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Vibe Coding a Speed Reading App with Python in Just 15 Minutes

Published

2 weeks ago

June 25, 2025

Bala Priya C

Vibe Coding a Speed Reading App with Python in Just 15 Minutes

Image by Author | Ideogram

Picture this: You have an idea for a speed reading app. Instead of spending hours researching which Python modules and libraries to use, coding the different components, and debugging syntax errors, you simply describe what you want in plain English. Within minutes, you’re tweaking font sizes and discussing user experience improvements with your AI coding partner.

This is “vibe coding” — a collaborative approach where natural language instructions help get to functional applications through iterative conversation. It’s not about replacing traditional coding skills, but about accelerating the journey from concept to working prototype.

Today, I’ll walk you through how I built a fully functional RSVP (Rapid Serial Visual Presentation) speed reading app in just 15 minutes using Python.

🔗 Link to the speed-reading app on GitHub

Going From Idea to Implementation

Say you have an idea, and would like to vibe-code it. If you already use ChatGPT, Claude, or Gemini, you can continue to use the same. I recommend you try out these prompts (or better versions of the same) to see what you’re able to build.

Step 1: Describe What You Want to Build

You can open with a simple request:

“I’d like to create a command-line speed reading application using Python that implements RSVP (Rapid Serial Visual Presentation) technique. The app should run on Ubuntu, display words sequentially at adjustable speeds, and include basic controls based on keyboard inputs. Could you provide a clean, well-structured implementation with proper error handling?”

No technical specifications. No detailed requirements. Just a clear intent. This is where vibe coding is super cool — you start with the what, not the how.

This gives us a good starting point. From that initial prompt, you should get a functional terminal-based speed reading application:

class RSVPReader:
    def __init__(self, text, wpm=250, chunk_size=1):
        self.text = text
        self.wpm = wpm
        self.words = self._prepare_text()
        self.current_index = 0
        self.is_paused = False
        self.delay = 60.0 / (wpm * chunk_size)

The initial implementation includes:

Text processing: Splitting content into readable chunks
Speed control: Configurable words-per-minute
Interactive controls: Pause, resume, navigate, speed adjustment
Progress tracking: Visual feedback with progress bars
File support: Read from text files or direct input

For the complete implementation of the class, you can check the rsvp_reader.py file.

Step 2: Enhance User Experience

When requesting improvements, we used descriptive, goal-oriented language:

“I’d like to enhance the visual presentation by centering the text display in the terminal window and increasing the font emphasis for better readability. Could you modify the code to utilize the terminal’s center area more effectively while maintaining clean, professional output?”

This prompted terminal manipulation:

def _get_terminal_size(self):
	"""Get terminal dimensions for responsive layout"""
	try:
    import shutil
    cols, rows = shutil.get_terminal_size()
    return cols, rows
	except OSError:
    	    return 80, 24  # Sensible fallbacks

Now the speed-reading app still works. However, we can add some final improvements.

Step 3: Refine User Interface Requirements As Needed

Our final iteration request specifies the requirements clearly:

“I’d like to refine the interface design with these specific requirements: 1) Display text in the center 40% of the terminal screen, 2) Reduce default reading speed for better comprehension, 3) Create a static control interface that doesn’t refresh, with only the reading text updating dynamically, 4) Maintain clean borders around the active display area. Could you implement these changes while preserving all existing functionality?”

This resulted in the following terminal control:

def _get_display_area(self):
    """Get the 40% center rectangle dimensions"""
    cols, rows = self._get_terminal_size()
    
    display_width = int(cols * 0.4)
    display_height = int(rows * 0.4)
    
    start_col = (cols - display_width) // 2
    start_row = (rows - display_height) // 2
    
    return start_col, start_row, display_width, display_height

def _draw_static_interface(self):
    """Draw the static interface"""
    # Controls stay fixed, only words change

An Overview of the Technical Specifics

We have the following in the RSVP speed reading app we’ve built.

Threading for Responsive Controls

This method captures keyboard input in real-time without pausing the main program by switching the terminal to raw mode and using non-blocking I/O polling:

def _get_keyboard_input(self):
    """Non-blocking keyboard input handler"""
    old_settings = termios.tcgetattr(sys.stdin)
    try:
        tty.setraw(sys.stdin.fileno())
        while self.is_running:
            if select.select([sys.stdin], [], [], 0.1)[0]:
                # Handle real-time input without blocking

Smart Terminal Positioning

This method positions text at exact coordinates on the terminal screen using ANSI escape sequences, where the code moves the cursor to a specific row and column before printing the word:

def _display_word(self, word):
    # Use ANSI escape codes for precise positioning
    print(f'\033[{word_row};{word_start_col}H{large_word}')

Adaptive Speed Control

This dynamically adjusts reading speed based on word length, giving users 20% more time for long words (8+ characters) and 20% less time for short words (under 4 characters) to optimize comprehension:

# Longer words get more display time
word_delay = self.delay
if len(current_word) > 8:
    word_delay *= 1.2
elif len(current_word) < 4:
    word_delay *= 0.8

So yeah, you can run the app, and see for yourself how it works.

First, you can make it executable like so. Make sure you can add the shebang line at the top of the script:

$ chmod +x rsvp_reader.py

You can run it like so:

$ ./rsvp_reader.py sample.txt

You can find more details on the README file.

Conclusion

Our vibe coding session produced:

A fully functional terminal-based speed reading app in Python
Support for variable reading speeds (50-1000+ WPM)
Real-time controls for pause, navigation, and speed adjustment
Adaptive display that works on any terminal size
Clean, distraction-free interface focused on the 40% center area
Smart word timing based on length and complexity

In 15 minutes, we went from a simple idea to a functional application that someone can actually use.

Ready to try vibe coding yourself? Start with a simple idea, describe it in plain English, and see where the conversation takes you. The code will follow.

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.

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Jobs & Careers

HCLSoftware Launches Domino 14.5 With Focus on Data Privacy and Sovereign AI

Published

2 hours ago

July 7, 2025

Shalini Mondal

HCLSoftware, a global enterprise software leader, launched HCL Domino 14.5 on July 7 as a major upgrade, specifically targeting governments and organisations operating in regulated sectors that are concerned about data privacy and digital independence.

A key feature of the new release is Domino IQ, a sovereign AI extension built into the Domino platform. This new tool gives organisations full control over their AI models and data, helping them comply with regulations such as the European AI Act.

It also removes dependence on foreign cloud services, making it easier for public sector bodies and banks to protect sensitive information.

“The importance of data sovereignty and avoiding unnecessary foreign government influence extends beyond SaaS solutions and AI. Specifically for collaboration – the sensitive data within email, chat, video recordings and documents. With the launch of Domino+ 14.5, HCLSoftware is helping over 200+ government agencies safeguard their sensitive data,” said Richard Jefts, executive vice president and general manager at HCLSoftware

The updated Domino+ collaboration suite now includes enhanced features for secure messaging, meetings, and file sharing. These tools are ready to deploy and meet the needs of organisations that handle highly confidential data.

The platform is supported by IONOS, a leading European cloud provider. Achim Weiss, CEO of IONOS, added, “Today, more than ever, true digital sovereignty is the key to Europe’s digital future. That’s why at IONOS we are proud to provide the sovereign cloud infrastructure for HCL’s sovereign collaboration solutions.”

Other key updates in Domino 14.5 include achieving BSI certification for information security, the integration of security event and incident management (SEIM) tools to enhance threat detection and response, and full compliance with the European Accessibility Act, ensuring that all web-based user experiences are inclusive and accessible to everyone.

With the launch of Domino 14.5, HCLSoftware is aiming to be a trusted technology partner for public sector and highly regulated organisations seeking control, security, and compliance in their digital operations.

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Mitsubishi Electric Invests in AI-Assisted PLM Systems Startup ‘Things’

Published

2 hours ago

July 7, 2025

Smruthi Nadig

Mitsubishi Electric Corporation announced on July 7 that its ME Innovation Fund has invested in Things, a Japan-based startup that develops and provides AI-assisted product lifecycle management (PLM) systems for the manufacturing industry.

This startup specialises in comprehensive document management, covering everything from product planning and development to disposal. According to the company, this marks the 12th investment made by Mitsubishi’s fund to date.

Through this investment, Mitsubishi Electric aims to combine its extensive manufacturing and control expertise with Things’ generative AI technology. The goal is to accelerate the development of digital transformation (DX) solutions that tackle various challenges facing the manufacturing industry.

In recent years, Japan’s manufacturing sector has encountered several challenges, including labour shortages and the ageing of skilled technicians, which hinder the transfer of expertise. In response, DX initiatives, such as the implementation of PLM and other digital systems, have progressed rapidly. However, these initiatives have faced challenges related to development time, cost, usability, and scalability.

Komi Matsubara, an executive officer at Mitsubishi Electric Corporation, stated, “Through our collaboration with Things, we expect to generate new value by integrating our manufacturing expertise with Things’ generative AI technology. We aim to leverage this initiative to enhance the overall competitiveness of the Mitsubishi Electric group.”

Things launched its ‘PRISM’ PLM system in May 2023, utilising generative AI to improve the structure and usage of information in manufacturing. PRISM offers significant cost and scalability advantages, enhancing user interfaces and experiences while effectively implementing proofs of concept across a wide range of companies.

Atsuya Suzuki, CEO of Things, said, “We are pleased to establish a partnership with Mitsubishi Electric through the ME Innovation Fund. By combining our technology with Mitsubishi Electric’s expertise in manufacturing and control, we aim to accelerate the global implementation of pioneering DX solutions for manufacturing.”

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AI to Track Facial Expressions to Detect PTSD Symptoms in Children

Published

5 hours ago

July 7, 2025

Merin Susan John

A research team from the University of South Florida (USF) has developed an AI system that can identify post-traumatic stress disorder (PTSD) in children.

The project addresses a longstanding clinical dilemma: diagnosing PTSD in children who may not have the emotional vocabulary, cognitive development or comfort to articulate their distress. Traditional methods such as subjective interviews and self-reported questionnaires often fall short. This is where AI steps in.

“Even when they weren’t saying much, you could see what they were going through on their faces,” Alison Salloum, professor at the USF School of Social Work, reportedly said. Her observations during trauma interviews laid the foundation for collaboration with Shaun Canavan, an expert in facial analysis at USF’s Bellini College of Artificial Intelligence, Cybersecurity, and Computing.

The study introduces a privacy-first, context-aware classification model that analyses subtle facial muscle movements. However, instead of using raw footage, the system extracts non-identifiable metrics such as eye gaze, mouth curvature, and head position, ensuring ethical boundaries are respected when working with vulnerable populations.

“We don’t use raw video. We completely get rid of subject identification and only keep data about facial movement,” Canavan reportedly emphasised. The AI also accounts for conversational context, whether a child is speaking to a parent or a therapist, which significantly influences emotional expressivity.

Across 18 therapy sessions, with over 100 minutes of footage per child and approximately 185,000 frames each, the AI identified consistent facial expression patterns in children diagnosed with PTSD. Notably, children were more expressive with clinicians than with parents; a finding that aligns with psychological literature suggesting shame or emotional avoidance often inhibits open communication at home.

While still in its early stages, the tool is not being pitched as a replacement for therapists. Instead, it’s designed as a clinical augmentation, a second set of ‘digital’ eyes that can pick up on emotional signals even trained professionals might miss in real time.

“Data like this is incredibly rare for AI systems,” Canavan added. “That’s what makes this so promising. We now have an ethically sound, objective way to support mental health assessments.”

If validated on a larger scale, the system could transform mental health diagnostics for children—especially for pre-verbal or very young patients—by turning non-verbal cues into actionable insights.

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