Stefan Đokić | .NET

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[Stefan Đokić | .NET @TheCodeMan__]

This reality hits:

deadlines, new team members, rushed PRs, “we’ll fix it later.”

Most #dotnet projects don’t fall apart because of bad developers.

They fall apart because nobody decided how things should look when things get messy.

At the beginning:
✔ clean structure
✔ good intentions
✔ “we’ll keep it tidy”

Then reality hits:
• deadlines
• new team members
• rushed PRs
• “we’ll fix it later”

And suddenly the codebase starts fighting back.

This carousel shows the full curriculum of Pragmatic .NET Code Rules.

Not as a syllabus.
But as a system.

A system that:
- removes pointless decisions
- enforces consistency automatically
- keeps projects clean even when teams grow

I made this course for developers who:
• are tired of style debates
• want predictable projects
• care about long-term sanity, not quick wins

Already 50+ developers joined during pre-order.

And yes, you get access immediately.
And yes, you get access to the community.

You don’t wait.
You apply.

You improve your project while the course grows.

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(Only 4 spots left at the price and only today)

Original price is $150.

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Curious, what’s the one rule your current codebase is missing?

[Stefan Đokić | .NET @TheCodeMan__]

My .NET AI agent projects, with source code, are in AI for Developers (7-day free trial):
https://t.co/xXzk0utGBA

[Stefan Đokić | .NET @TheCodeMan__]

FunctionInvokingChatClient in .NET allows up to 40 model round trips per request by default.

One confused agent, 40 paid calls for a single question.

Set MaximumIterationsPerRequest to what your flow actually needs.

[Stefan Đokić | .NET @TheCodeMan__]

Your API endpoint takes 5 minutes to respond. That's not an endpoint. That's a hostage situation. 😅

- The user stares at a spinner.
- Your server holds a thread for the whole time.
- A load balancer quietly kills the request after 60 seconds.
- The client retries, and now the same report is being built twice.

A faster endpoint won't save you here. A different shape will.

Here's the flow (follow the numbers on the visual):

1. Client sends POST /reports
2. API answers in ~20 ms with 202 Accepted + a Location header pointing to /reports/42
3. API drops the job into a queue - nothing heavy happens in the request
4. A background worker picks it up and does the 5-minute work
5. Client polls GET /reports/42 and gets "Running"
6. Client polls again and gets "Done" + the result URL

What you get:

→ Request threads are free in milliseconds, not minutes
→ No more proxy timeouts, because no request lives longer than a few ms
→ Workers scale independently of the web tier - 10 workers, 2 API instances, your call
→ Retries are safe, because the client asks for the status of job 42 instead of starting a new one

Two small details that make it production-ready:

→ Return a Retry-After header, so clients know how often to poll
→ Swap polling for SignalR or a webhook when you need push instead of pull

The mental shift: long-running work is a resource you create, not a response you wait for.

How do you handle long-running work in your APIs today - polling, webhooks or SignalR?

Join 25k+ engineers to learn more every week: https://t.co/mqkOvjdlIJ

[Stefan Đokić | .NET @TheCodeMan__]

@AntonMartyniuk Love the “once you have evals, swapping models is easy” point. In .NET it gets even easier: code against IChatClient, run your evals with Microsoft.Extensions .AI.Evaluation, and trying a cheaper model becomes a config change plus one test run.

[Stefan Đokić | .NET @TheCodeMan__]

The full path, monolith -> modular monolith -> microservices at 100k users, with .NET code:
https://t.co/JPQv0gzwug

[Stefan Đokić | .NET @TheCodeMan__]

Most teams that move to microservices don't have a scaling problem.

They have an organization problem, and they pick a distributed system to solve it.

Start with a modular monolith. Extract a module when it really needs to scale on its own.

[Stefan Đokić | .NET @TheCodeMan__]

@mjovanovictech Simple rule I’d start with: if the answer depends on who’s asking, it doesn’t go in a shared cache. Then start with a strict threshold, log the near-misses, and only loosen it once you’ve checked that those matches were actually correct.

[Stefan Đokić | .NET @TheCodeMan__]

Je l vracamo Drzavni Posao? #drzavniposao

[Stefan Đokić | .NET @TheCodeMan__]

If you save to the database and then publish a message as two separate steps, you will eventually lose a message.

That's what the Outbox pattern fixes: data and message in one transaction, a background worker publishes later.

[Stefan Đokić | .NET @TheCodeMan__]

An AI agent in .NET is a loop:

IChatClient sends the message + tools -> the model asks for a tool -> FunctionInvokingChatClient runs your C# method -> the result goes back -> repeat.

The model never touches your database. It only asks.

[Stefan Đokić | .NET @TheCodeMan__]

I tried to commit a file, and git said no.

An AI read my staged diff, found a hardcoded Stripe key, and blocked the commit (Headless agent).

~10 lines of PowerShell:
claude -p + --json-schema + a pre-commit hook. Fail closed.

Full video: https://t.co/71uZgzogay

#ClaudeCode #dotnet

[Stefan Đokić | .NET @TheCodeMan__]

I tried to commit a file, and git said no.

An AI read my staged diff, found a hardcoded Stripe key, and blocked the commit (Headless agent).

~10 lines of PowerShell:
claude -p + --json-schema + a pre-commit hook. Fail closed.

Full video: https://t.co/71uZgzogay

#ClaudeCode #dotnet

[Stefan Đokić | .NET @TheCodeMan__]

The source code for my agent, RAG and MCP projects is in my community, AI for Developers (7-day free trial):
https://t.co/xXzk0utGBA

[Stefan Đokić | .NET @TheCodeMan__]

An AI agent in .NET is a loop:

IChatClient sends the message + tools -> the model asks for a tool -> FunctionInvokingChatClient runs your C# method -> the result goes back -> repeat.

The model never touches your database. It only asks.

[Stefan Đokić | .NET @TheCodeMan__]

@AntonMartyniuk The Miro MCP server is the bridge. Connect it to your AI tool (Claude Code / Cursor / Copilot) once, and the agent can read and write your boards directly

[Stefan Đokić | .NET @TheCodeMan__]

An AI reviewed my .NET pull request on a whiteboard and caught 3 real bugs hidden in a 60-file diff - before I hit merge.

Full walkthrough, every prompt: https://t.co/gtoQJxc4Uf

[Stefan Đokić | .NET @TheCodeMan__]

An AI reviewed my .NET pull request on a whiteboard and caught 3 real bugs hidden in a 60-file diff - before I hit merge.

Full walkthrough, every prompt: https://t.co/gtoQJxc4Uf

[Stefan Đokić | .NET @TheCodeMan__]

Before you blame the model, count how many times this week you typed "we don't use MediatR" to it.

That's not a model problem. That's a missing CLAUDE. md.

[Stefan Đokić | .NET @TheCodeMan__]

The biggest productivity gain I got from AI agents didn't come from a better model.

It came from writing our conventions down once, so I stopped re-explaining them every session.

[Stefan Đokić | .NET @TheCodeMan__]

A CLAUDE. md inside src/MyShop.Infrastructure/ only loads when the agent touches a file in that folder.

So your EF Core rules don't have to sit in context while it writes an endpoint.

Split instructions by where the code lives.