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. 40% OFF Preorder price: $59.89 (Only 4 spots left at the price and only today) Original price is $150. 👉 Get it now here: thecodeman.net/pragmatic-dot… Curious, what’s the one rule your current codebase is missing?

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.

Sunday question: which AI coding tool do you actually use every day at work - Claude Code, Codex, Cursor or Copilot? And which one did you try and drop?

Every dev is shipping AI agents now. But almost nobody can answer one question about them: in production, what did the agent actually do? Which tools did it call? In what order? How many times? How many tokens did it burn? Where did the time go? If you’re logging to the console, you have no idea. It’s a black box. So I built a real one in C# (an order-support agent with Microsoft.Extensions .AI), put it on GitHub, and added Sentry’s new Agent Monitoring for .NET. About two lines, no change to the agent logic: → every model call recorded - model, tokens in/out, latency → every tool call recorded - the exact inputs and outputs → all stitched into one trace: a parent agent span with a child span per step Then I ran it like production - locally and in GitHub Actions - and opened the run in Sentry. First the Agents view (the full conversation the agent had), then the trace waterfall for timing. And there it was: a run that looked fine actually failed. A tool threw - and instead of stopping, the model quietly retried the same broken tool, failed again, then gave me a half-answer. The trace marked both calls red, with the exact order id the model passed in. On my laptop, all I’d get is a polite half-answer and a clean console. Here I saw the exact failing tool, twice, and where it sat in the agent’s run. One fix and the trace went green. That’s the difference between guessing and knowing what your agent did. Full build + the open-source repo: youtube.com/watch?v=EfmFwzbY… What are you building AI agents for in .NET? 👇

Hey @KLM and @airfrance, I've been trying to resolve a compensation claim for weeks. Your team keeps requesting my bank account holder's name, but your claim portal has NO field to enter it. I've submitted the details multiple times and keep getting the same email. Where can I reach an actual human who can fix this?

If your AI feature breaks when the model starts its answer with "Sure! Here's the JSON:", the bug is in your code, not in the model.

One line that removed a whole class of LLM bugs from my code: var res = await client.GetResponseAsync<TicketTriage>(prompt); A typed result from a schema, not a string you parse and hope. Use TryGetResult, because models don't always honor the schema.

Result: 350GB free in a couple of minutes. And somebody will tell me how not to love the AI and Claude? 😂

lol