Showing posts with label ai. Show all posts
Showing posts with label ai. Show all posts

Reading Notes #676

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This #rd explores practical insights on leveraging GitHub Copilot for enhanced .NET testing, the rise of AI-driven documentation solutions, and the importance of security in coding agents. From dissecting Docker’s MCP servers to debating the merits of Minimal APIs, we cover a mix of .NET updates, developer workflows, and emerging best practices. Whether you’re refining build processes, optimizing codebases, or staying ahead of security trends, these notes offer a curated selection of ideas to spark your next project or refactor.



Let’s unpack what’s new and impactful in tech!

AI

Programming

~frank


Ask AI from Anywhere: No GUI, No Heavy Clients, No Friction

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Ever wished you could ask AI from anywhere without needing an interface? Imagine just typing ? and your question in any terminal the moment it pops into your head, and getting the answer right away! In this post, I explain how I wrote a tiny shell script that turns this idea into reality, transforming the terminal into a universal AI client. You can query Reka, OpenAI, or a local Ollama model from any editor, tab, or pipeline—no GUI, no heavy clients, no friction.

Small, lightweight, and surprisingly powerful: once you make it part of your workflow, it becomes indispensable.

💡 All the code scripts are available at: https://github.com/reka-ai/terminal-tools


The Core Idea

There is almost always a terminal within reach—embedded in your editor, sitting in a spare tab, or already where you live while building, debugging, and piping data around. So why break your flow to open a separate chat UI? I wanted to just type a single character (?) plus my question and get an answer right there. No window hopping. No heavy client.

How It Works

The trick is delightfully small: send a single JSON POST request to whichever AI provider you feel like (Reka, OpenAI, Ollama locally, etc.):

# Example: Reka
curl https://api.reka.ai/v1/chat
     -H "X-Api-Key: <API_KEY>" 
     -d {
          "messages": [
            {
              "role": "user",
              "content": "What is the origin of thanksgiving?"
            }
          ],
          "model": "reka-core",
          "stream": false
        }

# Example: Ollama local
curl http://127.0.0.1:11434/api/chat 
-d {  
      "model": "llama3",   
      "messages": [
        {
          "role": "user", 
          "content": "What is the origin of thanksgiving?"
        }], 
      "stream": false
    }

Once we get the response, we extract the answer field from it. A thin shell wrapper turns that into a universal “ask” verb for your terminal. Add a short alias (?) and you have the most minimalist AI client imaginable.

Let's go into the details

Let me walk you through the core script step-by-step using reka-chat.sh, so you can customize it the way you like. Maybe this is a good moment to mention that Reka has a free tier that's more than enough for this. Go grab your key—after all, it's free!

The script (reka-chat.sh) does four things:

  1. Captures your question
  2. Loads an API key from ~/.config/reka/api_key
  3. Sends a JSON payload to the chat endpoint with curl.
  4. Extracts the answer using jq for clean plain text.

1. Capture Your Question

This part of the script is a pure laziness hack. I wanted to save keystrokes by not requiring quotes when passing a question as an argument. So ? What is 32C in F works just as well as ? "What is 32C in F".

if [ $# -eq 0 ]; then
    if [ ! -t 0 ]; then
        QUERY="$(cat)"
    else
        exit 1
    fi
else
    QUERY="$*"
fi

2. Load Your API Key

If you're running Ollama locally you don't need any key, but for all other AI providers you do. I store mine in a locked-down file at ~/.config/reka/api_key, then read and trim trailing whitespace like this:

API_KEY_FILE="$HOME/.config/reka/api_key"
API_KEY=$(cat "$API_KEY_FILE" | tr -d '[:space:]')

3. Send The JSON Payload

Building the JSON payload is the heart of the script, including the API_ENDPOINT, API_KEY, and obviously our QUERY. Here’s how I do it for Reka:

RESPONSE=$(curl -s -X POST "$API_ENDPOINT" \
     -H "X-Api-Key: $API_KEY" \
     -H "Content-Type: application/json" \
     -d "{
  \"messages\": [
    {
      \"role\": \"user\",
      \"content\": $(echo "$QUERY" | jq -R -s .)
    }
  ],
  \"model\": \"reka-core\",
  \"stream\": false
}")

4. Extract The Answer

Finally, we parse the JSON response with jq to pull out just the answer text. If jq isn't installed we display the raw response, but a formatted answer is much nicer. If you are customizing for another provider, you may need to adjust the JSON path here. You can add echo "$RESPONSE" >> data_sample.json to the script to log raw responses for tinkering.

With Reka, the response look like this:

{
    "id": "cb7c371b-3a7b-48d2-829d-70ffacf565c6",
    "model": "reka-core",
    "usage": {
        "input_tokens": 16,
        "output_tokens": 460,
        "reasoning_tokens": 0
    },
    "responses": [
        {
            "finish_reason": "stop",
            "message": {
                "role": "assistant",
                "content": " The origin of Thanksgiving ..."
            }
        }
    ]
}
The value we are looking for and want to display is the `content` field inside `responses[0].message`. Using `jq`, we do: 
echo "$RESPONSE" | jq -r '.responses[0].message.content // .error // "Error: Unexpected response format"'

Putting It All Together

Now that we have the script, make it executable with chmod +x reka-chat.sh, and let's add an alias to your shell config to make it super easy to use. Add one line to your .zshrc or .bashrc that looks like this:

alias \\?=\"$REKA_CHAT_SCRIPT\"

Because ? is a special character in the shell, we escape it with a backslash. After adding this line, reload your shell configuration with source ~/.zshrc or source ~/.bashrc, and you are all set!

The Result

Now you can ask questions directly from your terminal. Wanna know what is origin of Thanksgiving, ask it like this:

? What is the origin of Thanksgiving

And if you want to keep the quotes, please you do you!

Extra: Web research

I couldn't stop there! Reka also supports web research, which means it can fetch and read web pages to provide more informed answers. Following the same pattern described previously, I wrote a similar script called reka-research.sh that sends a request to Reka's research endpoint. This obviously takes a bit more time to answer, as it's making different web queries and processing them, but the results are often worth the wait—and they are up to date! I used the alias ?? for this one.

On the GitHub repository, you can find both scripts (reka-chat.sh and reka-research.sh) along with a script to create the aliases automatically. Feel free to customize them to fit your workflow and preferred AI provider. Enjoy the newfound superpower of instant AI access right from your terminal!

What's Next?

With this setup, the possibilities are endless. Reka supports questions related to audio and video, which could be interesting to explore next. The project is open source, so feel free to contribute or suggest improvements. You can also join the Reka community on Discord to share your experiences and learn from others.


Resources




Reading Notes #675

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Here’s a compact roundup of links and highlights I found interesting this week. You’ll find updates on Git, Chrome DevTools tips, C# 14 and .NET 10 coverage, Blazor upgrade notes, a practical Copilot + Visual Studio guide, plus a few useful tools and AI announcements. Enjoy, and tell me which item you want me to explore next.


AI


Programming


Miscellaneous

  • ZoomIt v9.21 | Microsoft Community Hub (Alex Mihaiuc) - If you are on Windows, please do you do yourself a favour and try Zoom it. Since I switched to Mac and Linux, it's the thing that I miss the most.

Check-In Doc MCP Server: A Handy Way to Search Only the Docs You Trust

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Ever wished you could ask a question and have the answer come only from a handful of trusted documentation sites—no random blogs, no stale forum posts? That’s exactly what the Check-In Doc MCP Server does. It’s a lightweight Model Context Protocol (MCP) server you can run locally (or host) to funnel questions to selected documentation domains and get a clean AI-generated answer back.

What It Is

The project (GitHub: https://github.com/fboucher/check-in-doc-mcp) is a Dockerized MCP server that:

  • Accepts a user question.
  • Calls the Reka AI Research API with constraints (only allowed domains).
  • Returns a synthesized answer based on live documentation retrieval.

You control which sites are searchable by passing a comma‑separated list of domains (e.g. docs.reka.ai,docs.github.com). That keeps > results focused, reliable, and relevant.

What Is the Reka AI Research API?

Reka AI’s Research API lets you blend language model reasoning with targeted, on‑the‑fly web/document retrieval. Instead of a model hallucinating an answer from static training data, it can:

  • Perform limited domain‑scoped web searches.
  • Pull fresh snippets.
  • Integrate them into a structured response.

In this project, we use the research feature with a web_search block specifying:

  • allowed_domains: Only the documentation sites you trust.
  • max_uses: Caps how many retrieval calls it makes per query (controls cost & latency).

Details used here:

  • Model: reka-flash-research
  • Endpoint: http://api.reka.ai/v1/chat/completions
  • Auth: Bearer API key (generated from the Reka dashboard: https://link.reka.ai/free)

How It Works Internally

The core logic lives in ResearchService (src/Domain/ResearchService.cs). Simplified flow:

  1. Initialization
    Stores the API key + array of allowed domains, sets model & endpoint, logs a safe startup message.

  2. Build Request Payload
    The CheckInDoc(string question) method creates a JSON payload:

    var requestPayload = new {
  model,
  messages = new[] { new { role = "user", content = question } },
  research = new {
    web_search = new {
      allowed_domains = allowedDomains,
      max_uses = 4
    }
  }
};

  3. Send Request
    Creates a HttpRequestMessage (POST), adds Authorization: Bearer <APIKEY>, sends JSON to Reka.

  4. Parse Response
    Deserializes into a RekaResponse domain object, returns the first answer string.

Adding It to VS Code (MCP Extension)

You can run it as a Docker-based MCP server. Two simple approaches:

Option 1: Via “Add MCP Server” UI

  1. In VS Code (with MCP extension), click Add MCP Server.
  2. Choose type: Docker image.
  3. Image name: fboucher/check-in-doc-mcp.
  4. Enter allowed domains and your Reka API key when prompted.

Option 2: Via mcp.json (Recommended)

Alternatively, you can manually configure it in your mcp.json file. This will make sure your API key isn't displayed in plain text. Add or merge this configuration:

{
  "servers": {
    "check-in-docs": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "ALLOWED_DOMAINS=${input:allowed_domains}
        ",
        "-e",
        "APIKEY=${input:apikey}",
        "fboucher/check-in-doc-mcp"
      ]
    }
  },
  "inputs": [
    {
      "id": "allowed_domains",
      "type": "promptString",
      "description": "Enter the comma-separated list of documentation domains to allow (e.g. docs.reka.ai,docs.github.com):"
    },
    {
      "id": "apikey",
      "type": "promptString",
      "password": true,
      "description": "Enter your Reka Platform API key:"
    }
  ]
}

How to Use It

To use it ask to Check In Doc something or You can now use the SearchInDoc tool in your MCP-enabled environment. Just ask a question, and it will search only the specified documentation domains.

Final Thoughts

It’s intentionally simple—no giant orchestration layer. Just a clean bridge between a question, curated domains, and a research-enabled model. Sometimes that’s all you need to get focused, trustworthy answers.

If this sparks an idea, clone it and adapt away. If you improve it (citations, richer error handling, multi-turn context)—send a PR!

Watch a quick demo


Links & References

Reading Notes #674

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This week: Cake v6.0.0 is out, Docker Desktop adds helpful debugging tools, and .NET 10 brings a ton of changes worth exploring. Plus some thoughts on working with AI coding assistants and a great cybersecurity podcast.

AI

DevOps

  • Cake v6.0.0 released - Great news! I will have to upgrade my pipeline. Hopefully, the upgrade will be smooth.

  • Docker Desktop 4.50 Release (Deanna Sparks) - Nice update, and oh wow! I'm looking forward to try that debug, that's great news

Programming

Podcasts


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank


AI Vision: Turning Your Videos into Comedy Gold (or Cringe)

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I've spent most of my career building software in C# and .NET, and only used Python in IoT projects. When I wanted to build a fun project—an app that uses AI to roast videos, I knew it was the perfect opportunity to finally dig into Python web development.

The question was: where do I start? I hopped into a brainstorming session with Reka's AI chat and asked about options for building web apps in Python. It mentioned Flask, and I remembered friends talking about it being lightweight and perfect for getting started. That sounded right.

In this post, I share how I built "Roast My Life," a Flask app using the Reka Vision API.

The Vision (Pun Intended)

The app needed three core things:

  1. List videos: Show me what videos are in my collection
  2. Upload videos: Let me add new ones via URL
  3. Roast a video: Send a selected video to an AI and get back some hilarious commentary

See it in action

Part 1: Getting Started Environment Setup

The first hurdle was always going to be environment setup. I'm serious about keeping my Python projects isolated, so I did the standard dance:

Before even touching dependencies, I scaffolded a super bare-bones Flask app. Then one thing I enjoy from C# is that all dependencies are brought in one shot, so I like doing the same with my python projects using requirements.txt instead of installing things ad‑hoc (pip install flask then later freezing).

Dropping that file in first means the setup snippet below is deterministic. When you run pip install -r requirements.txt, Flask spins up using the exact versions I tested with, and you won't accidentally grab a breaking major update.

Here's the shell dance that activates the virtual environment and installs everything:

cd roast_my_life/workshop
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Then came the configuration. We will need an API key and I don't want to have it hardoced so I created a .env file to store my API credentials:

API_KEY=YOUR_REKA_API_KEY
BASE_URL=https://vision-agent.api.reka.ai

To get that API key, I visited the Reka Platform and grabbed a free one. Seriously, a free key for playing with AI vision APIs? I was in.

With python app.py, I fired up the Flask development server and opened http://127.0.0.1:5000 in my browser. The UI was there, but... it was dead. Nothing worked.

Perfect. Time to build.

The Backend: Flask Routing and API Integration

Coming from ASP.NET Core's controller-based routing and Blazor, Flask's decorator-based approach felt just like home. All the code code goes in the app.py file, and each route is defined with a simple decorator. But first things first: loading configuration from the .env file using python-dotenv:

from flask import Flask, request, jsonify
import requests
import os
from dotenv import load_dotenv

app = Flask(__name__)

# Load environment variables (like appsettings.json)
load_dotenv()
api_key = os.environ.get('API_KEY')
base_url = os.environ.get('BASE_URL')

All the imports packages are the same ones that needs to be in the requirements.txt. And we retreive the API key and base URL from environment variables, just like in .NET Core.

Now, to be able to get roasted we need first to upload a video to the Reka Vision API. Here's the code—I'll go over some details after.


@app.route('/api/upload_video', methods=['POST'])
def upload_video():
    """Upload a video to Reka Vision API"""
    data = request.get_json() or {}
    video_name = data.get('video_name', '').strip()
    video_url = data.get('video_url', '').strip()
    
    if not video_name or not video_url:
        return jsonify({"error": "Both video_name and video_url are required"}), 400
    
    if not api_key:
        return jsonify({"error": "API key not configured"}), 500
    
    try:
        response = requests.post(
            f"{base_url.rstrip('/')}/videos/upload",
            headers={"X-Api-Key": api_key},
            data={
                'video_name': video_name,
                'index': 'true',  # Required: tells Reka to process the video
                'video_url': video_url
            },
            timeout=30
        )
        
        response_data = response.json() if response.ok else {}
        
        if response.ok:
            video_id = response_data.get('video_id', 'unknown')
            return jsonify({
                "success": True,
                "video_id": video_id,
                "message": "Video uploaded successfully"
            })
        else:
            error_msg = response_data.get('error', f"HTTP {response.status_code}")
            return jsonify({"success": False, "error": error_msg}), response.status_code
            
    except requests.Timeout:
        return jsonify({"success": False, "error": "Request timed out"}), 504
    except Exception as e:
        return jsonify({"success": False, "error": f"Upload failed: {str(e)}"}), 500

Once the information from the frontend is validated we make a POST request to the Reka Vision API's /videos/upload endpoint. The parameters are sent as form data, and we include the API key in the headers for authentication. Here I was using URLs to upload videos, but you can also upload local files by adjusting the request accordingly. As you can see, it's pretty straightforward, and the documentation from Reka made it easy to understand what was needed.

The Magic: Sending Roast Requests to Reka Vision API

Here's where things get interesting. Once a video is uploaded, we can ask the AI to analyze it and generate content. The Reka Vision API supports conversational queries about video content:

def call_reka_vision_qa(video_id: str) -> Dict[str, Any]:
    """Call the Reka Video QA API to generate a roast"""
    
    headers = {'X-Api-Key': api_key} if api_key else {}
    
    payload = {
        "video_id": video_id,
        "messages": [
            {
                "role": "user",
                "content": "Write a funny and gentle roast about the person, or the voice in this video. Reply in markdown format."
            }
        ]
    }
    
    try:
        resp = requests.post(
            f"{base_url}/qa/chat",
            headers=headers,
            json=payload,
            timeout=30
        )
        
        data = resp.json() if resp.ok else {"error": f"HTTP {resp.status_code}"}
        
        if not resp.ok and 'error' not in data:
            data['error'] = f"HTTP {resp.status_code} calling chat endpoint"
        
        return data
        
    except requests.Timeout:
        return {"error": "Request to chat API timed out"}
    except Exception as e:
        return {"error": f"Chat API call failed: {e}"}

Here we pass the video ID and a prompt asking for a "funny and gentle roast." The API responds with AI-generated content, which we can then send back to the frontend for display. I try to give more "freedom" to the AI by asking it to reply in markdown format, which makes the output more engaging.

Try It Yourself!

The complete project is available on GitHub: reka-ai/api-examples-python

What Makes Reka Vision API So nice to use

What really stood out to me was how approachable the Reka Vision API is. You don't need any special SDK—just the requests library making standard HTTP calls. And honestly, it doesn't matter what language you're used to; an HTTP call is pretty much always simple to do. Whether you're coming from .NET, Python, JavaScript, or anything else, you're just sending JSON and getting JSON back.

Authentication is refreshingly straightforward: just pop your API key in the header and you're good to go. No complex SDKs, no multi-step authentication flows, no wrestling with binary data streams. The conversational interface lets you ask questions in natural language, and you get back structured JSON responses with clear fields.

One thing worth noting: in this example, the videos are pre-uploaded and indexed, which means the responses come back fast. But here's the impressive part—the AI actually looks at the video content. It's not just reading a transcript or metadata; it's genuinely analyzing the visual elements. That's what makes the roasts so spot-on and contextual.

Final Thoughts

The Reka Vision API itself deserves credit for making video AI accessible. No complicated SDKs, no multi-GB model downloads, no GPU requirements. Just simple HTTP requests and powerful AI capabilities. I'm not saying I'm switching to Python full-time, but expect to see me sharing more Python projects in the future!

References and Resources


Reading Notes #673

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This week’s notes focus on where AI meets everyday development: Copilot and Azure for tighter, faster workflows, a thoughtful overhaul of Aspire’s deploy CLI, and a hands‑on look at building MCP servers in C#. Security threads through it all with practical DevSecOps and Shadow IT reminders plus podcast picks on teaching, acronyms, and tackling imposter syndrome.


AI

Programming

Podcasts

Miscellaneous

Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #671

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From debugging Docker builds to refining your .NET setup, this week’s Reading Notes delivers a sharp mix of practical dev tips and forward-looking tech insights. We revisit jQuery’s place in today’s web stack, explore AI-enhancing MCP servers, and spotlight open-source projects shaping tomorrow’s tools. Plus, PowerToys gets a sleek upgrade to streamline your Windows workflow. 

Let’s check out the ideas and updates that keep your skills fresh and your systems humming.

Programming

AI

Miscellaneous

Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #670

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Dives into the intersection of AI and development, exploring tools like GitHub Copilot’s AGENTS.md and the MCP Toolkit for automations, alongside .NET 10.0’s performance gains and OpenAI’s recent updates. Whether you’re optimizing serverless APIs with AWS Lambda or mastering the Web Animation API, this post highlights breakthroughs in code efficiency, model customization, and cloud innovation. Dive into these thought-provoking reads to stay ahead in a rapidly changing world.




Have a nice week!

Suggestion of the week

Cloud

AI

Programming

  • How .NET 10.0 boosted JSON Schema performance by 18% (Matthew Adams) - Another example of the gain in performance just by upgrading to the latest .NET version.

  • The Web Animation API (Christian Nwamba) - It's the first time I've read about this web animation API, pretty cool even if we need to be careful, I think that precision offers could be very interesting for some animations.


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank



Reading Notes #669

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This week explores the synergy between Dapr and WebAssembly for modern microservices, highlights the transformative potential of Wasm 3.0 for .NET developers, and delves into best practices for structuring Rust web services. In the AI realm, we examine the emergence of developer-friendly AI frameworks like Microsoft’s Agent Framework and Google’s Jules Tools, which bridge AI capabilities directly into terminals and workflows. Additionally, we examine AI obfuscation techniques and their implications, alongside updates on Perplexity’s free Comet AI browser and its new background assistant. Whether you’re building scalable systems, optimizing code, or integrating cutting-edge AI tools, this post offers a snapshot of trends shaping tech today.


Cloud

Programming

AI

Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #668

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This week covers Microsoft’s open-source Agent Framework for agentic AI, prompt-injection risks and mitigations, and the causes of language model hallucinations. It also highlights NuGet package security updates, Azure SQL Data API Builder improvements, Reka’s new Parallel Thinking feature, and the latest in AI benchmarking.


Cloud

Programming

AI

Databases

Miscellaneous


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #667

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This week post explores the intersection of AI, cloud, and DevOps, featuring updates on Microsoft’s Logic Apps integration, practical .NET tools for system automation, and strategies to enhance documentation for AI-driven workflows. Whether you’re refining enterprise security practices with NuGet’s Trusted Publishing or diving into the ethical nuances of AI through vector databases, this post offers a blend of technical deep dives and thought-provoking discussions. Don’t miss the podcast highlights, from DevOps innovation to the business impact of employee well-being, perfect for developers, architects, and curious minds alike. Let’s connect the dots in a world where code, creativity, and collaboration drive progress.






Programming

AI

DevOps

Podcasts

Miscellaneous

Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #666

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Welcome to another edition of my reading notes! This week I've gathered some interesting finds across programming, AI, and general tech topics. From exciting Cake updates to practical AI implementation advice, here are the articles that caught my attention recently.



Programming

  • Cake.Sdk 5.0.25257.82-beta released (devlead) - I'm happy to see the new release of this cake version coming to a recent .NET version. I have one of my projects using cake, and I was waiting for it; this is amazing

AI

Miscellaneous


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank



Reading Notes #665

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In this edition, we explore modern development's evolving landscape. From Microsoft's .NET Aspire simplifying distributed applications to AI security considerations, Git workflow optimizations, and backlog management strategies, there's something here to spark your next breakthrough.


The tech world never sleeps, and neither does innovation. Let's explore what caught my attention this week and might just spark your next big idea or solve that problem you've been wrestling with.

Programming


AI


Open Source


Podcast


~frank

Reading Notes #664

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Welcome to another edition of my weekly reading notes! This week's collection brings together some fascinating developments across the tech landscape. From the intricacies of building cross-platform .NET tools to impressive AI breakthroughs like Warp's stellar performance on SWE-bench, there's plenty to explore. I've also discovered some thought-provoking content about leadership, product management, and the art of meaningful communication. Whether you're interested in the latest AI tools, looking for career insights, or simply want to stay current with industry trends, this week's selection has something valuable for every developer and tech professional.



Programming

  • Using and authoring .NET tools (Andrew Lock) - Interesting post that shares the behind-the-scenes when you're building a tool for multiple targets and the challenge that it represents. Those also share the new ways of .NET 10

AI

Podcasts

Miscellaneous

Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Using AI with .NET 10 Scripts: What Worked, What Didn’t, and Lessons Learned

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I wanted to kick the tires on the upcoming .NET 10 C# script experience and see how far I could get calling Reka’s Research LLM from a single file, no project scaffolding, no .csproj. This isn’t a benchmark; it’s a practical tour to compare ergonomics, setup, and the little gotchas you hit along the way. I’ll share what worked, what didn’t, and a few notes you might find useful if you try the same.

All the sample code (and a bit more) is here: reka-ai/api-examples-dotnet · csharp10-script. The scripts run a small “top 3 restaurants” prompt so you can validate everything quickly.

We’ll make the same request in three ways:

  1. OpenAI SDK
  2. Microsoft.Extensions.AI for OpenAI
  3. Raw HttpClient

What you need

The C# "script" feature used below ships with the upcoming .NET 10 and is currently available in preview. If you prefer not to install a preview SDK, you can run everything inside the provided Dev Container or on GitHub Codespaces. I include a .devcontainer folder with everything set up in the repo.

Set up your API key

We are talking about APIs here, so of course, you need an API key. The good news is that it's free to sign up with Reka and get one! It's a 2-click process, more details in the repo. The API key is then stored in a .env file, and each script loads environment variables using DotNetEnv.Env.Load(), so your key is picked up automatically. I went this way instead of using dotnet user-secrets because I thought it would be the way it would be done in a CI/CD pipeline or a quick script.

Run the demos

From the csharp10-script folder, run any of these scripts. Each line is an alternative

dotnet run 1-try-reka-openai.cs
dotnet run 2-try-reka-ms-ext.cs
dotnet run 3-try-reka-http.cs

You should see a short list of restaurant suggestions.

Propmt Result: 3 restaurants


OpenAI SDK with a custom endpoint

Reka's API is using the OpenAI format; therefore, I thought of using the NuGet package OpenAI. To reference a package in a script, you use the #:package [package name]@[package version] directive at the top of the file. Here is an example:

#:package OpenAI@2.3.0

// ...

var baseUrl = "http://api.reka.ai/v1";

var openAiClient = new OpenAIClient(new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
    Endpoint = new Uri(baseUrl)
});

var client = openAiClient.GetChatClient("reka-flash-research");

string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";

var completion = await client.CompleteChatAsync(
    new List<ChatMessage>
    {
        new UserChatMessage(prompt)
    }
);

var generatedText = completion.Value.Content[0].Text;

Console.WriteLine($" Result: \n{generatedText}");

The rest of the code is more straightforward. You create a chat client, specify the Reka API URL, select the model, and then you send a prompt. And it works just as expected. However, not everything was perfect, but before I share more about that part, let's talk about Microsoft.Extensions.AI.

Microsoft Extensions AI for OpenAI

Another common way to use LLM in .NET is to use one ot the Microsoft.Extensions.AI NuGet package. In our case Microsoft.Extensions.AI.OpenAI was used.

#:package Microsoft.Extensions.AI.OpenAI@9.8.0-preview.1.25412.6

// ....

var baseUrl = "http://api.reka.ai/v1";

IChatClient client = new ChatClient("reka-flash-research", new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
    Endpoint = new Uri(baseUrl)
}).AsIChatClient();

string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";

Console.WriteLine(await client.GetResponseAsync(prompt));

As you can see, the code is very similar. Create a chat client, set the URL, the model, and add your prompt, and it works just as well.

That's two ways to use Reka API with different SDKs, but maybe you would prefer to go "SDKless", let's see how to do that.

Raw HttpClient calling the REST API

Without any SDK to help, there is a bit more line of code to write, but it's still pretty straightforward. Let's see the code:

using var httpClient = new HttpClient();

var baseUrl = "http://api.reka.ai/v1/chat/completions";

var requestPayload = new
{
    model = "reka-flash-research",
    messages = new[]
            {
                new
                {
                    role = "user",
                    content = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in New York city"
                }
            }
};

using var request = new HttpRequestMessage(HttpMethod.Post, baseUrl);
request.Headers.Add("Authorization", $"Bearer {REKA_API_KEY}");
request.Content = new StringContent(jsonPayload, Encoding.UTF8, "application/json");

var response = await httpClient.SendAsync(request);

var responseContent = await response.Content.ReadAsStringAsync();

var jsonDocument = JsonDocument.Parse(responseContent);

var contentString = jsonDocument.RootElement
    .GetProperty("choices")[0]
    .GetProperty("message")
    .GetProperty("content")
    .GetString();

Console.WriteLine(contentString);

So you create an HttpClient, prepare a request with the right headers and payload, send it, get the response, and parse the JSON to extract the text. In this case, you have to know the JSON structure of the response, but it follows the OpenAI format.

What did I learn from this experiment?

I used VS Code while trying the script functionality. One thing that surprised me was that I didn't get any IntelliSense or autocompletion. I try to disable the DevKit extension and change the setting for OmniSharp, but no luck. My guess is that because it's in preview, and it will work just fine in November 2025 when .NET 10 will be released.

In this light environment, I encountered some issues where, for some reason, I couldn't use an https endpoint, so I had to use http. In the raw httpClient script, I had some errors with the Reflection that wasn't available. It could be related to the preview or something else, I didn't investigate further.

For the most part, everything worked as expected. You can use C# code to quickly execute some tasks without any project scaffolding. It's a great way to try out the Reka API and see how it works.

What's Next?

While writing those scripts, I encountered multiple issues that aren't related to .NET but more about the SDKs when trying to do more advanced functionalities like optimization of the query and formatting the response output. Since it goes beyond the scope of this post, I will share my findings in a follow-up post. Stay tuned!

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Reading Notes #663

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Here are my reading notes for the week: a mix of AI research and evaluation, .NET and Linux troubleshooting, testing framework changes, and JavaScript/TypeScript perspectives, plus a few podcast episodes on C#, work design, and software modernization that I found worthwhile. 


AI

Programming

Podcasts


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #662

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From Docker's security practices to the latest in GPT-5 discussions, there's quite a mix of topics to dive into. I particularly enjoyed the thought-provoking piece about junior developers in the age of LLMs - it's a conversation we should all be having.


As always, grab your favorite beverage, and let's explore what caught my attention this week!

DevOps

AI

Podcasts

Miscellaneous


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

Reading Notes #661

Labels: , , , , , , , , , , , , , ,

This week post collects concise links and takeaways across .NET, AI, Docker, open source security, DevOps, and broader developer topics. From the .NET Conf call for content and Copilot prompts to Docker MCP tooling, container debugging tips, running .NET as WASM, and a fresh look at the 10x engineer idea.


Suggestion of the week

AI

Open Source

DevOps

Programming

Miscellaneous


Sharing my Reading Notes is a habit I started a long time ago, where I share a list of all the articles, blog posts, and books that catch my interest during the week.

If you have interesting content, share it!

~frank

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