First autonomous ML and AI engineering Agent
Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
First autonomous ML and AI engineering Agent launched on January 22, 2026 with 5 votes, #3,400 of 14,584 launches that month and more than 75% of that year's launches.
What it does
Extension for Visual Studio Code - Neo is an autonomous AI agent for machine learning engineers. Train, fine-tune, test and deploy ML workflows from natural language in VS Code. Iterate on real ML experiments, from data to deployment.
Neo is an autonomous AI agent designed specifically for Machine learning engineers, AI engineers and data scientists . Neo doesn’t just help you write ML code, it executes experiments, evaluates models, and iterates like a real ML engineer. From training deep learning models and fine-tuning LLMs to building LLM and RAG pipelines and deploying production ready AI systems. Works seamlessly with PyTorch, TensorFlow, Hugging Face, and modern AI frameworks using natural language. 🤖 Gen AI & LLM Engineers : Build RAG systems, fine-tune latest models (Llama 3, Qwen, Gemma), deploy production LLM applications. 🎯 Data Scientists & ML Engineers : Train deep learning models, experiment…from marketplace.visualstudio.com
In the maker’s words, at launch
Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate…
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