managedcode

Interesting, thanks for sharing. I’m trying it ring now

I started making YouTube videos recently and got frustrated by how many tools it takes just to read a script on camera. Write in Google Docs, copy to a teleprompter app, open OBS for recording, lose all your pacing notes along the way. So I built something that keeps the whole flow in one browser tab. PrompterOne is a teleprompter studio that goes from writing to live delivery without leaving the browser. You write a script in a structured format (I call it TPS — it carries pacing markers, emphasis, speed modifiers, emotion tags), rehearse it with an RSVP speed-reading mode, then read it on the teleprompter with your camera feed behind the text. There's also a Go Live mode that does canvas composition, local recording, and streaming through LiveKit/VDO.Ninja — all client-side, no media server. The whole thing is a static Blazor WebAssembly app on .NET 10. No backend, no accounts, scripts stay in browser storage. Deployed on GitHub Pages. It's in active alpha — the write/rehearse/read/record loop works today, live streaming is still being polished. 7 languages supported. Demo: https://prompter.managed-code.com Source: https://github.com/managedcode/PrompterOne

What MCAF Is

MCAF is a framework for building software products together with AI coding agents.

It defines how to:

keep all relevant context in a single repository describe work clearly before coding use integration/API/UI tests, containerized environments, and static analysis as hard gates encode repository-specific rules for AI agents in AGENTS.md at the repo root The goal of MCAF:

Use AI to build real products in a way that is predictable, safe, and repeatable. MCAF has three core elements:

Context — code, tests, documentation, and AGENTS.md live together in the repository. Verification — behaviour and code quality are proven by automated tests and static analysis, not opinions. Instructions — AGENTS.md defines how AI agents work in this repo and how they learn from feedback. This Guide defines the framework. The root AGENTS.md in each repository applies it to that codebase.

At Managed Code, we’ve built ManagedCode.Communication with a clear goal — to provide a full-featured, production-ready Result Pattern implementation in .NET, all in a single project. The project contains multiple NuGet packages for specific scenarios (core library, ASP.NET Core integration, Orleans integration, SignalR integration), but they all share the same foundation and philosophy.

Instead of throwing exceptions, your methods return Result or Result<T> — explicit, type-safe outcomes that are easy to compose with Map, Bind, Match, Tap, and other railway-oriented methods. For web APIs, failures can be automatically converted into RFC 7807 Problem Details responses, providing clients with structured error information (type, title, detail, status, plus custom extensions). For collections, CollectionResult<T> combines data with paging metadata in a single, consistent return type.

The idea is to have everything you might need for Result Pattern development in one place: functional composition methods, rich error modeling, ready-to-use framework integrations — without having to stitch together multiple third-party libraries or hand-roll adapters for production.

On the roadmap: first-class support for commands (command handlers working directly with Result types), idempotency strategies for safe retries in distributed systems, and extended logging to trace a result’s journey through complex workflows (API → SignalR → Orleans → client).

We’re looking for honest feedback from developers who use Result Patterns in real projects. What’s missing? What would make this your go-to solution instead of writing your own?

This is a good job. Good example.

This is good news! Good job! Good example

Hello HN community, I’m thrilled to announce a special giveaway sponsored by Packt! We’re offering 2 print copies and 1 ebook of “Building Applications with Microsoft Semantic Kernel” by Lucas A. Meyer.

Why This Book? This book dives deep into the practical applications of Microsoft Semantic Kernel, a powerful tool for building intelligent applications. Whether you’re a seasoned developer or just starting with AI, this book provides valuable insights and hands-on examples.

How to Enter: Visit the LinkedIn Post Share Your Experience: Leave a comment on the post sharing your experience with Large Language Models (LLMs). We’re looking for interesting stories, challenges you’ve faced, or innovative solutions you’ve developed.

Selection Process: The winners will be chosen by Vinishka Kalra, who will pick the most compelling stories.

Don’t miss this opportunity to get your hands on a copy of this valuable resource.

Share your stories and good luck!

I've created a video where I explain the SOLID principles of programming using relatable analogies. This approach aims to make the concepts more understandable and engaging. I'd love to hear your feedback on whether this method is effective and if I should continue creating similar content.