Source URL: https://simonwillison.net/2025/Apr/1/half-stack-data-science/
Source: Simon Willison’s Weblog
Title: Half Stack Data Science: Programming with AI, with Simon Willison
Feedly Summary: Half Stack Data Science: Programming with AI, with Simon Willison
I participated in this wide-ranging 50 minute conversation with David Asboth and Shaun McGirr. Topics we covered included applications of LLMs to data journalism, the challenges of building an intuition for how best to use these tool given their “jagged frontier" of capabilities, how LLMs impact learning to program and how local models are starting to get genuinely useful now.
At 27:47:
If you’re a new programmer, my optimistic version is that there has never been a better time to learn to program, because it shaves down the learning curve so much. When you’re learning to program and you miss a semicolon and you bang your head against the computer for four hours […] if you’re unlucky you quit programming for good because it was so frustrating. […]
I’ve always been a project-oriented learner; I can learn things by building something, and now the friction involved in building something has gone down so much […] So I think especially if you’re an autodidact, if you’re somebody who likes teaching yourself things, these are a gift from heaven. You get a weird teaching assistant that knows loads of stuff and occasionally makes weird mistakes and believes in bizarre conspiracy theories, but you have 24 hour access to that assistant.
If you’re somebody who prefers structured learning in classrooms, I think the benefits are going to take a lot longer to get to you because we don’t know how to use these things in classrooms yet. […]
If you want to strike out on your own, this is an amazing tool if you learn how to learn with it. So you’ve got to learn the limits of what it can do, and you’ve got to be disciplined enough to make sure you’re not outsourcing the bits you need to learn to the machines.
Via @halfstackdatascience.com
Tags: podcasts, generative-ai, podcast-appearances, ai, llms, data-journalism
AI Summary and Description: Yes
Summary: The text highlights the role of Large Language Models (LLMs) in facilitating programming learning, emphasizing their potential to reduce complexities and enhance self-directed education in a project-oriented manner. It also suggests a disparity in effectiveness for structured classroom learning versus independent study.
Detailed Description: The conversation featuring Simon Willison delves into the evolving landscape of programming education through the lens of LLMs, addressing both the opportunities and challenges they present. Key points include:
– **Enhanced Learning Curve**: The introduction of LLMs as tools that streamline the programming process; they mitigate common frustrations, such as syntax errors, making it easier for new programmers to overcome learning hurdles.
– **Autodidactic Advantages**: Emphasizes the support LLMs provide for self-taught learners, positioning them as effective, on-demand teaching assistants that offer continuous assistance and resources.
– **Challenges in Formal Education**: Highlights a lag in structured learning environments as institutions grapple with integrating LLMs into their curricula effectively. The text points out that it may take time for these benefits to be fully realized in classroom settings.
– **Need for Understanding and Discipline**: Encourages learners to be aware of the limitations of LLMs, advocating for a balanced approach where students do not overly rely on AI for essential learning processes.
Overall, the discussion promotes a positive outlook on using AI technologies in the programming domain while addressing crucial considerations for learners and educators alike in adapting to these advancements.