What is Solveit? Showing some recent use cases

2025-10-03 · Source: Jeremy Howard · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

Solveit is an application described as a dialogue environment for users, code execution, and AI interaction. It functions as a Jupiter style ripple environment where variables are live and modifiable. Solveit allows integrating any Python tool and enables AI to interact with user-defined code and variables. Recent use cases include scraping data from websites, exploring machine learning models, and recreational mathematics, where the AI assists with tasks like extracting information, debugging, searching, and generating plotting code (e.g., Matplotlib). The tool supports an iterative, exploratory programming style, emphasizing incremental steps and human-computer collaboration rather than full AI automation. The author highlights its utility for tasks like data analysis for civil engineering, including generating regular expressions and performing geometric calculations like convex hulls. A new "Solveit" course is announced, with future pay-as-you-go subscriptions for cloud instances and AI access.

Key takeaway

For data scientists or ML engineers exploring new data or debugging models, Solveit offers a collaborative environment to accelerate iterative development. You can leverage its live REPL and AI assistance to quickly extract information, generate plotting code, or validate complex calculations like convex hulls. Consider adopting this human-in-the-loop approach to enhance your exploratory programming workflow and improve problem-solving efficiency.

Key insights

Solveit is an interactive REPL environment integrating live code execution with AI assistance for exploratory programming.

Principles

• Iterative, exploratory programming enhances understanding.
• AI acts as a co-worker, not a replacement.
• Live environments facilitate rapid testing and debugging.

Method

Integrate Python tools into a live REPL, define variables, and engage AI for assistance with data extraction, debugging, search, and code generation.

In practice

• Scrape web data with AI assistance.
• Debug ML models and explore mathematical concepts.
• Analyze engineering data, generate regex, perform convex hulls.

Topics

• Interactive Development Environments
• AI-assisted Programming
• REPL
• Exploratory Data Analysis
• Machine Learning Research
• Python Tools

Best for: AI Engineer, AI Scientist, Research Scientist, Machine Learning Engineer, Data Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Jeremy Howard.