Blog posts and technical reports welcome if you've already written this up!

#DeepSeek #DataLabeling

Particularly valuable would be:

- Production deployment experiences
- Alternatives to system prompts (discouraged with R1)
- Better approaches than few-shot prompting (since this results in lower performance)
- Effective prompt structures and patterns
- R1 vs V3 prompting differences

Will maybe write this up more over on my blog as I dive into how all the pieces work. I fiddled a bit with the learning rate + # of epochs but seems still can improve quite a bit.

Also excited to continue on in the book since the text lessons were something I actually didn't ever get too deeply into when I was doing the course the first time round so excited to dive into LSTMs etc and this whole pre-transformer world!

I generated about 8000 samples (across 100 different prompts, with different temperatures etc) to create the dataset, did a bit of cleanup (to remove `<think>` sections etc) and actually it did quite well! Surprised.

I actually implemented a version that spins up Pyodide / WASM and that lets you do actually run them in browser (CURL commands turned into Python 'requests') but I was uncertain if it would detract from the experience... Maybe I'll just push it and see...

Building this has been a great learning experience about Gemini's capabilities.

As someone who works on documentation at ZenML, I wanted to create something that complements the official resources with a different, example-first approach.

Contributions welcomed! github.com/strickvl/gem...

What you'll find:

- 📚 Concise, functional examples that focus on the 80/20 of what most developers need
- 🧠 Native support for llms.txt, bringing documentation directly into your IDE
- ⬅️ ➡️ Simple keyboard navigation (←/→) between examples
- 🌼 Clean, minimal design for quick reference

The fun part? I used Gemini (2.5-pro, obv) itself to help create many of the 30+ examples! My CLI tool asks for a feature and theme (yes, often cats), then Gemini generates code that I test and refine.

Quarto

I'm genuinely excited to see how teams will use this in their workflows. If you're building ML pipelines, I'd love to hear your thoughts on how conversational interfaces might fit into your development process.

#MLOps #MachineLearning

The current implementation is intentionally read-only (with limited exceptions) as we navigate security considerations. MCP has real promise, but the ecosystem needs further development before reaching its full potential.

With all the recent excitement around Model Context Protocol (MCP), we've built something practical: a way to query your infrastructure, analyze pipeline performance, and trigger runs through simple conversation rather than CLI commands.

@thomasruttig.bsky.social did you read it yet? You’re quoted a LOT

Yes. classic Claude