Kagi's AI assistant, previously in beta, is now available to all users. It aims to provide a more private and personalized search experience by focusing on factual answers, incorporating user feedback, and avoiding generic chatbot responses. Key features include personalized summarization of search results, the ability to ask clarifying questions, and ad-free, unbiased information retrieval powered by Kagi's independent search index. Users can access the assistant directly from the search bar or a dedicated sidebar.
Plandex v2 is an open-source AI coding agent designed for complex, large-scale projects. It leverages large language models (LLMs) to autonomously plan and execute coding tasks, breaking them down into smaller, manageable sub-tasks. Plandex uses a hierarchical planning approach, refining plans iteratively and adapting to unexpected issues or changes in requirements. The system also features error detection and debugging capabilities, automatically retrying failed tasks and adjusting its approach based on previous attempts. This allows for more robust and reliable autonomous coding, particularly for projects exceeding the typical context window limitations of LLMs. Plandex v2 aims to be a flexible tool adaptable to various programming languages and project types.
Hacker News users discussed Plandex v2's potential and limitations. Some expressed excitement about its ability to manage large projects and integrate with different tools, while others questioned its practical application and scalability. Concerns were raised about the complexity of prompts, the potential for hallucination, and the lack of clear examples demonstrating its capabilities on truly large projects. Several commenters highlighted the need for more robust evaluation metrics beyond simple code generation. The closed-source nature of the underlying model and reliance on GPT-4 also drew skepticism. Overall, the reaction was a mix of cautious optimism and pragmatic doubt, with a desire to see more concrete evidence of Plandex's effectiveness on complex, real-world projects.
Geoffrey Litt created a personalized AI assistant using a simple, yet effective, setup. Leveraging a single SQLite database table to store personal data and instructions, the assistant uses cron jobs to trigger automated tasks. These tasks include summarizing articles from his RSS feed, generating to-do lists, and drafting emails. Litt's approach prioritizes hackability and customizability, allowing him to easily modify and extend the assistant's functionality according to his specific needs, rather than relying on a complex, pre-built system. The system relies heavily on LLMs like GPT-4, which interact with the structured data in the SQLite table to generate useful outputs.
Hacker News users generally praised the simplicity and hackability of the AI assistant described in the article. Several commenters appreciated the "dogfooding" aspect, with the author using their own creation for real tasks. Some discussed potential improvements and extensions, like using alternative databases or incorporating more sophisticated NLP techniques. A few expressed skepticism about the long-term viability of such a simple system, particularly for complex tasks. The overall sentiment, however, leaned towards admiration for the project's pragmatic approach and the author's willingness to share their work. Several users saw it as a refreshing alternative to overly complex AI solutions.
Anthropic has announced that its AI assistant, Claude, now has access to real-time web search capabilities. This allows Claude to access and process information from the web, enabling more up-to-date and comprehensive responses to user prompts. This new feature enhances Claude's abilities across various tasks, including summarization, creative writing, Q&A, and coding, by grounding its responses in current information. Users can now expect Claude to deliver more factually accurate and contextually relevant answers by leveraging the vast knowledge base available online.
HN commenters discuss Claude's new web search capability, with several expressing excitement about its potential to challenge Google's dominance. Some praise Claude's more conversational and contextual search results compared to traditional keyword-based approaches. Concerns were raised about the lack of source links in the initial version, potentially hindering fact-checking and further exploration. However, Anthropic quickly responded to this criticism, stating they were actively working on incorporating source links and planned to release the feature soon. Several users noted Claude's strengths in summarizing and synthesizing information, suggesting its potential usefulness for research and complex queries. Comparisons were made to Perplexity AI, another conversational search engine, with some users finding Claude more conversational and less prone to hallucinations. There's general optimism about the future of AI-powered search and Claude's role in it.
Microsoft has introduced Dragon Ambient eXperience (DAX) Copilot, an AI-powered assistant designed to reduce administrative burdens on healthcare professionals. It automates note-taking during patient visits, generating clinical documentation that can be reviewed and edited by the physician. DAX Copilot leverages ambient AI and large language models to create summaries, suggest diagnoses and treatments based on doctor-patient conversations, and integrate information with electronic health records. This aims to free up doctors to focus more on patient care, potentially improving both physician and patient experience.
HN commenters express skepticism and concern about Microsoft's Dragon Copilot for healthcare. Several doubt its practical utility, citing the complexity and nuance of medical interactions as difficult for AI to handle effectively. Privacy is a major concern, with commenters questioning data security and the potential for misuse. Some highlight the existing challenges of EHR integration and suggest Copilot may exacerbate these issues rather than solve them. A few express cautious optimism, hoping it could handle administrative tasks and free up doctors' time, but overall the sentiment leans toward pragmatic doubt about the touted benefits. There's also discussion of the hype cycle surrounding AI and whether this is another example of overpromising.
Google's AI-powered tool, named RoboCat, accelerates scientific discovery by acting as a collaborative "co-scientist." RoboCat demonstrates broad, adaptable capabilities across various scientific domains, including robotics, mathematics, and coding, leveraging shared underlying principles between these fields. It quickly learns new tasks with limited demonstrations and can even adapt its robotic body plans to solve specific problems more effectively. This flexible and efficient learning significantly reduces the time and resources required for scientific exploration, paving the way for faster breakthroughs. RoboCat's ability to generalize knowledge across different scientific fields distinguishes it from previous specialized AI models, highlighting its potential to be a valuable tool for researchers across disciplines.
Hacker News users discussed the potential and limitations of AI as a "co-scientist." Several commenters expressed skepticism about the framing, arguing that AI currently serves as a powerful tool for scientists, rather than a true collaborator. Concerns were raised about AI's inability to formulate hypotheses, design experiments, or understand the underlying scientific concepts. Some suggested that overreliance on AI could lead to a decline in fundamental scientific understanding. Others, while acknowledging these limitations, pointed to the value of AI in tasks like data analysis, literature review, and identifying promising research directions, ultimately accelerating the pace of scientific discovery. The discussion also touched on the potential for bias in AI-generated insights and the importance of human oversight in the scientific process. A few commenters highlighted specific examples of AI's successful application in scientific fields, suggesting a more optimistic outlook for the future of AI in science.
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https://news.ycombinator.com/item?id=43724941
Hacker News users discussed Kagi Assistant's public release with cautious optimism. Several praised its speed and accuracy compared to alternatives like ChatGPT and Perplexity, particularly for coding tasks and factual queries. Some expressed concerns about the long-term viability of a subscription model for search, wondering if Kagi could maintain quality and compete with free, ad-supported giants. The integration with Kagi's existing search engine was generally seen as a positive, though some questioned its usefulness for simpler searches. A few commenters noted the potential for bias and the importance of transparency regarding the underlying model and training data. Others brought up the small company size and the challenge of scaling the service while maintaining performance and privacy. Overall, the sentiment was positive but tempered by pragmatic considerations about the future of paid search assistants.
The Hacker News post titled "Kagi Assistant is now available to all users" (linking to a blog post about Kagi's new AI assistant) generated a moderate amount of discussion, with several commenters expressing interest and sharing their initial experiences.
Several users praised Kagi's overall approach, particularly its subscription model and focus on privacy. One commenter specifically appreciated Kagi's commitment to not training their AI model on user data, seeing it as a refreshing change of pace from larger tech companies.
There was a discussion around the pricing, with some users finding it a bit steep while acknowledging the value proposition of a more private and potentially higher-quality search experience. One user suggested a tiered pricing model could be beneficial to cater to different usage needs and budgets.
Several commenters shared their early experiences with the assistant, highlighting its strengths in specific areas like coding and research. One user mentioned its proficiency in generating regular expressions, while another found it useful for quickly summarizing academic papers. Some also pointed out limitations, noting that the assistant was still under development and prone to occasional inaccuracies or hallucinations.
The conversation also touched upon the competitive landscape, comparing Kagi Assistant to other AI assistants like ChatGPT and Perplexity. Some users felt Kagi had the potential to carve out a niche for itself by catering to users who prioritize privacy and are willing to pay for a more curated and less ad-driven experience.
A few users expressed concerns about the long-term viability of smaller search engines like Kagi, questioning whether they could compete with the resources and data of tech giants. However, others countered this by arguing that there's a growing demand for alternatives that prioritize user privacy and offer a different approach to search.
Overall, the comments reflect a cautious optimism about Kagi Assistant, with users acknowledging its early stage of development while also expressing appreciation for its unique features and potential. Many commenters indicated a willingness to continue using and experimenting with the assistant to see how it evolves.