ML Intern transforms machine learning tasks into actionable run briefs, integrating insights from academic papers, Hub searches, GitHub inspections, and HF Jobs. It's designed for makers and tech enthusiasts looking to streamline their ML workflows and enhance project clarity.
We started building this after seeing how much energy gets lost when an important workflow depends on too many disconnected tools and too much manual cleanup.
With ML Intern, we wanted to create a turn an ML task into a usable run brief shaped around papers, Hub search, GitHub inspection, and HF Jobs that feels more practical in everyday use, easier to explain to a team, and easier to learn from over time.
We're still shaping it with real feedback, so if anything feels especially useful, confusing, or missing, we'd genuinely love to hear your thoughts.
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