DEV Community: Moniruzzaman Saikat

Bruno: The Git-Native API Client Developers Should Try

Moniruzzaman Saikat — Mon, 03 Aug 2026 16:09:15 +0000

Modern API development requires more than a tool that can send HTTP requests.

Developers need API collections that can be reviewed, versioned, tested, shared, and executed inside CI pipelines. However, many API clients treat collections as data stored inside their own platform.

Bruno takes a different approach.

Bruno is an open-source, local-first, and Git-native API client. Instead of hiding API collections inside a cloud workspace, Bruno stores them as readable files on your filesystem.

That simple decision makes Bruno fit naturally into a developer's existing workflow.

What Is Bruno?

Bruno is a desktop API client for developing and testing APIs.

You can use it to work with:

REST APIs
GraphQL APIs
Authentication flows
Environment variables
Request scripts
Response tests
API documentation
Automated collection runs

It covers many workflows developers already use tools like Postman or Insomnia for.

The major difference is how Bruno stores and manages API collections.

Your requests live inside regular folders and text files. You can open them in an editor, commit them to Git, review changes in pull requests, and keep them beside your application code.

Why Git-Native API Collections Matter

Imagine that your backend repository contains the following structure:

my-api/
├── app/
├── database/
├── routes/
├── tests/
└── api-collection/
    ├── users/
    ├── authentication/
    ├── products/
    └── bruno.json

The API collection can be committed to the same repository as the application.

When a developer changes an endpoint, they can update the related Bruno request in the same branch.

A pull request can then contain:

Updated user registration validation
Updated registration API request
Added registration error tests
Updated API documentation

The backend implementation and its API examples remain synchronized.

This is much cleaner than updating code in Git while separately maintaining a collection inside an external cloud workspace.

Collections Are Readable Files

Bruno stores requests as plain-text files.

A simplified Bruno request may look similar to this:

meta {
  name: Get Current User
  type: http
  seq: 1
}

get {
  url: {{baseUrl}}/api/user
  body: none
  auth: bearer
}

auth:bearer {
  token: {{accessToken}}
}

tests {
  test("should return the authenticated user", function () {
    expect(res.status).to.equal(200);
  });
}

Because the request is text-based, it works well with:

Git diffs
Pull requests
Code reviews
Merge history
IDE search
Automated agents
Repository backups

You do not need a proprietary interface just to understand what changed.

Local-First by Design

Bruno runs locally and does not require an account for its normal open-source workflow.

Your requests, environments, scripts, and collections stay on your machine or inside repositories you control.

This is useful when working with:

Private APIs
Internal company services
Sensitive authentication flows
Client projects
Local development environments
Restricted enterprise networks

A local-first tool also avoids making your API workflow dependent on the availability of an external cloud platform.

Creating Your First Bruno Collection

After installing Bruno, create a new collection and select a directory on your filesystem.

For example:

projects/
└── inventory-api/
    └── bruno/

Create an environment containing variables such as:

vars {
  baseUrl: http://localhost:8000
  accessToken:
}

You can then create a login request:

POST {{baseUrl}}/api/login

With a JSON body:

{
  "email": "developer@example.com",
  "password": "password"
}

After receiving the response, you can save the token using a post-response script:

if (res.status === 200) {
  bru.setEnvVar("accessToken", res.body.token);
}

Other authenticated requests can reuse the value:

Authorization: Bearer {{accessToken}}

This creates a repeatable authentication workflow without manually copying tokens after every login.

Testing API Responses

Bruno supports response tests, allowing a collection to work as both documentation and executable validation.

For example:

test("status should be 200", function () {
  expect(res.status).to.equal(200);
});

test("response should contain a user", function () {
  expect(res.body).to.have.property("user");
});

test("user should have an email", function () {
  expect(res.body.user).to.have.property("email");
});

You can also validate response types:

test("products should be an array", function () {
  expect(res.body.products).to.be.an("array");
});

Or check business rules:

test("product stock should not be negative", function () {
  res.body.products.forEach(function (product) {
    expect(product.stock).to.be.at.least(0);
  });
});

These tests help catch API regressions before they reach production.

Environments and Variables

Most applications have multiple environments:

Local
Development
Staging
Production

Instead of duplicating requests, you can create environment-specific variables.

Local environment:

vars {
  baseUrl: http://localhost:8000
}

Staging environment:

vars {
  baseUrl: https://staging-api.example.com
}

Production environment:

vars {
  baseUrl: https://api.example.com
}

Your request remains unchanged:

GET {{baseUrl}}/api/products

You only switch the active environment.

Sensitive values such as API keys and access tokens should be stored separately from committed collection files.

Bruno automatically creates recommended Git ignore rules when creating collections, but developers should still review files before committing them.

Collaboration Through Git

A Bruno collection can be managed like any other source code directory.

Initialize Git:

git init

Add the collection:

git add .

Create a commit:

git commit -m "Add authentication API collection"

Connect it to a remote repository:

git remote add origin https://github.com/username/project-api.git
git push -u origin main

Team members can clone the repository and open the same collection in Bruno.

Changes can be reviewed using normal Git commands:

git diff
git status
git log

This gives API collections the same review process as application code.

Running Collections From the CLI

Bruno provides a command-line interface called Bruno CLI.

Install it using npm:

npm install -g @usebruno/cli

Navigate to the collection directory:

cd path/to/bruno-collection

Run the entire collection:

bru run

Run it with a selected environment:

bru run --env local

Run only a specific folder:

bru run users

You can also pass environment variables directly:

bru run --env local --env-var ACCESS_TOKEN=your-token

To run requests with selected tags:

bru run --tags=smoke,sanity

To exclude unfinished or skipped requests:

bru run --exclude-tags=skip,draft

This makes Bruno useful beyond manual API exploration.

Your collection can become part of your automated testing system.

Using Bruno in CI/CD

A simple CI workflow can install Bruno CLI and run an API collection after starting the application.

A simplified GitHub Actions example could look like this:

name: API Tests

on:
  push:
    branches:
      - main
  pull_request:

jobs:
  api-tests:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: 22

      - name: Install Bruno CLI
        run: npm install -g @usebruno/cli

      - name: Run API tests
        working-directory: ./bruno
        run: bru run --env ci

For a real application, you would also start the backend service and database before executing the collection.

For example:

- name: Install backend dependencies
  run: composer install --no-interaction

- name: Prepare environment
  run: |
    cp .env.example .env
    php artisan key:generate
    php artisan migrate --force

- name: Start Laravel server
  run: php artisan serve --host=127.0.0.1 --port=8000 &

- name: Run Bruno tests
  working-directory: ./bruno
  run: bru run --env ci

Now every pull request can verify that important API endpoints still work.

Generating Test Reports

Bruno CLI supports report formats that can be used in automated pipelines.

Depending on your workflow, you can generate formats such as:

JSON
JUnit
HTML

JUnit output is especially useful because many CI platforms can display test results directly.

A collection can therefore serve several purposes:

Manual API exploration
Developer documentation
Regression testing
CI validation
Integration testing

This reduces the need to maintain separate API examples and testing assets.

Importing Existing Collections

Teams do not need to rebuild every request manually before trying Bruno.

Bruno supports importing collections from common formats and tools, including Postman collections.

A practical migration process is:

Export the existing collection.
Import it into Bruno.
Verify environment variables and authentication.
Review scripts and tests.
Store the new collection inside the project repository.
Commit it to Git.
Add CLI execution to CI.

Some tool-specific scripts may require adjustment, but importing provides a useful starting point.

API Documentation Inside the Collection

Bruno supports Markdown documentation at different levels, including:

Workspace
Collection
Folder
Request

You can document an authentication folder with information such as:

# Authentication

All protected endpoints require a bearer token.

## Login

Send the user's email and password to `/api/login`.

The response contains an access token that should be included in the
`Authorization` header for future requests.

Documentation stays close to the executable requests.

When an endpoint changes, developers can update the request, tests, and explanation together.

Bruno can also generate standalone HTML documentation from collections, making it possible to publish readable API references without maintaining a completely separate source.

Bruno 4 and AI Assistance

Bruno 4.0 introduced Bring Your Own Key AI capabilities.

Instead of forcing developers to use a fixed AI provider, Bruno supports OpenAI-compatible providers and custom model configurations.

Its AI features can assist with:

Writing request documentation
Generating scripts
Creating response tests
Reviewing changes
Working with the active request or collection
Providing scripting autocomplete

The Bring Your Own Key approach gives developers more control over the provider, model, and credentials they use.

Bruno also includes security controls that explain what context is shared with the selected AI provider.

AI assistance is most useful here when it improves the files developers already own instead of moving their entire API workflow into another closed platform.

Where Bruno Fits Best

Bruno is particularly suitable for teams that:

Already use Git for collaboration
Want collections stored beside application code
Prefer local-first development tools
Need readable API request files
Want API tests inside CI pipelines
Work with private or internal APIs
Want to avoid mandatory cloud accounts
Need an open-source API client
Use coding agents or IDE-based workflows

It is also a strong option for solo developers because collections can be backed up and shared through any Git repository.

Things to Consider

Bruno's workflow may feel different if your team is accustomed to browser-based collaboration.

Git-based collaboration assumes that team members understand concepts such as:

Repositories
Commits
Branches
Pull requests
Merge conflicts

This is usually not a major issue for engineering teams, but it can require onboarding for non-technical users.

Some advanced Git interface features and team capabilities are also associated with paid Bruno editions. You should compare the current editions before standardizing it across a larger organization.

The core open-source workflow, however, remains highly useful for individual developers and technical teams.

Bruno vs Traditional Cloud-Based API Clients

The main difference is not the request editor.

Most established API clients can send requests, manage environments, and execute tests.

The important difference is ownership and workflow.

A traditional cloud-based workflow often looks like this:

Application code -> Git repository
API collection -> External platform
Documentation -> Another platform
Automated tests -> Separate test suite

A Bruno-based workflow can look like this:

Application code -> Git repository
API collection -> Same Git repository
API documentation -> Same collection
API tests -> Same collection
CI execution -> Bruno CLI

This does not eliminate every tool, but it reduces fragmentation.

A Practical Project Structure

For a Laravel API, I would organize the project like this:

inventory-platform/
├── app/
├── database/
├── routes/
├── tests/
├── bruno/
│   ├── authentication/
│   │   ├── login.bru
│   │   ├── logout.bru
│   │   └── current-user.bru
│   ├── products/
│   │   ├── list-products.bru
│   │   ├── create-product.bru
│   │   └── update-product.bru
│   ├── orders/
│   │   ├── create-order.bru
│   │   └── get-order.bru
│   ├── environments/
│   ├── collection.bru
│   └── bruno.json
├── composer.json
└── README.md

Each backend feature should include:

Its implementation
Automated application tests
Bruno requests
Bruno response tests
Relevant API documentation

A feature is not complete until its executable API examples are updated.

Conclusion

Bruno is not interesting simply because it is another API client.

It is interesting because it treats API collections as part of the codebase.

Collections can be stored locally, reviewed through Git, executed through the command line, validated in CI, and edited with normal developer tools.

That makes the API workflow easier to own and maintain.

For developers who value local tools, open-source software, readable files, and Git-based collaboration, Bruno is worth testing on a real project.

You can explore it at usebruno.com and keep your first collection inside your next API repository.

React Native vs Flutter in 2026: Which One Should You Choose?

Moniruzzaman Saikat — Tue, 28 Jul 2026 14:50:16 +0000

Every year someone declares a winner in the React Native vs Flutter debate.

The reality is much simpler.

Neither framework is universally better. The right choice depends on your team, product, timeline, and long term goals.

After watching both ecosystems evolve, here is how I think about choosing between them in 2026.

React Native

React Native lets you build mobile applications using JavaScript or TypeScript with React.

If your team already builds web applications with React, the learning curve is relatively small.

Strengths

Excellent for React developers
Large ecosystem and community
Strong third party library support
Easier code sharing between web and mobile
Huge hiring pool

Challenges

Native modules are sometimes required
Performance can require additional optimization
Dependency compatibility occasionally becomes frustrating
Platform specific issues still exist

React Native is often the fastest path if your company already has experienced React engineers.

Flutter

Flutter uses Dart and renders its own UI instead of relying on native platform widgets.

That gives developers remarkable control over the user interface.

Strengths

Beautiful and consistent UI
Excellent animation support
High performance
Great developer tooling
Predictable rendering across Android and iOS

Challenges

Requires learning Dart
Larger application size
Smaller talent pool compared to React
Less code sharing with existing React web applications

Flutter shines when UI quality and consistency are top priorities.

Performance

For most business applications, users will not notice a meaningful difference.

Modern versions of both frameworks perform extremely well.

Performance only becomes a deciding factor for applications with:

Complex animations
Heavy graphics
Real time visual rendering
Large interactive interfaces

Even then, architecture matters more than the framework itself.

Developer Experience

React Native feels familiar if you already know JavaScript.

Flutter feels surprisingly productive once you become comfortable with Dart.

Both provide excellent hot reload capabilities, making development much faster than traditional native development.

Community

React Native has years of maturity and an enormous ecosystem.

Flutter continues to grow rapidly with excellent documentation and an active community.

Finding solutions online is easy with either framework.

Hiring Developers

This is where React Native still has an advantage.

JavaScript is one of the most widely used programming languages, making React Native developers easier to find.

Flutter developers are becoming more common every year, but experienced engineers are still less abundant in many regions.

When I Would Choose React Native

I would choose React Native when:

The company already uses React
Fast hiring is important
Code sharing with web applications matters
Existing JavaScript expertise is strong

When I Would Choose Flutter

I would choose Flutter when:

Pixel perfect UI is important
The product relies heavily on animations
Design consistency matters
Starting a completely new mobile project

The Bigger Picture

Frameworks change.

Programming languages evolve.

New tools appear every year.

The fundamentals remain the same.

A well designed architecture, clean code, thoughtful testing, and understanding your users will have a far bigger impact than choosing React Native or Flutter.

The best mobile applications are rarely successful because of the framework they use.

They succeed because they solve real problems.

Final Thoughts

Stop looking for the perfect framework.

Instead, choose the one that fits your team, your product, and your long term maintenance goals.

Both React Native and Flutter are capable of building excellent mobile applications in 2026.

The better choice is the one your team can build, maintain, and improve with confidence.

What would you choose for your next project in 2026? React Native or Flutter?

The Future of Software Engineering in the Age of AI

Moniruzzaman Saikat — Mon, 27 Jul 2026 16:57:39 +0000

Artificial intelligence is changing software engineering faster than most developers expected.

AI tools can already generate code, explain unfamiliar repositories, write tests, detect bugs, create documentation, and suggest architectural improvements. This has created an important question for software engineers:

What should developers learn and plan for if AI can write code?

The future of software engineering is not about competing with AI at typing speed. It is about becoming better at understanding problems, designing systems, validating decisions, and using AI responsibly.

AI Will Change the Developer Workflow

Traditional software development often follows this process:

Understand the requirement
Design the solution
Write the code
Test the implementation
Fix bugs
Deploy the application
Maintain the system

AI can assist in almost every step, but assistance is not the same as ownership.

An AI tool may generate a controller, database migration, API endpoint, React component, or unit test within seconds. However, it does not automatically understand the complete business context, long-term product direction, infrastructure limitations, security requirements, or user expectations.

The engineer still needs to decide:

Whether the generated solution is correct
Whether it follows the existing architecture
Whether it introduces security risks
Whether it can scale
Whether it is maintainable
Whether it solves the actual business problem

AI will reduce repetitive coding, but it will increase the importance of engineering judgment.

Coding Skills Will Still Matter

Some people believe developers will no longer need to understand programming because AI can generate code.

That is unlikely.

Developers who understand programming deeply will use AI more effectively than developers who only know how to request code.

Without technical knowledge, it becomes difficult to detect:

Incorrect assumptions
Hidden performance issues
Security vulnerabilities
Poor database queries
Unnecessary dependencies
Broken edge cases
Inconsistent architecture
Hallucinated libraries or methods

A developer should still understand data structures, algorithms, databases, networking, operating systems, debugging, testing, security, and software architecture.

The difference is that developers may spend less time writing basic boilerplate and more time reviewing, integrating, and improving generated solutions.

The Software Engineer Will Become a System Orchestrator

Future software engineers may work more like orchestrators.

Instead of manually implementing every small function, they may divide a large problem into smaller tasks and use multiple tools or agents to complete them.

For example, an engineer may ask AI to:

Analyze an existing repository
Propose a database structure
Generate an initial implementation
Write automated tests
Review the implementation for security issues
Create API documentation
Prepare deployment instructions

The engineer will then review the output, resolve conflicts, test the system, and make the final decisions.

This requires more than prompt writing. It requires the ability to structure work clearly and evaluate results accurately.

Problem Solving Will Be More Valuable Than Syntax

Programming syntax is becoming easier to access.

A developer can ask AI how to write a loop, create a Laravel relationship, build a React hook, configure Docker, or write a SQL query.

The more valuable skill is understanding what should be built.

A strong engineer should be able to answer questions such as:

What is the actual problem?
Who experiences the problem?
What are the constraints?
What is the simplest reliable solution?
What could fail?
What data should be stored?
What should not be stored?
What are the security risks?
How will the system be maintained?
How will success be measured?

AI can generate several solutions, but the engineer must choose the right one.

Software Architecture Will Become More Important

AI can produce working code that is locally correct but globally inconsistent.

For example, an AI-generated feature may work in isolation while violating the project's architectural standards. It may duplicate existing services, bypass authorization rules, introduce tight coupling, or create unnecessary database queries.

Future engineers will need strong architectural awareness.

Important areas include:

Modular design
Separation of concerns
Domain modeling
API design
Database design
Caching strategies
Event-driven systems
Distributed systems
Observability
Fault tolerance
Scalability
Maintainability

The ability to understand the whole system will become more valuable than the ability to generate individual files quickly.

Code Review Will Become a Core Skill

As AI-generated code increases, code review will become one of the most important engineering activities.

Developers will need to review code for:

Functional correctness
Security
Performance
Readability
Maintainability
Test coverage
Architectural consistency
Compliance requirements
Unexpected side effects

AI can also assist with code review, but human accountability remains necessary.

A useful future workflow may include multiple review stages:

AI generates the initial code
AI performs a first review
Automated tests and static analysis run
A developer reviews the implementation
The feature is tested against real requirements
The final change is approved

The engineer becomes responsible for validating machine-generated work rather than trusting it automatically.

Testing Will Be More Important, Not Less

AI can generate code quickly, which means teams may produce more code in less time.

More code also creates more opportunities for defects.

Testing must remain a major part of the development process.

Future engineers should understand:

Unit testing
Integration testing
End-to-end testing
Contract testing
Load testing
Security testing
Regression testing
Property-based testing

AI can help create test cases, but developers must ensure the tests represent meaningful behavior.

A generated test may confirm that the code behaves exactly as written while failing to verify whether the business requirement is correct.

The best engineers will use AI to increase test coverage without replacing critical thinking.

Security Knowledge Will Become Essential

AI-generated code can contain vulnerabilities.

It may create unsafe queries, expose sensitive information, use insecure authentication patterns, mishandle file uploads, or depend on outdated packages.

Software engineers need to understand common security risks, including:

SQL injection
Cross-site scripting
Cross-site request forgery
Broken access control
Insecure direct object references
Authentication weaknesses
Secrets exposure
Dependency vulnerabilities
Unsafe deserialization
Server-side request forgery

Developers must treat AI-generated code as untrusted until it has been reviewed and tested.

Security cannot be delegated completely to an AI assistant.

Domain Knowledge Will Differentiate Engineers

Generic coding knowledge is widely available through AI tools.

Domain knowledge is harder to replace.

An engineer who understands healthcare, finance, logistics, education, e-commerce, legal operations, manufacturing, or another specialized industry can make better product decisions.

Domain knowledge helps engineers understand:

Real user workflows
Industry terminology
Regulatory requirements
Operational risks
Common exceptions
Data sensitivity
Business priorities

A developer who understands both technology and the business domain will remain highly valuable.

Communication Skills Will Matter More

Software engineering has never been only about code.

Engineers communicate with clients, product managers, designers, testers, support teams, infrastructure teams, and other developers.

As AI handles more implementation work, human communication becomes even more important.

Future engineers should be able to:

Ask clear questions
Challenge unclear requirements
Explain technical decisions
Document assumptions
Describe risks
Present tradeoffs
Write useful specifications
Give and receive feedback
Coordinate across teams

Clear communication also improves AI output. A vague requirement usually produces a vague implementation.

Developers Should Learn How to Work With AI Tools

AI-assisted development should become a practical engineering skill.

Developers should learn how to:

Provide relevant project context
Break large tasks into smaller steps
Define clear constraints
Request explanations before implementation
Ask for multiple solution options
Generate and review tests
Compare architectural approaches
Verify generated APIs and libraries
Request security reviews
Use repository-level coding agents safely
Control which files an agent can modify
Review changes before committing them

The goal is not to accept every generated answer. The goal is to use AI as a productive collaborator while maintaining control.

Prompt Engineering Alone Is Not a Career Plan

Prompt engineering is useful, but it is not enough by itself.

Good prompts depend on good thinking.

A developer needs technical understanding to provide the correct context, identify missing information, define constraints, and evaluate the output.

Instead of focusing only on prompt tricks, engineers should improve:

Technical fundamentals
System design
Product thinking
Debugging
Testing
Security
Communication
Domain expertise
AI-assisted workflows

Prompting is one part of the process, not the entire profession.

Junior Developer Roles Will Change

AI may affect junior developers more quickly because many entry-level tasks involve repetitive implementation.

Examples include:

Generating CRUD modules
Writing basic tests
Fixing simple bugs
Creating documentation
Converting designs into components
Writing database migrations
Building standard API endpoints

These tasks will not disappear completely, but teams may expect junior developers to complete them faster with AI assistance.

Junior engineers should focus on understanding why the code works.

They should practice:

Reading existing codebases
Debugging without blindly copying solutions
Writing tests
Understanding database behavior
Learning version control
Reviewing AI-generated code
Explaining their decisions
Building complete projects
Deploying and maintaining applications

The ability to learn and reason will matter more than memorizing syntax.

Senior Engineers Will Also Need to Adapt

Senior engineers are not automatically protected from AI-driven changes.

A senior developer who refuses to use modern tools may become slower than a less experienced engineer who combines technical knowledge with effective AI usage.

Senior engineers should learn how to:

Design AI-assisted development workflows
Define repository rules
Review large generated changes
Protect architectural consistency
Improve team productivity
Establish AI security policies
Select appropriate tools
Mentor developers in responsible AI usage
Measure the actual value of AI adoption

Experience remains valuable, but it must be combined with adaptability.

A Practical Future Plan for Software Engineers

Software engineers can prepare for the AI-driven future with a structured plan.

1. Strengthen the Fundamentals

Learn programming deeply instead of depending entirely on frameworks.

Focus on:

Data structures
Algorithms
Databases
Networking
Operating systems
Object-oriented programming
Functional programming concepts
Concurrency
Memory management
Software design principles

Frameworks change, but fundamentals remain useful.

2. Master One Technical Stack

Avoid learning every new framework at a shallow level.

Choose one stack and understand it properly.

Learn:

Project structure
Dependency management
Authentication
Authorization
Database optimization
Background jobs
Caching
Testing
Deployment
Monitoring
Security

Deep knowledge makes it easier to evaluate AI-generated code.

3. Learn System Design

Study how complete systems are designed.

Start with:

REST APIs
Database indexing
Caching
Queues
File storage
Search systems
Notification systems
Rate limiting
Logging
Monitoring

Then move toward distributed systems, fault tolerance, scalability, and event-driven architecture.

4. Build Real Projects

Tutorial projects are useful, but real projects reveal deeper problems.

Build applications that include:

Multiple user roles
Permissions
Payments
Notifications
File uploads
Background jobs
Reports
Search
Audit logs
Automated tests
Production deployment

Use AI during development, but review and understand every important decision.

5. Improve Debugging Skills

AI can suggest solutions, but debugging still requires investigation.

Practice:

Reading stack traces
Inspecting logs
Reproducing bugs
Isolating variables
Checking database queries
Profiling performance
Using debuggers
Identifying race conditions
Testing assumptions

A developer who can find the actual cause of a problem will remain valuable.

6. Learn Security

Security should be part of everyday development.

Study secure authentication, authorization, input validation, data protection, dependency management, secrets handling, and common web vulnerabilities.

Review AI-generated code with a security-first mindset.

7. Learn AI-Assisted Development

Use AI for real engineering tasks.

Experiment with:

Code generation
Repository analysis
Refactoring
Test generation
Documentation
Bug investigation
SQL optimization
Architecture comparison
Code review
Migration planning

Track which tasks save time and which tasks require more manual correction.

8. Develop Product Thinking

Understand how software creates value.

Learn how to:

Identify user pain points
Prioritize features
Reduce unnecessary complexity
Validate ideas
Measure outcomes
Balance quality and delivery speed
Understand business constraints

The engineer who helps build the right product is more valuable than the engineer who only writes requested code.

9. Improve Communication

Practice writing technical documentation, architecture notes, issue descriptions, pull request summaries, and project proposals.

Learn to explain complex topics in simple language.

10. Build a Public Body of Work

Create evidence of your skills.

This may include:

Open-source contributions
Technical articles
Complete GitHub projects
Architecture case studies
Performance optimization reports
Security reviews
Developer tools
Libraries
Documentation improvements

A strong portfolio should show how you think, not only how much code you can generate.

Skills That May Become More Valuable

The following skills are likely to become increasingly important:

System design
Software architecture
Security engineering
Cloud infrastructure
DevOps
Observability
Data engineering
AI integration
Model evaluation
Product engineering
Technical leadership
Domain expertise
Code review
Testing strategy
Performance optimization

Developers do not need to master everything. They should combine strong fundamentals with one or two areas of deeper specialization.

Skills That May Become Less Differentiating

Some skills may remain useful while becoming less valuable as standalone abilities.

Examples include:

Memorizing syntax
Writing repetitive boilerplate
Building basic CRUD systems without deeper logic
Converting simple designs into static pages
Writing basic documentation manually
Creating simple unit tests
Copying solutions without understanding them

AI can perform many of these tasks quickly.

The developer must add value through reasoning, verification, integration, and ownership.

Will AI Replace Software Engineers?

AI is more likely to replace parts of software engineering work than the entire profession.

It may reduce the amount of manual effort required for many tasks. It may also allow smaller teams to build larger products.

However, software projects still require people who can:

Understand unclear requirements
Make technical decisions
Manage risk
Protect user data
Resolve production incidents
Coordinate teams
Balance business and engineering priorities
Take responsibility for outcomes

The role will change, but responsibility will remain human.

The Best Strategy Is Adaptation

Developers should not ignore AI, and they should not depend on it blindly.

The strongest approach is to combine human judgment with machine speed.

Use AI to:

Explore ideas faster
Reduce repetitive work
Generate initial implementations
Improve documentation
Expand test coverage
Analyze unfamiliar code
Compare possible solutions

Use engineering knowledge to:

Define the problem
Select the architecture
Verify correctness
Protect security
Test edge cases
Maintain quality
Make final decisions

The future belongs to engineers who can use AI without surrendering their judgment.

Conclusion

Software engineering is entering a new stage.

Writing code will remain important, but it will no longer be the only defining skill of a developer. The profession will place greater value on problem solving, architecture, testing, security, communication, domain knowledge, and responsible AI usage.

Developers should not ask whether AI will eliminate software engineering.

A more useful question is:

How can I become the engineer who knows what to build, how to validate it, and how to use AI to deliver it better?

That is the future plan worth following.

From Electron to Native Desktop Apps: How I Rebuilt TimoDesk for macOS and Windows

Moniruzzaman Saikat — Fri, 17 Jul 2026 18:15:44 +0000

From Electron to Native Desktop Apps: How I Rebuilt TimoDesk for macOS and Windows

Over the last month, I have spent most of my time rebuilding TimoDesk.

This was not a normal update or a small feature release. TimoDesk needed a complete desktop application rewrite.

The previous application was built with Electron and React. It worked well, but after running for long periods, we faced problems such as higher memory usage, larger application bundles, occasional performance issues, and an experience that did not always feel fully native.

Because TimoDesk is designed to run silently in the background for many hours, efficiency matters.

So I made a major decision.

I rebuilt the macOS application using Swift and the Windows application using .NET WPF.

Both applications are now in production.

Why This Was a Big Challenge for Me

Before starting this work, I had never written a single line of Swift in my entire development career.

I also had no native desktop application running in production.

My background was mainly in JavaScript. JavaScript was one of my first loves in programming. Over the years, I wrote production applications with React, worked with Vue around 2020 and 2021, used Node.js, and learned enough Electron to understand how desktop applications could be created with web technologies.

However, knowing how to create a basic Electron application and building a production-grade desktop application are two very different things.

TimoDesk was my first serious desktop product.

During the rewrite, I had to learn new technologies, understand operating-system-specific behavior, handle background services, manage performance, and make sure the applications could work reliably for real users.

I built both native applications myself while working under constant pressure and learning completely new technology stacks at the same time.

Seeing both applications reach production is a major personal achievement for me.

What TimoDesk Does

TimoDesk is a complete time tracking and productivity platform for remote teams, companies, agencies, and professionals.

The desktop application runs in the background and can:

Track working time
Record applications used during work
Track visited URLs
Capture screenshots at configured intervals
Synchronize time tracking data with the backend dashboard
Associate tracked time with projects and tasks
Operate silently without interrupting the user

For organizers, team managers, and company owners, TimoDesk also provides real-time visibility into team activity.

Managers can view:

Which team members are currently active
Who is idle or inactive
Which task each person is working on
Current project activity
Logged time and productivity information
Periodic screenshots and application usage

We also provide browser extensions for Chrome and Mozilla Firefox. These extensions help connect browser activity with the broader TimoDesk productivity system.

Why We Moved Away from Electron

Electron helped us build the original version quickly because it allowed us to use JavaScript and React.

For many applications, Electron is still a practical choice. However, TimoDesk has requirements that make native development especially valuable.

A time tracking application may remain active for an entire working day. It continuously handles timers, background synchronization, application monitoring, URL tracking, screenshots, and local system events.

Over long working sessions, every unnecessary resource matters.

The Electron version performed well in many situations, but we also experienced challenges such as:

Higher memory consumption
Larger installation packages
Resource pressure during long sessions
Less direct operating system integration
An interface that did not always feel completely native

The native rewrite gives us greater control over performance, background processing, memory usage, system permissions, and operating system integration.

The goal is to make TimoDesk lightweight, reliable, efficient, and closely connected to the operating system.

Building the macOS Version with Swift

The macOS version was developed using Swift.

This was one of the most challenging parts of the project because Swift was completely new to me.

I had to learn how macOS handles permissions, application monitoring, screenshots, background tasks, local storage, synchronization, system events, and user interface behavior.

macOS has strict privacy and security requirements, especially for applications that interact with screen recording, accessibility features, and application activity.

Building these features properly required more than simply translating JavaScript code into Swift. I had to redesign many parts of the application around the macOS environment.

The result is a native application that feels more closely integrated with macOS and gives us a stronger foundation for future development.

Building the Windows Version with .NET WPF

For Windows, I rebuilt TimoDesk using .NET and WPF.

The Windows application needed to handle many of the same responsibilities as the macOS application, but Windows has its own APIs, lifecycle behavior, installation requirements, background processes, permissions, and system-level challenges.

The WPF version gives us greater access to the Windows environment and allows us to optimize the experience specifically for Windows users.

Instead of maintaining one general desktop application for every operating system, we can now improve each version based on the strengths and behavior of its platform.

Linux May Be Next

I am also considering building a native Linux version.

The long-term goal is to make TimoDesk highly efficient across all major operating systems.

Linux presents its own challenges because desktop environments, display systems, permissions, and application packaging can vary significantly.

However, a native Linux version would allow TimoDesk to serve more developers, technical teams, remote companies, and organizations that rely heavily on Linux workstations.

TimoDesk Is Becoming More Than a Time Tracker

Time tracking is only one part of the larger vision.

We are building TimoDesk as a complete work operating system for remote teams.

Our team is actively working on:

Project management
Task management
Payroll management
Remote team operations
Productivity analytics
Mobile applications
AI-powered recommendations
Cost and performance insights

The objective is simple: teams should not need to move between many disconnected platforms to manage their daily work.

A company should be able to manage projects, assign tasks, track time, monitor productivity, calculate payroll, review team performance, and communicate important work information from one connected system.

Mobile Applications Are Also Coming

We are also developing mobile applications for TimoDesk.

The mobile application will make it easier for users and managers to stay connected to their work from anywhere.

Managers will be able to review team activity, tracked hours, projects, tasks, notifications, and productivity information without always opening the web dashboard.

The mobile experience is an important part of making TimoDesk a complete work solution.

AI Features for Better Productivity

We are also working on AI-powered features.

The purpose of these features is not simply to add AI because it is popular. The goal is to use existing work data to generate useful recommendations.

Based on how teams already use TimoDesk, the platform may be able to suggest:

Better task allocation
More accurate project estimates
Productivity improvements
Potential workflow problems
Time-consuming activities
Opportunities to reduce project costs
Work patterns that may affect delivery
Better planning for future projects

The idea is to help teams make better decisions using the work information they already generate.

What I Learned

This project reminded me that developers should not be limited by the technologies they already know.

A month ago, Swift was completely unfamiliar to me.

Today, I have a native macOS application written in Swift and a Windows application written in .NET WPF running in production.

The process was difficult. There were bugs, pressure, unfamiliar APIs, operating system restrictions, and many moments where the next step was not obvious.

But production software is often built that way.

You learn, solve one problem at a time, test constantly, and continue until the product becomes real.

This experience gave me more confidence in my ability to move beyond web development and build deeper, system-level software.

Try TimoDesk

TimoDesk is built for remote teams, agencies, companies, and professionals who need better visibility into projects, time, and productivity.

You can use it to:

Track project time
Monitor productivity
Review application and URL usage
Capture periodic screenshots
Manage remote team activity
Understand how working hours are spent

You can explore TimoDesk at:

https://timodesk.com

This native rewrite is only the beginning. The next stage is to make TimoDesk faster, smarter, more efficient, and capable of becoming the complete work platform remote teams can rely on every day.

Here is the clean Markdown version, ready for DEV.to, GitHub, Medium, or any Markdown editor:

From Electron to Native Desktop Apps: How I Rebuilt TimoDesk for macOS and Windows
Over the last month, I have spent most of my time rebuilding TimoDesk.

This was not a normal update or a small feature release. TimoDesk needed a complete desktop application rewrite.

Because TimoDesk is designed to run silently in the background for many hours, efficiency matters.

So I made a major decision.

I rebuilt the macOS application using Swift and the Windows application using .NET WPF.

Both applications are now in production.

Why This Was a Big Challenge for Me
Before starting this work, I had never written a single line of Swift in my entire development career.

I also had no native desktop application running in production.

However, knowing how to create a basic Electron application and building a production-grade desktop application are two very different things.

TimoDesk was my first serious desktop product.

I built both native applications myself while working under constant pressure and learning completely new technology stacks at the same time.

Seeing both applications reach production is a major personal achievement for me.

What TimoDesk Does
TimoDesk is a complete time tracking and productivity platform for remote teams, companies, agencies, and professionals.

The desktop application runs in the background and can:

Track working time

Record applications used during work

Track visited URLs

Capture screenshots at configured intervals

Synchronize time-tracking data with the backend dashboard

Associate tracked time with projects and tasks

Operate silently without interrupting the user

For organizers, team managers, and company owners, TimoDesk also provides real-time visibility into team activity.

Managers can view:

Which team members are currently active

Who is idle or inactive

Which task each person is working on

Current project activity

Logged time and productivity information

Periodic screenshots and application usage

We also provide browser extensions for Chrome and Mozilla Firefox. These extensions help connect browser activity with the broader TimoDesk productivity system.

Why We Moved Away from Electron
Electron helped us build the original version quickly because it allowed us to use JavaScript and React.

For many applications, Electron is still a practical choice. However, TimoDesk has requirements that make native development especially valuable.

A time-tracking application may remain active for an entire working day. It continuously handles timers, background synchronization, application monitoring, URL tracking, screenshots, and local system events.

Over long working sessions, every unnecessary resource matters.

The Electron version performed well in many situations, but we also experienced challenges such as:

Higher memory consumption

Larger installation packages

Resource pressure during long sessions

Less direct operating system integration

An interface that did not always feel completely native

The native rewrite gives us greater control over performance, background processing, memory usage, system permissions, and operating system integration.

The goal is to make TimoDesk lightweight, reliable, efficient, and closely connected to the operating system.

Building the macOS Version with Swift
The macOS version was developed using Swift.

This was one of the most challenging parts of the project because Swift was completely new to me.

I had to learn how macOS handles permissions, application monitoring, screenshots, background tasks, local storage, synchronization, system events, and user interface behavior.

macOS has strict privacy and security requirements, especially for applications that interact with screen recording, accessibility features, and application activity.

Building these features properly required more than simply translating JavaScript code into Swift. I had to redesign many parts of the application around the macOS environment.

The result is a native application that feels more closely integrated with macOS and gives us a stronger foundation for future development.

Building the Windows Version with .NET WPF
For Windows, I rebuilt TimoDesk using .NET and WPF.

The WPF version gives us greater access to the Windows environment and allows us to optimize the experience specifically for Windows users.

Instead of maintaining one general desktop application for every operating system, we can now improve each version based on the strengths and behavior of its platform.

Linux May Be Next
I am also considering building a native Linux version.

The long-term goal is to make TimoDesk highly efficient across all major operating systems.

Linux presents its own challenges because desktop environments, display systems, permissions, and application packaging can vary significantly.

However, a native Linux version would allow TimoDesk to serve more developers, technical teams, remote companies, and organizations that rely heavily on Linux workstations.

TimoDesk Is Becoming More Than a Time Tracker
Time tracking is only one part of the larger vision.

We are building TimoDesk as a complete work operating system for remote teams.

Our team is actively working on:

Project management

Task management

Payroll management

Remote team operations

Productivity analytics

Mobile applications

AI-powered recommendations

Cost and performance insights

The objective is simple: teams should not need to move between many disconnected platforms to manage their daily work.

Mobile Applications Are Also Coming
We are also developing mobile applications for TimoDesk.

The mobile application will make it easier for users and managers to stay connected to their work from anywhere.

Managers will be able to review team activity, tracked hours, projects, tasks, notifications, and productivity information without always opening the web dashboard.

The mobile experience is an important part of making TimoDesk a complete work solution.

AI Features for Better Productivity
We are also working on AI-powered features.

The purpose of these features is not simply to add AI because it is popular. The goal is to use existing work data to generate useful recommendations.

Based on how teams already use TimoDesk, the platform may be able to suggest:

Better task allocation

More accurate project estimates

Productivity improvements

Potential workflow problems

Time-consuming activities

Opportunities to reduce project costs

Work patterns that may affect delivery

Better planning for future projects

The idea is to help teams make better decisions using the work information they already generate.

What I Learned
This project reminded me that developers should not be limited by the technologies they already know.

A month ago, Swift was completely unfamiliar to me.

Today, I have a native macOS application written in Swift and a Windows application written in .NET WPF running in production.

The process was difficult. There were bugs, pressure, unfamiliar APIs, operating system restrictions, and many moments when the next step was not obvious.

But production software is often built that way.

You learn, solve one problem at a time, test constantly, and continue until the product becomes real.

This experience gave me more confidence in my ability to move beyond web development and build deeper, system-level software.

Try TimoDesk
TimoDesk is built for remote teams, agencies, companies, and professionals who need better visibility into projects, time, and productivity.

You can use it to:

Track project time

Monitor productivity

Review application and URL usage

Capture periodic screenshots

Manage remote team activity

Understand how working hours are spent

You can explore TimoDesk at:

https://timodesk.com

This native rewrite is only the beginning. The next stage is to make TimoDesk faster, smarter, more efficient, and capable of becoming the complete work platform remote teams can rely on every day.

This version uses Markdown-compatible headings, lists, and a clickable link.

bro markdown in # or whatever

From Electron to Native Desktop Apps: How I Rebuilt TimoDesk for macOS and Windows

Over the last month, I have spent most of my time rebuilding TimoDesk.

This was not a normal update or a small feature release. TimoDesk needed a complete desktop application rewrite.

Because TimoDesk is designed to run silently in the background for many hours, efficiency matters.

So I made a major decision.

I rebuilt the macOS application using Swift and the Windows application using .NET WPF.

Both applications are now in production.

Why This Was a Big Challenge for Me

Before starting this work, I had never written a single line of Swift in my entire development career.

I also had no native desktop application running in production.

However, knowing how to create a basic Electron application and building a production-grade desktop application are two very different things.

TimoDesk was my first serious desktop product.

I built both native applications myself while working under constant pressure and learning completely new technology stacks at the same time.

Seeing both applications reach production is a major personal achievement for me.

What TimoDesk Does

TimoDesk is a complete time tracking and productivity platform for remote teams, companies, agencies, and professionals.

The desktop application runs in the background and can:

Track working time
Record applications used during work
Track visited URLs
Capture screenshots at configured intervals
Synchronize time-tracking data with the backend dashboard
Associate tracked time with projects and tasks
Operate silently without interrupting the user

For organizers, team managers, and company owners, TimoDesk also provides real-time visibility into team activity.

Managers can view:

Which team members are currently active
Who is idle or inactive
Which task each person is working on
Current project activity
Logged time and productivity information
Periodic screenshots and application usage

We also provide browser extensions for Chrome and Mozilla Firefox. These extensions help connect browser activity with the broader TimoDesk productivity system.

Why We Moved Away from Electron

Electron helped us build the original version quickly because it allowed us to use JavaScript and React.

For many applications, Electron is still a practical choice. However, TimoDesk has requirements that make native development especially valuable.

Over long working sessions, every unnecessary resource matters.

The Electron version performed well in many situations, but we also experienced challenges such as:

Higher memory consumption
Larger installation packages
Resource pressure during long sessions
Less direct operating system integration
An interface that did not always feel completely native

The native rewrite gives us greater control over performance, background processing, memory usage, system permissions, and operating system integration.

The goal is to make TimoDesk lightweight, reliable, efficient, and closely connected to the operating system.

Building the macOS Version with Swift

The macOS version was developed using Swift.

This was one of the most challenging parts of the project because Swift was completely new to me.

I had to learn how macOS handles permissions, application monitoring, screenshots, background tasks, local storage, synchronization, system events, and user interface behavior.

macOS has strict privacy and security requirements, especially for applications that interact with screen recording, accessibility features, and application activity.

Building these features properly required more than simply translating JavaScript code into Swift. I had to redesign many parts of the application around the macOS environment.

The result is a native application that feels more closely integrated with macOS and gives us a stronger foundation for future development.

Building the Windows Version with .NET WPF

For Windows, I rebuilt TimoDesk using .NET and WPF.

The WPF version gives us greater access to the Windows environment and allows us to optimize the experience specifically for Windows users.

Instead of maintaining one general desktop application for every operating system, we can now improve each version based on the strengths and behavior of its platform.

Linux May Be Next

I am also considering building a native Linux version.

The long-term goal is to make TimoDesk highly efficient across all major operating systems.

Linux presents its own challenges because desktop environments, display systems, permissions, and application packaging can vary significantly.

However, a native Linux version would allow TimoDesk to serve more developers, technical teams, remote companies, and organizations that rely heavily on Linux workstations.

TimoDesk Is Becoming More Than a Time Tracker

Time tracking is only one part of the larger vision.

We are building TimoDesk as a complete work operating system for remote teams.

Our team is actively working on:

Project management
Task management
Payroll management
Remote team operations
Productivity analytics
Mobile applications
AI-powered recommendations
Cost and performance insights

The objective is simple: teams should not need to move between many disconnected platforms to manage their daily work.

Mobile Applications Are Also Coming

We are also developing mobile applications for TimoDesk.

The mobile application will make it easier for users and managers to stay connected to their work from anywhere.

Managers will be able to review team activity, tracked hours, projects, tasks, notifications, and productivity information without always opening the web dashboard.

The mobile experience is an important part of making TimoDesk a complete work solution.

AI Features for Better Productivity

We are also working on AI-powered features.

The purpose of these features is not simply to add AI because it is popular. The goal is to use existing work data to generate useful recommendations.

Based on how teams already use TimoDesk, the platform may be able to suggest:

Better task allocation
More accurate project estimates
Productivity improvements
Potential workflow problems
Time-consuming activities
Opportunities to reduce project costs
Work patterns that may affect delivery
Better planning for future projects

The idea is to help teams make better decisions using the work information they already generate.

What I Learned

This project reminded me that developers should not be limited by the technologies they already know.

A month ago, Swift was completely unfamiliar to me.

Today, I have a native macOS application written in Swift and a Windows application written in .NET WPF running in production.

The process was difficult. There were bugs, pressure, unfamiliar APIs, operating system restrictions, and many moments when the next step was not obvious.

But production software is often built that way.

You learn, solve one problem at a time, test constantly, and continue until the product becomes real.

This experience gave me more confidence in my ability to move beyond web development and build deeper, system-level software.

Try TimoDesk

TimoDesk is built for remote teams, agencies, companies, and professionals who need better visibility into projects, time, and productivity.

You can use it to:

Track project time
Monitor productivity
Review application and URL usage
Capture periodic screenshots
Manage remote team activity
Understand how working hours are spent

You can explore TimoDesk at:

https://timodesk.com

This native rewrite is only the beginning. The next stage is to make TimoDesk faster, smarter, more efficient, and capable of becoming the complete work platform remote teams can rely on every day.

How to Run a Successful Software Business in 2026

Moniruzzaman Saikat — Fri, 10 Jul 2026 06:29:36 +0000

Executive summary

Assumption: this article is written for founders and senior product and technology leaders building a horizontal software business in 2026, typically B2B SaaS with some prosumer lessons where useful. No specific industry vertical is assumed.

The strategic reality of 2026 is not that “every company needs AI.” It is that every software company now operates in a market where AI is widely adopted, buyers can research products faster, and the winning firms are those that turn AI from a demo into a governed, repeatable workflow advantage. McKinsey’s late-2025 survey found that 88% of organizations report regular AI use in at least one business function, 62% are at least experimenting with AI agents, and yet only about one-third say they have begun scaling AI across the enterprise. In other words, AI is mainstream, but scaled value is still scarce. That is the opening for disciplined software companies. citeturn20view0turn0search6

The market is also moving from experimentation to budgeted spend. Menlo Ventures estimates enterprise generative AI spending rose from $11.5 billion in 2024 to $37 billion in 2025, with $19 billion going to the application layer alone. Gartner forecast worldwide AI spending at nearly $1.5 trillion in 2025 and $2.52 trillion in 2026. Stanford’s 2026 AI Index reports that global corporate AI investment more than doubled in 2025 and that generative AI reached roughly 53% population-level adoption within three years of mass-market launch. citeturn29view0turn0search12turn0search2turn19view0turn30view0

The practical implication is straightforward: do not compete on “having AI.” Compete on proprietary context, workflow embedding, distribution, trust, and economics. The strongest business models in 2026 combine one of four patterns: bundled AI to defend the core suite, premium AI add-ons for governed enterprise workflows, seat-plus-usage hybrids for variable-cost workloads, and freemium-to-premium upgrades where AI expands supply and engagement. Current company behavior from GitHub, ServiceNow, Zoom, Atlassian, and Duolingo shows all four patterns working when tied to clear customer outcomes. citeturn24view4turn24view9turn25view0turn24view0turn24view2turn24view5turn19view4turn26view0

Introduction

In 2026, software is being sold into an environment where “deep research” is no longer a specialist capability. Knowledge workers can ask AI systems to compare vendors, synthesize customer references, summarize filings, inspect documentation, and pressure-test claims in minutes. That compresses information asymmetry and makes surface-level feature differentiation easier to copy and easier to expose. At the same time, agents are spreading into enterprise applications: Gartner projected in 2025 that 40% of enterprise apps would feature task-specific AI agents by 2026, while McKinsey found that agent use is most commonly reported in IT and knowledge management. citeturn0search6turn20view0

That changes the definition of a successful software business. The winning company is not the one with the flashiest model. It is the one that owns a high-frequency job to be done, sits on the system of record or system of action for that workflow, makes trustworthy decisions with proprietary context, and monetizes value without letting inference cost outrun gross margin. If 2024 was the year of pilots and 2025 the year of broad adoption, 2026 is the year of operational discipline. citeturn20view0turn29view0turn19view0

Market trends from 2024 to 2026

Three signals matter most. First, adoption is broadening faster than enterprise value is compounding. McKinsey reported 78% of organizations using AI in at least one business function in its March 2025 survey, up from 72% in early 2024 and 55% a year earlier; by November 2025, that figure had climbed to 88%. Yet only 39% reported any enterprise-level EBIT impact, and McKinsey’s “AI high performers” represented only about 6% of respondents. That gap between usage and measurable company-level value is exactly where durable software businesses can win. citeturn19view5turn20view0

Second, budgets are moving decisively toward applications. Menlo estimates the application layer captured $19 billion of enterprise generative AI spending in 2025, more than half the total, and coding alone represented $4.0 billion of departmental AI spend. Menlo also found that enterprises now prefer buying to building: 76% of AI use cases were purchased in 2025, versus an almost even split in 2024. Founders should read that as a distribution and packaging opportunity, not proof that in-house AI teams do not matter. citeturn29view0

pie showData
    title 2025 Enterprise Generative AI Spend
    "Application layer" : 19
    "Other layers" : 18

Menlo’s estimate that the application layer accounted for $19 billion of $37 billion in enterprise generative AI spend is a strong signal that the most immediate software value is still being captured in products that sit closest to the user and the workflow, not in raw model infrastructure. citeturn29view0

Third, the agent era is raising the bar for product design and governance. McKinsey found 62% of organizations were at least experimenting with AI agents by late 2025, and Gartner projected that task-specific agents would spread rapidly into enterprise applications by 2026. At the same time, Stanford’s AI Index warns that investment and capabilities are accelerating faster than governance and measurement frameworks. That means “agentic” is not a moat by itself; the moat is safe orchestration, grounded context, and measurable business outcomes. citeturn20view0turn0search6turn19view0

Business models, pricing, and case studies

The most resilient 2026 business model is usually a hybrid monetization stack: a base software subscription, optional premium AI add-ons where governance or specialization matters, and measured usage for expensive or autonomous workflows. Pure seat-based pricing works best when AI is lightweight and broadly used. Pure consumption works best when the customer already understands unit economics. Most companies should avoid copying API-style pricing blindly and instead price around the customer’s scarce unit of value: a developer seat, a workflow, a resolved case, a converted lead, or a high-intent research task. This synthesis reflects what we see in GitHub, Zoom, ServiceNow, Atlassian, and Duolingo. citeturn24view4turn24view9turn24view2turn25view0turn24view5turn19view4

Model	Best fit	Advantage	Primary risk
Bundled AI in core plans	Collaboration and suite products	Drives adoption fast and defends the base product	Hidden inference costs if usage spikes
Seat + usage credits	Developer tools and power-user workflows	Aligns margin with higher-intensity workloads	Billing complexity can slow adoption
Premium AI add-on	Enterprise workflow products	Best for governance, ROI proofs, and upsell	Requires strong sales enablement
Freemium + premium AI tier	Prosumer and consumer education/productivity	Expands funnel and converts power users	High support and compute costs at scale
Outcome-based automation	Narrow, high-value workflows	Strongest ROI narrative	Harder contracting and measurement

This comparison synthesizes current market patterns visible in Zoom’s no-extra-cost AI bundling, GitHub’s seat-plus-credit plans and 2026 usage-based extension, ServiceNow’s premium AI ACV growth, Atlassian’s Rovo bundle-plus-standalone pricing, and Duolingo’s freemium-to-premium conversion strategy. citeturn24view2turn24view3turn24view4turn24view9turn25view0turn24view0turn24view5turn19view4

GitHub Copilot is the clearest lesson in category creation and monetization evolution. Microsoft said in May 2025 that 15 million developers were already using GitHub Copilot; GitHub later reported that Copilot code review usage had grown 10x and accounted for more than one in five code reviews on GitHub. GitHub’s organization plans are seat-based, but in 2026 the company extended usage-based billing for heavier workloads. Lesson: start with simple adoption pricing, then add consumption when agents materially increase variable cost and delivered value. citeturn24view7turn24view8turn24view4turn24view9

ServiceNow shows how to sell AI as governed enterprise workflow expansion rather than as a generic assistant. Its 2025 annual-report disclosures said Now Assist surpassed $600 million in ACV in 2025 and aimed to exceed $1 billion ACV in 2026; Q1 2026 results said customers spending more than $1 million in ACV on Now Assist grew more than 130% year over year. Lesson: premium AI monetizes best where the software already owns approvals, workflows, and compliance-sensitive operations. citeturn5search2turn24view0

Zoom demonstrates the strategic logic of bundling. Zoom repeatedly emphasized that AI Companion remains available at no additional cost for paid Zoom Workplace accounts, while also launching customization and bring-your-own-data paths. Lesson: if AI strengthens the core suite, protects retention, and creates a future add-on market, bundling can be smarter than immediate monetization. citeturn24view2turn24view3

Duolingo is a useful reminder that AI should expand supply, not just trim labor. The company reported 12.2 million paid subscribers and 52.7 million DAUs as of Q4 2025, and said AI let it publish 20,500 skills in Q1 2026 versus 7,100 per quarter in 2025 and 1,800 in 2024. Even while expanding higher-cost AI features, it reported Q2 2025 gross margin of 72.4%, with lower-than-expected AI costs helping margins. Lesson: the best AI businesses use automation to widen their content or service frontier while protecting monetization and margin. citeturn19view4turn26view0turn26view1turn30view1turn30view2

Atlassian shows why the context layer matters. By late 2025, Atlassian said Rovo had rolled out to every paying cloud customer at no extra cost, that AI usage had more than doubled across its customer base, and that a standalone Rovo offer was coming at $5 per user. Its FY26 investor forum materials also highlighted faster ARR growth among customers using Rovo, while its GA MCP server emphasized admin controls, auditability, and open connectivity to external AI clients. Lesson: in the deep-research era, the product that wins is the one with the best permissions-aware knowledge graph, not merely the best chat box. citeturn24view5turn12search0turn24view6

Product strategy and AI integration

A robust 2026 product strategy starts with a narrow wedge: one painful, frequent, expensive workflow where the product can prove a measurable before-and-after delta. The second design rule is architectural: separate the system of record, the retrieval and data layer, the orchestration layer, and the execution layer. For dynamic knowledge tasks, retrieval-augmented generation remains the default because it improves updateability and provenance; for action-taking systems, tool use matters because model quality alone is not enough. The original RAG paper explicitly frames explicit memory as a way to improve factuality and update knowledge, and Toolformer formalizes model use of external tools and APIs. citeturn16search16turn23view4

The technical stack most founders should assemble looks like this: a product event and application database; a permissions-aware retrieval index over first-party content; model routing across at least a small, fast model and a stronger reasoning model; prompt and workflow versioning; offline eval sets; online experimentation; audit logs; and policy controls for data handling and action execution. The warning from Google’s “Hidden Technical Debt in Machine Learning Systems” still applies: in ML systems, complexity compounds silently, which is why prompt chains, retrieval adapters, evaluation code, and fallback logic must be treated as first-class production assets. citeturn23view2turn16search3

Safety and explainability are not “governance extras”; they are product features. NIST’s AI RMF defines trustworthy AI in terms that include validity, safety, security, transparency, explainability, privacy enhancement, and fairness, and organizes execution around GOVERN, MAP, MEASURE, and MANAGE. For LLM applications specifically, OWASP’s current guidance highlights prompt injection, insecure output handling, data poisoning, denial of service, supply-chain issues, and sensitive-information disclosure as core design risks. A production product should therefore show sources when possible, constrain tool permissions, sandbox actions, support human approval for high-impact steps, and log what the model saw, inferred, and executed. citeturn23view0turn23view1

Evaluation also needs to be tougher in 2026 than “the demo looked good.” SWE-bench and SWE-bench Verified matter beyond coding because they illustrate the broader point: realistic AI evaluation requires messy, long-context, multi-step tasks and human-validated benchmarks, not just synthetic prompts. In practice, that means every software company should maintain a living eval suite for its own top workflows: grounding accuracy, task completion, latency, safety violations, action reversals, and user acceptance. citeturn23view5turn23view6

AI delivery checklist

Tie every AI feature to a single workflow KPI before launch.
Default to retrieval and tools before fine-tuning.
Ship citations, audit logs, and approval gates early.
Version prompts, policies, and eval sets like code.
Track gross-margin impact per account, not just global token spend.

Go-to-market, legal, organization, and operations

Go-to-market in 2026 should sell workflow economics, not abstractions. McKinsey’s data show that cost benefits are most often reported in software engineering, manufacturing, and IT, while revenue gains are most often reported in marketing and sales, strategy and finance, and product or service development. The same research shows that high performers redesign workflows and set growth or innovation objectives alongside efficiency. So the best sales motion is a short, instrumented value proof with baseline metrics, redesigned process steps, and a buyer-visible dashboard of time saved, quality improved, or revenue unlocked. citeturn20view0

On pricing, founders should think in layers. Bundle lightweight assistance that improves core retention. Sell governed or specialized AI as a premium add-on. Add usage pricing only where autonomous or high-volume workloads create real variable cost or outsized customer value. GitHub’s shift toward usage-based billing for heavier Copilot workloads is a good signal that agentic features eventually need economic separation, while Zoom and Atlassian show the complementary logic of broad AI bundling to accelerate adoption and defend the suite. citeturn24view9turn24view2turn24view5

Legally, privacy and AI compliance now have to be designed in from day one. If you process personal data, GDPR applies in the EU, and California’s CCPA gives consumers rights over personal information collected by businesses. The European Data Protection Board’s December 2024 opinion on AI models addressed anonymity, legitimate interest, and the consequences of unlawful personal-data processing in AI development and deployment. Separately, the EU AI Act entered into force on August 1, 2024; prohibited practices and AI literacy obligations started applying on February 2, 2025; GPAI obligations applied from August 2, 2025; and the Act became broadly applicable on August 2, 2026, with some embedded-product obligations deferred further. For GPAI providers, the Commission’s code of practice centers on transparency, copyright, and safety and security. citeturn15search13turn21view4turn21view3turn21view0turn21view1turn21view2

Intellectual property remains unsettled enough that you should operate conservatively. The U.S. Copyright Office’s Part 2 report says copyrightability of AI outputs turns on the nature and extent of human contribution and maintains the human-authorship baseline; its Part 3 training report describes ongoing debate and unresolved litigation over the use of copyrighted works in model training. The practical response is not paralysis. It is provenance logs, documented dataset rights, customer contract clarity, output review for high-risk content, and indemnity language you can actually stand behind. citeturn22view1turn22view2turn21view5turn21view6turn22view3

Organizationally, the best setup is usually a compact cross-functional pod per wedge: a GM or PM, design lead, engineering lead, applied AI engineer, data or ML engineer, platform engineer, and a part-time security/privacy partner, plus a solutions engineer for customer feedback loops. McKinsey reports that software engineers and data engineers are among the most in-demand AI-related roles, that half of AI-using organizations need more data scientists, and that workforce reskilling is increasing. Infrastructure-wise, keep cost discipline through model routing, caching, retrieval optimization, asynchronous work queues, batch jobs, and hard spend guardrails. Ethical risk management should be reviewed monthly at the product-operations level: not only bias and privacy, but also inaccuracy, explainability gaps, over-automation, and customer deception. citeturn19view5turn20view0turn23view0turn23view1

A 12–18 month tactical roadmap with milestones and KPIs

The roadmap below assumes a company with an existing core product and a modest product-engineering base. The aim is not to launch ten AI features. It is to create one durable wedge, prove economics, then scale safely.

gantt
    title 12–18 Month Roadmap
    dateFormat  YYYY-MM-DD
    axisFormat  %b %Y

    section Foundation
    Select wedge workflow and baseline metrics      :a1, 2026-08-01, 45d
    Data contracts, permissions, logging, eval set  :a2, after a1, 45d

    section First product
    Build RAG + tool orchestration MVP             :b1, 2026-11-01, 60d
    Pilot with design partners                      :b2, 2027-01-01, 60d

    section Commercialization
    Launch paid beta and sales playbook             :c1, 2027-03-01, 60d
    Add premium governance/admin features           :c2, after c1, 60d

    section Scaling
    Model routing, caching, cost controls           :d1, 2027-05-01, 60d
    Expand to second workflow and partner channel   :d2, 2027-07-01, 90d

Milestones and KPIs

In the first 90 days, define the wedge and instrument it. The only acceptable launch criteria are a clear benchmark and a clear denominator: time to complete task, resolution rate, acceptance rate, conversion rate, or expansion signal. By month six, the pilot should show both user value and economic viability. By month twelve, the product should have a repeatable sales narrative and a cost envelope. By month eighteen, the company should be able to scale a second workflow without rewriting the platform. This sequencing mirrors what the strongest AI performers do: redesign workflows, scale selectively, and invest behind use cases that already show measurable value. citeturn20view0turn29view0

A practical KPI stack for 2026 is: activation-to-first-value in under one day for self-serve or under 30 days for enterprise; weekly active teams; net revenue retention; pilot-to-production conversion; model-grounded answer rate; citation coverage on knowledge tasks; human acceptance rate of generated outputs; auto-resolution rate for target workflows; p95 latency; inference cost per active account; gross margin by plan; security or policy incidents; and percentage of workflows with auditable logs. For enterprise AI, a board-level metric that matters more than raw MAUs is revenue or cost impact per redesigned workflow. citeturn20view0turn23view0

Founder checklist for the next quarter

Pick one workflow where the product can own both context and action.
Choose a pricing model that maps to customer value, not vendor token pricing.
Stand up evals, observability, and privacy reviews before general availability.
Require every AI feature to show one adoption KPI and one margin KPI.
Treat legal clarity and user trust as growth levers, not overhead.

A short conclusion: the successful software business of 2026 is not an “AI wrapper,” nor a traditional SaaS product with a chatbot bolted onto it. It is a software company that uses AI to turn proprietary context into better decisions, better workflows, and better economics than competitors can match. In a deep-research market, the firms that win will be the ones that are easiest to trust, easiest to prove, and hardest to replace.

The Future of Software Development: What Developers Should Expect

Moniruzzaman Saikat — Tue, 23 Jun 2026 18:52:27 +0000

Software development is changing faster than ever.

Over the past decade, we have seen the rise of cloud computing, mobile applications, DevOps, containers, and artificial intelligence. Technologies that once felt revolutionary have become standard tools in a developer's workflow.

As we look toward the future, one question continues to emerge:

Will developers still be needed?

The short answer is yes.

However, the role of developers is evolving. The future will not belong to developers who simply write code. It will belong to developers who can solve problems, understand business requirements, leverage AI effectively, and continuously adapt to new technologies.

Let's explore what the future of software development may look like from a developer's perspective.

AI Will Become a Standard Development Tool

Artificial intelligence is already transforming how developers work.

Tools such as AI coding assistants can generate boilerplate code, explain complex functions, write tests, and even suggest bug fixes.

Many developers fear AI will replace programmers.

In reality, AI is more likely to replace repetitive tasks than developers themselves.

Future developers will spend less time writing basic code and more time focusing on:

System design
Business logic
Architecture decisions
Security considerations
User experience
Problem solving

Learning how to collaborate with AI may become as important as learning a programming language.

Coding Will Become More Accessible

Historically, software development required deep technical knowledge.

Today, low-code and no-code platforms allow non-technical users to build simple applications.

This trend will continue.

Businesses will increasingly use visual development tools for straightforward applications and workflows.

However, complex systems will still require experienced developers.

Enterprise software, financial systems, healthcare platforms, and large-scale SaaS applications cannot rely entirely on drag-and-drop tools.

Developers will continue to play a critical role where complexity exists.

The Demand for Problem Solvers Will Increase

Companies do not hire developers because they need code.

They hire developers because they need solutions.

As AI becomes better at generating code, technical implementation becomes less of a competitive advantage.

Understanding the problem becomes more valuable than writing the solution.

Future developers who can bridge the gap between technology and business will stand out.

This means developing skills such as:

Communication
Critical thinking
Product understanding
Requirements analysis
Leadership

The ability to solve real-world problems will become a major differentiator.

Full Stack Skills Will Become More Valuable

The boundaries between frontend, backend, infrastructure, and operations are becoming increasingly blurred.

Modern developers often work with:

Frontend frameworks
APIs
Databases
Cloud services
CI/CD pipelines
Monitoring tools

While specialization will remain important, developers who understand the entire software lifecycle will have significant advantages.

The future favors developers who can see the bigger picture.

Security Will Become Everyone's Responsibility

Cybersecurity threats continue to grow every year.

In the past, security was often handled by dedicated teams.

Today, secure development practices are becoming part of every developer's responsibilities.

Future developers will need a stronger understanding of:

Authentication
Authorization
Data protection
Secure coding practices
Vulnerability management
Compliance requirements

Security awareness will no longer be optional.

It will be a core development skill.

Remote Development Is Here to Stay

The global software industry has proven that remote teams can build world-class products.

This has created opportunities for developers worldwide.

A talented developer in Bangladesh, India, Nigeria, or Brazil can now compete for opportunities that were once limited to specific geographic regions.

The future workforce will be increasingly global.

Developers who can communicate effectively, collaborate asynchronously, and work independently will thrive.

Continuous Learning Will Be Mandatory

One constant in software development is change.

Programming languages evolve.

Frameworks change.

New tools emerge.

Entire technology stacks can become obsolete within a few years.

The most successful developers will not be those who know a particular framework.

They will be those who know how to learn quickly.

Adaptability will become one of the most valuable skills in the industry.

Developer Productivity Will Increase Dramatically

Modern tools are already helping developers accomplish more with less effort.

In the future, developers may build applications in days that previously required weeks or months.

AI-assisted development, automation, cloud services, and mature frameworks will continue to accelerate software delivery.

This does not mean developers will work less.

Instead, expectations will increase.

Businesses will expect faster development cycles, quicker releases, and higher quality products.

Developers who leverage modern tools effectively will have a major advantage.

What Skills Should Developers Focus on Today?

If you want to prepare for the future, focus on skills that remain valuable regardless of technological changes.

These include:

Problem solving
Software architecture
System design
Communication
Security fundamentals
Cloud computing
AI-assisted development
Database design
Business understanding

Technologies may change.

Fundamental skills remain valuable.

Final Thoughts

The future of software development is exciting, not threatening.

AI will change how we write software.

Automation will eliminate repetitive work.

New tools will make development faster than ever before.

But technology alone does not create successful software.

People do.

Developers who embrace change, continuously learn, and focus on solving meaningful problems will remain in demand for years to come.

The future does not belong to the developers who write the most code.

It belongs to the developers who create the most value.

How to Use AI to Achieve 10x Productivity

Moniruzzaman Saikat — Tue, 23 Jun 2026 18:47:47 +0000

A few years ago, being productive meant working longer hours.

Today, it means working smarter.

Artificial Intelligence has become one of the biggest productivity multipliers available to developers, entrepreneurs, designers, marketers, and business owners. The people getting the most value from AI are not using it to replace their work. They are using it to eliminate repetitive tasks, speed up decision making, and focus on high value activities.

In this article, I will share how I use AI to dramatically increase productivity and how you can do the same.

Stop Thinking of AI as a Chatbot

Most people open ChatGPT, ask a random question, get an answer, and close the tab.

That is not where the real productivity gains come from.

Think of AI as a digital assistant that can help with:

Research
Writing
Coding
Planning
Documentation
Customer support
Content creation
Data analysis
Brainstorming

The goal is not to ask better questions occasionally.

The goal is to build workflows where AI becomes part of your daily process.

1. Use AI for Research

Research can consume hours.

Instead of opening twenty browser tabs, start with AI.

For example, instead of searching:

What is Laravel queue processing?

Ask:

Explain Laravel queues like I am a junior developer. Include practical examples and common mistakes.

You get a structured overview within seconds.

This does not replace verification, but it drastically reduces the time needed to understand a topic.

2. Turn AI Into Your Personal Writing Assistant

Writing documentation, emails, proposals, blog posts, and social media content takes time.

AI can generate first drafts almost instantly.

Some examples:

Blog outlines
Product descriptions
Documentation
Client emails
Meeting summaries

The biggest productivity boost comes from eliminating the blank page problem.

Instead of starting from zero, start with a draft and improve it.

3. Use AI for Coding

This is where many developers experience massive productivity gains.

AI can help:

Generate boilerplate code
Explain unfamiliar code
Debug issues
Create database schemas
Write test cases
Generate documentation

For example, instead of spending thirty minutes creating a CRUD structure, you can generate the foundation in minutes and focus on business logic.

The key is understanding the code before using it.

AI should accelerate development, not replace engineering judgment.

4. Automate Repetitive Tasks

Every repetitive task is a candidate for AI assistance.

Examples include:

Formatting data
Creating reports
Summarizing meetings
Generating content variations
Writing support responses

If you perform the same task more than three times per week, ask yourself:

Can AI help me do this faster?

The answer is often yes.

5. Use AI as a Thinking Partner

One underrated use of AI is brainstorming.

Whenever I am stuck on a problem, I ask AI to challenge my assumptions.

For example:

Give me five alternative solutions to this business problem.

Or:

What risks am I missing in this project?

This often reveals ideas I would not have considered on my own.

6. Build Prompt Templates

Many people waste time rewriting the same prompts.

Create reusable templates.

Blog Post Prompt

Act as a technical writer.

Write a detailed blog post about [TOPIC].

Target audience: [AUDIENCE]

Include:
- Introduction
- Practical examples
- Common mistakes
- Conclusion

Once you have a library of prompts, your productivity increases significantly.

7. Focus on Output, Not Activity

AI changes how we measure productivity.

Being busy is not the goal.

Producing results is.

If AI helps you complete a task in 10 minutes instead of 1 hour, that is not cheating.

That is efficiency.

The most productive professionals are not necessarily the hardest workers.

They are often the best at leveraging tools.

Common Mistakes

Blindly Trusting AI

Always verify important information.

AI can make mistakes.

Using Vague Prompts

Poor prompts lead to poor outputs.

Provide context, goals, and constraints.

Trying to Automate Everything

Not every task should be delegated.

Focus on repetitive and low value work first.

Final Thoughts

AI is not a magic button.

It will not automatically make you productive.

The real advantage comes from integrating AI into your daily workflow and using it consistently.

Start small.

Choose one repetitive task you do every day.

Use AI to reduce the time required for that task.

Then repeat the process.

Over time, the small improvements compound.

That is where the real 10x productivity gains come from.

[Boost]

Moniruzzaman Saikat — Mon, 29 Sep 2025 03:58:20 +0000

Top 10 Most Design Patterns You Should Learn

Moniruzzaman Saikat — Tue, 15 Jul 2025 21:04:47 +0000

You should learn design patterns because they give you proven, reusable solutions to common software design problems. Instead of reinventing the wheel every time, patterns help you write code that is clean, scalable, flexible, and easy to maintain. They teach you how to think in structures, not just syntax — making it easier to communicate with other developers, architect complex systems, and avoid spaghetti code. Whether you're working on a small app or a big enterprise system, design patterns help you build software that lasts.

And here are top 10 of them you should start learning now.

1. Factory Pattern

Creates objects without exposing the instantiation logic to the client.
Used when you want to delegate the creation of objects to a single point — instead of scattering new Class() everywhere.

2. Strategy Pattern

Enables selecting an algorithm at runtime by encapsulating each behavior in a separate class. Avoids if-else/switch by letting you swap logic like payment methods or sorting strategies dynamically.

3. Singleton Pattern

Ensures a class has only one instance and provides global access to it. Perfect when one shared thing (like a database connection or logger) needs to be accessed everywhere.

4. Observer Pattern

Defines a one-to-many dependency so that when one object changes state, all its dependents are notified. Aka event listeners — used in UI, notifications, activity logs.

5. Decorator Pattern

Adds new behaviors to objects dynamically without changing their structure. Wrap an object to extend its functionality (great alternative to subclassing).

6. Adapter Pattern

Converts the interface of a class into another interface that a client expects. Useful for integrating incompatible systems or third-party APIs.

7. Builder Pattern

Constructs complex objects step-by-step, allowing different representations using the same construction process. Best for creating objects with many optional parameters.

8. Facade Pattern

Provides a simplified, unified interface to a complex subsystem. Hides all the complexity behind one simple class/interface.

9. Command Pattern

Encapsulates a request as an object, allowing it to be queued, logged, or undone. Perfect for systems where actions can be delayed, repeated, or undone.

10. Chain of Responsibility Pattern

Passes a request along a chain of handlers until one handles it. Each handler decides whether to handle the request or pass it along.

ফ্যাক্টরি – একটি অবজেক্ট ক্রিয়েশনাল প্যাটার্ন

Moniruzzaman Saikat — Tue, 15 Jul 2025 20:41:42 +0000

এটি একটি ডিজাইন প্যাটার্ন যা অবজেক্ট তৈরির লজিককে আলাদা করে রাখে, যাতে আপনার মূল অ্যাপ্লিকেশন কোড নির্দিষ্ট ক্লাসের নামের উপর নির্ভরশীল না হয়। শুধু ফ্যাক্টরিকে বলে দেন, আর আপনি যেই অবজেক্ট চাবেন তা পেয়ে যাবেন।

ফ্যাক্টরি প্যাটার্নকে একটি পাওয়ার প্লাগ অ্যাডাপ্টারের মতো করে ভাবেন। আপনি যেকোনো একটা ডিভাইস (ল্যাপটপ, ফোন, টিভি) এতে যুক্ত করেন, আর এটি সঠিক কারেন্ট সরবরাহ করে — এটা কীভাবে কাজ করছে তা নিয়ে চিন্তা করার দরকার নাই। ফ্যাক্টরি প্যাটার্নের মূল লক্ষ্য হইতেছে অবজেক্ট তৈরিকে বিজিনেস লজিক থেকে আলাদা করে ফেলা, যাতে আপনার কোড আরও ফ্লেক্সিবল, টেস্টেবল এবং সহজে রক্ষণাবেক্ষণযোগ্য হয়।

চলেন একটা উদাহরণ দেখি, ফ্যাক্টরি প্যাটার্নর যেটা ডকুমেন্ট এক্সপোর্টার নিচ্ছে।

interface Exporter {
    public function export(array $data): string;
}

class PdfExporter implements Exporter {
    public function export(array $data): string {
        return "Exporting as PDF";
    }
}

class CsvExporter implements Exporter {
    public function export(array $data): string {
        return "Exporting as CSV";
    }
}

class ExportFactory {
    public static function getExporter(string $format): Exporter {
        return match($format) {
            'pdf' => new PdfExporter(),
            'csv' => new CsvExporter(),
            default => throw new Exception("Invalid export format"),
        };
    }
}

// Usage
$exporter = ExportFactory::getExporter('csv');
echo $exporter->export([]);

ফ্যাক্টরি প্যাটার্ন কেন ইউজ করা লাগে?

আপনি অবজেক্ট তৈরিকে ছেন্ট্রালাইজড করতে চান
আপনি কোড ডিকপল করতে চান (ক্লায়েন্ট জানবে না যে সে কোন ক্লাস ব্যবহার করছে)
ক্লায়েন্ট কোড টাচ না করেই আপনি কোন অবজেক্ট তৈরি করা হয়েছে তা পরিবর্তন করার ফ্লেক্সিবিলিটি চান

এখানে একটি নোটিফিকেশন সেন্ডার (ইমেল, এসএমএস, পুশ, যাই হোক না কেন) সিস্টেমের আরেকটি উদাহরণ দেওয়া হল:

interface Notifier {
    public function send(string $message): string;
}

class EmailNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending Email: $message";
    }
}

class SMSNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending SMS: $message";
    }
}

class PushNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending Push Notification: $message";
    }
}

class NotificationFactory {
    public static function create(string $type): Notifier {
        return match($type) {
            'email' => new EmailNotifier(),
            'sms' => new SMSNotifier(),
            'push' => new PushNotifier(),
            default => throw new Exception("Invalid notifier type"),
        };
    }
}

// Usage
$notifier = NotificationFactory::create('sms');
echo $notifier->send('Factory pattern rocks!');

হাহা, এখন চলেন একটা গল্প পড়ি যাতে বুঝতে আরো সুবিধা হয়, ঠিক আছে?

ছোট্ট টেকনোলজি টাউন ছিল, যেখানে সৈকত নামে এক জন বাচ্চা প্রোগ্রামার থাকতো। আর হালায় একটা পপুলার রেস্টুরেন্ট “কোড ক্রাস্ট পিৎজা”র জন্য অনলাইন ফুড অর্ডারিং সিস্টেম বানাইতেছিল।

কিন্তু প্রতি বার যখন কাস্টমার মার্গারিটা, পেপারোনি বা ভেজি পিৎজা অর্ডার দিত, সৈকতের অ্যাপের কাজ হইত এরকম:

$margherita = new MargheritaPizza();
$pepperoni = new PepperoniPizza();
$veggie = new VeggiePizza();

আর বুঝেন ভাই, এভাবে এখানে ওখানে সব জায়গায় পিৎজা তৈরির কোড থাকলে কেমন খারাপ-টা লাগে! দুই-তিনটা ক্লাস পর্যন্ত তো ঠিক ছিল, কিন্তু একসময় রেস্টুরেন্ট আর দশটা পিৎজা টাইপ যোগ করলো আর কিছু পিৎজার বেক করার ধরনও বদলাতে চাইলো। তখন বুঝেন ব্যাপারটা কেমন গোলমাল হয়ে গেল!

ওওওওঁ, একটা আইডিয়া আসল, ধরেন যদি আমি কাউকে সব নতুন পিৎজা বানানোর কাজ দিয়ে দিই (মোটামুটি সব প্রসেস), আর ক্লায়েন্ট সাইড থেকে সেটা খুব সহজে ইউজ করতে পারি! তখনই ভাবল একটা PizzaFactory বানানো যাক — যে বুদ্ধিমান(হাহা) রান্নাঘরের সহকারী, যাকে বলে দিলেই যেকোন পিৎজা তৈরী করতে পারে।

আর এখানে হল সেইটা:

interface Pizza {
    public function prepare(): string;
    public function deliverd(): boolean;
}

class MargheritaPizza implements Pizza {
    public function prepare() {
        return "Preparing Margherita Pizza";
    }
}

class PepperoniPizza implements Pizza {
    public function prepare() {
        return "Preparing Pepperoni Pizza";
    }
}

class PizzaFactory {
    public static function make(string $type): Pizza {
        return match($type) {
            'margherita' => new MargheritaPizza(),
            'pepperoni'  => new PepperoniPizza(),
            default => throw new Exception("Pizza type not found"),
        };
    }
}

আর ইউজ করার ধরনটা এমন (স্রেফ কাস্টমারের অর্ডার করা পিৎজার নামটা পাস করো, বাকিটা ফ্যাক্টরি নিজেই করে ফেলে দিয়ে দিবে, দারুন):

$pizza = PizzaFactory::make('pepperoni');
echo $pizza->prepare();
echo $pizza->deliverd(); // shanti shanti shanti

আর কোথাও new ClassName() দিয়ে দিয়ে প্যারা খাওয়া লাগবে না। কিচেন (ফ্যাক্টরি) নিজের মতো করে সব সামলায়, আর সৈকত পেয়ে যায় শান্তি :)

মোরাল অব দা স্টোরিঃ

ফ্যাক্টরি প্যাটার্ন একদম রেস্টুরেন্টের রান্নাঘরেরই মতো — ক্লায়েন্টরা (সৈকতের অ্যাপ) সরাসরি খাবার বানায় না। তারা রান্নাঘরের (ফ্যাক্টরির) কাছে যায়, আর রান্নাঘর সঠিক ডিশ বানায়ে বানায়ে পরিবেশন করে।

Factory - The Object Creational Pattern

Moniruzzaman Saikat — Tue, 15 Jul 2025 20:14:43 +0000

It's a design pattern that encapsulates object creation logic so your main application code doesn’t depend on concrete class names. Just ask the factory and get what you need.

Think of the Factory Pattern as a power plug adapter. You plug in any device (Laptop, Phone, TV), and it gives you the correct current — without worrying about how it works. The Factory Pattern’s main goal is to decouple object creation from business logic, making your code flexible, testable, and easy to maintain.

Here is an example of factory pattern using a case of document exporter.

interface Exporter {
    public function export(array $data): string;
}

class PdfExporter implements Exporter {
    public function export(array $data): string {
        return "Exporting as PDF";
    }
}

class CsvExporter implements Exporter {
    public function export(array $data): string {
        return "Exporting as CSV";
    }
}

class ExportFactory {
    public static function getExporter(string $format): Exporter {
        return match($format) {
            'pdf' => new PdfExporter(),
            'csv' => new CsvExporter(),
            default => throw new Exception("Invalid export format"),
        };
    }
}

// Usage
$exporter = ExportFactory::getExporter('csv');
echo $exporter->export([]);

Why should you use factory pattern ?

You want to centralize object creation
You want to decouple code (client doesn’t know which exact class it is using)
You want flexibility to change which object is created, without touching client code

Here is another example by a notification sender (Email, SMS, Push, Whatever) system:

interface Notifier {
    public function send(string $message): string;
}

class EmailNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending Email: $message";
    }
}

class SMSNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending SMS: $message";
    }
}

class PushNotifier implements Notifier {
    public function send(string $message): string {
        return "Sending Push Notification: $message";
    }
}

class NotificationFactory {
    public static function create(string $type): Notifier {
        return match($type) {
            'email' => new EmailNotifier(),
            'sms' => new SMSNotifier(),
            'push' => new PushNotifier(),
            default => throw new Exception("Invalid notifier type"),
        };
    }
}

// Usage
$notifier = NotificationFactory::create('sms');
echo $notifier->send('Factory pattern rocks!');

Hahaha, Lemme tell you a story for better understand okay:

Once upon a time in a small tech town, there lived a programmer named Saikat. And halay was building an online food ordering system for a popular restaurant called "Code Crust Pizza".

But each time a customer ordered a pizza — Margherita, Pepperoni, or Veggie — Saikat's app had to do something like:

$margherita = new MargheritaPizza();
$pepperoni = new PepperoniPizza();
$veggie = new VeggiePizza();

and you know how bad is this when in the system you do all these here and there. it's fine with 2/3 classes until the restaurant added 10 more pizza types and wanted to change the way some pizzas were baked.

oh oh oh, an idea like okay: what if I order someone to build all the things for a new pizza(all the processes in it) and it will just be used from the client codebase in a very simple way. So this is the time to build a PizzaFactory — a smart kitchen helper who knows how to prepare any pizza.

and here it's :

interface Pizza {
    public function prepare(): string;
    public function deliverd(): boolean;
}

class MargheritaPizza implements Pizza {
    public function prepare() {
        return "Preparing Margherita Pizza";
    }
}

class PepperoniPizza implements Pizza {
    public function prepare() {
        return "Preparing Pepperoni Pizza";
    }
}

class PizzaFactory {
    public static function make(string $type): Pizza {
        return match($type) {
            'margherita' => new MargheritaPizza(),
            'pepperoni'  => new PepperoniPizza(),
            default => throw new Exception("Pizza type not found"),
        };
    }
}

and the use case is like(just pass the customer's ordered pizza name there, all things are done in it's own way, ouch):

$pizza = PizzaFactory::make('pepperoni');
echo $pizza->prepare();
echo $pizza->deliverd(); // shanti shanti shanti

No more new ClassName() all over the place. The kitchen (factory) handles it, and it's peace :)

Moral of the story:

The Factory Pattern is like a kitchen in a restaurant — clients (your app) don’t create dishes directly. They ask the kitchen (factory), which prepares and serves the right item.

Understanding the CRM Sales Process

Moniruzzaman Saikat — Thu, 15 May 2025 03:25:12 +0000

A solid CRM system revolves around a predictable and trackable sales process. Here's a breakdown of the typical flow followed in most CRM platforms like Salesforce, EspoCRM, Zoho, or your own custom-built solution.

💼 Sales Process Overview:

Lead ➜ Opportunity ➜ Quote ➜ Order ➜ Invoice

This is the backbone of most B2B sales workflows, where each stage reflects a deeper commitment from the potential customer.

1. 🧲 Lead — Initial Interest

A Lead represents someone who has shown interest in your product or service but hasn’t been qualified yet.

📌 Example:

John from “Acme Corp” fills out the Contact Us form on your website, asking about your CRM solution.

Typical Fields in a CRM:

Name, Email, Phone
Company Name
Source (Website, Ad, Referral)
Assigned Sales Rep
Lead Score
Status (New, Contacted, Disqualified, etc.)

📈 Goal: Determine if the lead is a good fit — and if so, convert them into a Contact, Account, and Opportunity.

2. 💰 Opportunity — Qualified Deal in Progress

Once the lead is confirmed to be serious and potentially ready to buy, it's converted into an Opportunity.

📌 Example:

You have a discovery call with John. He's a decision-maker with a budget and wants to purchase a CRM within 30 days.
You create:
📝 Opportunity Name: “CRM Deal with Acme Corp”

Common Fields:

Deal Value (e.g., \$2,500)
Stage (e.g., Proposal, Negotiation)
Expected Close Date
Probability %
Linked Contact & Account

📈 Goal: Track deal progress and activities like calls, demos, or meetings.

3. 📄 Quote — Formal Price Proposal

When your customer wants exact pricing, you issue a Quote, which includes itemized costs, terms, and validity.

📌 Example:

🧾 Quote #Q-2025-043
"CRM Pro Plan – 10 users"

Quote Fields:

Quote Number
Products/Services
Unit Price, Quantity, Taxes, Discounts
Total Amount
Expiry Date
Status (Draft, Sent, Accepted, Rejected)

📈 Goal: Share formal pricing → wait for customer acceptance.

4. ✅ Order — Purchase Confirmed

The customer accepts the quote. An Order is generated, indicating commitment.

📌 Example:

📦 Order #ORD-34324
Quote accepted — the sales team triggers account setup.

Order Fields:

Linked Quote
Billing Info
Delivery Terms or Provisioning Status
Order Date
Internal Notes

📈 Goal: Fulfill the order → activate services or ship product.

5. 💳 Invoice — Payment Request

Once the service is delivered or the order is processed, the system issues an Invoice to collect payment.

📌 Example:

📃 Invoice #INV-2025-102
\$500 due by June 5, 2025

Invoice Fields:

Invoice Number
Linked Order/Customer
Due Date
Payment Status (Unpaid, Paid, Overdue)
Tax Breakdown

📈 Goal: Get paid → mark invoice as settled.

🧭 Recap: Sales Flow in Action

Lead (Interest) 
   ↓
Opportunity (Qualified)
   ↓
Quote (Price Proposal)
   ↓
Order (Confirmed)
   ↓
Invoice (Payment)

Each step involves specific data, people, and workflows that help your team manage and scale the sales cycle efficiently.