r/csharp

Hi everyone,

For the past 1.5 years I've been learning C#/.NET stack for development, and 

Recently, I started connecting with developers and product managers (many aren’t directly from the dotnet field ), and I keep hearing a few things in the loop like-  “dotnet has a small and slow market.”, "Switch to Java/Python and have more opportunities.”, “Fintechs rarely uses C#.”, “The salaries are lower and stagnant.”

Hearing this over and over again has honestly made me a bit discouraged and sceptical, as I’ve invested a lot of time learning the stack (did my graduation in Non-IT field)

My take is-
> Microsoft invests heavily into dotnet/Visual Studio, etc
> Many Large enterprise systems still run on Legacy system, VB
> Framework evolved  from .NET Core to .NET 11 (MS is not stupid)

So, one part of me feels like the ecosystem is still very strong, but at the same time the mixed opinions are confusing.

Currently I’m building an ERP system for a small auto-parts shop as a learning project and understand what it takes to become a real-problem solver.

My question to the community: What is your honest view of the dotnet job market today (especially for juniors)? Did anyone here start their career with similar doubts? Would you still recommend someone to invest deeply in the dotnet  ecosystem today?
> Can a fellow donet MVP be willing to share their perspective? 

Regards

Hi!

I’ve recently started programming with C sharp in Jetbrains.

I kinda understand the concept of the basics but it’s so hard to remember everything !

So do you have any tips of the basics what to learn? And how to learn it easier ?

I built a Serilog sink that outputs logs as clean HTML so they’re actually readable, searchable, and filterable by level.

.WriteTo.HtmlFile("logs-{date}.html")

I have a number of older Visual Foxpro apps that we have decided to go ahead and rewrite using C#. Many of the older apps store data in our SQL server databases, which were designed back in the late 90's and early 2000s. Table structures and field names were different back then, variables and fields often used Hungarian notation. Most fields in most tables have a short 2-3 character table prefix then an underscore then a fieldname (example, Order table has order number field C(10) ord_ordernumber). Some tables are named for single record, others are named for plural records. Most sites listing best practices these days for database apps say to not use this naming convention for fields and tables.

If this were you redesigning an older app into a modern application framework, would you redefine the entire database structure using current best practices for naming everything, or would you keep the old existing data as-is, and just write your new app and use the old original table names?

Every AI tool comparison I see online uses greenfield demo projects. "Look, I told it to build a REST API and it generated the whole thing!" Great. That tells me nothing about how the tool performs in a 5-year-old C# codebase with 800k lines, DDD architecture, custom middleware, 15 internal NuGet packages, and entity framework migrations dating back to EF Core 2.

I've been running an evaluation on our actual production codebase and the results are very different from what the YouTube demos suggest. The elephant in the room is context. Our codebase has patterns that evolved over 5 years. We have custom Result types, specific repository patterns, our own middleware pipeline, domain events that follow a particular convention. No AI tool knows about any of this out of the box.

Tools that just look at the current file generate generic C# that compiles but doesn't fit. They suggest using standard exception handling when we use Result monads. They generate repositories that don't follow our interface pattern. They create controllers that bypass our custom middleware.

The tools that try to understand your codebase (by indexing repos or connecting to docs) are meaningfully better for enterprise C#. Not perfect, but the gap between "knows your codebase" and "doesn't know your codebase" is the biggest differentiator I've found. Bigger than model quality, bigger than suggestion speed, bigger than chat features.

For teams evaluating AI tools for established C# codebases, how much weight are you putting on the tool's ability to learn your specific codebase patterns?

https://preview.redd.it/9kdo9pi99ktg1.png?width=1053&format=png&auto=webp&s=c9afb75bda635b2750d09b69974274704dc0c988

Considering the new Dell Pro 13 Premium for my daily driver.

Specs:

• CPU: Intel Core Ultra 7 268V (8C/8T)

• RAM: 32GB LPDDR5x (On-package)

• GPU: Intel Arc 140V (Integrated)

• Screen: 13.3" QHD+ Touch

Workflow:

• Unity: 2D and light 3D development.

• C# / .NET: Primary IDE is Visual Studio. Working on backend APIs and general C# projects.

• Web Dev: Full-stack, Docker, Laravel.

Concerns: How does the 268V handle compilation and Unity baking without Hyper-Threading? Is the thermal throttling an issue in this 13-inch chassis under sustained load?

Would you recommend this for a portable dev setup or should I look for something with a dGPU?

Thanks!