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I’ve been working on ADK-Rust, an open-source framework for building and deploying AI agents in Rust.
The motivation came from building agent systems where performance, safety, and predictable behavior mattered more than rapid prototyping. Most agent frameworks and workflow tools today are Python- or JS-first and tend to be runtime-heavy when taken to production.
Recently, I added ADK-Studio — a visual, low-code environment for building AI agent workflows on top of ADK-Rust.
You can think of ADK-Studio as a Rust-native alternative to tools like n8n, but focused specifically on AI agents: - Visual, drag-and-drop workflow design (sequential, parallel, loop, router agents) - Tool integration (functions, MCP servers, browser automation, search) - Real-time execution with SSE streaming and event traces - Code generation: visual workflows compile down to production Rust code - Build and run agents as native executables directly from the studio
The goal is to let people prototype agent workflows visually, then ship them as fast, memory-safe Rust binaries instead of long-running JS/Python services.
Making AI Agents with ADK Studio is super simple:
1. ADK-Studio install: `cargo install adk-studio` 2. Start ADK Studio server: `adk-studio --port 6000` 3. Open in browser: open http://localhost:6000
I would really appreciate feedback from folks building agent systems, workflow engines, or AI inference infrastructure — especially around design tradeoffs vs existing tools like n8n.
Project site: https://adk-rust.com GitHub: https://github.com/zavora-ai/adk-rust
Best James
Facing financial challenges and nearing bankruptcy, I decided to document my ideas in a book and sell it on Amazon KDP. The first book, "The Complete LangGraph Blueprint - Build 50+ AI Agents for Business Success", sold over 300 copies in just four weeks and became a best new release in December after being primarily marketed on HN. That reception gave me hope, and I’m deeply grateful to the HN community for your support!
Encouraged by this, I published another book, "People VS AI: The Radical Business Case for Artificial Intelligence Adoption", which also hit #1 in the "Industrial Business Management" category on Amazon (it's still hanging up there on the top spot!)
Now, I’m excited to share my third book: "The Complete Hugging Face Blueprint: A Hands-On Guide to Developing and Deploying Models on Hugging Face with 150+ Practical Lessons." It’s FREE for the next two days (save $49) to encourage your feedback and reviews: Grab it here: https://www.amazon.com/Complete-Hugging-Face-Blueprint-Hands...
This book focuses on practical machine learning without overwhelming theory or math. A key highlight is the Hugging Face Inference API, which I’ve dedicated an entire chapter to. From the feedback I have received so far, it seems few developers know about the Hugging Face Inference API. And they find it amazing when they discover it. I have dedicated a whole chapter in the book to this topic and gladly sharing this below:
What is the Hugging Face Inference API? The Hugging Face Inference API is a user-friendly way to access pre-trained models on the Hugging Face Hub for tasks like: Text classification, summarization, translation, Q&A, Image classification, object detection, Audio transcription and much more.
Key Features: No Deployment Needed: Direct access to thousands of models. Scalability: Handles high-volume requests in real-time. Multi-Framework Support: Works with TensorFlow, PyTorch, and JAX models. Enterprise-Grade Security: Reliable and secure. Low Resources: Does not require GPUs or expensive hardware.
Common Use Cases: Integrating AI into web/mobile apps. Prototyping ML solutions. Real-time predictions for text, image, and audio tasks.
Setting Up the Inference API Step 1: Get an API Token Log in to Hugging Face. Navigate to your "Access tokens" page under settings and generate a token. Step 2: Install the python client pip install huggingface_hub Step 3: Authenticate from huggingface_hub import login login("your_api_token") Example: Sentiment Analysis from huggingface_hub import InferenceClient
client = InferenceClient(model="distilbert-base-uncased-finetuned-sst-2-english") response = client.text_classification("I love using Hugging Face APIs!") print(response) Example: Image Classification client = InferenceClient(model="microsoft/resnet-50") response = client.image_classification("./example.jpg") print(response)
I’ve included over 20 practical examples for real-life use cases in the book. If you’re curious about leveraging Hugging Face or learning Machine Learning through building practical ML applications, I’d love for you to check it out, learn, and share your review! Your support not only helps me improve but also fuels my dream of building Zavora.ai, an AI Agent startup. Thanks so much, HN, for being an amazing community!
Grab the book: https://www.amazon.com/Complete-Hugging-Face-Blueprint-Hands...
Best, James Karanja
If your boss or co-founder is into tech, AI Agents, or just loves a thought-provoking read, you’ve got to check out People vs AI on Amazon. It’s the ultimate Christmas gift – a deep dive into how AI is reshaping businesses, job roles, and decision-making.
This book is perfect for anyone budgeting for AI Agents in 2025 or just curious about how AI will impact their industry. Whether they’re an exec, a founder, or just someone fascinated by AI’s potential, this book delivers insights they’ll appreciate.
Here’s the kicker: the Kindle version is FREE for the next two days (yes, free!). If you’re like me and love the feel of a real book, the paperback is just $19.99. A thoughtful and affordable gift for any tech enthusiast—or even yourself (because why not?).
Grab it here: https://www.amazon.com/dp/B0DPN1YHFW
Merry Christmas, HN!
Grab the book: https://www.amazon.com/dp/B0DP69QV7K