DataTopics: All Things Data, AI & Tech #81 AI Code Assistants: The Good, The Bad & The Overhyped, plus Python’s UV Glow-Up & Postman’s Existential Crisis
Feb 27, 2025
Discover the latest in AI-assisted coding, exploring how LLMs can enhance or complicate the programming world. Dive into tools like Cline and Cursor, debating their impact on developer productivity. Reflect on the evolution of Python's UV package manager and its emerging role in the community. Consider the balance between software quality and user experience, along with the importance of human intuition in a tech-driven landscape. Plus, enjoy some light-hearted generational humor about how slang evolves over time!
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Three-Step LLM Greenfield Workflow
- Structure Greenfield work: start with idea-honing, then planning, then execution using LLMs to generate skeletons.
- Save the spec and to-dos so generated code maps to concrete tasks you can review and test.
Limit LLM Access And Review Diffs
- Give LLMs contextual access to the codebase when editing legacy projects so they produce relevant changes.
- Restrict read/write permissions and review diffs to avoid opaque, large-scale edits that are hard to understand.
You Can Get 'Over Your Skis' With LLMs
- Powerful LLM edits can leave you "over your skis" with many simultaneous changes that are hard to trace.
- Comfort and strong testing practices become essential when accepting large AI-generated code outputs.