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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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Canva Partners With NCERT to Launch AI-Powered Teacher Training

Published

35 minutes ago

July 7, 2025

Ankush Das

Canva has signed a memorandum of understanding (MoU) with the National Council of Educational Research and Training (NCERT) to launch free teacher training and certification programs hosted on the education ministry’s DIKSHA platform.

The initiative aims to enhance digital literacy, creativity, and AI proficiency among educators across India, in alignment with the objectives of the National Education Policy (NEP) 2020.

As part of the agreement, Canva will offer Indian teachers free access to its education platform and provide learning materials tailored for visual and collaborative instruction. NCERT will ensure that the course content aligns with the national curriculum and is made regionally accessible. Available in multiple Indian languages, the course will also be broadcast via PM e-Vidya DTH channels to extend its reach beyond internet-enabled classrooms.

The certification program includes training on using Canva’s design tools to create engaging lesson plans, infographics, and presentations. Teachers will also learn to co-create content with students and apply AI tools to improve classroom outcomes. Upon completion, participants will receive a joint certificate from NCERT and Canva.

“This partnership is a powerful step toward equipping educators with practical digital skills that not only save time but spark imagination in every classroom,” Jason Wilmot, head of education at Canva, said in a press statement.

Chandrika Deb, country manager for India at Canva stated, “By delivering this program free of cost, in multiple languages, and through a trusted national platform like NCERT, we are not only advancing digital fluency and creative confidence in classrooms across the country, but also deepening Canva’s long-term commitment to India, which plays a pivotal role in our vision to democratize design and creativity at scale.”

Moreover, the company shared some interesting figures. Canva has seen significant global momentum, with over 100 million students and teachers using its platform. In 2024, over 1 billion designs were created, many powered by Canva’s AI tools like Dream Lab, which enables teachers to generate custom visuals instantly. Teacher usage of AI tools has increased by 50% over the past year, with student engagement rising by 107%.

We may see further developments in this partnership as the training program for teachers progresses over time.

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Capgemini to Acquire WNS for $3.3 Billion with Focus on Agentic AI

Published

1 hour ago

July 7, 2025

Mohit Pandey

Capgemini has announced a definitive agreement to acquire WNS, a mid-sized Indian IT firm, for $3.3 billion in cash. This marks a significant step towards establishing a global leadership position in agentic AI.

The deal, unanimously approved by the boards of both companies, values WNS at $76.50 per share—a premium of 28% over the 90-day average and 17% above the July 3 closing price.

The acquisition is expected to immediately boost Capgemini’s revenue growth and operating margin, with normalised EPS accretion of 4% by 2026, increasing to 7% post-synergies in 2027.

“Enterprises are rapidly adopting generative AI and agentic AI to transform their operations end-to-end. Business process services (BPS) will be the showcase for agentic AI,” Aiman Ezzat, CEO of Capgemini, said.

“Capgemini’s acquisition of WNS will provide the group with the scale and vertical sector expertise to capture that rapidly emerging strategic opportunity created by the paradigm shift from traditional BPS to agentic AI-powered intelligent operations.”

Pending regulatory approvals, the transaction is expected to close by the end of 2025.

WNS’ integration is expected to strengthen Capgemini’s presence in the US market while unlocking immediate cross-selling opportunities through its combined offerings and clientele.

WNS, which reported $1.27 billion in revenue for FY25 with an 18.7% operating margin, has consistently delivered a revenue growth of around 9% over the past three fiscal years.

“As a recognised leader in the digital BPS space, we see the next wave of transformation being driven by intelligent, domain-centric operations that unlock strategic value for our clients,” Keshav R Murugesh, CEO of WNS, said. “Organisations that have already digitised are now seeking to reimagine their operating models by embedding AI at the core—shifting from automation to autonomy.”

The companies expect to drive additional revenue synergies between €100 million and €140 million, with cost synergies of up to €70 million annually by the end of 2027.

“WNS and Capgemini share a bold, future-focused vision for Intelligent Operations. I’m confident that Capgemini is the ideal partner at the right time in WNS’ journey,” Timothy L Main, chairman of WNS’ board of directors, said.

Capgemini, already a major player with over €900 million in GenAI bookings in 2024 and strategic partnerships with Microsoft, Google, AWS, Mistral AI, and NVIDIA, aims to solidify its position as a transformation partner for businesses looking to embed agentic AI at scale.

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Piyush Goyal Announces Second Tranche of INR 10,000 Cr Deep Tech Fund

Published

21 hours ago

July 6, 2025

Siddharth Jindal

IIT Madras and its alumni association (IITMAA) held the sixth edition of their global innovation and alumni summit, ‘Sangam 2025’, in Bengaluru on 4 and 5 July. The event brought together over 500 participants, including faculty, alumni, entrepreneurs, investors and students.

Union Commerce and Industry Minister Shri Piyush Goyal, addressing the summit, announced a second tranche of ₹10,000 crore under the government’s ‘Fund of Funds’, this time focused on supporting India’s deep tech ecosystem. “This money goes to promote innovation, absorption of newer technologies and development of contemporary fields,” he said.

The Minister added that guidelines for the fund are currently being finalised, to direct capital to strengthen the entire technology lifecycle — from early-stage research through to commercial deployment, not just startups..

He also referred to the recent Cabinet decision approving $12 billion (₹1 lakh crore) for the Department of Science and Technology in the form of a zero-interest 50-year loan. “It gives us more flexibility to provide equity support, grant support, low-cost support and roll that support forward as technologies get fine-tuned,” he said.

Goyal said the government’s push for indigenous innovation stems from cost advantages as well. “When we work on new technologies in India, our cost is nearly one-sixth, one-seventh of what it would cost in Switzerland or America,” he said.

The Minister underlined the government’s focus on emerging technologies such as artificial intelligence, machine learning, and data analytics. “Today, our policies are structured around a future-ready India… an India that is at the forefront of Artificial Intelligence, Machine Learning, computing and data analytics,” he said.

He also laid out a growth trajectory for the Indian economy. “From the 11th largest GDP in the world, we are today the fifth largest. By the end of Calendar year 2025, or maybe anytime during the year, we will be the fourth-largest GDP in the world. By 2027, we will be the third largest,” Goyal said.

Sangam 2025 featured a pitch fest that saw 20 deep tech and AI startups present to over 250 investors and venture capitalists. Selected startups will also receive institutional support from the IIT Madras Innovation Ecosystem, which has incubated over 500 ventures in the last decade.

Key speakers included Aparna Chennapragada (Chief Product Officer, Microsoft), Srinivas Narayanan (VP Engineering, OpenAI), and Tarun Mehta (Co-founder and CEO, Ather Energy), all IIT Madras alumni. The summit also hosted Kris Gopalakrishnan (Axilor Ventures, Infosys), Dr S. Somanath (former ISRO Chairman) and Bengaluru South MP Tejasvi Surya.

Prof. V. Kamakoti, Director, IIT Madras, said, “IIT Madras is committed to playing a pivotal role in shaping ‘Viksit Bharat 2047’. At the forefront of its agenda are innovation and entrepreneurship, which are key drivers for National progress.”

Ms. Shyamala Rajaram, President of IITMAA, said, “Sangam 2025 is a powerful confluence of IIT Madras and its global alumni — sparking bold conversations on innovation and entrepreneurship.”

Prof. Ashwin Mahalingam, Dean (Alumni and Corporate Relations), IIT Madras, added, “None of this would be possible without the unwavering support of our alumni community. Sangam 2025 embodies the strength of that network.”

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