DEV Community: Ali Kolahdoozan

From Turbo Pascal to TypeScript: Anders Hejlsberg on Language Design, Scale, and AI

Ali Kolahdoozan — Sat, 07 Feb 2026 05:48:04 +0000

Anders Hejlsberg is one of the most influential programming language designers in modern computing. From Turbo Pascal and Delphi to C# and TypeScript, his work has shaped how millions of developers write software every day. In a wide-ranging conversation, Hejlsberg reflects on language design, developer experience, open source, and how AI is changing the future of programming.

The Importance of Developer Feedback Loops

Hejlsberg’s early experience with 8-bit computers deeply influenced his philosophy. Limited memory forced developers to keep entire programs in their heads, encouraging simplicity and focus. This constraint led to one of Turbo Pascal’s key innovations: a dramatically shortened edit-compile-run cycle.

Rather than waiting minutes or hours for compilation, Turbo Pascal compiled directly into memory and executed immediately. Errors dropped the developer straight back into the editor. This tight feedback loop made programming more interactive, productive, and enjoyable—an approach that continues to shape modern tooling.

Democratizing Software Development

Turbo Pascal wasn’t just fast—it was affordable. When Borland priced it at $49.95, Hejlsberg initially doubted the decision. In retrospect, it proved brilliant. Lower pricing massively expanded adoption, helping democratize programming and influencing an entire generation of developers.

This lesson—accessibility matters as much as technical excellence—has echoed throughout Hejlsberg’s career.

Building Platforms, Not Just Languages

At Microsoft, Hejlsberg helped lead the transition from Visual J++ to the creation of C# and the .NET platform. The goal wasn’t simply to design a new language, but to deliver a cohesive ecosystem: runtime, compiler, libraries, tooling, and debugging working together as one experience.

Users, Hejlsberg argues, don’t care whether a feature belongs to the language, the framework, or the IDE. What matters is the overall experience. Fragmented silos lead to complexity and inefficiency; holistic design leads to productivity.

Why TypeScript Exists

The rise of large-scale JavaScript applications exposed a fundamental problem: JavaScript lacked the tooling needed for big teams and complex systems. When Microsoft teams began compiling C# into JavaScript just to get type checking and tooling, it became clear that JavaScript itself needed fixing.

TypeScript emerged as a pragmatic solution—not a replacement, but a strict superset of JavaScript. By adding static types while preserving compatibility, TypeScript met developers where they were. This decision dramatically lowered adoption barriers and allowed JavaScript and TypeScript to share the same tooling investment.

Open Source as a Turning Point

TypeScript’s success accelerated when Microsoft fully embraced open source. Early releases on CodePlex failed to engage the community. The move to GitHub—and true open development via pull requests and public discussions—changed everything.

Transparency built trust. Developers could see decisions being made, influence priorities, and contribute directly. This shift transformed TypeScript from a corporate project into a community-driven ecosystem.

Scaling Beyond JavaScript

As TypeScript adoption exploded, performance became a bottleneck. The compiler—written in JavaScript—could no longer scale efficiently for massive codebases. After experimentation, the team chose to port the compiler to Go.

This wasn’t a rewrite, but a careful, function-by-function port to preserve exact semantics. The result: up to 10× performance improvements through native execution and shared-memory concurrency. Crucially, existing users wouldn’t need to change anything—the behavior remained identical, just faster.

AI, MCP, and the Future of Tooling

Hejlsberg sees AI not as a replacement for programming languages, but as a shift in how tools are used. AI excels when it has seen large volumes of existing code—making popular languages like JavaScript and TypeScript particularly well suited for AI-assisted development.

Rather than inventing new languages for AI, Hejlsberg argues that the future lies in better integration: exposing semantic tooling through protocols like MCP, enabling AI agents to query, refactor, and reason about code deterministically.

In this emerging model, developers increasingly supervise rather than manually author every line. IDEs matter less as interfaces, but their underlying services—type checking, refactoring, analysis—matter more than ever.

The Human Role Remains Essential

Despite automation, Hejlsberg is skeptical of a future without human developers. AI can reduce repetitive work, manage large code migrations, and triage issues—but judgment, design, and responsibility remain human strengths.

A key challenge ahead is talent development: if AI reduces entry-level tasks, how do junior engineers grow into seniors? The industry must consciously redesign learning pathways to avoid hollowing out the profession.

Looking Forward

TypeScript will continue evolving alongside JavaScript standards, while its tooling adapts to an AI-native world. The broader lesson from Hejlsberg’s career is consistent: successful technology is pragmatic, accessible, ecosystem-aware, and relentlessly focused on developer experience.

From Turbo Pascal’s instant feedback loops to MCP-enabled AI workflows, the tools may change—but the philosophy remains the same.

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Ali Kolahdoozan — Sat, 30 Aug 2025 00:43:03 +0000

More often indirectly – By framing debates in ways that avoid scrutiny. Instead of asking “Which AI, for which problem, at what cost?” the discussion becomes “You’re either with us, or you’re left behind.”

By omission – Leaving out ecological and human costs, or burying them in footnotes and appendices.

What Real Transparency Would Look Like

If CEOs were truly honest, we would see:

Resource accounting – Exact electricity, water, and carbon use for each product and training run.

Data provenance – Where data comes from, under what consent, and how people can opt out.

Labor visibility – Who is labeling and moderating, under what conditions, and at what pay.

Impact metrics – Beyond benchmarks, measuring actual problem-solving and social effects.

Community rights – Local communities hosting infrastructure should have real veto power.

What Can We Do?

Demand details, not slogans: ask “Which model, which data, what cost?”

Shape policies in schools, workplaces, and governments to set responsible AI standards.

Defend data rights: insist on informed consent and fair use of personal content.

Push for transparent reporting on energy, water, and labor.

Support smaller, efficient, local AI solutions over defaulting to mega-models.

Conclusion

Tech CEOs may not always “lie” outright, but their skillful framing, selective disclosures, and sweeping promises create a distorted picture. The narrative of “inevitable AI progress” only continues if we accept it passively.

The real question isn’t whether CEOs lie—it’s: how do we build systems where, even if they wanted to, they couldn’t?

The Only Trait for Success in the AI Era — How to Build It

Ali Kolahdoozan — Sat, 23 Aug 2025 02:26:57 +0000

Abstract
Artificial intelligence (AI) has advanced to the point of rivaling human creativity and problem-solving. Large language models (LLMs) and related technologies can now solve tasks once thought uniquely human, such as highly original mathematical problems. This shift redefines the essential skills required for future success. This article explores why critical thinking, empathy, and the ability to create value for others represent the single most important trait for human success in the AI era, and how education and social entrepreneurship can be structured to build it.

1. Introduction

With the advent of advanced AI systems, traditional definitions of human uniqueness are being challenged. Capabilities such as idea generation, problem-solving, and even creativity are now within the reach of computational systems. Consequently, the question arises: what remains uniquely human, and what should be emphasized to ensure survival and relevance in an era dominated by intelligent machines?

The central argument of this article is that the defining human trait for success in the AI era is the ability to think critically while authentically creating value for others. This trait requires both cognitive and social-emotional dimensions, extending beyond technical proficiency.

2. AI’s Expanding Creativity

AI’s ability to solve problems previously thought unsolvable by machines illustrates a transformation in the understanding of creativity. For instance, advanced AI systems have successfully solved several International Mathematical Olympiad problems, which are deliberately designed to be novel and resistant to repetition. This demonstrates that creativity is no longer an exclusive human domain, and that human success must be reframed around skills that cannot be automated as easily.

3. Risks of Dependency in Education

The widespread use of AI by students introduces serious risks for cognitive development:

Erosion of critical skills: Using AI to complete writing or problem-solving tasks may bypass essential learning processes.

Analogy with physical exercise: Just as physical strength requires active exercise, intellectual resilience requires active engagement in problem-solving. Reliance on AI during formative years undermines this development.

Loss of independent reasoning: Overdependence risks creating generations of individuals who lack logical analysis and are vulnerable to manipulation.

Therefore, educational systems must place stronger emphasis on reading, writing, logical reasoning, and communication—skills that serve as the foundation for independent thought.

4. Building Critical Thinking Through Empathy

Critical thinking in the AI era must be redefined to include empathy. Solving real-world problems requires the capacity to understand and simulate the perspectives of others. Without empathy, problem-solving becomes detached from human needs and fails to generate meaningful solutions.

Thus, the only sustainable human advantage lies in the fusion of analytical reasoning with social intelligence. This combination enables humans to collaborate effectively, build trust, and create value that extends beyond technical outcomes.

5. Social Entrepreneurship as a Training Ground

Social entrepreneurship offers a scalable pathway for developing this essential trait. By combining academic training with interpersonal skill-building, innovative models can prepare the next generation to thrive. For example:

Peer-led education: High school students trained in communication can mentor younger learners, reinforcing both groups’ skills.

Integration of arts and sciences: Professional actors and comedians can teach future leaders to inspire, persuade, and engage others, while simultaneously improving their ability to convey complex ideas.

Win-win design: These models benefit both the mentors, who gain leadership and charisma, and the learners, who gain critical thinking skills.

Such frameworks provide evidence that education designed around empathy, collaboration, and communication can strengthen societal resilience.

6. The Societal Dimension

AI systems are not neutral; they are shaped by the biases of their creators and the data they process. As a result, the increasing reliance on a small number of global AI providers risks narrowing the diversity of perspectives. To counteract this, societies must:

Encourage individuals to consume diverse sources of information.

Promote the habit of evaluating competing narratives critically.

Develop networks of thoughtful, empathetic leaders capable of addressing civilizational challenges.

The robustness of human civilization depends on cultivating independent, critical thinkers who can resist manipulation and maintain autonomy.

Conclusion

AI has already matched or exceeded human creativity in specific contexts. As technological systems continue to advance, the defining human trait for success will not be superior problem-solving alone but the capacity to combine critical thinking with empathy to authentically create value for others.

Educational reform and social entrepreneurship present effective mechanisms for developing this trait at scale. By fostering thoughtful, empathetic, and collaborative individuals, humanity can ensure not only survival in the AI era but also the flourishing of civilization.

🆚 SQL Server vs. MongoDB: Why MongoDB Is a Better Choice for Modern Applications

Ali Kolahdoozan — Fri, 06 Jun 2025 04:22:46 +0000

In today’s fast-paced, data-intensive world, developers and organizations face a crucial decision when choosing a database: stick with traditional relational databases like SQL Server, or move forward with flexible, document-based NoSQL systems like MongoDB. While SQL Server has a long legacy of serving enterprise needs, MongoDB is purpose-built for the demands of modern, scalable, cloud-native applications.

In this article, we’ll compare the two and explain why MongoDB outshines SQL Server in key areas that matter most in today’s software landscape.

1. ⚙️ Data Model: Flexibility vs. Rigidity
SQL Server relies on a strict relational model. This means data must be normalized, tables strictly defined, and schema changes require costly migrations and downtime.

MongoDB, in contrast, uses a flexible document model:

Schema-less: Documents can vary in structure.

JSON-like format (BSON): Natural for developers to work with.

Embedded documents: No need for complex JOINs.

🚀 MongoDB adapts to real-world data faster, reducing friction in development and iteration.

2. 🚀 Performance and Scalability
SQL Server was designed for vertically scaled systems — more CPU, more RAM. This leads to:

Expensive licensing

Performance bottlenecks at scale

MongoDB was designed for horizontal scalability:

Built-in sharding

Elastic scaling across clusters

Can handle massive volumes of data across commodity hardware

With cloud-native design (like MongoDB Atlas), it auto-scales based on usage. SQL Server, by contrast, struggles in elastic or microservice environments.

3. ☁️ Cloud-Native and DevOps Friendly
MongoDB Atlas is a managed cloud platform that integrates seamlessly with modern DevOps workflows:

One-click deployment on AWS, Azure, or GCP

RESTful API, CLI, and Terraform support

Native integration with Kubernetes

SQL Server is harder to containerize, requires Windows licensing (for older versions), and lacks seamless cloud-native features unless heavily reconfigured.

4. ⚒️ Developer Productivity
MongoDB is developer-first:

Schema-less design means faster prototyping.

Native JSON support means fewer transformations.

Drivers and tooling for all major languages.

SQL Server:

Requires rigid schema planning

Involves stored procedures, triggers, and legacy T-SQL syntax

Developers often end up constrained by DBAs and bureaucracy

In MongoDB, developers own the data model. In SQL Server, the database owns the developer.

5. 💵 Cost Efficiency
SQL Server licensing is expensive (per core, per user, per feature).

Enterprise features like replication, compression, or analytics require add-ons or higher-tier licenses.

MongoDB offers:

Free and open-source community edition

Cost-effective managed service (Atlas)

Auto-scaling to reduce idle resource costs

In many cases, MongoDB is 10x more cost-efficient at scale.

6. 📊 Real-Time Analytics & Search
MongoDB includes:

Aggregation Framework: Real-time pipelines without ETL

Atlas Search: Built-in full-text search (Lucene-based)

Change Streams: Native event streams for reactive architectures

SQL Server? You’ll need:

Separate integration with external analytics tools

Additional data warehousing setup

Complex ETL pipelines

MongoDB simplifies the stack. SQL Server inflates it.

Conclusion: SQL Server Is for Yesterday, MongoDB Is for Tomorrow

SQL Server is solid, reliable, and trusted — but it’s a product of the past.
MongoDB is agile, scalable, and built for the modern era of distributed, cloud-based, high-velocity development.

🟢 If you're building for the future, MongoDB is the way forward.

Building Better Software: Lessons from Industry for Scalable Systems 🏗️💻

Ali Kolahdoozan — Tue, 06 May 2025 04:57:52 +0000

In today’s fast-moving tech world, creating software that’s robust, flexible, and scalable is crucial. Apps are becoming more complex, and users expect them to work flawlessly. To meet these demands, developers can take cues from traditional industries by breaking systems into smaller components, adhering to standard practices, and maintaining clear roles. This article explores how these industry-inspired strategies can help build software that's easier to manage, update, and scale—without sounding like a textbook. 🚀

Why Think Like a Factory Builder? 🏭
When you think of “industry,” images of assembly lines producing identical parts or construction sites with precise blueprints might come to mind. This approach is beneficial for software development too. In the early days, coding was akin to crafting custom furniture—unique but challenging to modify or expand. Now, with software integral to everything from hospitals to banks, we need systems that are predictable and manageable.

By adopting industrial principles, we can create software that's clear, repeatable, and durable. This doesn't stifle creativity; instead, it provides developers with a solid framework to focus on what makes their app exceptional. 🎨🛠️

The Basics: Smaller Pieces and Common Rules 🧩📏

1. Modularity: Break It Down 🔧
Large software projects can resemble a tangled mess—difficult to navigate and fix. Modularity addresses this by dividing the system into small, focused components, each handling a specific task. This makes development, testing, and maintenance more straightforward.

Consider a car: the engine propels, the brakes stop, and the radio entertains. If the radio malfunctions, you don't need to overhaul the engine. Similarly, in software, modifying the payment system shouldn't disrupt user login functionality. Clear boundaries between components lead to reduced stress and fewer bugs. 🚗🔍

2. Standards: Play Nice Together 🤝
While modularity ensures components are self-contained, standards ensure they interact seamlessly. By following shared protocols—like common data formats or service connections—components integrate smoothly without extra effort.

It's akin to cities using standard pipe sizes to ensure water flows to every house. In software, standards like REST APIs, JSON, or OAuth2 serve as these "pipes," allowing new features or integrations without starting from scratch. Standards facilitate teamwork and simplify upgrades. 🌐🔌

Complexity: Keep the Good, Ditch the Bad 🧠🧹
Every software project involves some complexity—it's part of the process. However, distinguishing between necessary and unnecessary complexity is vital.

Necessary complexity arises from the problem you're solving, such as intricate tax calculations or live sports updates. It's unavoidable but manageable with proper planning.

Unnecessary complexity is self-inflicted—like messy code, ambiguous naming, or redundant features.

The goal is to eliminate the unnecessary. Writing clear code, choosing intelligent structures, and utilizing effective tools help maintain order. Cloud platforms, pre-built libraries, and managed services act as a cleanup crew, allowing you to focus on delivering value to users. ☁️🛠️

Why “As-a-Service” Is a Lifesaver 💡
Cloud services—such as AWS for computing power, Heroku for app hosting, or Slack for team communication—offload much of the operational burden. They handle servers, scaling, and security updates, enabling you to concentrate on development.

This approach isn't about laziness; it's about efficiency. It's comparable to a chef using pre-chopped vegetables to save time. By entrusting experts with the underlying infrastructure, your team can focus on enhancing the app's core features. Additionally, these services are designed to scale, accommodating growth seamlessly. 📈🍽️

Real-Life Examples: Containers and Logins 📦🔐
Containers: Pack It, Ship It, Done 🚢
Containers, like those provided by Docker, have revolutionized software deployment. They package your application with all its dependencies, ensuring it runs consistently across environments—from development to production.

Containers also support microservices architecture, where applications are divided into small, independent services. This modularity allows for isolated updates, such as modifying the search feature without affecting the entire system. It's like organizing your app into neatly labeled boxes instead of a chaotic suitcase. 🧳🗂️

Logins: One System, No Fuss 🔑
Previously, each app had its own login system, leading to complexity and security concerns. Now, standards like OAuth2 and tools like Auth0 or Keycloak simplify authentication. They manage user logins, permissions, and security, eliminating the need to build these systems from scratch.

Outsourcing this complex task to specialized services saves time, reduces bugs, and enhances user data security. This exemplifies industrial thinking: standardize complex processes and reuse them across applications. 🛡️🔐

Build Software Like a City 🏙️
Visualize your software as a city. There's a downtown for businesses, suburbs for residences, and industrial zones for manufacturing. Roads, power lines, and water pipes connect everything, built to standardized specifications. Adding a new park doesn't necessitate rebuilding the entire city.

Your software should emulate this structure:

Group by purpose: Divide your app into domains like payments, user accounts, or reports.

Stick to shared systems: Utilize consistent tools for data storage, messaging, or service connections.

Plan for growth: Implement changes without disrupting existing functionalities.

This approach ensures your system remains flexible, maintainable, and prepared for future developments. 🛣️🏗️

Wrapping Up 🎁
Incorporating industrial principles into software development isn't about transforming developers into machines. It's about applying practical strategies—modularity, standardization, and strategic planning—to build applications that are reliable, maintainable, and scalable.

The most effective developers design systems that withstand real-world challenges. They prioritize clarity, focus on essential features, and build with foresight. By thinking like city planners or factory engineers, you can create software that's not only powerful but also a pleasure to maintain.

So, before diving into your next project, take a moment to establish a solid foundation. It will save you time, reduce stress, and perhaps even prevent a few gray hairs down the line. 🧱🧠

🔥 Top 10 Things Every .NET Developer Should Do in 2025

Ali Kolahdoozan — Mon, 05 May 2025 14:13:22 +0000

As the .NET ecosystem continues to evolve, so should our approach to architecture, tooling, and mindset. The practices that worked five years ago may now be holding us back. Here’s a curated list of 10 key things .NET developers should focus on in 2025 to stay effective, efficient, and future-ready.

1. ❌ Reconsider the Use of AutoMapper and MediatR
While AutoMapper and MediatR were once staples in modern .NET projects, they often introduce unnecessary complexity and indirection.

✅ Instead: Use explicit mapping and direct service communication. It leads to better performance, easier debugging, and improved readability.

2. 🧱 Build with Modular Monoliths, Not Microservices
Microservices aren't the only path to scalability. For many teams, especially smaller ones, a Modular Monolith is far easier to manage.

✅ Why:

Clear domain boundaries

Simpler deployments

Fewer runtime issues

Start with a monolith, and split only when you actually need to.

3. 🧹 Prioritize Code Quality from Day One
Technical debt compounds quickly. Enforce code quality standards early using static analysis tools, code reviews, and automated linters.

✅ Tools to consider:

Roslyn Analyzers

Code style rules in .editorconfig

Integration with CI/CD

4. 📊 Add OpenTelemetry to Every Project
Observability should be a default in 2025. Implementing OpenTelemetry gives you visibility into distributed systems and helps diagnose performance issues faster.

✅ Bonus: It’s vendor-neutral and works well with tools like Jaeger, Zipkin, and Azure Monitor.

5. 🧪 Focus on Integration Testing
Unit tests are great, but integration tests validate the real behavior of the full system—across APIs, databases, and services.

✅ How to get started:

Use WebApplicationFactory

Consider Testcontainers for testing with real dependencies like SQL Server or Redis

6. 🧩 Use .NET Aspire for Local Development
.NET Aspire simplifies local orchestration of microservices, databases, queues, and more—all from your .NET project.

✅ Benefits:

Unified configuration

Better local dev experience

Improved testability

7. 🧠 Learn at Least One New Technology This Year
Stay ahead of the curve by picking up a new framework or toolset. Consider technologies like:

Orleans for distributed apps

Blazor United

WebAssembly

MAUI for cross-platform

8. 📚 Read at Least One Good Technical Book
Quick tutorials and videos are helpful, but deep understanding often comes from books.

✅ A few ideas:

Clean Architecture

The Pragmatic Programmer

Designing Data-Intensive Applications

9. 🎯 Keep It Explicit and Simple
Avoid the temptation of clever abstractions and hidden magic. Favor explicit logic and clear boundaries.

✅ Why:

Easier onboarding

Better long-term maintainability

Fewer hidden bugs

10. 🔍 Be Context-Aware, Not Trend-Driven
Choose patterns and tools based on your project’s needs, not just community hype. Whether it's Clean Architecture, CQRS, or a simple layered approach—choose what solves your actual problems.

🚀 Final Thoughts
2025 is the year to simplify, focus, and improve. Refactor outdated patterns, boost observability, write better tests, and keep your skills sharp. Your future projects—and your team—will thank you for it.

dotnet #csharp #Blazor #DotNetDeveloper #CleanArchitecture #OpenTelemetry #ModularMonolith #IntegrationTesting #Aspire #VSCode #DeveloperTips #CodeQuality #DevLife #2025Tech

Why Big Tech Is Slowly Ghosting Golang

Ali Kolahdoozan — Sun, 04 May 2025 03:59:40 +0000

🦥 “Go” Away Already: Why Big Tech Is Slowly Ghosting Golang

Ah, Go — the language that promised to keep things simple, and boy did it deliver. So simple, in fact, that Big Tech is now collectively yawning, stretching, and slowly walking away without making eye contact.

In the 2010s, Go was hailed as the antidote to "enterprisey" bloatware. “No generics!” they cheered. “No magic!” they boasted. “No modern features whatsoever!” And for a brief, beautiful moment, it worked — right until engineers realized they actually wanted to do things.

Now, in 2025, the hype is flatter than Go's type system. Let’s explore the eulogy evolution of Go's decline.

🧠 1. Simplicity That Stops the Moment Things Get Complex
Go is like that friend who’s great at helping you move a chair but disappears when it’s time to assemble IKEA furniture.

Its "simplicity" is a paper-thin shield that breaks the moment your project scales beyond a hello-world microservice. Need rich abstractions? Pattern matching? Useful generics? Sorry. Go wants you to embrace for-loops and hand-craft every generic structure like it’s 1999.

Meanwhile, Rust, Kotlin, and basically every other language on the planet are evolving. Go? Still bragging about “how little there is to learn,” which is ironic because there’s even less to use.

💥 2. Error Handling: A Ritual of Suffering
Nothing screams "modern language" like writing the same if err != nil line 40 times in a file.

Go’s error handling system is a celebration of verbosity and repetition — perfect for teaching interns pain tolerance. In real-world enterprise systems, though, this approach quickly turns into copy-paste purgatory.

Meanwhile, Rust handles errors with elegance and Kotlin lets you throw and catch like it’s civilized. Go? It still thinks you need the full workout of typing everything out manually to "truly understand what's going on."

🔥 3. Concurrency You Can’t Trust
Yes, goroutines were cool — in the same way unguarded chainsaws are cool. At first glance, they seem sleek and efficient. Then you blink and your system is flooded with 10,000 orphaned routines deadlocked in a trench war over a mutex.

Go’s concurrency model is like giving every toddler in the room a pair of scissors and hoping for the best. Rust, meanwhile, brings compile-time guarantees. Even Java — the O.G. enterprise tortoise — has grown up and added safer models.

Go gives you power without accountability. And we know how that ends.

🐢 4. Garbage Collection… in a Real-Time World?
Go’s GC is “optimized,” sure. Just like your 2012 laptop is “still functional.” When you're running latency-sensitive systems like HFT or embedded analytics, a GC pause isn’t just inconvenient — it’s catastrophic.

Rust’s “you control the memory” model isn't just about power. It's about reliability. Go hands the keys to the GC and prays. Big Tech stopped praying years ago.

🤖 5. Developer Joy Is Overrated Anyway
If you ask the Go team, programming joy should come from not having features.

No IDE support to speak of? No rich generics? Limited reflection? Boring, repetitive code? That's not a shortcoming — it's a feature! Go is the Excel of programming languages: simple, powerful-ish, and absolutely exhausting when used at scale.

In the meantime, Rust offers clippy, cargo, and world-class tooling. TypeScript lets you build front-to-back in one flow. Kotlin is like writing in poetry. Go is... a compliance form.

🧱 6. WASM? Web? What Are Those?
WebAssembly? Yeah, Go has a page on it somewhere, buried under 7 layers of build flags. Meanwhile, Rust and TypeScript are practically the royal family of WASM. Google may have invented Go, but it sure looks like they forgot to give it a passport to the future.

🤫 7. Big Tech’s Silent Breakup
Big Tech isn’t yelling at Go — it’s just leaving quietly. Like a bad date you never hear from again.

Cloudflare? Rust.

Dropbox? Rust.

Amazon? Rust.

Google? Funding Rust.

You can still see Go’s toothbrush in the bathroom, but let’s be honest — it’s just a matter of time.

🪦 Final Words: Not Dead, Just Retired Early
Go isn’t dead — it’s just… semi-retired. It’ll always be useful for “small services,” “CLI tools,” and teaching juniors what a null pointer is. But the dream? The glorious future of distributed simplicity?

That dream has a new name: C# & || Rust.

And Go? Go quietly fades into the sunset, clutching its err != nil, proudly simple to the very end.

⚙️Microservices: Power vs. Complexity

Ali Kolahdoozan — Sat, 03 May 2025 12:40:07 +0000

Microservices: A Strategic Balance of Agility, Complexity, and Engineering Discipline_

Microservices have earned a reputation as a modern and agile approach to building software systems, particularly for organizations dealing with rapid change, distributed teams, and complex business domains. Yet, like any architectural decision, microservices carry a dual nature—bringing both significant advantages and notable trade-offs.

This article explores the true motivations behind adopting microservices, outlines what makes them powerful yet costly, and discusses when they’re the right choice for your software landscape.

🔄 Continuous Delivery as a Driving Force
One of the most compelling reasons to adopt microservices is their alignment with Continuous Delivery (CD). In fast-paced environments, the ability to push changes to production reliably, repeatedly, and quickly is critical.

Microservices support this by design:

They are small, independently deployable units, minimizing the risk of changes.

Their size makes them faster to test and easier to isolate operationally.

Teams can work in parallel, delivering new features or fixes without waiting on others.

However, the relationship between microservices and CD is bidirectional. Just adopting microservices doesn’t guarantee continuous delivery. You must invest in automated pipelines, comprehensive monitoring, and DevOps culture to unlock the synergy.

Without this investment, microservices can slow you down instead—each service becomes a deployment and testing challenge of its own. In this sense, DevOps isn't optional; it's part of the deal.

🧱 The Myth of Simplicity
At first glance, microservices look clean and modular. But distributed systems are inherently complex, and microservices are no exception. Consider these realities:

Inter-service communication, often over HTTP or messaging queues, is significantly slower than in-process calls.

Debugging a flow that spans multiple services is exponentially harder than tracing through a monolith.

Cross-service refactoring is painful, especially when code boundaries aren't clean.

Every service brings with it its own lifecycle—build, test, deploy, monitor. Multiply that by 20 or 50 services, and your operational load skyrockets unless you have solid automation.

The performance concern is also real. If you design microservices like you would a monolith—making granular, synchronous remote calls—you will introduce latency. Smart microservice systems embrace async communication, local caching, and event-driven architecture to avoid this pitfall.

🛠️ Maintainability Through Isolation
What microservices excel at is containment. When a service is well-scoped:

The team working on it doesn’t need to understand the rest of the system.

It has clear ownership of its data, which reduces unintended interference.

It can evolve independently, embracing new technologies or design patterns without impacting others.

This high degree of modularity improves maintainability—provided you maintain discipline around boundaries. Poorly defined service scopes or data leakage across services can destroy the very maintainability benefits microservices promise.

Also important is the technology footprint. Because services are replaceable, it's best to avoid bloated frameworks or speculative abstractions. Instead, prefer lightweight, targeted tools that meet the current need. After all, rewriting a microservice is part of its lifecycle—not a failure.

🌱 Greenfield vs. Brownfield: When Should You Microservice?
Should you go all-in on microservices for a new project? The answer is: not always.

Microservices originated as a response to the challenges of growing monoliths. If your system starts simple, it might not need the overhead of a full microservices setup. However, if you:

Anticipate multiple teams working in parallel,

Require frequent independent deployments,

Have a complex domain with clear subdomains,

Or expect significant scaling variance across features,

Then microservices from day one could be the right move—if you’re ready to invest in automation, observability, and deployment infrastructure.

For other greenfield projects, a modular monolith might be a smarter start—preserving flexibility while avoiding early complexity. You can break it apart later using patterns like the Strangler Fig, as your system matures.

🔄 Code Reuse: The Hidden Danger
It’s tempting to DRY (Don’t Repeat Yourself) everything across services. But reusing business logic across microservices often creates more problems than it solves:

It introduces coupling between services that should be independent.

Updates for one service might break another unnecessarily.

Shared libraries create versioning headaches and reduce deployment autonomy.

Instead, embrace business logic duplication where needed—it’s cheaper than coupling.

However, shared technical infrastructure is fair game. Common concerns like logging, authentication, resilience policies, or service discovery can (and should) be centralized into well-maintained libraries or platforms.

The key distinction: share infrastructure, not domain logic.

🚀 Scalability and Resilience Through Granularity
A microservice architecture lets you scale the right thing. Got a service that’s hit by 90% of user traffic? You can scale just that piece. Need redundancy for critical functionality? Deploy multiple instances of just that service.

When done right, microservices support eventual consistency, graceful degradation, and fault isolation. But they require you to design for failure: retries, circuit breakers, and timeouts aren’t optional—they're survival tools.

✅ Final Thoughts: Are Microservices Worth It?
Microservices are not just an architecture. They’re an ecosystem of automation, culture, and design discipline.

They offer massive rewards: agility, scalability, team independence. But they come with steep costs: complexity, operational overhead, and the need for serious engineering maturity.

Choose them intentionally.

Start small. Define clear service boundaries. Automate everything. And keep a constant eye on whether the benefits you expected are actually being delivered.

Sometimes, the best architecture isn’t the most fashionable one—it’s the one that fits your team, your system, and your business reality today.

🚀 EF Core vs Dapper Benchmark – Real .NET 10 Test Results

Ali Kolahdoozan — Mon, 21 Apr 2025 23:17:15 +0000

I ran a full BenchmarkDotNet suite comparing:

EF Core (Tracked ✅ / NoTracking ❌)

Dapper (Raw SQL, no ORM fluff)

We tested:

SelectById
SelectMany (1000 rows)
Insert speed
Memory usage & allocations

💥 Results?

Dapper crushed EF in insert performance (65x faster)

Tracking in EF adds more overhead than you think

This is a real-world test, not just theory.
📹 Watch the video and see the numbers for yourself.

Full Blazor WebAssembly + .NET 10 Web API (Minimal API) Todo App — powered entirely by VS Code Agent Mode! 🤖⚡

Ali Kolahdoozan — Sun, 20 Apr 2025 08:25:49 +0000

🧠 Meet VS Code Agent Mode — Your AI Coding Sidekick

Microsoft just brought AI into the editor with VS Code Agent Mode, giving you context-aware coding help right inside your project. No more switching tabs. No more losing flow. It reads your code, understands your structure, and assists like a real-time coding partner.

And to put it to the test…

I built a full Blazor WebAssembly + .NET 10 Web API (Minimal API) Todo App — powered entirely by VS Code Agent Mode! 🤖⚡

✅ Watch how the agent speeds up everything — from setting up the API to crafting UI logic
✅ See Minimal APIs and Blazor WASM in action with clean, modern architecture
✅ A great walkthrough if you're exploring .NET 10, full-stack C#, or just want to see AI + dev in perfect sync

🎥 [Click below to watch the video!]

Wish you all the best ...

How Small Teams Are Changing the World 🎯

Ali Kolahdoozan — Fri, 14 Mar 2025 03:11:10 +0000

How Small Teams Are Changing the World: The Power of Efficiency and Talent

In today’s fast-paced startup ecosystem, small teams are making a huge impact. The traditional belief that large corporations with vast resources dominate the business landscape is increasingly being challenged by ultra-lean, high-performing startups. The data in the image above highlights how small, focused teams have successfully built multi-million-dollar businesses at an astonishing speed. This phenomenon raises an essential question: Is the future of startups defined by efficiency rather than size?

This article explores how small teams achieve extraordinary growth, why they often outperform larger organizations, and what lessons founders can learn from their success.

The Rise of Small but Powerful Teams
The examples in the image illustrate a remarkable trend:

✅ Lovable grew to $17M ARR (Annual Recurring Revenue) in just 3 months—with only 15 employees.
✅ Cursor hit $100M ARR in 21 months—with just 20 employees.
✅ Bolt by StackBlitz scaled to $20M ARR in 2 months—with a team of only 15 people.
✅ ElevenLabs reached $100M ARR in just 2 years—with 50 employees.
✅ Aragon.ai grew to $10M ARR in 2 years—with only 9 employees.
✅ Gamma surpassed $10M ARR—with 28 employees.
✅ Tally made $2M+ ARR—with just 5 employees.

These numbers are staggering. They challenge the conventional wisdom that growth requires massive teams and large-scale operations. Instead, they highlight how focused teams of highly skilled individuals can achieve what was once thought impossible.

Why Do Small Teams Succeed?

1. Agility and Speed 🚀
Large companies are often burdened by bureaucracy, slow decision-making, and layers of management. Small teams move faster, iterate quickly, and adapt to changing market conditions in real-time.

2. Efficiency and Focus 🎯
With fewer employees, every team member must contribute at a high level. There is no room for inefficiency or wasted resources. Everyone knows their role, and they work together with a clear mission in mind.

3. The Power of A-Players 🔥
The real difference is not just the number of employees but who those employees are. One A-player will always outperform ten C-players. These companies prioritize quality over quantity, ensuring that every team member is an asset, not just a number.

4. Lean Business Models 💰
Startups that succeed with small teams often do so because their business models allow them to scale without requiring massive operational overhead. Many of these companies rely on automation, AI, and software-driven growth rather than human-intensive processes.

5. Culture of Ownership 🏆
In a small team, everyone has a stake in success. There is a sense of ownership, accountability, and passion that is often lost in larger organizations. Employees are more engaged, work harder, and feel a stronger connection to the company’s vision.

The Outliers vs. The Reality
It’s important to acknowledge that these are outliers—the 1% of the 1%. Not every company can or should aim to be this lean.

👉 Some industries require large teams (e.g., manufacturing, healthcare, logistics).
👉 Some business models don’t scale as efficiently with small teams.
👉 Some companies prioritize long-term growth over rapid scaling.

However, even for startups that do need larger teams, there is still a valuable lesson:

💡 It’s not about hiring less—it’s about hiring intentionally.

Many startups make the mistake of scaling their teams too quickly. They hire more people than they actually need, leading to inefficiency, misalignment, and wasted capital. The real takeaway from these success stories is that hiring the right people is far more important than hiring a lot of people.

What Founders Can Learn from These Companies If you’re a startup founder, here’s what you can take away from these success stories:

✅ Hire the best, not the most. Prioritize quality over quantity. One highly skilled employee is worth more than five mediocre ones.

✅ Embrace automation and leverage AI. Many successful startups minimize human labor by using automation, software, and scalable systems.

✅ Stay lean and efficient. Avoid unnecessary expenses, unnecessary hiring, and bloated teams.

✅ Move fast and iterate. Speed is a startup’s greatest advantage over large corporations. Test, learn, and adapt quickly.

✅ Build a culture of ownership. Small teams succeed when every member cares deeply about the company’s mission and success.

Final Thoughts: The Future of Startups is Lean & Smart

The traditional approach to building a successful company—hiring hundreds of employees, raising massive funding rounds, and growing at all costs—is being challenged. The startups in this image prove that small, focused, and highly skilled teams can achieve incredible results without unnecessary complexity.

🚀 The future of startups isn’t about how many people you hire.
💡 It’s about who you hire and how well they execute.

And that’s a lesson every founder should take to heart.

🚀 The Future of Blazor: A Strong and Promising Path

Ali Kolahdoozan — Fri, 07 Mar 2025 08:29:55 +0000

Blazor, Microsoft's Single Page Application (SPA) framework, continues to gain attention in the web development world. While some developers have expressed concerns about its future, the long-term outlook for Blazor remains strong 💪, particularly due to its deep integration with the expansive .NET ecosystem.

This article explores why Blazor is a solid choice for web development and why its future remains bright. 🌟

🔥 Blazor’s Strength: The Power of .NET
One of the biggest advantages of choosing Blazor is that it seamlessly integrates into the entire .NET universe 🌎. Developers who invest in Blazor are not just learning a standalone framework—they are immersing themselves in a broader, constantly evolving ecosystem.

✅ 1. Future-Proofing Through .NET
By choosing Blazor, developers can leverage cutting-edge tools like .NET Aspire for scalable applications and .NET AI libraries 🤖 for integrating artificial intelligence seamlessly into their projects.

✅ 2. Simplified Development with Powerful Features
Microsoft has a history of making development easier by abstracting complex functionalities, such as security 🔐, database interaction 🗄️, and real-time updates ⚡. Blazor allows developers to build rich, interactive web applications without heavy reliance on JavaScript.

✅ 3. A Stepping Stone into the .NET Ecosystem
Learning Blazor inherently means understanding key .NET concepts like:

🔄 Dependency Injection (DI)
📊 LINQ & Entity Framework Core
🔐 ASP.NET Identity
🎨 Razor Syntax
This broad knowledge base makes Blazor developers highly versatile in the job market.

🤔 Addressing the Concerns About Blazor’s Adoption
Some developers argue that Blazor has fewer job opportunities compared to React ⚛️ or Angular 🔺. While React and Angular have a larger job market, Blazor developers rarely learn it in isolation.

Blazor developers are also proficient in C# and .NET 🏆, making them highly valuable. Many companies still use older frameworks like ASP.NET Core MVC or Web Forms 📜, and while they may be slow to adopt Blazor, its advantages make it an inevitable choice for future projects.

🚀 Why Blazor Stands Out Among Web Frameworks
Blazor offers several advantages over traditional JavaScript-based frameworks:

1️⃣ Full-Stack Development with C#
✔️ Write both client-side and server-side code using C# and .NET 🖥️
✔️ Less reliance on JavaScript while promoting code sharing 🔄

2️⃣ Easy Migration for .NET-Based Companies
🏢 Organizations using .NET can transition to Blazor easily without needing JavaScript expertise.

3️⃣ Flexible Hosting Models
🔹 Blazor Server – Real-time interaction ⚡ with SignalR & secure code execution 🔒
🔹 Blazor WebAssembly (WASM) – Fully runs in the browser 🌐 with offline support 📴
🔹 Blazor Hybrid – Cross-platform desktop & mobile apps 📱💻 via .NET MAUI
🔹 Blazor with SSR & Streaming Rendering – Improved performance with dynamic updates 🔥

4️⃣ Seamless Integration with .NET Ecosystem
✔️ Entity Framework Core 🗄️ for databases
✔️ ASP.NET Identity & Azure AD 🔐 for authentication
✔️ Dependency Injection 🔄 for modular development

5️⃣ Strong Security & Scalability
✔️ Built-in security features like authentication, authorization, and anti-forgery protection 🛡️
✔️ Blazor WebAssembly ensures sandboxed execution for added security 🔒

6️⃣ Reduced Reliance on JavaScript (But Supports Interop)
✔️ Avoids breaking changes in third-party JavaScript libraries 🚧
✔️ Supports JS interop when needed (e.g., for Chart.js 📊 or D3.js 📉)

7️⃣ Component-Based Architecture
✔️ Encourages modular & reusable UI components 🧩
✔️ Similar to React or Angular but within .NET 🎯

8️⃣ Performance Optimizations
✔️ AOT (Ahead-of-Time) compilation for WebAssembly 🚀
✔️ Streaming rendering & lazy loading for faster load times ⚡
✔️ Pre-rendering for SEO & better UX 🏆

📈 The Growing Blazor Ecosystem & Microsoft’s Commitment
🔹 Microsoft is actively investing in Blazor 💰, ensuring long-term stability
🔹 More third-party UI libraries like Radzen, Telerik, and MudBlazor 🎨
🔹 Enterprise adoption is increasing – Blazor is the future for .NET-based modern web applications

🌟 Final Thoughts: The Future of Blazor is Bright
Blazor is not just a front-end framework – it is a strategic investment in .NET-based web development 🏆.

✔️ Seamless .NET integration 🖥️
✔️ Flexible hosting models 🌎
✔️ Enhanced security & scalability 🔒
✔️ Continuous Microsoft support 💪

While adoption may take time, Blazor’s potential is undeniable. Businesses will increasingly recognize its efficiency, performance, and maintainability, making Blazor an ideal choice for future projects.

For developers considering Blazor:
🎯 Learn C# & .NET first, then explore Blazor as a natural extension of your skills.

Blazor is here to stay – and its future looks stronger than ever! 🚀🔥