The gap between “I want to build something with AI” and actually shipping it is still huge—fragmented tooling, constant model churn, tutorials that assume you already know everything. So how do you figure out what n8n project to pursue or why your agent keeps hallucinating? Or even what tools are most useful for your AI projects, no matter the scale?

We're reinvigorating our Dev.to account for putting AI tutorials and technical deep dives in your feed, straight from our technical writing staff. Look forward to guides on topics like running DeepSeek R1 on a GPU Droplet, debugging LangGraph agents, and setting up vLLM for inference—plus video walkthroughs when reading isn't enough.

At DigitalOcean, we're not just a cloud company anymore. With Gradient—our unified AI cloud—we're giving developers GPU infrastructure and an agent platform for running inference at scale. And we've learned a lot along the way that we want to share.

This will include:

Breakdowns of new models, benchmarks, and technical concept foundations.
Tutorials on deploying AI tools, LLM fine-tuning and memory management, and AI agent creation.
Step-by-step articles on model debugging, data control, reinforcement learning, and using DigitalOcean infrastructure.
Videos on vLLM deployment, LangGraph agents, and n8n automation.

How exactly do vibe coding tools work to create applications? Get a rundown of how to use Lovable and n8n to generate a Yuka-style app—and download the prompts to test it out for yourself.

If you want to keep up with our hands-on tutorials, video walkthroughs, and DigitalOcean best practices, stick around; you might even catch a few discussions, contests, or memes in your feed.

StackFeed

DigitalOcean on Dev.to: Practical AI Insights for Builders