Source URL: https://simonwillison.net/2025/Jan/4/what-we-learned-copying-all-the-best-code-assistants/
Source: Simon Willison’s Weblog
Title: What we learned copying all the best code assistants
Feedly Summary: What we learned copying all the best code assistants
Steve Krouse describes Val Town’s experience so far building features that use LLMs, starting with completions (powered by Codeium and Val Town’s own codemirror-codeium extension) and then rolling through several versions of their Townie code assistant, initially powered by GPT 3.5 but later upgraded to Claude 3.5 Sonnet.
This is a really interesting space to explore right now because there is so much activity in it from larger players. Steve classifies Val Town’s approach as “fast following" – trying to spot the patterns that are proven to work and bring them into their own product.
It’s challenging from a strategic point of view because Val Town’s core differentiator isn’t meant to be AI coding assistance: they’re trying to build the best possible ecosystem for hosting and iterating lightweight server-side JavaScript applications. Isn’t this stuff all a distraction from that larger goal?
Steve concludes:
However, it still feels like there’s a lot to be gained with a fully-integrated web AI code editor experience in Val Town – even if we can only get 80% of the features that the big dogs have, and a couple months later. It doesn’t take that much work to copy the best features we see in other tools. The benefits to a fully integrated experience seems well worth that cost. In short, we’ve had a lot of success fast-following so far, and think it’s worth continuing to do so.
It continues to be wild to me how features like this are easy enough to build now that they can be part-time side features at a small startup, and not the entire project.
Via Hacker News
Tags: prompt-engineering, ai-assisted-programming, val-town, generative-ai, steve-krouse, ai, llms
AI Summary and Description: Yes
Summary: The text discusses Val Town’s development of code assistant features using Large Language Models (LLMs), highlighting the strategic challenge of integrating AI technology within their primary goal of creating a JavaScript application ecosystem. This demonstrates a growing trend where even small startups can effectively leverage AI tools for enhanced software development capabilities.
Detailed Description: The content outlines the journey of Val Town in building features utilizing LLMs for code assistance, marking its evolution from using earlier models like GPT 3.5 to later incorporating Claude 3.5 Sonnet. This reflects a broader movement in the technology space, where integrating sophisticated AI tools is becoming more accessible, even for smaller companies. The main points include:
– **Exploration of AI in Development**: Val Town’s experience represents a significant trend in tech where AI tools and assistance become core components of software development.
– **Strategic Positioning**: The “fast following” strategy is employed by Val Town to quickly adopt and integrate successful features from other tools, which showcases the competitive dynamics of the space.
– **Core Mission vs. AI Features**: There is a noted tension between Val Town’s primary goal of fostering a robust ecosystem for server-side JavaScript applications and the potential distraction of pursuing AI integrations.
– **Integration Benefits**: Despite these challenges, the author suggests that the integration of a web AI code editor could allow Val Town to provide a competitive user experience, with significant advantages outweighing the costs of feature development.
– **Accessibility of AI Features**: The text underscores the decreasing barrier to entry for startups to incorporate advanced features like AI assistance, suggesting a shift in the industry dynamics where innovation in developer tools is no longer limited to large companies.
This insight is particularly relevant for professionals in AI, cloud, and infrastructure security, as it highlights emerging best practices and considerations around integrating AI tools within existing infrastructure, emphasizing the balance between leveraging AI capabilities and maintaining focus on core business objectives.