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In this article I wrote for my newsletter (Entry State), I share how I’m approaching Scripting in Rive. Not from a “here’s the method” angle, but more like a set of messy field notes.
I’ve been learning in a very exploratory way, and I wanted to share what’s been working for me so far.
Inside you’ll find:
● How I explored different tools when AI credits ran out
● What helped me prompt better and spend fewer credits
● The fundamentals I’m learning and how I’m practicing them
If you’re also learning Scripting, I hope you can find something useful! 👇
linkedin.com
Finding my Scripting workflow in Rive
A designer’s field notes A few weeks ago, I shared my first impressions of Scripting in Rive. In that post, I talked about what I called the “blind‑coding” stage — those first attempts where you throw ideas at the Agent, watch things appear almost instantly, and think: okay, this is both amazing and
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#characteranimation #animation #riveanimator #interactiveanimation
Smooth and cute!
Small interactions can carry a lot of weight.
I designed and implemented this @Rive animated character for Rideaway’s analytics consent banner.
The goal was not to push people toward “Accept”. It was to make an easily ignored privacy decision more noticeable, more human, and worth a moment of attention, while keeping both choices equally clear.
See it live: https://rideaway.app
Does motion improve this moment, or would you prefer a completely static consent banner?
🚀 AI Engineer | AI Developer | GenAI & LLM Systems
AI should do more than generate text — it should solve real business problems.
I build production-ready AI systems that combine LLMs, RAG, AI Agents, automation, and cloud infrastructure to turn complex workflows into intelligent products.
🔹 What I build
→ AI Agents & multi-agent systems
→ RAG / knowledge assistants
→ LLM-powered applications
→ Document & data intelligence
→ AI automation workflows
→ NLP & Generative AI solutions
→ Cloud-based AI platforms
🔹 Tech stack
Python • PyTorch • LangChain • LlamaIndex • OpenAI • Azure AI • GCP • AWS • RAG • Vector Databases • Docker • Kubernetes • React • TypeScript
I focus on the full lifecycle — from AI architecture and prototyping to deployment and production optimization.
Looking for someone who can take an AI idea from “this could be useful” → “this is running in production”?
📩 Let’s build it.
#AI #ArtificialIntelligence #AIAgents #GenerativeAI #LLM #RAG #MachineLearning #AIEngineer #Python #AzureAI #GCP #Freelance
@Norbert Łaszanowski Covering architecture through production optimization is a useful distinction. How do you typically evaluate RAG quality before deploying on Azure or GCP?