, 4]]
list2 = [[5, 6], [7, 8]]
flattened_list = []
for sublist in list1 + list2:
    for item in sublist:
        flattened_list.append(item)
print(flattened_list)  # [1, 2, 3, 4, 5, 6, 7, 8]

However, there's a more elegant and efficient way to achieve this using itertools.chain. You can pass multiple lists to itertools.chain using the * operator, which unpacks the lists into separate arguments. Here's an example:

import itertools
list1 = [[1, 2], [3, 4]]
list2 = [[5, 6], [7, 8]]
flattened_list = list(itertools.chain(*list1 + list2))
print(flattened_list)  # [1, 2, 3, 4, 5, 6, 7, 8]

The itertools.chain approach is not only more concise but also more memory-efficient, as it avoids creating intermediate lists.
The takeaway is that using itertools.chain(*lists) is a more efficient and Pythonic way to flatten multiple lists than using nested loops.

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