Heyneo.so – We built the first autonomous Machine Learning Engineer
NEO is an autonomous machine learning engineering AI agent capable of implementing complex ML tasks - From data cleaning, preprocessing, handling structural issues to model exploration, training, optimization with its own reasoning and code execution capabilities. We are soon releasing our early beta access. Join our waitlist. Would highly appreciate your feedback and use-cases you could potentially use NEO for.
Heyneo.so – We built the first autonomous Machine Learning Engineer launched on February 28, 2025 with 6 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
NEO helps AI engineers build RAG systems, run LLM evals, create ML pipelines, debug AI workflows, and ship production AI faster across Claude Code, Cursor, VS Code, and web.
AI agent for RAG pipelines, LLM evals, prompt optimization, fine-tuning, and ML training. Works with Claude Code, Cursor, and VS Code IDE. Bring Neo into your editor, connect it to Claude Code over MCP, or run it in the cloud with zero setup. Pick how you want to build. Install the Neo extension and delegate ML engineering — RAG systems, LLM evals, fine-tuning, and pipelines — without leaving VS Code or Cursor. NEO helps you build, test, and improve AI systems from a single task prompt. It can research approaches, write code, run experiments, debug failures, benchmark models, evaluate outputs, and produce reports. Instead of managing scripts, logs, evals, and manual fixes yourself, NEO…from heyneo.so
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First autonomous ML and AI engineering AgentJan 2026 · marketplace.visualstudio.com · ▲5Founder 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…
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