DEV Community: Samuel Oyerinde

Reflections on OpenAI's Scaling Laws for Neural Language Models: Why Models Improve, Why Compute Matters, and What It Means for Africa

Samuel Oyerinde — Thu, 02 Jul 2026 04:24:22 +0000

I recently took the time to read one of the most influential papers in modern AI:

Scaling Laws for Neural Language Models (OpenAI, 2020)

📄 Paper: Scaling Laws for Neural Language Models

Despite being published several years ago, it remains one of the most insightful papers I've read in a long time.

What struck me most wasn't just the mathematics behind scaling. It was how many questions I've been asking over the years were already being investigated by researchers long before the current AI boom.

The Questions I've Been Asking

As someone working across speech AI, NLP, and low-resource languages, I've always been curious about why AI systems continue to improve so dramatically across different domains:

Text generation
Speech recognition
Speech synthesis
Computer vision
Multimodal systems
Coding assistants
Domain-specific AI applications

The questions that kept coming up were:

Is AI getting better simply because engineering is improving?
Is the main driver more training data?
Is compute the real secret?
Do larger parameter counts matter most?
How important are architecture choices such as width and depth?

It turns out these were exactly the questions the paper set out to answer.

What Scaling Laws Actually Tell Us

One of the paper's most important findings is surprisingly simple:

Model performance improves predictably as we increase model size, dataset size, and compute.

These three variables are deeply interconnected.

A larger model can learn more sophisticated patterns, but only if it has access to enough data and enough compute to fully utilize its capacity.

Likewise:

More data alone is not enough.
More parameters alone are not enough.
More compute alone is not enough.

The gains come from balancing all three.

The paper demonstrated that loss curves follow remarkably consistent power-law relationships across a wide range of model scales.

In other words:

AI improvement is not random. It follows surprisingly predictable trends.

For me, this provided a useful framework for understanding why each generation of AI systems often feels significantly more capable than the one before it.

The Scaling Triangle

A useful way to think about scaling laws is as a triangle.

                Compute
                   ▲
                   │
                   │
                   │
                   │
                   │
Data ◄─────────────┼─────────────► Model Size

Each corner influences the others.

If one side becomes a bottleneck, overall performance suffers.

For example:

Large Model + Small Dataset

Huge capacity
Little knowledge

Result:

Overfitting
Poor generalization

Large Dataset + Small Model

Massive knowledge
Limited capacity

Result:

Under-utilization of data
Missed patterns

Large Model + Large Dataset + Limited Compute

Potential exists
Training never fully converges

Result:

Wasted capacity

Scaling laws showed that optimal performance emerges when these factors grow together.

Looking at Today's Models

Fast-forward a few years, and we can see these ideas playing out in practice.

Some of today's most influential open-weight models include:

Each generation tends to improve through a combination of:

Larger effective model capacity
More training data
Greater compute budgets
Better architectures
Improved post-training
Alignment and reinforcement learning

What makes modern AI fascinating is that progress rarely comes from a single factor.

The gains are usually cumulative.

The Growing Opacity of Frontier Models

Something else stood out while reflecting on the paper.

During earlier generations of AI research, it was common to know:

Parameter counts
Dataset sizes
Compute budgets
Training methodologies

Today, that transparency has largely disappeared.

Organizations such as:

OpenAI
Anthropic
Google DeepMind

increasingly keep these details private for proprietary reasons
.

As a result, direct comparisons between frontier systems have become much more difficult than they were just a few years ago.

We often observe the outputs.

The inputs that created them are becoming less visible.

The Elephant in the Room: Compute

The deeper I went into the paper, the more I realized something important.

The AI conversation often focuses on models.

But the real bottleneck is usually compute.

And compute is an entirely different beast.

When we hear about:

Google building AI-focused data centers
Meta investing tens of billions into infrastructure
NVIDIA becoming one of the world's most valuable companies
xAI building massive GPU clusters
Anthropic securing large-scale compute partnerships

we're seeing where a significant portion of the AI race is actually being fought.

Not at the model layer.

At the infrastructure layer.

The Hidden Stack Behind Every AI Model

Most people interact with:

ChatGPT
Claude
Gemini
DeepSeek

What they don't see is:

Massive GPU clusters
Data centers
High-speed networking
Storage systems
Power infrastructure
Cooling systems
Distributed training frameworks

The model is the visible product.

The compute infrastructure is what makes the product possible.

This is one reason why AI development has become increasingly capital-intensive.

Why Open-Weight Models Matter

This is where open-weight models become incredibly important.

Organizations such as:

have dramatically lowered the barrier to entry.

Without open-weight models, many startups, researchers, and independent developers would have little practical access to state-of-the-art AI capabilities.

For many parts of the world, open-weight models are not simply convenient.

They are foundational.

They enable adaptation of AI systems to:

Local languages
Local cultures
Local businesses
Specialized industries
Emerging modalities

without requiring billions of dollars in training costs.

Where Does Africa Fit Into This Story?

Reading the paper eventually led me to a different question:

Where does Africa fit into the future of AI?

From a business perspective, supply generally follows demand.

Companies invest where markets are large and where purchasing power is strongest.

AI ecosystems often follow similar dynamics.

Africa possesses:

Exceptional talent
Rich linguistic diversity
Large populations
Important problems worth solving

What we often lack are:

Large-scale compute infrastructure
Frontier hardware access
Research funding
Venture capital at comparable scale

This creates a structural challenge.

Not because capability is lacking.

But because modern AI increasingly depends on computational resources.

Why I'm Still Optimistic

Despite these challenges, I remain optimistic.

The rise of open-weight AI has fundamentally changed what is possible.

Today, researchers can build meaningful systems without training trillion-parameter models from scratch.

Instead, they can focus on adaptation.

For African AI researchers, this shift is enormously important.

The opportunity is no longer:

Build the next frontier model.

The opportunity is:

Adapt frontier capabilities to local problems.

My Experience with Yorùbá Speech AI

Over the past few years, I've explored this idea through research in Yorùbá speech and language technologies.

Some of my projects include:

Research Repository

Academic Research Repository

Yorùbá OmniTTS

Yorùbá OmniTTS Project

These projects leverage existing foundation models and adapt them for a low-resource language context.

Without open-weight ecosystems, work like this would be significantly more difficult.

Open-weight AI allows researchers to spend less time rebuilding foundational infrastructure and more time solving real-world problems.

What Scaling Laws Mean for Low-Resource Languages

One lesson from scaling laws is often overlooked.

The paper is not only about larger models.

It's also about where future opportunities exist.

For many low-resource languages, including African languages:

The foundation models already exist.
The architectures already exist.
The training recipes largely exist.

The missing pieces are often:

High-quality datasets
Evaluation benchmarks
Domain adaptation
Fine-tuning
Deployment infrastructure

This changes the nature of the challenge.

The question becomes less about competing with frontier labs and more about leveraging their advances effectively.

Final Thoughts

Reading Scaling Laws for Neural Language Models helped me understand why AI systems continue to improve so consistently.

But it also highlighted a deeper reality.

The future of AI is not determined solely by better models.

It is also shaped by:

Compute
Capital
Infrastructure
Research ecosystems
Access

Scaling laws explain how models improve.

Compute explains who gets to participate.

For regions such as Africa, the immediate opportunity may not be building the next frontier model tomorrow.

The opportunity is leveraging today's open-weight models to build solutions for:

Local languages
Education
Healthcare
Agriculture
Finance
Government services
Knowledge preservation

The rise of open-weight AI has already lowered the barrier.

The question now is whether it can lower it enough to make advanced AI truly global.

Fine-Tuning OmniVoice for Yoruba Zero-Shot Voice Cloning: Lessons from 9.6 Hours of Speech Data

Samuel Oyerinde — Thu, 02 Jul 2026 03:56:55 +0000

However, when you move beyond high-resource languages and start working with low-resource, tonal languages like Yoruba, many of the assumptions behind standard TTS pipelines begin to fail.

Over the past week, I fine-tuned OmniVoice for Yoruba using approximately 9.6 hours of speech data collected from 156 speakers. The goal was to build a model capable of zero-shot voice cloning while preserving the tonal characteristics that are fundamental to Yoruba speech.

This article documents the dataset construction process, the engineering decisions that mattered, why some popular multilingual baselines struggle with Yoruba, and how I evaluated synthesis quality.

Why Yoruba is Different

Unlike English, Yoruba is a tonal language.

Words that appear similar can have completely different meanings depending on tone:

Word	Meaning
ọkọ	husband
òkò	hoe
ọ̀kọ̀	vehicle

For humans, tone is obvious.

For TTS models, tone must be learned through the interaction between:

Text representations
Acoustic patterns
Prosodic modeling

A model that ignores tonal information will often generate speech that is intelligible acoustically but semantically incorrect.

This creates unique challenges for text normalization, tokenization, and model training.

Dataset Construction

To create a balanced Yoruba speech corpus, I merged two complementary datasets:

1. Google Yoruba Speech Dataset (OpenSLR 86)

A collection of high-quality studio recordings with clean acoustic conditions.

Benefits:

Consistent microphone setup
Minimal background noise
Reliable pronunciation

2. IroyinSpeech

A crowd-sourced Yoruba speech dataset containing diverse accents and speaking styles.

Benefits:

Greater speaker diversity
Real-world recording conditions
More representative pronunciation variation

Final Dataset Statistics

Metric	Value
Total Utterances	6,989
Total Duration	~9.6 Hours
Total Speakers	156
Sample Rate	24 kHz
Channels	Mono

The idea was simple:

Use studio-quality recordings to establish acoustic stability while using crowd-sourced speech to improve speaker diversity and robustness.

Data Pipeline

The preprocessing pipeline looked like this:

Google OpenSLR 86 (Studio)
          +
IroyinSpeech (Crowd)
          │
          ▼
     24 kHz Mono WAV
          │
          ▼
 Discrete Audio Tokenization
 (HiggsAudio 8-Codebook VQ)
          │
          ▼
     OmniVoice Training

Rather than feeding raw waveforms directly into the language model, OmniVoice operates on discrete audio tokens generated through vector quantization.

For this project, I used the HiggsAudio 8-Codebook VQ tokenizer, which converts continuous speech into sequences of discrete acoustic symbols that can be modeled autoregressively.

Understanding the Zero-Shot Paradigm

One common misconception about fine-tuning voice cloning models is that fine-tuning teaches the model a single voice.

That is not what happens here.

Even after fine-tuning, the model remains a zero-shot voice cloning system.

The architecture consists of:

Qwen3-0.6B as the autoregressive language model
Discrete audio tokens as targets
Reference audio for speaker conditioning

During training, the model learns:

Yoruba Text
     +
Audio Tokens
     ↓
Speech Generation

At inference time:

Text Prompt
     +
5–10 Second Reference Clip
     ↓
Cloned Speaker Output

The reference clip provides speaker identity dynamically.

This means the model can synthesize speech in voices it has never seen before, provided there is a short reference sample.

Diacritics Are Acoustic Features, Not Formatting

This was arguably the most important lesson from the project.

Many NLP pipelines treat diacritics as optional formatting.

For Yoruba TTS, this assumption is catastrophic.

Characters such as:

ẹ
ọ
ṣ
à
á
ì
ó

are not stylistic variations.

They encode phonological and tonal information directly tied to pronunciation.

Removing diacritics forces the model to map identical text sequences to multiple conflicting acoustic realizations.

The result is:

Tone confusion
Prosody degradation
Lower intelligibility
Inconsistent synthesis

Safeguard #1: Unicode NFC Normalization

Before training, all transcripts were normalized using Unicode Normalization Form C (NFC).

Without normalization:

ẹ

can be represented as:

e + U+0323

instead of a single Unicode codepoint.

This can silently break tokenization and create inconsistent text representations.

Using NFC ensured that all Yoruba characters were encoded consistently across the corpus.

Safeguard #2: Tokenizer Vocabulary Audit

Before launching training, I audited the tokenizer vocabulary to verify that Yoruba characters existed as native tokens.

Specifically, I checked support for:

ẹ
ọ
ṣ
à
á
è
é
ì
í
ò
ó
ù
ú

The objective was to ensure that none of these characters were being mapped to:

<UNK>

Unknown-token substitutions would effectively erase tonal information before the model even saw the text.

Why Meta's MMS Struggles with Modern Yoruba

Meta's Massive Multilingual Speech (MMS) project is one of the most significant contributions to multilingual speech technology.

However, it exposes an important issue that affects many low-resource languages:

Domain Mismatch

A substantial portion of publicly available Yoruba speech data originates from:

Bible readings
Religious recordings
Formal narration

These datasets are valuable linguistically but represent a narrow style of speech.

As a result, models trained primarily on such data often produce speech that sounds:

Formal
Archaic
Unnaturally scripted

When generating contemporary Yoruba conversations, this mismatch becomes noticeable.

Why OpenSLR + IroyinSpeech Helped

The datasets used in this project contain:

News content
Modern writing
Everyday language patterns

This broader linguistic coverage produced synthesis that sounded substantially closer to modern spoken Yoruba.

The improvement was especially noticeable in:

Prosody
Phrase rhythm
Conversational flow

ASR-in-the-Loop Evaluation

Evaluating TTS remains one of the hardest problems in speech research.

The gold standard remains:

Mean Opinion Score (MOS)

Human listeners rate speech quality and naturalness.

The downside:

Expensive
Slow
Difficult to scale

To obtain a faster quantitative signal, I implemented an ASR-in-the-loop evaluation pipeline.

Text Prompt
      │
      ▼
[ Fine-Tuned OmniVoice ]
      │
      ▼
Synthesized Audio
      │
      ▼
[ Fine-Tuned Yoruba ASR ]
      │
      ▼
Word Error Rate (WER)

The ASR model used was:

ccibeekeocwhisper-small-yoruba-07-17

Results

The synthesized speech achieved:

Normalized WER ≈ 11.5%

This is not a replacement for MOS evaluation.

However:

If an independent, diacritic-aware Yoruba ASR system can accurately recover the original text from synthesized speech, that provides strong evidence that the speech is intelligible and linguistically faithful.

For rapid iteration, this evaluation loop proved extremely useful.

Key Takeaways

After training and evaluation, three conclusions stood out:

1. Data Quality Matters More Than Model Size

The combination of studio recordings and crowd-sourced diversity provided stronger gains than simply increasing parameter count.

2. Diacritics Must Be Preserved End-to-End

For Yoruba TTS:

Diacritics are acoustic features.

Treating them as optional formatting introduces ambiguity the model cannot reliably resolve.

3. Domain Coverage Matters

Modern conversational datasets produce noticeably more natural synthesis than corpora dominated by religious or formal speech.

What's Next?

With the TTS pipeline stable, the next step is building the reverse direction:

Streaming Yoruba ASR

The current roadmap includes:

Low-latency transcription
WebSocket streaming
Silero VAD
Real-time inference
Code-switch handling
Speaker diarization

The long-term goal is a production-grade speech platform supporting:

Yoruba ASR
Yoruba TTS
Voice cloning
Conversational AI
Educational speech applications

Resources

Model

Sam4rano/omnivoice-yoruba-tts

Repository

Search for:

sam4rano_tts

🚀 Master VS Code on Mac: The Ultimate Keyboard Shortcuts Guide for Next.js & Python Developers

Samuel Oyerinde — Mon, 30 Jun 2025 07:29:29 +0000

Originally published on dev.to

As a developer working with Next.js and Python on macOS, I've discovered that mastering VS Code keyboard shortcuts is one of the fastest ways to supercharge your productivity. Instead of constantly reaching for your mouse, these shortcuts will have you navigating, editing, and debugging code like a seasoned pro.

Quick Mac Tip: Most VS Code shortcuts replace Ctrl with Cmd and Alt with Option on Mac. I'll highlight any exceptions as we go!

Why Keyboard Shortcuts Matter

Before we dive in, let's be honest—learning shortcuts feels tedious at first. But once they become muscle memory, you'll wonder how you ever coded without them. The time you save adds up quickly, especially when you're deep in a coding flow state.

📝 Basic Text Editing: Your Daily Drivers

These are the shortcuts you'll use every single day:

Essential Copy/Paste Operations

Cmd + X - Cut selected text (or entire line if nothing's selected)
Cmd + C - Copy selected text (or entire line if nothing's selected)
Cmd + V - Paste from clipboard
Cmd + A - Select all content in file

Undo/Redo & Comments

Cmd + Z - Undo last action
Cmd + Shift + Z - Redo (Mac's version of Ctrl+Y)
Cmd + / - Toggle line comment (works on multiple selected lines too!)

⚡ Advanced Text Editing: Level Up Your Game

Once you're comfortable with the basics, these shortcuts will make you feel like a coding wizard:

Smart Selection & Autocomplete

Cmd + Space - Trigger autocomplete suggestions
Cmd + . - Quick fix for errors/warnings under cursor
Cmd + D - Select word under cursor, then select next occurrence

Line Manipulation

Cmd + Shift + K - Delete current line (without copying to clipboard)
Cmd + Enter - Insert new line below cursor
Cmd + Shift + Enter - Insert new line above cursor
Option + Up/Down - Move current line up/down

Code Folding & Indentation

Tab - Indent line
Cmd + ] - Increase indentation
Cmd + Option + [ - Collapse code block
Cmd + Option + ] - Expand code block
Shift + Option + A - Block comment selected code

🗂️ Tab Management: Stay Organized

Managing multiple files efficiently is crucial for larger projects:

Opening & Closing Tabs

Cmd + Shift + T - Reopen last closed tab
Cmd + W - Close current tab
Cmd + K, Cmd + W - Close all tabs

Navigation Between Tabs

Ctrl + Tab - Show recent tabs list (note: Ctrl, not Cmd!)
Ctrl + Shift + Tab - Show tabs in reverse order

Panel Management

Cmd + Option + Left/Right - Move tab to left/right panel
Ctrl + 1/2/3 - Switch focus between panels

🎛️ Panel & Sidebar Control

Maximize your screen real estate and access tools quickly:

Terminal & Panels

Ctrl + ~ - Toggle terminal (remember: Ctrl not Cmd)
Ctrl + Shift + ~ - Open new terminal
Cmd + Shift + M - Open problems panel
Cmd + Shift + U - Open output panel

Sidebar Navigation

Cmd + B - Toggle sidebar
Cmd + Shift + E - Focus Explorer
Cmd + P - Quick file search
Cmd + Shift + P - Command Palette (your best friend!)

Settings & Shortcuts

Cmd + , - Open settings
Cmd + K, Cmd + S - View/edit keyboard shortcuts

🔍 Find, Replace & Symbol Navigation

These shortcuts are game-changers when working with large codebases:

Search Operations

Cmd + F - Find in current file
Cmd + Shift + F - Global search across project
Cmd + Option + F - Find and replace in file
Cmd + Shift + H - Global find and replace

Symbol Navigation (Perfect for Next.js components and Python functions!)

F2 - Rename symbol everywhere
Shift + F12 - Show all references inline
Shift + Option + F12 - Open references in side panel
Option + F12 - Peek definition
F12 - Go to definition
Cmd + Click - Go to definition (mouse + keyboard combo)

Symbol Search

Cmd + Shift + O - Search symbols in current file
Cmd + T - Search symbols globally

🎯 Advanced Selection & Multi-Cursor Magic

Multi-cursor editing is where VS Code really shines:

Text Selection

Shift + Arrow Keys - Select character by character
Option + Left/Right - Jump by word
Shift + Option + Left/Right - Select by word
Ctrl + Shift + Left/Right - Expand selection to logical blocks

Multi-Cursor Mastery

Option + Click - Add cursor at click position
Cmd + U - Undo last cursor placement
Cmd + Option + Up/Down - Add cursor above/below

Box Selection

Shift + Option + Drag - Select rectangular text block
Cmd + Shift + Option + Arrows - Adjust box selection

🐛 Debugging Shortcuts

Essential for troubleshooting your Next.js apps and Python scripts:

Debug Control

F5 - Start debugging / Continue execution
Shift + F5 - Stop debugger
F9 - Toggle breakpoint
Shift + F9 - Add inline breakpoint

Step Through Code

F11 - Step into function
F10 - Step over function
Shift + F11 - Step out of function

💡 Pro Tips for Maximum Efficiency

Start Small: Pick 5-10 shortcuts that match your most common actions and practice them for a week.
Use the Command Palette: Cmd + Shift + P is your gateway to discovering new features and their shortcuts.
Customize When Needed: Access Cmd + K, Cmd + S to modify shortcuts that don't feel natural.
Practice with Real Projects: The best way to memorize shortcuts is by using them in your actual Next.js and Python development work.

📚 Additional Resources

Want to dive deeper? Check out these helpful resources:

🎯 Your Next Steps

The journey to keyboard shortcut mastery doesn't happen overnight, but every shortcut you learn is a small investment in your future productivity. Start with the basic text editing shortcuts, then gradually work your way up to the advanced multi-cursor and debugging features.

Which shortcuts are you most excited to try? Drop a comment below and let me know how these shortcuts have improved your development workflow!

Found this helpful? Give it a ❤️ and share it with your fellow developers. Happy coding! 🚀

Tags: #vscode #productivity #macos #nextjs #python #shortcuts #webdev #developer

Next.js Performance Optimization with Real-World Example

Samuel Oyerinde — Sat, 12 Apr 2025 14:25:09 +0000

Introduction

Building a performant web application involves balancing static and dynamic content effectively. Next.js offers a suite of features to achieve this balance, including Streaming React Server Components (RSC), Partial Prerendering (PPR), Incremental Static Regeneration (ISR), Server-Side Rendering (SSR), Static Site Generation (SSG), caching, prefetching, and suspense boundaries. Let's explore these concepts through a real-world example: a blog website with a mix of static and dynamic content.

Scenario: Blog Homepage

Our blog homepage will display:

Static Header and Blog Posts: These are mostly static, updated occasionally.
Dynamic "Latest Comments" Section: This updates frequently.

Step-by-Step Implementation

Step 1: Project Setup

Directory Structure:

   /app
     /layout.tsx         # Root layout for the app
     /page.tsx           # Homepage route
     /components
       /BlogPostList.tsx # Component for rendering the blog post list
       /LatestComments.tsx # Component for rendering latest comments
     /lib
       /data.ts         # Functions to fetch blog posts and comments

Mock Data Source: Use a JSON file or API for blog posts and comments.

Step 2: Define Data Fetching Functions

In /lib/data.ts, create functions to fetch blog posts and comments:

// lib/data.ts
export async function getBlogPosts() {
  await new Promise((resolve) => setTimeout(resolve, 1000)); // Simulate delay
  return [
    { id: 1, title: "Blog Post 1", content: "This is the first post." },
    { id: 2, title: "Blog Post 2", content: "This is the second post." },
  ];
}

export async function getLatestComments() {
  await new Promise((resolve) => setTimeout(resolve, 1500)); // Simulate delay
  return [
    { id: 1, text: "Great post!", user: "User1" },
    { id: 2, text: "Thanks for sharing!", user: "User2" },
  ];
}

Step 3: Create Components

BlogPostList Component: Use SSG with ISR for blog posts.

   // components/BlogPostList.tsx
   import { getBlogPosts } from "../lib/data";

   export default async function BlogPostList() {
     const posts = await getBlogPosts();

     return (

         Blog Posts

           {posts.map((post) => (

               {post.title}
               {post.content}

           ))}


     );
   }

LatestComments Component: Use SSR with a suspense boundary for dynamic comments.

   // components/LatestComments.tsx
   import { getLatestComments } from "../lib/data";

   export default async function LatestComments() {
     const comments = await getLatestComments();

     return (

         Latest Comments

           {comments.map((comment) => (


                 {comment.user}: {comment.text}


           ))}


     );
   }

Step 4: Build the Homepage

Combine static and dynamic content using Streaming SSR and Suspense.

// app/page.tsx
import { Suspense } from "react";
import BlogPostList from "../components/BlogPostList";
import LatestComments from "../components/LatestComments";

export const revalidate = 600; // Enable ISR

export default async function Home() {
  return (

      My Blog

      {/* Static Blog Posts with SSG + ISR */}


      {/* Dynamic Comments with Streaming SSR and Suspense */}
      Loading latest comments...}>



  );
}

Step 5: Optimize Caching and Prefetching

Prefetching: Use prefetch={true} in links for faster navigation.

   // components/BlogPostList.tsx (updated)
   import Link from "next/link";
   import { getBlogPosts } from "../lib/data";

   export default async function BlogPostList() {
     const posts = await getBlogPosts();

     return (

         Blog Posts

           {posts.map((post) => (



                   {post.title}


               {post.content}

           ))}


     );
   }

Cache Headers for Static Assets: Set cache headers in next.config.js.

   // next.config.js
   module.exports = {
     async headers() {
       return [
         {
           source: "/:path*",
           headers: [
             {
               key: "Cache-Control",
               value: "public, max-age=31536000, immutable", // Cache static assets for 1 year
             },
           ],
         },
       ];
     },
   };

Explanation of Rendering Strategies

SSG with ISR for BlogPostList: Pre-rendered at build time, revalidated every 10 minutes.
Streaming SSR with PPR for LatestComments: Dynamic content is streamed as it becomes available.
Suspense Boundary: Ensures a smooth loading experience by showing a fallback UI.

Benefits

Fast Initial Load: Static content loads immediately.
Dynamic Updates: Fresh data is fetched at request time.
Improved UX: Suspense boundaries prevent page blocking.
Caching: Reduces server requests on repeat visits.
Prefetching: Enhances navigation speed.

Tags:

#NextJS #React #WebDevelopment #PerformanceOptimization #StreamingRSC #ISR #SSG #SSR #SuspenseBoundary #Caching #Prefetching

This approach ensures a fast and user-friendly experience by leveraging Next.js's advanced features to balance static and dynamic content effectively.

Building an API with Express.js and Connecting It to the Frontend

Samuel Oyerinde — Thu, 27 Mar 2025 09:47:56 +0000

Building an API with Express.js and Connecting It to the Frontend

When building modern web applications, APIs serve as the bridge between the backend (server-side logic) and the frontend (user interface). In this blog post, we’ll break down how an Express.js-based API works, its key components, and how it connects to a frontend application.

1. What is a Router in Express.js?

A router in Express.js is a way to organize API endpoints into modular and reusable components. Think of it as a "sub-application" that groups related routes together.

Why Use Routers?
- Keeps code clean and modular.
- Allows you to separate concerns (e.g., authentication routes in auth.js, product routes in products.js).

Example:

const express = require('express');
const router = express.Router();

// Define routes
router.post('/register', (req, res) => {
  res.send('Register endpoint');
});
router.post('/login', (req, res) => {
  res.send('Login endpoint');
});

// Export the router
module.exports = router;

This router can then be mounted in the main app:

const authRouter = require('./routes/auth');
app.use('/api/auth', authRouter);

2. Backend API Workflow

Here’s how a typical authentication API works:

a. POST `/api/auth/register`

Purpose: Registers a new user.
Steps:
1. Check if the email already exists in the database.
2. Hash the password using bcrypt.
3. Save the user to the database.
4. Return a JWT token for future authenticated requests.

b. POST `/api/auth/login`

Purpose: Logs in an existing user.
Steps:
1. Verify if the email exists.
2. Compare the provided password with the hashed password stored in the database.
3. If valid, return a JWT token.

c. GET `/api/auth/user`

Purpose: Fetches data of the logged-in user.
Steps:
1. Validate the JWT token using middleware.
2. Return user data, excluding sensitive information like passwords.

Code Example for /register:

const bcrypt = require('bcrypt');
const jwt = require('jsonwebtoken');
const User = require('./models/User'); // Mongoose model

router.post('/register', async (req, res) => {
  const { email, password } = req.body;

  // Check if user exists
  const existingUser = await User.findOne({ email });
  if (existingUser) return res.status(400).send('User already exists');

  // Hash password
  const hashedPassword = await bcrypt.hash(password, 10);

  // Save user to database
  const newUser = new User({ email, password: hashedPassword });
  await newUser.save();

  // Generate JWT token
  const token = jwt.sign({ id: newUser._id }, 'secretKey', { expiresIn: '1h' });

  res.status(201).json({ token });
});

3. Connecting the Backend to the Frontend

The frontend communicates with this API through HTTP requests.

Step 1: Registration/Login

The frontend sends a POST request to /api/auth/register or /api/auth/login with user credentials.

Example Frontend Code:

// Register a user
const response = await fetch('/api/auth/register', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ email: 'test@example.com', password: '123456' }),
});

const data = await response.json();
console.log(data.token); // JWT token

Step 2: Storing JWT Token

After receiving the JWT token, store it securely (e.g., in localStorage or cookies):

localStorage.setItem('token', data.token);

Step 3: Making Authenticated Requests

For protected routes like /api/auth/user, include the JWT token in the request headers:

const token = localStorage.getItem('token');

const response = await fetch('/api/auth/user', {
  headers: { Authorization: `Bearer ${token}` },
});

const userData = await response.json();
console.log(userData);

4. Key Components of an Express API

Component	Role
Express Router	Organizes routes into modules for cleaner code.
Bcrypt	Hashes passwords securely before storing them in a database.
JWT (JSON Web Token)	Generates tokens for authenticating users across sessions.
Mongoose Models	Defines schemas for MongoDB collections (e.g., users).
Auth Middleware	Validates JWT tokens for protected routes like `/api/auth/user`.

Summary: Frontend vs Backend Roles

Frontend	Backend
Sends HTTP requests to API endpoints.	Handles requests and sends responses with data/logic.
Stores and uses tokens for auth.	Generates and validates tokens using JWTs.
Displays user data (e.g., profile).	Fetches and secures data from databases like MongoDB.

Why This Matters

Security: Passwords are hashed, and JWT ensures only authenticated users access protected routes.
Scalability: Modular routing and middleware make it easier to grow your application.
Separation of Concerns: The frontend focuses on UI/UX while the backend handles logic and data.

This approach forms the backbone of most modern web apps, enabling seamless communication between frontend interfaces and backend systems.

Tags:

#NodeJS #ExpressJS #API #Authentication #WebDevelopment #FrontendBackendIntegration

Citations:
[1] https://rapidapi.com/blog/nodejs-express-rest-api-example/
[2] https://www.freecodecamp.org/news/the-express-handbook/
[3] https://dev.to/mrrishimeena/understanding-expressjs-router-nodejs-a-comprehensive-guide-59o8
[4] https://developer.mozilla.org/en-US/docs/Learn/Server-side/Express_Nodejs/Introduction
[5] https://dev.to/reiallenramos/create-an-express-api-static-and-dynamic-routes-33lb
[6] https://www.codecademy.com/article/what-is-express-js
[7] https://www.youtube.com/watch?v=1XxQBXl4Lv8
[8] https://www.freecodecamp.org/news/rest-api-design-best-practices-build-a-rest-api/
[9] https://www.topcoder.com/thrive/articles/routing-in-express-js
[10] https://kinsta.com/knowledgebase/what-is-express-js/

Automated Download Folder Organization with python

Samuel Oyerinde — Fri, 14 Mar 2025 10:03:27 +0000

The Problem

Lately, my Downloads folder had become a chaotic mess. Finding a single file meant endless scrolling and searching—frustrating and time-consuming. But then, I stumbled upon an article on Dev.to about Python automation, which sparked an idea. After some research, I wrote a script that automates the organization of my Downloads folder.

The Solution

The script performs the following tasks:

Scans my Downloads folder
Identifies file types (e.g., PDFs, images, videos, etc.)
Moves each file into its designated folder

Here's a visual representation of how the organized folder structure looks:

Image description

The Code

Step 1: Check Folder Existence and Create Folders

First, we need to ensure the specified Downloads folder exists and create the necessary subfolders if they don't already exist.

import os
import shutil

# Define file types and their corresponding folders
FILE_TYPES = {
    "Documents": ['.pdf', '.docx', '.txt'],
    "Images": ['.jpg', '.png', '.gif'],
    "Videos": ['.mp4', '.mkv', '.avi'],
    "Other": []  # Default folder for unknown file types
}

def organize_downloads(download_folder):
    # Check if the folder exists
    if not os.path.exists(download_folder):
        print(f"The folder {download_folder} does not exist.")
        return

    # Create folders if they don't exist
    for folder in FILE_TYPES.keys():
        folder_path = os.path.join(download_folder, folder)
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)

Step 2: Organize Files

Next, we iterate through all files in the Downloads folder, identify their types, and move them to their respective folders.

    # Iterate through files in the download folder
    for file in os.listdir(download_folder):
        file_path = os.path.join(download_folder, file)

        # Skip directories
        if os.path.isdir(file_path):
            continue

        file_extension = os.path.splitext(file)[1].lower()

        # Find the right folder for the file
        destination_folder = "Other"
        for folder, extensions in FILE_TYPES.items():
            if file_extension in extensions:
                destination_folder = folder
                break

        # Move the file to the appropriate folder
        destination_path = os.path.join(download_folder, destination_folder, file)
        shutil.move(file_path, destination_path)
        print(f"Moved {file} to {destination_folder}")

# Call and run the script
if __name__ == "__main__":
    download_folder = input("Enter the path to your downloads folder: ")
    organize_downloads(download_folder)
    print("Download folder organized successfully!")

What’s Next?

I’m now working on enhancing this script to run automatically based on specific triggers (e.g., time intervals or folder size). The goal is to make file organization completely hands-off, so my system stays organized without any manual intervention.

Share Your Thoughts!

If you’re curious about the code or want to try it out yourself, feel free to adapt it to your needs or suggest improvements—I’d love to hear your thoughts! You can find the code on GitHub: https://github.com/sam4rano/python-automation.

Decoupling Hoisting In React

Samuel Oyerinde — Thu, 23 Mar 2023 19:58:10 +0000

**Table Of Content

Brief overview of hoisting in JavaScript
Explanation of how hoisting works in ReactJS
Hoisting in ReactJS
Benefits of using hoisting in ReactJS
Examples of hoisting in ReactJS
Best Practices for Hoisting in ReactJS
Tips for optimizing performance with hoisting in ReactJS
Conclusion
Reference**

Brief overview of hoisting in JavaScript
Hoisting is a JavaScript mechanism where variables and function declarations are moved to the top of their respective scope before the code is executed. This means that you can use a variable or function before it has been declared in your code. However, only the declaration is hoisted, not the initialization, which means that if you try to access a variable before it has been assigned a value, it will be undefined. Hoisting can be a powerful tool, but it can also lead to unexpected results if not used properly.

Variable declarations are hoisted to the top of their respective function or global scope.

a. For example, consider the following code

Even though the console.log(x) statement appears before the declaration of the x variable, the code still runs without error because the declaration is hoisted to the top of the function scope.
b. Function declarations are also hoisted to the top of their respective function or global scope. For example, consider the following code:

Even though the example() function is called before it is declared, the code still runs without error because the function declaration is hoisted to the top of the global scope.

Note: It's important to note that while variable and function declarations are hoisted, any assignments or initializations are not hoisted.
For example, consider the following code:

In this code, the declaration of the x variable is hoisted to the top of the global scope, but the initialization of x is not hoisted. As a result, the console.log(x) statement will output undefined because x has not yet been assigned a value.

Explanation of how Hoisting works in React
In ReactJS, hoisting works in a similar way to how it works in regular JavaScript. React components are defined as functions, and just like any other JavaScript function, the function declaration is hoisted to the top of its scope. This means that you can use the component before it has been defined in your code.

Hoisting in React
For example, consider the following React code:

//1. Hoisting a function component
renderMyComponent();

function renderMyComponent() {
  ReactDOM.render(<MyComponent />, document.getElementById('root'));
}

function MyComponent() {
  return <h1>Hello, world!</h1>;
}

In this example, the MyComponent function component is declared after the renderMyComponent function. However, because the function declaration is hoisted to the top of the scope,
the renderMyComponent function can still call the MyComponent component and render it to the DOM.

Benefits of using hoisting in ReactJS

a. Improved code organization: Hoisting allows you to declare variables and functions at the top of a code block, which can make your code more organized and easier to read.

b. Faster execution: Hoisting can improve the performance of your code by allowing the JavaScript engine to pre-process function and variable declarations before executing the rest of the code.

c. Avoiding errors: Hoisting can prevent errors caused by using variables or functions before they are declared, which can be especially helpful in large ReactJS projects.

Example of Hoisting In React

// 2. Hoisting a state variable
import {useState} from 'react';

const MyComponent = () => {
  const [count, setCount] = useState(0);

  const handleClick = () => {
    setCount(count + 1);
  }

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}
export default MyComponent;

In this example, the count state variable is declared and initialized using the useState hook. Even though count is declared after the handleClick function that uses it,
the function can still access count because the declaration is hoisted to the top of the component function scope.

import { useState, useEffect } from "react";
const MyComponent = () => {
  const [count, setCount] = useState(0);

  const handleClick = () => {
    setCount(count + 1);
  }

  useEffect(() => {
    // do some side effect
  }, []);

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}
export default MyComponent;

In this example, the handleClick function is declared below the useState hook, but it can still be called in the JSX element because of hoisting. This improves the organization and readability of the code because related code is grouped together.

Similarly, the useState hook is declared above the handleClick function, but it can still be used in the function because of hoisting.
This allows for more flexible placement of code
and can improve performance by allowing the JavaScript engine to pre-process the hook before executing the rest of the code.

Best practices for Hoisting in React
a. Declare state hooks and other hooks at the top of the component, before any other logic or functions:

import { useState, useEffect } from "react";

const MyComponent = () => {
  const [count, setCount] = useState(0);

  useEffect(() => {
    // do some side effect
  }, []);

  const handleClick = () => {
    setCount(count + 1);
  }

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}
export default MyComponent;

b. Declare variables and functions before using them:

import { useState } from "react";

const MyComponent = () => {
  const [count, setCount] = useState(0);

  // Good: Declare handleClick before using it in JSX
  const handleClick = () => {
    setCount(count + 1);
  }

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}
export default MyComponent;

c. Declare functions that use hooks below the hooks they depend on

import { useState, useEffect } from "react";

const MyComponent = () => {
  const [count, setCount] = useState(0);

  useEffect(() => {
    // do some side effect
  }, []);

  const handleClick = ()  {
    setCount(count + 1);
  }

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}

export default MyComponent;

d. Avoid declaring functions and hooks inside other functions, as this can make it harder to reason about the code

import { useState, useEffect } from "react";

const MyComponent = () => {
  const [count, setCount] = useState(0);

  const handleClick = () => {
    const innerFunction = () => {
      setCount(count + 1); // This won't work as expected
    }

    innerFunction();
  }

  useEffect(() => {
    // do some side effect
  }, []);

  return (
    <div>
      <h1>Count: {count}</h1>
      <button onClick={handleClick}>Increment</button>
    </div>
  );
}
export default MyComponent;

Tips for optimizing performance with hoisting in ReactJS

Use hoisting with caution and only when necessary to avoid overcomplicating your code.

Be mindful of the order in which your code is executed to avoid unexpected behavior.

Use profiling tools to identify performance issues and optimize your code accordingly.

Conclusion

Hoisting is an important concept in ReactJS that can improve code organization and performance when used properly. It is important to understand the best practices and pitfalls of hoisting to avoid errors and optimize code performance.

Reference
https://facebook.github.io/react/docs/thinking-in-react.html
https://facebook.github.io/react/docs/lifting-state-up.html
https://www.digitalocean.com/community/tutorials/understanding-hoisting-in-javascript
https://react.dev/blog/2023/03/16/introducing-react-dev

Fibonacci Sequence: Algorithm and Python implementation simplified

Samuel Oyerinde — Fri, 22 Jul 2022 17:16:00 +0000

1.1 The Fibonacci Sequence

The Fibonacci numbers, sometimes known as Fn, create a series in which each number is the sum
of the two numbers before it. Although some authors ignore the initial words and begin the sequence
from 1 and 1 or from 1 and 2, the pattern typically starts from 0 and 1.
The following values follow 0 and 1 in the sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34,...

1.2 The previous two digits are combined to yield the following number:

The 1 is found by adding the two numbers that precede it (0+1),
The 2 by adding the two numbers that come before it (1+1),
The 3 by adding the two numbers that come before it (1+2),
The 5 by adding the two numbers that come before it (2+3), and so on!

1.3 Mathematically, Fibonacci can be represented by:

F(1) = 1
F(2) = 1
F(n) = F(n − 1) + F(n − 2)

1.4 pictorial representation

2 Fibonacci Algorithm

2.1 Non-Recursive Iterative Algorithm for Fibonacci
Procedures or subroutines written in a programming language that do not refer to themselves
are known as non-recursive functions.

2.1.1 Algorithm non-recursive Fibonacci

Procedure Iterative_Fibonacci(n):
    int f0, f1, fib
    f0 = 0
    f1 = 1
    display f0, f1
    for int i:= 1 to n:
        fib := f0 + f1   
        f0 := f1
        f1 := fib
        display fib
    END for loop
END Iterative_Fibonacci

2.1.2 Python implementation

num_terms = int(input("Number of terms? "))

# first two terms
f1, f2 = 0, 1
count = 0

# checking for the validity of the number of terms
if num_terms <= 0:
   print("Enter a positive integer")
# if there is only one term, return f1
elif num_terms == 1:
   print("Fibonacci sequence to",num_terms,":")
   print(f1)
# generate fibonacci sequence
else:
   print("Fibonacci sequence:")
   while count < num_terms:
       print(f1)
       nths = f1 + f2
       # update values
       f1 = f2
       f2 = nths
       count += 1

2.2 Recursive implementation of the Fibonacci sequence

Recursion is a powerful programming concept that provides an effective way to solve large problems by
breaking them down into simpler subproblems, using this approach, it's often possible to unravel
seemingly complex problems via repeated application of very simple solutions to the subproblems.
When we speak about recursion in programming, we are simply talking about recursive functions.
A recursive function is a function that calls itself somewhere in its definition, The function is defined in terms of itself.

2.2.1 Properties of Recursion

There must be a reachable base case where the function stops calling itself otherwise infinite loop will occur
The argument of the function must be modified with each call.
Performing the identical operations multiple times with different inputs.
For each step, we apply smaller inputs to reduce the complexity of the problem.

2.2.2 Algorithm of Recursive Fibonacci

START
Procedure Fibonacci(n)
   declare f0, f1, fib, loop 

   set f0 to 0
   set f1 to 1

   display f0, f1

   for loop ← 1 to n

      fib ← f0 + f1   
      f0 ← f1
      f1 ← fib

      display fib
   end for

END

2.2.3 Python implementation

def fibo(n):
   if n <= 1:
       return n
   else:
       return(fibo(n-1) + fibo(n-2))

nterms = int(input('enter an integer? '))

# check if the number of terms is valid
if nterms <= 0:
   print("Please enter a positive integer")
else:
   print("Fibonacci sequence:")
   for i in range(nterms):
       print(recur_fibo(i))

Thanks for reading, kindly follow for more interesting content on Data structure and Algorithm

DEV Community: Samuel Oyerinde

Reflections on OpenAI's Scaling Laws for Neural Language Models: Why Models Improve, Why Compute Matters, and What It Means for Africa

The Questions I've Been Asking

What Scaling Laws Actually Tell Us

The Scaling Triangle

Large Model + Small Dataset

Large Dataset + Small Model

Large Model + Large Dataset + Limited Compute

Looking at Today's Models

The Growing Opacity of Frontier Models

The Elephant in the Room: Compute

The Hidden Stack Behind Every AI Model

Why Open-Weight Models Matter

Where Does Africa Fit Into This Story?

Why I'm Still Optimistic

My Experience with Yorùbá Speech AI

Research Repository

Yorùbá OmniTTS

What Scaling Laws Mean for Low-Resource Languages

Final Thoughts

Further Reading

Fine-Tuning OmniVoice for Yoruba Zero-Shot Voice Cloning: Lessons from 9.6 Hours of Speech Data

Why Yoruba is Different

Dataset Construction

1. Google Yoruba Speech Dataset (OpenSLR 86)

2. IroyinSpeech

Final Dataset Statistics

Data Pipeline

Understanding the Zero-Shot Paradigm

Diacritics Are Acoustic Features, Not Formatting

Safeguard #1: Unicode NFC Normalization

Safeguard #2: Tokenizer Vocabulary Audit

Why Meta's MMS Struggles with Modern Yoruba

Domain Mismatch

Why OpenSLR + IroyinSpeech Helped

ASR-in-the-Loop Evaluation

Mean Opinion Score (MOS)

Results

Key Takeaways

1. Data Quality Matters More Than Model Size

2. Diacritics Must Be Preserved End-to-End

3. Domain Coverage Matters

What's Next?

Streaming Yoruba ASR

Resources

Model

Repository

Tags

🚀 Master VS Code on Mac: The Ultimate Keyboard Shortcuts Guide for Next.js & Python Developers

Why Keyboard Shortcuts Matter

📝 Basic Text Editing: Your Daily Drivers

⚡ Advanced Text Editing: Level Up Your Game

🗂️ Tab Management: Stay Organized

🎛️ Panel & Sidebar Control

🔍 Find, Replace & Symbol Navigation

🎯 Advanced Selection & Multi-Cursor Magic

🐛 Debugging Shortcuts

💡 Pro Tips for Maximum Efficiency

📚 Additional Resources

🎯 Your Next Steps

Next.js Performance Optimization with Real-World Example

Introduction

Scenario: Blog Homepage

Step-by-Step Implementation

Step 1: Project Setup

Step 2: Define Data Fetching Functions

Step 3: Create Components

Step 4: Build the Homepage

Step 5: Optimize Caching and Prefetching

Explanation of Rendering Strategies

Benefits

Tags:

Building an API with Express.js and Connecting It to the Frontend

Building an API with Express.js and Connecting It to the Frontend

1. What is a Router in Express.js?

2. Backend API Workflow

a. POST /api/auth/register

b. POST /api/auth/login

c. GET /api/auth/user

3. Connecting the Backend to the Frontend

a. POST `/api/auth/register`

b. POST `/api/auth/login`

c. GET `/api/auth/user`