After over a decade using Vim/Neovim, the author experimented with Zed, a new electron-based editor. While appreciating Zed's native performance, smooth scrolling, and collaborative features, the author found the Vim mode lacking compared to their highly customized Neovim setup. Specifically, plugins and keybindings didn't translate seamlessly, hindering their workflow. Although impressed by Zed's potential, particularly its speed and built-in collaboration, the author ultimately returned to Neovim, finding its flexibility and familiarity more valuable than Zed's current advantages. They remain optimistic about Zed's future and plan to revisit it as it matures.
Shantanu Goel, a long-time Vim/Neovim user with over a decade of experience, recounts his initial exploration of Zed, a collaborative code editor built using Rust and WebAssembly. Having deeply ingrained Vim habits, he approaches Zed with a mixture of curiosity and skepticism, eager to see if it can live up to the hype and potentially offer a compelling alternative to his established workflow.
Goel begins by acknowledging the impressive performance of Zed, particularly its speed and responsiveness. He highlights the near-instant startup time, a stark contrast to his experience with increasingly complex and sometimes sluggish Neovim setups. This immediate responsiveness extends to general usage, with smooth scrolling and quick operations, even with large files. He attributes this performance to Zed's architectural foundation, built on Rust and leveraging WebAssembly.
Despite appreciating Zed's performance, Goel encounters several friction points stemming from his entrenched Vim muscle memory. He details his struggles adapting to Zed's different modal editing approach. While superficially similar to Vim, subtle variations in command execution and keybindings create a learning curve. Specifically, he mentions the necessity of using "Escape" to exit insert mode, as opposed to other viable Vim options like "Ctrl-[", a nuance that disrupts his ingrained workflow. He also notes the different behavior of the "." (dot) command, which repeats the last action, and observes that it doesn't always function as expected in Zed compared to his Vim experience.
Further, Goel explores Zed's built-in collaboration features, which are a core selling point of the editor. While recognizing the potential of real-time collaborative editing, he expresses reservations about its practical implementation. He finds the collaborative experience somewhat cumbersome, particularly in managing shared cursors and navigating the presence of multiple users within the same document.
Beyond the collaboration aspects, Goel also examines Zed's plugin ecosystem. Coming from the rich and extensively developed Vim plugin environment, he finds Zed's offerings comparatively limited. He acknowledges that Zed is still in its early stages of development, and therefore anticipates growth in this area. However, the current lack of comprehensive plugin support presents a significant obstacle for a user accustomed to the extensive customization and functionality offered by Vim plugins.
Finally, Goel concludes his initial assessment of Zed with a balanced perspective. He recognizes the editor's promising performance and the innovative potential of its collaborative features. However, he emphasizes that, for a seasoned Vim user like himself, overcoming deeply ingrained habits and adapting to a new editing paradigm presents a significant challenge. He remains open to further exploration of Zed as it matures, particularly as its plugin ecosystem expands, but for now, he intends to stick with his well-established and highly customized Neovim environment. He implies that Zed might be more readily adopted by users who are not already heavily invested in the Vim ecosystem.
Summary of Comments ( 208 )
https://news.ycombinator.com/item?id=43045606
Hacker News users generally expressed enthusiasm for Zed's new edit prediction feature powered by the Zeta model. Several praised the speed and accuracy of the predictions, noting its potential to significantly improve coding workflow. Some discussed the implications of open-sourcing the model, hoping it would foster community contributions and adaptations for other editors. A few questioned the licensing details of the open-sourced components and how they relate to Zed's overall business model. Others drew comparisons to existing AI-powered coding assistants like GitHub Copilot, speculating on Zeta's potential competitive advantages and disadvantages. Finally, some expressed interest in how the model handles complex edits beyond simple completions, like refactoring and debugging.
The Hacker News post discussing Zed's new edit prediction feature using the Zeta model generated a moderate amount of discussion, with a mix of praise, skepticism, and technical curiosity.
Several commenters expressed excitement about the potential of AI-assisted coding and saw this as a significant step forward. One commenter likened it to "autocomplete on steroids" and anticipated its usefulness in streamlining coding workflows. Another appreciated Zed's commitment to open-sourcing the model, emphasizing the benefits for community involvement and improvement. The potential for reducing repetitive coding tasks and improving overall developer productivity was a recurring theme.
However, some users voiced concerns about the practical implementation and potential downsides. One commenter questioned the model's ability to handle complex codebases and larger edits, expressing doubt that it could accurately predict beyond simple changes. Another user raised the issue of potential over-reliance on such tools, speculating that it might hinder developers from fully understanding the code they are working with, leading to a decline in overall code quality. The possibility of the model introducing subtle bugs or making incorrect predictions that go unnoticed was also brought up as a potential drawback.
A few commenters delved into more technical aspects. One asked about the specific architecture of the Zeta model and how it differs from other large language models used for code generation. Another inquired about the training data used and whether it included code from private repositories, raising privacy concerns. There was also discussion about the latency of the predictions and how it might impact the user experience, with some suggesting that real-time performance is crucial for such a feature to be truly useful.
Finally, some commenters offered suggestions for improvement, such as incorporating support for more programming languages and integrating with other popular code editors. One user suggested the possibility of training the model on a user's specific coding style to further personalize the predictions.
Overall, the comments reflect a cautious optimism about the potential of AI-powered edit prediction while acknowledging the challenges and potential pitfalls that need to be addressed. The open-source nature of the project and the active engagement of the community suggest that these concerns will be explored and potentially mitigated as the technology continues to develop.