Stories with Tag AI Assisted Coding

• Zed now predicts your next edit with Zeta, our new open model

  permalink

  Posted: 2025-02-14 06:50:47

  Verbose tl;dr

  Zed, a code editor, has introduced Zeta, an open-source large language model (LLM) designed specifically for predicting code edits. Zeta powers a new "Suggest Edit" feature within Zed that anticipates the user's next change and offers it as a suggestion, potentially streamlining the coding process. Trained on a massive dataset of edits from real-world projects, Zeta understands context and offers increasingly relevant suggestions as you type. This model is available for anyone to download and use, fostering community development and customization for various programming languages and workflows.

  The Zed project has unveiled Zeta, a novel, open-source large language model (LLM) meticulously designed for the purpose of predicting edits within code. This capability is deeply integrated into the Zed editor, enhancing the user experience by proactively suggesting the next changes a developer might make while writing. Zeta's underlying architecture leverages a transformer-based neural network trained on an extensive dataset of real-world code edits, enabling it to learn intricate patterns and conventions across various programming languages. Rather than merely offering generic code completions, Zeta specifically anticipates edits, encompassing insertions, deletions, and modifications of code segments. This predictive editing capability dynamically adapts to the context of the code being written, taking into account the current file, surrounding code, and even the user’s ongoing editing session.

  Zed's integration with Zeta facilitates a more streamlined coding workflow. As developers type, Zeta proactively presents potential edit suggestions, allowing them to accept these predictions with minimal effort. This reduces keystrokes and can accelerate the coding process, especially for repetitive tasks or common code structures. The model's focus on edit prediction, as opposed to general-purpose code generation, allows it to offer more relevant and targeted suggestions, reducing the cognitive load on the developer and minimizing interruptions.

  The open-source nature of Zeta is a key differentiator, fostering transparency and enabling community contributions for further refinement and expansion of the model. This openness also allows researchers and developers to explore the model's inner workings, adapt it to specific coding scenarios, and potentially integrate it into other tools and platforms beyond the Zed editor. The developers highlight Zeta's ability to generate multiple edit predictions simultaneously, offering users a choice of potential next steps. This flexibility further enhances the adaptability and utility of the prediction system. The blog post showcases examples of Zeta's edit predictions in action, demonstrating its practical application and efficacy in a real-world coding environment. The Zed team anticipates that Zeta will significantly contribute to developer productivity and looks forward to community engagement in the ongoing development and evolution of this promising technology.

  • Zed
  • zeta
  • edit prediction
  • Code Editor
  • open model
  • machine learning
  • AI Assisted Coding
  • Software Development
  • developer tools
  • IDE

  Summary of Comments ( 208 )
  https://news.ycombinator.com/item?id=43045606

  Verbose tl;dr

  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.

• Llama.vim – Local LLM-assisted text completion

  permalink

  Posted: 2025-01-23 18:06:42

  Verbose tl;dr

  Llama.vim is a Vim plugin that integrates large language models (LLMs) for text completion directly within the editor. It leverages locally running GGML-compatible models, offering privacy and speed advantages over cloud-based alternatives. The plugin supports various functionalities, including code generation, translation, summarization, and general text completion, all accessible through simple Vim commands. Users can configure different models and parameters to tailor the LLM's behavior to their needs. By running models locally, Llama.vim aims to provide a seamless and efficient AI-assisted writing experience without relying on external APIs or internet connectivity.

  Llama.vim is a Vim plugin that leverages the power of large language models (LLMs) locally, specifically those based on the ggml format like the "llama.cpp" implementation, to provide advanced text completion and generation capabilities directly within the Vim editor. This means users can harness the power of sophisticated AI models for writing, coding, and other text-based tasks without needing an internet connection or relying on external services, preserving privacy and potentially offering faster performance.

  The plugin works by communicating with a locally running instance of a compatible LLM, sending the current buffer content and cursor position as context. The LLM then processes this information and generates completion suggestions which are presented to the user within Vim's familiar completion menu. Users can select the desired completion, or cycle through different options, seamlessly integrating the LLM's output into their workflow.

  Llama.vim boasts several customizable features, allowing users to tailor the behavior of the LLM to their specific needs. This includes adjusting parameters such as the "temperature" (controlling the creativity and randomness of the generated text), the number of tokens to generate, and the specific model to utilize. The plugin also supports prompt engineering through the use of special comments within the Vim buffer, enabling users to provide more specific instructions or context to guide the LLM's generation. Furthermore, it offers features like displaying the probability of suggested completions, allowing users to assess the confidence of the model. The installation process is straightforward, requiring users to have a compatible ggml-based LLM executable and to install the plugin using a standard Vim plugin manager.

  By bringing the power of LLMs directly into the Vim editing environment, Llama.vim aims to significantly enhance productivity and creativity for users engaged in various text-based tasks, offering a privacy-focused and efficient alternative to cloud-based LLM services. It empowers users with sophisticated text generation capabilities without ever leaving their preferred editing environment.

  • vim
  • Neovim
  • LLM
  • Large Language Model
  • Text Completion
  • Code Completion
  • Plugin
  • ggml
  • Local
  • offline
  • AI Assisted Coding
  • machine learning
  • natural language processing

  Summary of Comments ( 21 )
  https://news.ycombinator.com/item?id=42806328

  Verbose tl;dr

  Hacker News users generally expressed enthusiasm for Llama.vim, praising its speed and offline functionality. Several commenters appreciated the focus on simplicity and the avoidance of complex dependencies like Python, highlighting the benefits of a pure Vimscript implementation. Some users suggested potential improvements like asynchronous updates and better integration with specific LLM APIs. A few questioned the practicality for larger models due to resource constraints, but others countered that it's useful for smaller, local models. The discussion also touched upon the broader implications of local LLMs becoming more accessible and the potential for innovative Vim integrations.

  The Hacker News post for Llama.vim, a local LLM-assisted text completion tool, generated a moderate amount of discussion with 19 comments. Many of the comments focus on the practicalities and implications of using local LLMs for coding.

  Several users express enthusiasm for the potential of local LLMs, highlighting the benefits of privacy, speed, and offline availability. One commenter points out that while cloud-based models might offer superior performance, the advantage of local models lies in their ability to work with sensitive data that one wouldn't want to send to a third-party server. This sentiment is echoed by others who appreciate the enhanced privacy and security aspects. The speed advantage of local models is also mentioned, with one user noting that even if cloud latency is only 50ms, it can still disrupt the flow of coding compared to near-instantaneous local responses.

  The discussion also delves into the resource requirements of running LLMs locally. One comment acknowledges the substantial RAM demands of these models but notes that prices for 64GB and even 128GB of RAM are becoming increasingly reasonable. Another user suggests that the ability to run smaller, specialized models locally might be a more practical approach for many users, compared to trying to run the largest, most general models.

  The conversation touches on the broader trend of decentralization and the potential for local LLMs to become a significant part of that movement. One commenter expresses hope that local, personalized AI models will become increasingly prevalent.

  A few comments offer practical advice and observations about the Llama.vim project specifically. One user mentions using a different, unspecified LLM plugin for Vim and highlights its ability to provide inline suggestions as they type. Another user points out that the ggml format, which Llama.vim utilizes, is not necessarily optimal for GPUs and expresses a desire for more readily available quantized models for GPUs.

  Finally, there are some brief comments expressing general interest in the project and its potential. While not offering deep analysis, these comments contribute to the overall positive reception of Llama.vim on Hacker News.