Steve Yegge is highly impressed with Claude Code, a new coding assistant. He finds it significantly better than GitHub Copilot, praising its superior reasoning abilities, ability to follow complex instructions, and aptitude for refactoring. He highlights its proficiency in Python but notes its current weakness with JavaScript. Yegge believes Claude Code represents a leap forward in AI coding assistance and predicts it will transform programming practices.
Software engineer Steve Yegge has published an effusive preliminary review of Claude Code, a new code generation tool from Anthropic, based on his experiences using it for a couple of days. He prefaces his remarks by acknowledging the rapidly evolving landscape of AI coding assistants and the possibility that Claude Code might be surpassed quickly. Nevertheless, he expresses a strong belief that Claude Code represents a significant leap forward in the field.
Mr. Yegge highlights several key advantages of Claude Code. He finds its code quality noticeably superior to that of GitHub Copilot, specifically mentioning fewer hallucinations and a greater aptitude for producing correct and functional code. He emphasizes that this improved accuracy translates to a substantial reduction in debugging time, a major boon for developers.
Beyond code generation, Mr. Yegge lauds Claude Code's proficiency in understanding natural language prompts. He describes providing the tool with complex, multi-step instructions involving a variety of tasks, including code generation, analysis, explanation, and documentation, and reports that Claude Code executes these instructions with impressive competence. This sophisticated understanding of natural language, he argues, allows for a more fluid and intuitive interaction with the AI assistant.
The author elaborates on Claude Code's ability to handle longer contexts, citing an example of processing 100,000 lines of code, albeit with some caveats about potential instability. He contrasts this capability with the limitations of other models, suggesting that Claude Code's capacity for handling extensive codebases opens new possibilities for large-scale code analysis and manipulation.
Furthermore, Mr. Yegge expresses enthusiasm for Claude Code's potential as a debugging aid. He describes using the tool to diagnose and fix issues in his own code with considerable success, praising its ability to pinpoint problems and propose effective solutions.
Overall, Mr. Yegge portrays Claude Code as a highly promising development in the realm of AI-powered coding tools. While acknowledging the nascent stage of this technology, he believes that Claude Code's superior code quality, robust natural language understanding, and impressive context handling capabilities represent a substantial advancement over existing alternatives and portend a significant shift in the way software is developed. He concludes with a strong recommendation for developers to experiment with Claude Code and experience its capabilities firsthand.
Summary of Comments ( 123 )
https://news.ycombinator.com/item?id=43307809
Hacker News users discussing their experience with Claude Code generally found it impressive. Several commenters praised its ability to handle complex instructions and multi-turn conversations, with some even claiming it surpasses GPT-4 in certain areas like code generation and maintaining context. Others highlighted its strong reasoning abilities and fewer hallucinations compared to other LLMs. However, some users expressed caution, pointing out potential limitations in specific domains like math and the lack of access for most users. The cost of Claude Pro was also a topic of discussion, with some debating its value compared to GPT-4. Overall, the sentiment leaned towards optimism about Claude's potential while acknowledging its current limitations and accessibility issues.
The Hacker News post "I've been using Claude Code for a couple of days" (linking to a 2011 tweet about an internal Google coding tool) sparked a discussion thread with several insightful comments. Many commenters noted the historical context of the tweet, highlighting that it originated in 2011 and referred to an internal Google tool, not the more recently released Anthropic Claude.
Several commenters expressed a sense of nostalgia, remembering the internal Google tool fondly and reminiscing about its capabilities. They pointed out features like its code search, documentation integration, and refactoring capabilities. One commenter mentioned how valuable such a tool is internally at Google, enabling developers to easily navigate and understand the company's massive codebase. They also expressed a wish for similar tools to be publicly available.
A recurring theme in the comments was the difficulty of building and maintaining such comprehensive code analysis and assistance tools. Commenters discussed the challenges of scaling these tools to handle the complexity of real-world codebases and the ongoing effort required to keep them up-to-date with evolving languages and frameworks.
Some users discussed the various attempts to create similar tools outside of Google, acknowledging both successful projects and those that have fallen short. They mentioned tools like Kythe, which aims to provide a standardized platform for code analysis, and other open-source efforts aimed at replicating some of the functionality of internal Google tools.
The discussion also touched upon the importance of code intelligence tools for developer productivity and how they can significantly reduce the cognitive load associated with navigating large and complex codebases. Commenters speculated on why more tools of this caliber haven't emerged publicly, suggesting factors like the high development cost and the challenge of effectively monetizing such tools. There was also a discussion on how companies often keep these kinds of powerful internal tools proprietary to maintain a competitive advantage.
Finally, some users drew parallels between the capabilities described in the tweet and more recent advancements in AI-powered coding assistants, like GitHub Copilot and the aforementioned Anthropic Claude, highlighting the progress made in this domain over the past decade. They wondered how these tools compared to Google's internal tools and expressed hope for even more powerful and accessible code intelligence tools in the future.