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A twitter client using Flask and Redis

A twitter client using Flask and Redis

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Joydeep
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December 29, 2016
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3 min read
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In our previous redis blog we gave a brief introduction on how to interface between python and redis. In this post, we will use Redis as a cache, to build the backend of our basic twitter app.

We first start the server, if it’s in a stopped state.

sudo service redis_6379 start
sudo service redis_6379 stop

In case you have not installed the redis server, you can install the server and configure it with python using the previous tutorial.

We will work on creating our own custom Twitter and post tweets to this. Users should be able to post tweets, and there should be a timeline forthe posts. The screenshot of the final product is shown below.

We will use flask and redis for this. Flask is a good python web microframework which lets you focus only on things you need. There is more focus on the modularity of your code base. Redis is a key-value datastore that can be used as a database. Redis is an excellent choice for caching and for constant real-time analysis of data coming in, hence redis is a great tool to build a twitter-like platform.

Let us start building the module. There are some build dependencies; therefore ensure the following dependencies are installed.

sudo apt-get install build-essential
sudo apt-get install python3-dev
sudo apt-get install libncurses5-dev

Once done, fire-up a virtualenv and install the requirements.

virtualenv venv -p python3.5
source venv/bin/activate
wget https://raw.githubusercontent.com/infinite-Joy/retwis-py/master/requirements.txt
pip install -r requirements.txt

Create a folder structure of the following format.

mkdir retwis
cd retwis

Frontend using Jinja templates

Flask lets us create the template files - layout.html, login.html and signup.html. These templates are designed using the Jinja2 templates which Flask uses. We can use template inheritance and login and signup pages will inherit from layout.html.

Check out the three template files shown below.

<!doctype html>
<title>Retwis</title>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css">
<link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='style.css') }}">
<nav class="navbar navbar-default navbar-fixed-top">
  <div class="container-fluid">
    <div class="navbar-header">
      <h1>Retwis</h1>
    </div>
    <div id="navbar" class="navbar-collapse collapse">
      <ul class="nav navbar-nav navbar-right">
        <li>
        {% if not session.username %}
          <a href="{{ url_for('login') }}">log in</a>
        {% else %}
          <a href="{{ url_for('logout') }}">log out</a>
        {% endif %}
        </li>
      </ul>
    </div>
  </div>
</nav>
<div class="main-body">
  <div class="container">
    {% block body %}{% endblock %}
  </div>
</div>

Note that we have abstracted out the common elements of all the pages. We have defined the header with the title and then in the body; if a session is present, there will be the login link, else there will be the logout link.

Check out the login and the signup html which are almost similar.

{% extends "layout.html" %}
{% block body %}
  <h2>Login</h2>
  {% if error %}<p class="error"><strong>Error:</strong> {{ error }}{% endif %}
  <form action="{{ url_for('login') }}" method="post">
    <div class="form-group">
      <label for="username">Username</label>
      <input class="form-control" type="text" name="username">
    </div>
    <div class="form-group">
      <label for="password">Password</label>
      <input class="form-control" type="password" name="password">
    </div>
    <button class="btn btn-default" type="submit">Login</button>
  </form>
  <a class="btn btn-default" href="{{ url_for('signup') }}">Sign up</a>
{% endblock %}
{% extends "layout.html" %}
{% block body %}
  <h2>Signup</h2>
  {% if error %}<p class="error"><strong>Error:</strong> {{ error }}{% endif %}
  <form action="{{ url_for('signup') }}" method="post">
    <div class="form-group">
      <label for="username">Username</label>
      <input class="form-control" type="text" name="username">
    </div>
    <div class="form-group">
      <label for="password">Password</label>
      <input class="form-control" type="password" name="password">
    </div>
    <button class="btn btn-default" type="submit">Sign up</button>
  </form>
{% endblock %}

As you can see, if there is no error, then we define the username and the password fields that are bound with the “post” method.

We can now create the basic flask app and see if the two templates get rendered correctly. We create two endpoints for the templates and then render them. Check out the code below.

from flask import Flask
from flask import render_template

app = Flask(__name__)
DEBUG = True

@app.route('/signup')
def signup():
    error = None
    return render_template('signup.html', error=error)

@app.route('/')
def login():
    error = None
    return render_template('login.html', error=error)

if __name__ == "__main__":
    app.run()

To run the server use the following command.

python views.py

On your browser, open http://127.0.0.1:5000/signup

And hit http://127.0.0.1:5000/

You should be able to see the two pages above.

We will also need to create the home page which the user will fall back to once he is logged in. Create a home.html in the templates folder and then write the tweets block.

{% extends "layout.html" %}
{% block body %}
  <form action="{{ url_for('home') }}" method="post">
    <div class="form-group">
      <input class="form-control" type="text" name="tweet" placeholder="What are you thinking?">
    </div>
    <button class="btn btn-default" type="submit">Post</button>
  </form>
  {% for post in timeline %}
    <li class="tweet">
      {{ post.username }} at {{ post.ts }}
      {{ post.text }}
    </li>
  {% else %}
    <h2>No posts!</h2>
  {% endfor %}
{% endblock %}

As you see, if there are posts on the timeline, then list the username, time, and the text, else put “No posts” in header format. Let’s build the code for that in view.py and see how it looks.

@app.route('/home')
def home():
    return render_template('home.html', timeline=[{"username": "dummy_username",
                                                   "ts": "today",
                                                   "text": "dummy text"}])

If you check out the url http://localhost:5000/home, you should get the page below.

Now that we have all the pages and have built the frontend, in the next post we will build the redis backend that will handle the user information, the session data, and the posts that the users submit.

Sessions and user information

We will be using redis to get user information. If you don't have redis-py already installed in your virtual environment, install it using pip.

pip install redis

Next, we need to plugin redis to our flask app and see that it gets instantiated before each request.

import redis

from flask import Flask
from flask import render_template

app = Flask(__name__)
DEBUG = True

def init_db():
    db = redis.StrictRedis(
        host=DB_HOST,
        port=DB_PORT,
        db=DB_NO)
    return db

@app.before_request
def before_request():
    g.db = init_db()

# remaining code here.

We will interface the signup page with redis and on signing up, the user information should get populated in the redis datastore.

We change the signup function to the code below.

import redis

from flask import Flask
from flask import render_template
from flask import request
from flask import url_for
from flask import session
from flask import g

app = Flask(__name__)

# other code …

@app.route('/signup', methods=['GET', 'POST'])
def signup():
    error = None
    if request.method == 'GET':
        return render_template('signup.html', error=error)
    username = request.form['username']
    password = request.form['password']
    user_id = str(g.db.incrby('next_user_id', 1000))
    g.db.hmset('user:' + user_id, dict(username=username, password=password))
    g.db.hset('users', username, user_id)
    session['username'] = username
    return redirect(url_for('home'))

Here, we take the username and the password from the form and push them to the redis database. Note that we increment the keys by 1000. This is a standard for redis keys. For more information, consult the official docs.

We will also need to set a secret key to use session information which is used in the code above. You can read about sessions and how to set session keys from the official docs. We will also do a little bit of refactoring and keep the settings information together.

# import statements

app = Flask(__name__)

# settings
DEBUG = True

# I am using a SHA1 hash. Use a more secure algo in your PROD work
SECRET_KEY = '8cb049a2b6160e1838df7cfe896e3ec32da888d7'
app.secret_key = SECRET_KEY

# Redis setup
DB_HOST = 'localhost'
DB_PORT = 6379
DB_NO = 0

# def init_db(): ...
# def before_request(): ...
# def signup(): ...
# def login(): ...
# def home(): ...

if __name__ == "__main__":
    app.run()

Check out the form now and try to submit some user information.

Check on the redis end and check out the values that have been populated.

?  redis-cli
127.0.0.1:6379> HGETALL *
(empty list or set)
127.0.0.1:6379> KEYS *
1) "users"
2) "user:1000"
3) "next_user_id"
127.0.0.1:6379> HGETALL "users"
1) "hackerearth"
2) "1000"
127.0.0.1:6379> HGETALL "user:1000"
1) "username"
2) "hackerearth"
3) "password"
4) "hackerearth"

Once the session and signup functions work fine, we can then focus on the home page where people can login once they have signed up. These two pages should fall back safely to the home page.

@app.route('/', methods=['GET', 'POST'])
def login():
    error = None
    if request.method == 'GET':
        return render_template('login.html', error=error)
    username = request.form['username']
    password = request.form['password']
    user_id = str(g.db.hget('users', username), 'utf-8')
    if not user_id:
        error = 'No such user'
        return render_template('login.html', error=error)
    saved_password = str(g.db.hget('user:' + str(user_id), 'password'), 'utf-8')
    if password != saved_password:
        error = 'Incorrect password'
        return render_template('login.html', error=error)
    session['username'] = username
    return redirect(url_for('home'))

The code tells us if the request method is “GET”, then we render the login page. This is the first page that comes up when we go to the page http://localhost:5000/.

After that, we will fill up the fields with the previous values. The entered username and password is pulled from the form. Using this username, we get the user ID from the redis database and this user ID is used to retrieve the password. This password is then matched with the entered password. If there is a match, then we will be redirected to the “home page.”

We now need to work on the home page. The home page is the biggest of the three modules as these do several things simultaneously. It should handle the session information. If the session information is not there, it should transfer to the login page. It should retrieve the posts of the user and push them to the redis database and get the data in turn. So we will replace the home function in views.py with the code below.

@app.route('/home', methods=['GET', 'POST'])
def home():
    if not session:
        return redirect(url_for('login'))
    user_id = g.db.hget('users', session['username'])
    if request.method == 'GET':
        return render_template('home.html', timeline=_get_timeline(user_id))
    text = request.form['tweet']
    post_id = str(g.db.incr('next_post_id'))
    g.db.hmset('post:' + post_id, dict(user_id=user_id,
                                       ts=datetime.utcnow(), text=text))
    g.db.lpush('posts:' + str(user_id), str(post_id))
    g.db.lpush('timeline:' + str(user_id), str(post_id))
    g.db.ltrim('timeline:' + str(user_id), 0, 100)
    return render_template('home.html', timeline=_get_timeline(user_id))

def _get_timeline(user_id):
    posts = g.db.lrange('timeline:' + str(user_id), 0, -1)
    timeline = []
    for post_id in posts:
        post = g.db.hgetall('post:' + str(post_id, 'utf-8'))
        timeline.append(dict(
            username=g.db.hget('user:' + str(post[b'user_id'], 'utf-8'), 'username'),
            ts=post[b'ts'],
            text=post[b'text']))
    return timeline

Note, the timeline part is handled in the _get_timeline function. We get the timeline from the redis database and then for all the posts we put the username, time and the post text to a timeline list. This list is returned to the home function, which takes the user tweet post and pushes it to redis, after which it renders the current posts in the timeline. We will also need to “import datetime.”

import redis

import datetime

from flask import Flask
from flask import render_template
from flask import request
from flask import url_for
from flask import session
from flask import g
from flask import redirect

# rest of the code

We need to build the url for logout for the template to work correctly.

@app.route('/logout')
def logout():
    session.pop('username', None)
    return redirect(url_for('login'))

Now, check it in the browser. Hit http://localhost:5000; login with your credentials. You should be able to post tweets now to the post.

Please refactor the code to make it more organized. Also, use Test Driven Development and good logging practises when building production-grade apps (although it isn’t in this post). Please find the whole code in this github repo.

Credits

A big shoutout to kushmansingh/retwis-py who inspired me to write the blog.

References
quora: Why-use-Redis

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December 29, 2016
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3 min read
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How I used VibeCode Arena platform to build code using AI and leant how to improve it

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

  • Clean, semantic HTML
  • Smooth CSS transitions
  • Keyboard navigation support
  • ARIA labels for accessibility
  • Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

  • No DNS-prefetch hints for external image domains
  • Missing width/height attributes causing layout shift
  • No preload directives for critical resources
  • Missing CSS containment properties
  • No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

  • No 'use strict' directive in JavaScript
  • Missing package.json for dependency management
  • No test files
  • Missing README documentation
  • No .gitignore or version control setup
  • Could use functional array methods for cleaner code
  • Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

  • Security vulnerabilities
  • Performance/Efficiency issues
  • Performance optimization opportunities
  • Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

  • What actually improved (and by how much)
  • What new issues I might have introduced
  • Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

  • How to implement Content Security Policy correctly
  • Why DOM depth matters for rendering performance
  • What CSS containment does and when to use it
  • How to structure code for better maintainability
  • Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

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Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

Vibe Coding Difference

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

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