I'm excited to share that my new n8n template has been approved and is now available for everyone to use! This template automates the process of creating AI-generated video clips from YouTube videos and sending notifications directly to your inbox.
If you've ever wanted to automatically create short clips from long YouTube videos, this template is for you. It watches a YouTube channel of your choice, and whenever a new video is published, it uses AI to generate engaging short clips perfect for social media. You get notified by email when your clip is ready to download.
How It Works
The workflow is straightforward and runs completely on autopilot:
Monitor YouTube channels - The template watches the RSS feed of any YouTube channel you specify. When a new video appears, the automation kicks off.
Request AI clip generation - Using Reka's Vision API, the workflow sends the video for AI processing. You have full control over the output:
Write a custom prompt to guide the AI on what kind of clip to create
Choose whether to include captions
Set minimum and maximum clip duration
Smart status checking - When the clips are ready, you receive a success email with your download link. As a safety feature, if the job takes too long, you'll get an error notification instead.
Getting Started is Easy
The best part? You can install this template with just one click from the n8n Templates page. No complex setup required!
After installation, you'll just need two quick things:
A Gmail account (or use any email provider you like)
That's it! The template comes ready to use. Simply add your YouTube channel RSS feed, connect your API key, and you're ready to start generating clips automatically. The whole setup takes just a few minutes.
If you run into any questions or want to share what you've built, join the Reka Discord community. I'd love to hear how you're using this template!
Show Me
In this short video I show you how to get that template into your n8n and how to configure it.
I love the cloud, in fact most people probably know me because of my shared content related to that. But sometimes our apps don't need scaling, or redundancy. Sometimes we just want to host them somewhere.
It was the holidays, and during my time off I worked on a few small personal projects. I packaged them in containers so it's easy to deploy anywhere. I deployed them on a mini-PC that I have at home and it is great... as long as I stay home. But what if I would like to access it from elsewhere (ex: my in-laws' house)?
I set up a nice Cloudflare tunnel to a Traefik container that proxies the traffic to the correct container based on the prefix or second-level domain. So dev.c5m.ca goes to container X and test.c5m.ca goes to container Y. In this post, I wanted to share how I did it (and also have it somewhere for me in case I need to do it again 😉). It's simple once you know all the pieces work together.
generated by Microsoft designer
The Setup
The architecture is straightforward: Cloudflare Tunnel creates a secure connection from my home network to Cloudflare's edge, and Traefik acts as a reverse proxy that routes dynamically incoming requests to the appropriate container based on the subdomain. This way, I can access multi ple services through different subdomains without exposing my home network directly to the internet.
Step 1: Cloudflare Tunnel
First, assuming you already owne a domain name, you'll need to create a Cloudflare tunnel. You can do this through the Cloudflare dashboard under Zero Trust → Networks → Tunnels. Once created, you'll get a tunnel token that you'll use in the configuration.
The tunnel token is stored in a .env file for security. The --no-autoupdate flag prevents the container from trying to update itself automatically, which is useful in a controlled environment.
Step 2: DNS Configuration
In Cloudflare dashboard, create a CNAME Record with a wildcard. For example for my domain "c5m.ca" that record will look like this: *.c5m.ca.
Step 3: Traefik Configuration
Traefik is the reverse proxy that will route traffic to your containers. I have two configuration files: one for Traefik itself and one for the Docker Compose setup.
I've configured two entry points: web on port 8082 (HTTP) and websecure on port 8043 (HTTPS). I did it that way because the default 80 and 443 where already taken. The Docker provider watches for containers with Traefik labels and automatically configures routing. exposedByDefault: false means containers won't be exposed unless explicitly enabled with labels. You won't have to change Traefik config to add more containers, it's all dynamic.
traefik.enable=true: This container should be exposed
nginxtest is the unique name for routing this container.
traefik.http.routers.nginxtest.rule=Host(...): Route requests for test.c5m.ca to this container
traefik.http.routers.nginxtest.entrypoints=web: Use the web entry point (port 8082)
Bonus: A More Complex Example
For a more realistic scenario, let's share how I could expose 2D6 Dungeon App
here's a simplified version of my 2d6-docker-compose.yaml which includes a multi-container application:
Multiple services working together (database, API, web app)
Only the webapp is exposed through Traefik (the database and API are internal)
The webapp uses both web and websecure entry points
Important note here is that container part of the same network can use their internal port (ex: 5000 for DAB, 3306 for MySQL)
The external network is the proxy created previously
Cloudflare Tunnel Configuration
In your Cloudflare dashboard, you'll need to configure the tunnel to route traffic to Traefik. Create a public hostname that points to http://<local-ip>:8082. Use the local IP of your server something like "192.168.1.123" You can use wildcards like *.c5m.ca to route all subdomains to Traefik, which will then handle the routing based on the hostname.
Wrapping Up
That's it! Once everything is set up:
The Cloudflare tunnel creates a secure connection from your home to Cloudflare
Traffic comes in through Cloudflare and gets routed to Traefik
Traefik reads the hostname and routes to the appropriate container
Each service can be accessed via its own subdomain
Only the containers with the Traefik labels are accessible from outside my network
It's dynamic! Any new container, with the labels, will be routed without changing the config in Traefik nor Cloudflare
It's a simple setup that works great for personal projects. The best part is that you don't need to expose any ports on your router or deal with dynamic DNS, Cloudflare handles all of that.
Next step will be to add some authentication and authorization (ex: using Keycloak), but that's for another post. For now, this gives me a way to access my home-hosted services from anywhere, and I thought it could be useful to share.
My colleague Annie loves clipping videos from her favorite creators. You know that feeling when you catch a great moment and turn it into a perfect short? That's her jam. But she kept running into this frustrating problem: by the time she saw a new video and got around to clipping it, everyone else had already done it. She was always late to the party.
When she told me about this, I thought, "What if we could automatically clip videos the moment they're published?" That way, she'd have her clips ready to post while the content is still fresh.
So I put my experience with integration tools to work and built something for her—and for anyone else who has this same problem. And you know what? I'm pretty excited to share it with you.
I put together an open-source n8n templates that automatically clips YouTube videos using AI. Here's how it works:
It watches for new videos from your favorite YouTube channel
Sends the video to Reka's AI to create clips automatically
Checks when the clips are ready and sends you an email with the download link
The whole thing runs on n8n (it's a free automation platform), and it uses Reka's Clips API to do the AI magic. Best part? It's completely free to use and set up.
How It Actually Works
I built this using two n8n workflows that work together:
Workflow 1: Submit Reel Creation
This one's the watcher. It monitors a YouTube channel's RSS feed, and the moment a new video drops, it springs into action:
Grabs the video URL
Sends it to Reka's API with instructions like "Create an engaging short video highlighting the best moments"
Gets back a job ID so we can track the progress
Saves everything to a n8n data table
The cool thing is you can customize how the clips are made. Want vertical videos for TikTok? Done. Need subtitles? Got it. You can set the clip length anywhere from 0 to 30 seconds. It's all in the JSON configuration.
{
"video_urls": ["{{ $json.link }}"],
"prompt": "Create an engaging short video highlighting the best moments",
"generation_config": {
"template": "moments",
"num_generations": 1,
"min_duration_seconds": 0,
"max_duration_seconds": 30
},
"rendering_config": {
"subtitles": true,
"aspect_ratio": "9:16"
}
}
Workflow 2: Check Reel Status
This one's the patient checker. Since AI takes time to analyze a video and create clips (could be several minutes depending on the video length), we need to check in periodically:
Looks at all the pending jobs in our data table
Asks Reka's API "Hey, is this one done yet?"
When a clip is ready, sends you an email with the download link
Marks the job as complete so we don't check it again
I set mine to check every 15-30 minutes. No need to spam the API—good things take time! 😉
Setting It Up (It's Easier Than You Think)
When I was helping Annie set this up (you can watch the full walkthrough below), we got it working in just a few minutes. Here's what you need to do:
Step 1: Create Your Data Table
In n8n, create a new data table. Here's a pro tip I learned the hard way: don't name it "videos"—use something like "clip_jobs" or "reel_records" instead. Trust me on this one; it'll save you some headaches.
Your table needs four columns (all strings):
video_title - The name of the video
video_url - The YouTube URL
job_id - The ID Reka gives us to track the clip
job_status - Where we are in the process (queued, processing, completed, etc.)
Step 2: Import the Workflows
Download the two JSON files from the GitHub repo and import them into n8n. They'll show up with some errors at first—that's totally normal! We need to configure them.
Step 3: Configure "Submit Reel Creation"
RSS Feed Trigger: Replace my YouTube channel ID with the one you want to monitor. You can find any channel's ID in their channel URL.
API Key: Head to platform.reka.ai and grab your free API key. Pop it into the Bearer Auth field. Give it a memorable name like "Reka API key" so you know what it is later.
Clip Settings: This is where you tell the AI what kind of clips you want. The default settings create one vertical video (9:16 aspect ratio) up to 30 seconds long with subtitles. But you can change anything:
The prompt ("Create an engaging short video highlighting the best moments")
Duration limits
Aspect ratio (square, vertical, horizontal—your choice)
Whether to include subtitles
Data Table: Connect it to that table you created in Step 1.
Step 4: Configure "Check Reel Status"
Trigger: Start with the manual trigger while you're testing. Once everything works, switch it to a schedule trigger (I recommend every 15-30 minutes).
API Key: Same deal as before—add your Reka API key.
Email: Update the email node with your email address. You can customize the subject and body if you want, but the default works great.
Data Table: Make sure all the data table nodes point to your table from Step 1.
Watching It Work
When Annie and I tested it live, that moment when the first clip job came through with a "queued" status? That was exciting. Then checking back and seeing "completed"? Even better. And when that email arrived with the download link? Perfect.
The clips Reka AI creates are actually really good. It analyzes the entire video, finds the best key moments (or what ever your prompt asks), adds subtitles, and packages it all up in a format ready for social media.
Wrap Up
This tool works great whether you're a clipper enthusiast or a content creator looking to generate clips for your own channel. Once you set it up, it just runs. New video drops at 3 AM? Your clip is already processing. You wake up to a download link in your inbox.
It's open source and free to use. Take it, customize it, make it your own. And if you come up with improvements or have ideas, I'd love to hear about them. Share your updates on GitHub or join the conversation in the Reka Community Discord.
Watch the Full Setup
I recorded the entire setup process with Annie (she was testing it for the first time). You can see every step, every click, and yes, even the little mistakes we made along the way. That's real learning right there.
Get Started
Ready to try it? Here's everything you need:
🔗 n8n template/ Github: https://link.reka.ai/n8n-clip
🔗 Reka API key: https://link.reka.ai/free (renewable & free)
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.
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:
Captures your question
Loads an API key from ~/.config/reka/api_key
Sends a JSON payload to the chat endpoint with curl.
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="$*"
fi
2. Load Your API Key
If you're running Ollama locally you don't need any key, but for all other AI providers you do. I store mine in a locked-down file at ~/.config/reka/api_key, then read and trim trailing whitespace like this:
Finally, we parse the JSON response with jq to pull out just the answer text. If jq isn't installed we display the raw response, but a formatted answer is much nicer. If you are customizing for another provider, you may need to adjust the JSON path here. You can add echo "$RESPONSE" >> data_sample.json to the script to log raw responses for tinkering.
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.
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:
Initialization
Stores the API key + array of allowed domains, sets model & endpoint, logs a safe startup message.
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
}
}
};
Send Request
Creates a HttpRequestMessage (POST), adds Authorization: Bearer <APIKEY>, sends JSON to Reka.
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
In VS Code (with MCP extension), click Add MCP Server.
Choose type: Docker image.
Image name: fboucher/check-in-doc-mcp.
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!
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:
List videos: Show me what videos are in my collection
Upload videos: Let me add new ones via URL
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:
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.
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!
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:
OpenAI SDK
Microsoft.Extensions.AI for OpenAI
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.
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!
Introducing Reka Research: Your AI Research Assistant
Meet Reka Research a powerful AI agent that can search the web and analyze your files to answer complex questions in minutes. Whether you're staying up to date with AI news, screening resumes, or researching technical topics, Reka Research does the heavy lifting for you.
What Makes Reka Research Special?
Reka Research stands out in four key areas:
Top Performance: Best in class results on research benchmarks
Fast Results: Get thorough answers in 1-3 minutes
Full Transparency: See exactly how the AI reached its conclusions. All steps are visible.
Smart Web Search That Actually Works
Ever wished you could ask someone to research the latest AI developments while you focus on other work? That's exactly what Reka Research does.
Watch how it works:
In this demo, Jess and Sharath shows how Reka Research can automatically gather the most important AI news from the past week. The AI visits multiple websites, takes notes, and presents a clean summary with sources. You can even restrict searches to specific domains or set limits on how many sites to check.
File Search for Your Private Documents
Sometimes the information you need isn't on the web - it's in your company's documents, meeting notes, or file archives. Reka Research can search through thousands of private files to find exactly what you're looking for.
See it in action:
In this example, ess and Sharath shows how HR teams can use Reka Research to quickly screen resumes. Instead of manually reviewing hundreds of applications, the AI finds candidates who meet specific requirements (like having a computer science degree and 3+ years of backend experience) in seconds!
Writing Better Prompts Gets Better Results
Like any AI tool, Reka Research works best when you know how to ask the right questions. The key is being specific about what you want and providing context.
Learn the techniques:
Jess and Yi shares practical tips for getting the most out of Reka Research. Instead of asking "summarize meeting minutes," try "summarize April meeting minutes about public participation." The more specific you are, the better your results will be.
Ready to Try Reka Research?
Reka Research is currently available for everyone! Try it via the playground, or using directly the API. Whether you're researching competitors, analyzing documents, or staying current with industry trends, it can save you hours of work.
Want to learn more and connect with other users? Join our Discord community where you can:
In a recent post, I shared how to set up a CI/CD pipeline for a .NET Aspire project on GitLab. The pipeline includes unit tests, security scanning, and secret detection, and if any of those fail, the pipeline would fail. Great, but what about code coverage for the unit tests? The pipeline included code coverage commands, but the coverage was not visible in the GitLab interface. Let's fix that.
One thing I initially thought was that the regex used to extract the coverage was incorrect. The regex used in the pipeline was:
coverage: '/Total\s*\|\s*(\d+(?:\.\d+)?)%/'
That regex came directly from the GitLab documentation, so I thought it should work correctly. However, coverage still wasn't visible in the GitLab interface.
So with the help of GitHub Copilot, we wrote a few commands to validate:
That the coverage.cobertura.xml was in a consistent location (instead of being in a folder with a GUID name)
That the coverage.cobertura.xml file was in a valid format
What exactly the regex was looking for
Everything checked out fine, so why was the coverage not visible?
The Solution
It turns out that the coverage command with the regex expression is scanning the console output and not the coverage.cobertura.xml file. Aha! One solution was to install dotnet-tools to changing where the the test results was persisted; to the console instead of the XML file, but I preferred keeping the .NET environment unchanged.
The solution I ended up implementing was executing a grep command to extract the coverage from the coverage.cobertura.xml file and then echoing it to the console. Here's what it looks like:
I hope this helps others save time when setting up code coverage for their .NET projects on GitLab. The key insight is that GitLab's coverage regex works on console output, not on the files (XML or other formats).
If you have any questions or suggestions, feel free to reach out!
Getting a complete CI/CD pipeline for your .NET Aspire solution doesn't have to be complicated. I've created a template that gives you everything you need to get started in minutes.
Replace the sample project with your own .NET Aspire code
Push to your GitLab repository
Watch your CI/CD pipeline run automatically
That's it! You immediately get automated builds, testing, and security scanning.
Pro Tip: The best time to set up CI/CD is when you're just starting your project because everything is still simple.
Part 2: Building the Template with GitLab Duo
Now let me share my experience creating this template using GitLab's AI assistant, GitLab Duo.
Starting Simple, Growing Smart
I didn't build this complex pipeline all at once. I started with something very basic and used GitLab Duo to gradually add features. The AI helped me:
Add secret detection when I asked: "How can I scan for accidentally committed secrets?"
Fix test execution issues when my unit tests weren't running properly
Optimize the pipeline structure for better performance
Working with GitLab in VS Code
While you can edit .gitlab-ci.yml files directly in GitLab's web interface, I prefer VS Code. Here's my setup:
Install the official GitLab extension from the VS Code marketplace
Once you've signed in, this extension gives you:
Direct access to GitLab issues and work items
AI-powered chat with GitLab Duo
GitLab Duo in Action
GitLab Duo became my pair programming partner. Here's how I used it:
Understanding Code: I could type /explain and ask Duo to explain what any part of my pipeline configuration does by highlighting that section.
Solving Problems: When my solution didn't compile, I described the issue to Duo and got specific suggestions. For example, it helped me realize some projects weren't in .NET 9 because dotnet build required the Aspire workload. I could either keep my project in .NET 8 and add a before_script instruction to install the workload or upgrade to .NET 9; I picked the latest.
Adding Features: I started with just build and test, then incrementally asked Duo to help me add security scanning, secret detection, and better error handling.
Adding Context: Using /include to add the project file or the .gitlab-ci.yml file while asking questions helped Duo understand the context better.
Learn More with the Docs: During my journey, I knew Duo wasn't just making things up as it was referencing the documentation. I could continue my learning there and read more examples of how before_script is used in different contexts.
The AI-Assisted Development Experience
What impressed me most was how GitLab Duo helped me learn while building. Instead of just copying configurations from documentation, each conversation taught me something new about GitLab CI/CD best practices.
Conclusion
I think this template can be useful for anyone starting a .NET Aspire project. Ready to try it? Clone the template at cloud5mins/aspire-template and start building with confidence.
Whether you're new to .NET Aspire or CI/CD, this template gives you a good foundation. And if you want to customize it further, GitLab Duo is there to help you understand and modify the configuration.
If you think we should add more features or improve the template, feel free to open an issue in the repository. Your feedback is always welcome!
Recently, someone asked me an interesting question: "Can GitHub Copilot or AI help me convert an application from one language to another?" My answer was a definitive yes! AI can not only help you write code in a new language, but it can also improve team collaboration and bridge the knowledge gap between developers who know different programming languages.
To demonstrate this capability, I decided to convert a COBOL application to Java—a perfect test case since I don't know either language well, which means I really needed Copilot to do the heavy lifting. All the code is available on GitHub.
The first step was setting up a proper development environment. I used a dev container and asked Copilot to help me build it. I also asked for recommendations on the best VS Code extensions for Java development. Within just a few minutes, I had a fully configured environment ready for Java development.
Choosing the Right Copilot Agent
When working with GitHub Copilot for code conversion, you have different mode to choose from:
Ask: Great for general questions (like asking about Java extensions)
Edit: Perfect for simple document editing (like modifying the generated code)
Agent: The powerhouse for complex tasks involving multiple files, imports, and structural changes
For code conversion projects, the Agent is your best friend. It can look at different source files, understand project structure, edit code, and even create new files on your behalf.
The Conversion Process
I used Claude 3.5 Sonnet for this conversion. Here's the simple prompt I used:
"Convert this hello business COBOL application into Java"
Copilot didn't just convert the code, it also provided detailed information about how to execute the Java application, which was invaluable since I had no Java experience.
The results varied depending on the AI model used (Claude, GPT, Gemini, etc.), but the core functionality remained consistent across different attempts. Since the original application was simple, I converted it multiple times using different prompts and models to test the consistency. Sometimes it generated a single file, other times it created multiple files: a main application and an Employee class (which wasn't in my original COBOL version). Sometimes it updated the Makefile to allow compilation and execution using make, while other times it provided instructions to use javac and java commands directly.
This variability is expected with generative AI results will differ between runs, but the core functionality remains reliable.
Real-World Challenges
Of course, the conversion wasn't perfect on the first try. For example, I encountered runtime errors when executing the application. The issue was with the data format—the original file used a flat file format with fixed length records (19 characters per record) and no line breaks.
I went back to Copilot, highlighted the error message from the terminal, and provided additional context about the 19 character record format. This iterative approach is key to successful AI assisted conversion.
"It's not working as expected, check the error in #terminalSelection. The records have fixed length of 19 characters without line breaks. Adjust the code to handle this format"
The Results
After the iterative improvements, my Java application successfully:
Compiled without errors
Processed all employee records
Generated a report with employee data
Calculated total salary (a nice addition that wasn't in the original)
While the output format wasn't identical to the original COBOL version (missing leading zeros, different line spacing), the core functionality was preserved.
Video Demonstration
Watch the complete conversion process in action:
Best Practices for AI-Assisted Code Conversion
Based on this experience, here are my recommendations:
1. Start with Small Pieces
Don't try to convert thousands of lines at once. Break your conversion into manageable modules or functions.
2. Set Up Project Standards
Consider creating a .github folder at your project root with an instructions.md file containing:
Best practices for your target language
Patterns and tools to use
Specific versions and frameworks
Enterprise standards to follow
3. Stay Involved in the Process
You're not just a spectator - you're an active participant. Review the changes, test the output, and provide feedback when things don't work as expected.
4. Iterate and Improve
Don't expect perfection on the first try. In my case, the converted application worked but produced slightly different output formatting. This is normal and expected, after all you are converting between two different languages with different conventions and styles.
Can AI Really Help with Code Conversion?
Absolutely, yes! GitHub Copilot can significantly:
Speed up the conversion process
Help with syntax and language specific patterns
Provide guidance on running and compiling the target language
Bridge knowledge gaps between team members
Generate supporting files and documentation
However, remember that it's generative AI, results will vary between runs, and you shouldn't expect identical output every time.
Final Thoughts
GitHub Copilot is definitely a tool you need in your toolkit for conversion projects. It won't replace the need for human oversight and testing, but it will dramatically accelerate the process and help teams collaborate more effectively across different programming languages.
The key is to approach it as a collaborative process where AI does the heavy lifting while you provide guidance, context, and quality assurance. Start small, iterate often, and don't be afraid to ask for clarification or corrections when the output isn't quite right.
Have you tried using AI for code conversion? I'd love to hear about your experiences in the comments below! Visit c5m.ca/copilot to get started with GitHub Copilot.
As developers, we're constantly looking for tools that can help us stay in the flow and be more productive. Today, I want to share a powerful tool that's been gaining traction in the developer community: GitKraken CLI. This command-line interface brings together several key features that modern developers love - it's AI-powered, terminal-based, and incredibly efficient for managing Git workflows.
GitKraken CLI (accessible via the gk command) stands out because it simplifies complex Git workflows while adding intelligent automation. Unlike traditional Git commands, it provides a more intuitive workflow management system that can handle multiple repositories simultaneously.
Getting Started
Installation is straightforward. On Windows, you can install it using:
winget install gitkraken.cli
Once installed, you'll have access to the gk command, which becomes your gateway to streamlined Git operations.
The Workflow in Action
Let's walk through a typical development session using GitKraken CLI:
1. Starting a Work Session
Instead of manually creating branches and switching contexts, you can start a focused work session:
gk w start "Add Behind my Cloud feed" -i "Add Behind my Cloud feed #1"
This single command:
Creates a new branch based on your issue/feature name
Switches to that branch automatically
Links the work session to a specific issue
Sets up your development environment for focused work
2. Managing Multiple Work Sessions
You can easily see all your active work sessions:
gk w list
This is particularly powerful when working across multiple repositories or juggling several features simultaneously.
3. Committing with Intelligence
After making your changes, adding files works as expected:
gk add .
But here's where the AI magic happens. Instead of writing commit messages manually:
gk w commit --ai
The AI analyzes your changes and generates meaningful, descriptive commit messages automatically. No more "quick fix" or "update stuff" commits!
4. Pushing and Creating Pull Requests
Publishing your work is equally streamlined:
gk w push
And when you're ready to create a pull request:
gk w pr create --ai
Again, AI assistance helps generate appropriate PR titles and descriptions based on your work.
5. Wrapping Up
Once your work is complete and merged, clean up is simple:
gk w end
This command:
Switches you back to the main branch
Deletes the feature branch, locally and on GitHub
Closes the work session
Leaves your repository clean and ready for the next task
Why This Matters
The beauty of GitKraken CLI lies in its ability to keep you in the zone. You don't need to:
Switch between multiple tools
Remember complex Git commands
Write commit messages from scratch
Manually manage branch lifecycle
Everything flows naturally from one command to the next, maintaining your focus on what matters most: writing code.
Multi-Repository Power
One of the standout features is GitKraken CLI's ability to manage multiple repositories simultaneously. This is invaluable for:
Microservices architectures
Full-stack applications with separate frontend/backend repos
Organizations with multiple related projects
Try It Yourself
GitKraken CLI is part of a broader suite of developer tools that GitKraken offers. The CLI itself is free to use, which makes it easy to experiment with and integrate into your workflow without any upfront commitment. If you find value in the CLI and want to explore their other tools, GitKraken has various products that might complement your development setup.
The learning curve is genuinely minimal since it builds on Git concepts you already know while adding helpful automation. I've found that even small workflow improvements can compound over time, especially when you're working on multiple projects or dealing with frequent context switching.
If you're curious about what else GitKraken offers beyond the CLI, you can explore their full product lineup here. For those who decide the Pro features would benefit their workflow, as an ambassador of GitKraken I can share my code to provide a 50% discount for your GitKraken Pro subscription.
The combination of AI assistance and intuitive commands addresses real pain points that many developers face daily. Whether GitKraken CLI becomes a core part of your toolkit will depend on your specific workflow, but it's worth trying given that it's free and takes just a few minutes to set up.
The best tools are the ones that get out of your way and let you focus on building. GitKraken CLI aims to do exactly that.
Testing has always been one of those tasks that developers know is essential but often find tedious. When I decided to add comprehensive unit tests to my NoteBookmark project, I thought: why not make this an experiment in AI-assisted development? What followed was a fascinating 4-hour journey that resulted in 88 unit tests, a complete CI/CD pipeline, and some valuable insights about working with AI coding assistants.
NoteBookmark is a .NET application built with C# that helps users manage and organize their reading notes and bookmarks. The project includes an API, a Blazor frontend, and uses Azure services for storage. You can check out the complete project on GitHub.
The Challenge: Starting from Zero
I'll be honest - it had been a while since I'd written comprehensive unit tests. Rather than diving in myself, I decided to see how different AI models would approach this task. My initial request was deliberately vague: "add a test project" without any other specifications.
Looking back, I realize I should have been more specific about which parts of the code I wanted covered. This would have made the review process easier and given me better control over the scope. But sometimes, the best learning comes from letting the AI surprise you.
The Great AI Model Comparison
GPT-4.1: Competent but Quiet
GPT-4.1 delivered decent results, but the experience felt somewhat mechanical. The code it generated was functional, but I found myself wanting more context. The explanations were minimal, and I often had to ask follow-up questions to understand the reasoning behind certain test approaches.
Gemini: The False Start
My experience with Gemini was... strange. Perhaps it was a glitch or an off day, but most of what was generated simply didn't work. I didn't persist with this model for long, as debugging AI-generated code that fundamentally doesn't function defeats the purpose of the exercise. Note that at the time of this writing, Gemini was still in preview, so I expect it to improve over time.
Claude Sonnet: The Clear Winner
This is where the magic happened. Claude Sonnet became my co-pilot of choice for this project. What set it apart wasn't just the quality of the code (though that was excellent), but the quality of the conversation. It felt like having a thoughtful colleague thinking out loud with me.
The explanations were clear and educational. When Claude suggested a particular testing approach, it would explain why. When it encountered a complex scenario, it would walk through its reasoning. I tried different versions of Claude Sonnet but didn't notice significant differences in results - they were all consistently good.
The Development Process: A 4-Hour Journey
Hour 1-2: Getting to Compilation
The first iteration couldn't compile. This wasn't surprising given the complexity of the codebase and the vague initial request. But here's where the AI collaboration really shined. Instead of manually debugging everything myself, I worked with Copilot to identify and fix issues iteratively.
We went through several rounds of:
Identify compilation errors
Discuss the best approach to fix them
Let the AI implement the fixes
Review and refine
After about 2 hours, we had a test project with 88 unit tests that compiled successfully. The AI had chosen xUnit as the testing framework, which I was happy with - it's a solid choice that I might not have picked myself if I was rusty on the current .NET testing landscape.
Hour 2.5-3.5: Making Tests Pass
Getting the tests to compile was one thing; getting them to pass was another challenge entirely. This phase taught me a lot about both my codebase and xUnit features I wasn't familiar with.
I relied heavily on the /explain feature during this phase. When tests failed, I'd ask Claude to explain what was happening and why. This was invaluable for understanding not just the immediate fix, but the underlying testing concepts.
One of those moment was learning about [InlineData(true)] and other xUnit data attributes. These weren't features I was familiar with, and having them explained in context made them immediately useful.
Hour 3.5-4: Structure and Style
Once all tests were passing, I spent time ensuring I understood each test and requesting structural changes to match my preferences. This phase was crucial for taking ownership of the code. Just because AI wrote it doesn't mean it should remain a black box. Let's repeat this: Understanding the code is essential; just because AI wrote it doesn't mean it's good.
Beyond Testing: CI/CD Integration
With the tests complete, I asked Copilot to create a GitHub Actions workflow to run tests on every push to main and v-next branches, plus PR reviews. Initially it started modifiying my existing workflow that takess care of the Azure deployment. I wanted a separate workflow for testing, so I interrupted (that's nice I wasn't "forced" to wait), and asked it to create a new one instead. The result was the running-unit-tests.yml workflow that worked perfectly on the first try.
This was genuinely surprising. CI/CD configurations often require tweaking, but the generated workflow handled:
Multi-version .NET setup
Dependency restoration
Building and testing
Test result reporting
Code coverage analysis
Artifact uploading
The PR Enhancement Adventure
Here's where things got interesting. When I asked Copilot to enhance the workflow to show test results in PRs, it started adding components, then paused and asked if it could delete the current version and start from scratch.
I said yes, and I'm glad I did. The rebuilt version created beautiful PR comments showing:
Test results summary
Code coverage reports (which I didn't ask for but appreciated)
Detailed breakdowns.
The Finishing Touches
No project is complete without proper status indicators. I added a test status badge to the README, giving anyone visiting the repository immediate visibility into the project's health.
Key Takeaways
What Worked Well
AI as a Learning Partner: Having Copilot explain testing concepts and xUnit features was like having a patient teacher
Iterative Refinement: The back-and-forth process felt natural and productive
Comprehensive Solutions: The AI didn't just write tests; it created a complete testing infrastructure
Quality Over Speed: While it took 4 hours, the result was thorough and well-structured
What I'd Do Differently
Be More Specific Initially: Starting with clearer scope would have streamlined the process
Set Testing Priorities: Identifying critical paths first would have been valuable
Plan for Visual Test Reports: Thinking about test result visualization from the start
Lessons About AI Collaboration
Model Choice Matters: The difference between AI models was significant
Conversation Quality Matters: Clear explanations make the collaboration more valuable
Trust but Verify: Understanding every piece of generated code is crucial
Embrace Iteration: The best results come from multiple refinement cycles
The Bigger Picture
This experiment reinforced my belief that AI coding assistants are most powerful when they're true collaborators rather than code generators. The value wasn't just in the 88 tests that were written, but in the learning that happened along the way.
For developers hesitant about AI assistance in testing: this isn't about replacing your testing skills, it's about augmenting them. The AI handles the boilerplate and suggests patterns, but you bring the domain knowledge and quality judgment.
Conclusion
Would I do this again? Absolutely. The combination of comprehensive test coverage, learning opportunities, and time efficiency made this a clear win. The 4 hours invested created not just tests, but a complete testing infrastructure that will pay dividends throughout the project's lifecycle.
If you're considering AI-assisted testing for your own projects, my advice is simple: start the conversation, be prepared to iterate, and don't be afraid to ask "why" at every step. The goal isn't just working code - it's understanding and owning that code.
The complete test suite and CI/CD pipeline are available in the NoteBookmark repository if you want to see the results of this AI collaboration in action.
