anon-kode is an open-source fork of Claude-code, a large language model designed for coding tasks. This project allows users to run the model locally or connect to various other LLM providers, offering more flexibility and control over model access and usage. It aims to provide a convenient and adaptable interface for utilizing different language models for code generation and related tasks, without being tied to a specific provider.
Anthropic has announced Claude 3.7, their latest large language model, boasting improved performance across coding, math, and reasoning. This version demonstrates stronger coding abilities as measured by Codex HumanEval and GSM8k benchmarks, and also exhibits improvements in generating and understanding creative text formats like sonnets. Notably, Claude 3.7 can now handle longer context windows of up to 200,000 tokens, allowing it to process and analyze significantly larger documents, including technical documentation, books, or even multiple codebases at once. This expanded context also benefits its capabilities in multi-turn conversations and complex reasoning tasks.
Hacker News users discussed Claude 3.7's sonnet-writing abilities, generally expressing impressed amusement. Some debated the definition of a sonnet, noting Claude's didn't strictly adhere to the form. Others found the code generation capabilities more intriguing, highlighting Claude's potential for coding assistance and the possible disruption to coding-related professions. Several comments compared Claude favorably to GPT-4, suggesting superior performance and a less "hallucinatory" output. Concerns were raised about the closed nature of Anthropic's models and the lack of community access for broader testing and development. The overall sentiment leaned towards cautious optimism about Claude's capabilities, tempered by concerns about accessibility and future development.
Onit is an open-source desktop application providing a unified interface for various large language models (LLMs), including ChatGPT, Claude, Gemini, and local models. It aims to simplify access and management of these models, offering features like prompt templates, conversation history, and an intuitive user interface. The project is available on GitHub and designed to be extensible, allowing users to easily integrate new models and features.
HN users generally expressed enthusiasm for Onit, praising its clean UI, open-source nature, and support for multiple LLMs (including local models). Several commenters highlighted the value of running models locally for privacy and cost savings, with specific interest in the upcoming support for llama.cpp. Some pointed out existing similar projects like llama-gpt and queried about Onit's differentiating features. A few users requested additional functionality, such as better prompt management and the ability to export chat logs. The developer actively engaged with comments, addressing questions and acknowledging feature requests.
Summary of Comments ( 17 )
https://news.ycombinator.com/item?id=43254351
Hacker News users discussed the potential of anon-kode, a fork of Claude-code allowing local and diverse LLM usage. Some praised its flexibility, highlighting the benefits of using local models for privacy and cost control. Others questioned the practicality and performance compared to hosted solutions, particularly for resource-intensive tasks. The licensing of certain models like CodeLlama was also a point of concern. Several commenters expressed interest in contributing or using anon-kode for specific applications like code analysis or documentation generation. There was a general sense of excitement around the project's potential to democratize access to powerful coding LLMs.
The Hacker News post "Show HN: Fork of Claude-code working with local and other LLM providers" (https://news.ycombinator.com/item?id=43254351) sparked a brief but interesting discussion with a few key points raised.
One commenter expressed skepticism about the practical usefulness of local LLMs for coding tasks, arguing that the quality difference compared to cloud-based models like GPT-4 is significant enough to negate the benefits of local processing, especially given the increasing availability of cheaper cloud alternatives. They specifically mentioned that even if local models eventually catch up in performance, the convenience and speed of cloud-based models might still be preferable.
Another commenter highlighted the licensing issue, pointing out that closed-source models can't be used commercially. They argued that this is a major drawback, especially for companies, and that this restriction limits the utility of projects like this one. They implied that open-source models are essential for broader adoption in commercial settings.
A third commenter explored the potential advantages of local models for specific niche use cases, suggesting that even with lower quality, they could be valuable for tasks like code suggestion or autocompletion within a local IDE, particularly if the codebase being worked on is sensitive and cannot be shared with external cloud services. They mentioned that speed and privacy are the primary drivers for such use cases.
Finally, the original poster (OP) responded to some of the comments, acknowledging the current limitations of local LLMs compared to cloud-based options but expressing optimism about the rapid pace of improvement in open-source LLMs. They also clarified the project's aim, emphasizing that it’s focused on providing a framework for using different LLMs locally rather than promoting any specific local model. They seem hopeful that this approach will become more compelling as local LLM technology matures.
In summary, the discussion revolved around the trade-offs between cloud-based and local LLMs for coding, with commenters highlighting the current performance gap, licensing restrictions, and potential niche applications of local models. The OP defended the project by focusing on its flexibility and the future potential of local LLMs.