The art of simplicity

So far I always used JWT.io to decode my JWT tokens. But today I discovered that I don't have to leave my Visual Studio IDE anymore. But before I show you this feature, let’s just briefly summarize what JWT tokens are. JWT what? JWT (JSON Web Token) is a compact, URL-safe token format used for securely transmitting information between parties. It is commonly used in authentication and authorization scenarios, especially in web and mobile applications. A JWT consists of three parts: Header : Contains metadata about the token, such as the signing algorithm used (e.g., HS256 or RS256). Payload : Contains the claims, which are statements about the user or other data (e.g., user ID, roles). This data is not encrypted, so it should not include sensitive information. Signature : A cryptographic signature that ensures the token has not been tampered with. It is created by encoding the header and payload, then signing them using a secret key or public/private key pair.

In a previous posted I introduced you to Continue in combination with Ollama, as a way to run a fully local AI Code Assistant. Remark: This post is part of a bigger series. Here are the other related posts: Part 1 – Introduction Part 2 -  Configuration Part 3 – Editing and Actions Part 4  -  Learning from your codebase Part 5 – Read your documentation Part 6 (this post) – Troubleshooting Although Continue really looks promising, I stumbled on some hurdles along the way. Here are some tips in case you encounter issues: Tip 1 - Check the Continue logs My first tip is to always check the logs. Continue provides good logging inside the IDE, So go to the output tab and switch to the Continue source to get the generated output: Tip 2 – Check the Continue LLM logs Next to the output of Continue itself, you can find all the LLM specific logs in the Continue LLM Prompt/Conversation output. So don’t forget to check that output as well: Tip 3 – Be patient

In a previous posted I introduced you to Continue in combination with Ollama, as a way to run a fully local AI Code Assistant. Remark: This post is part of a bigger series. Here are the other related posts: Part 1 – Introduction Part 2 -  Configuration Part 3 – Editing and Actions Part 4  -  Learning from your codebase Part 5 (this post) – Read your documentation Today I want to continue by having a look at how Continue can scrape your documentation website and make the content accessible inside your IDE The @docs context provider To use this feature you need to use the @docs context provider: Once you type @docs you already get a long list of available documentation: This is because Continue offers out-of-the-box a selection of pre-indexed documentation sites. (You can find the full list here ) If you now ask a question, the indexed documentation is used to answer your question: You can see the context used by expanding the context items section: Index you

In modern .NET applications, background processing plays a crucial role in handling tasks that don't need to be executed immediately, such as sending emails, processing large datasets, or scheduling recurring jobs. A library I like to use to manage background tasks in .NET is Hangfire , thanks to its simplicity, flexibility, and wide range of storage options. As these background tasks can be scheduled in the future, some kind of storage needs to be configured where Hangfire keeps all the information related to background job processing. Out of the box multiple storage systems are supported, one of them being SQL Server . Although using SQL Server can be a convenient option to use as the main storage for your Hangfire, it comes with one big disadvantage:  Polling is used to fetch new jobs. This has 2 consequences: It increases the load on your SQL server instance The time to pickup a new job and execute it  is signifcantly longer(here is a comparison with Redis from t

If you are a student and enthusiastic about AI and Cloud in general and Microsoft technologies specifically, than I would encourage you to check out the Microsoft Innovator Series . This series offers a range of events that can help you improve your technical skills and are a good headstart for the Imagine Cup , Microsoft's premier global technology startup competition for student founders. There are still some events planned between now and the end of this year, so certainly have a look!   More information Event Series | Microsoft Reactor A Journey of Innovation and Inspiration with Microsoft Imagine Cup 2025 - Microsoft Community Hub

Plugins are a key element when building agents in Semantic Kernel. They allow you to extend the capabilities of your Large Language Model with extra functions. This post is not about writing your own plugin but Just as a reminder, registering a plugin can be done like this: Here is the code I used to try to invoke the TimePlugin in a prompt: However when I did this the following error was returned: System.Collections.Generic.KeyNotFoundException: 'The plugin collection does not contain a plugin and/or function with the specified names. Plugin name – 'time', function name - 'Date'.' The problem is caused because I had configured an alias name for the plugin but I was using the plugin name in the prompt. I have 2 ways to fix the issue above. Either I update the prompt to point to the configured alias: Or I remove the alias from my configuration: Hope that helps… More information Plugins in Semantic Kernel | Microsoft Learn