Meilisearch is an open-source, easy-to-use search engine API. It features a typo-tolerant, fast search experience and offers AI-powered hybrid search capabilities combining keyword and semantic search for more relevant results. Developers can easily integrate Meilisearch into their applications using various SDKs and customize ranking rules, synonyms, and other settings for optimal performance and tailored search experiences.
PostgreSQL's full-text search functionality is often unfairly labeled as slow. This perception stems from common misconfigurations and inefficient usage. The blog post demonstrates that with proper setup, including using appropriate data types (like tsvector for indexed documents and tsquery for search terms), utilizing GIN indexes on tsvector columns, and leveraging stemming and other linguistic features, PostgreSQL's full-text search can be extremely performant, even on large datasets. Furthermore, optimizing queries by using appropriate operators and understanding how ranking works can significantly improve search speed. The post emphasizes that understanding and correctly implementing these techniques are key to unlocking PostgreSQL's full-text search potential.
Hacker News users generally agreed with the article's premise that PostgreSQL full-text search can be performant if implemented correctly. Several commenters shared their own positive experiences, highlighting the importance of proper indexing and configuration. Some pointed out that while PostgreSQL's full-text search might not outperform specialized solutions like Elasticsearch or Algolia for very large datasets or complex queries, it's more than adequate for many use cases. A few cautioned against using stemming without careful consideration, as it can lead to unexpected results. The discussion also touched upon the benefits of using pg_trgm for fuzzy matching and the trade-offs between different indexing strategies.
The author argues that Google's search quality has declined due to a prioritization of advertising revenue and its own products over relevant results. This manifests in excessive ads, low-quality content from SEO-driven websites, and a tendency to push users towards Google services like Maps and Flights, even when external options might be superior. The post criticizes the cluttered and information-poor nature of modern search results pages, lamenting the loss of a cleaner, more direct search experience that prioritized genuine user needs over Google's business interests. This degradation, the author claims, is driving users away from Google Search and towards alternatives.
HN commenters largely agree with the author's premise that Google search quality has declined. Many attribute this to increased ads, irrelevant results, and a focus on Google's own products. Several commenters shared anecdotes of needing to use specific search operators or alternative search engines like DuckDuckGo or Bing to find desired information. Some suggest the decline is due to Google's dominant market share, arguing they lack the incentive to improve. A few pushed back, attributing perceived declines to changes in user search habits or the increasing complexity of the internet. Several commenters also discussed the bloat of Google's other services, particularly Maps.
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.
Ecosia's founders have legally restructured the company to prevent it from ever being sold, even by future owners. This ensures that Ecosia's profits will always be used to plant trees and pursue its environmental mission. The change involves a new legal structure called a "steward ownership model" and a purpose foundation that holds all voting rights. This effectively makes selling Ecosia for profit impossible, guaranteeing its long-term commitment to environmental sustainability.
Hacker News users generally praised Ecosia's commitment to its mission, viewing the legal restructuring as a positive move. Some expressed skepticism about the long-term viability of the business model and wondered how Ecosia would adapt to future challenges without the option of selling. Others questioned the specific legal mechanisms employed and compared them to other charitable structures. A few commenters also raised concerns about potential future leadership changes and how those could impact Ecosia's stated commitment. Several users shared their personal experiences with the search engine, generally positive, and discussed the tradeoffs between Ecosia and other search options.
Ecosia and Qwant, two European search engines prioritizing privacy and sustainability, are collaborating to build a new, independent European search index called the European Open Web Search (EOWS). This joint effort aims to reduce reliance on non-European indexes, promote digital sovereignty, and offer a more ethical and transparent alternative. The project is open-source and seeks community involvement to enrich the index and ensure its inclusivity, providing European users with a robust and relevant search experience powered by European values.
Several Hacker News commenters express skepticism about Ecosia and Qwant's ability to compete with Google, citing Google's massive data advantage and network effects. Some doubt the feasibility of building a truly independent index and question whether the joint effort will be significantly different from using Bing. Others raise concerns about potential bias and censorship, given the European focus. A few commenters, however, offer cautious optimism, hoping the project can provide a viable privacy-respecting alternative and contribute to a more decentralized internet. Some also express interest in the technical challenges involved in building such an index.
The Department of Justice is reportedly still pushing for Google to sell off parts of its Chrome business, even as it prepares its main antitrust lawsuit against the company for trial. Sources say the DOJ believes Google's dominance in online advertising is partly due to its control over Chrome and that divesting the browser, or portions of it, is a necessary remedy. This potential divestiture could include parts of Chrome's ad tech business and potentially even the browser itself, a significantly more aggressive move than previously reported. While the DOJ's primary focus remains its existing ad tech lawsuit, pressure for a Chrome divestiture continues behind the scenes.
HN commenters are largely skeptical of the DOJ's potential antitrust suit against Google regarding Chrome. Many believe it's a misguided effort, arguing that Chrome is free, open-source (Chromium), and faces robust competition from other browsers like Firefox and Safari. Some suggest the DOJ should focus on more pressing antitrust issues, like Google's dominance in search advertising and its potential abuse of Android. A few commenters discuss the potential implications of such a divestiture, including the possibility of a fork of Chrome or the browser becoming part of another large company. Some express concern about the potential negative impact on user privacy. Several commenters also point out the irony of the government potentially mandating Google divest from a free product.
The author attempted to build a free, semantic search engine for GitHub using a Sentence-BERT model and FAISS for vector similarity search. While initial results were promising, scaling proved insurmountable due to the massive size of the GitHub codebase and associated compute costs. Indexing every repository became computationally and financially prohibitive, particularly as the model struggled with context fragmentation from individual code snippets. Ultimately, the project was abandoned due to the unsustainable balance between cost, complexity, and the limited resources of a solo developer. Despite the failure, the author gained valuable experience in large-scale data processing, vector databases, and the limitations of current semantic search technology when applied to a vast and diverse codebase like GitHub.
HN commenters largely praised the author's transparency and detailed write-up of their project. Several pointed out the inherent difficulties and nuances of semantic search, particularly within the vast and diverse codebase of GitHub. Some suggested alternative approaches, like focusing on a smaller, more specific domain within GitHub or utilizing existing tools like Elasticsearch with careful tuning. The cost of running such a service and the challenges of monetization were also discussed, with some commenters skeptical of the free model. A few users shared their own experiences with similar projects, echoing the author's sentiments about the complexity and resource intensity of semantic search. Overall, the comments reflected an appreciation for the author's journey and the lessons learned, contributing further insights into the challenges of building and scaling a semantic search engine.
A new Safari extension allows users to set ChatGPT as their default search engine. The extension intercepts search queries entered in the Safari address bar and redirects them to ChatGPT, providing a conversational AI-powered search experience directly within the browser. This offers an alternative to traditional search engines, leveraging ChatGPT's ability to synthesize information and respond in natural language.
Hacker News users discussed the practicality and privacy implications of using a ChatGPT extension as a default search engine. Several questioned the value proposition, arguing that search engines are better suited for information retrieval while ChatGPT excels at generating text. Privacy concerns were raised regarding sending every search query to OpenAI. Some commenters expressed interest in using ChatGPT for specific use cases, like code generation or creative writing prompts, but not as a general search replacement. Others highlighted potential benefits, like more conversational search results and the possibility of bypassing paywalled content using ChatGPT's summarization abilities. The potential for bias and manipulation in ChatGPT's responses was also mentioned.
Phind 2, a new AI search engine, significantly upgrades its predecessor with enhanced multi-step reasoning capabilities and the ability to generate visual answers, including diagrams and code flowcharts. It utilizes a novel method called "grounded reasoning" which allows it to access and process information from multiple sources to answer complex questions, offering more comprehensive and accurate responses. Phind 2 also features an improved conversational mode and an interactive code interpreter, making it a more powerful tool for both technical and general searches. This new version aims to provide clearer, more insightful answers than traditional search engines, moving beyond simply listing links.
Hacker News users discussed Phind 2's potential, expressing both excitement and skepticism. Some praised its ability to synthesize information and provide visual aids, especially for coding-related queries. Others questioned the reliability of its multi-step reasoning and cited instances where it hallucinated or provided incorrect code. Concerns were also raised about the lack of source citations and the potential for over-reliance on AI tools, hindering deeper learning. Several users compared it favorably to other AI search engines like Perplexity AI, noting its cleaner interface and improved code generation capabilities. The closed-source nature of Phind 2 also drew criticism, with some advocating for open-source alternatives. The pricing model and potential for future monetization were also points of discussion.
Google altered its Super Bowl ad for its Bard AI chatbot after it provided inaccurate information in a demo. The ad showcased Bard's ability to simplify complex topics, but it incorrectly stated the James Webb Space Telescope took the very first pictures of a planet outside our solar system. Google corrected the error before airing the ad, highlighting the ongoing challenges of ensuring accuracy in AI chatbots, even in highly publicized marketing campaigns.
Hacker News commenters generally expressed skepticism about Google's Bard AI and the implications of the ad's factual errors. Several pointed out the irony of needing to edit an ad showcasing AI's capabilities because the AI itself got the facts wrong. Some questioned the ethics of heavily promoting a technology that's clearly still flawed, especially given Google's vast influence. Others debated the significance of the errors, with some suggesting they were minor while others argued they highlighted deeper issues with the technology's reliability. A few commenters also discussed the pressure Google is under from competitors like Bing and the potential for AI chatbots to confidently hallucinate incorrect information. A recurring theme was the difficulty of balancing the hype around AI with the reality of its current limitations.
DeepSeek, a platform offering encoder APIs for developers, chose to open-source its core technology due to the inherent difficulty in building trust with users regarding data privacy and security when handling sensitive information like codebases and internal documentation. By open-sourcing, DeepSeek aims to foster transparency and allow users to self-host, ensuring complete control over their data. This approach mitigates concerns around vendor lock-in and allows the community to contribute to the project's development and security, ultimately building greater trust and fostering wider adoption.
Hacker News users discussed the open-sourcing of DeepSeek, primarily focusing on the challenges of monetizing open-source AI infrastructure. Many commenters were skeptical of Lago's business model, questioning how they could successfully build a proprietary offering on top of an open-source core, especially given the intense competition in the vector database space. Some suggested that open-sourcing DeepSeek was a necessary move due to the difficulty of attracting paying customers for a closed-source product. Others pointed out potential advantages, such as faster iteration and community contributions, but remained unconvinced of long-term viability. Several users expressed a desire for more technical details about DeepSeek's implementation and performance compared to existing solutions. The most compelling comments revolved around the inherent tension between open-sourcing and profitability in the current AI landscape.
Marginalia is a search engine designed to surface non-commercial content, prioritizing personal websites, blogs, and other independently published works often overshadowed by commercial results in mainstream search. It aims to rediscover the original spirit of the web by focusing on unique, human-generated content and fostering a richer, more diverse online experience. The search engine utilizes a custom index built by crawling sites linked from curated sources, filtering out commercial and spammy domains. Marginalia emphasizes quality over quantity, presenting a smaller, more carefully selected set of results to help users find hidden gems and explore lesser-known corners of the internet.
Hacker News users generally praised Marginalia's concept of prioritizing non-commercial content, viewing it as a refreshing alternative to mainstream search engines saturated with ads and SEO-driven results. Several commenters expressed enthusiasm for the focus on personal websites, blogs, and academic resources. Some questioned the long-term viability of relying solely on donations, while others suggested potential improvements like user accounts, saved searches, and more granular control over source filtering. There was also discussion around the definition of "non-commercial," with some users highlighting the inherent difficulty in objectively classifying content. A few commenters shared their initial search experiences, noting both successes in finding unique content and instances where the results were too niche or limited. Overall, the sentiment leaned towards cautious optimism, with many expressing hope that Marginalia could carve out a valuable space in the search landscape.
IRCDriven is a new search engine specifically designed for indexing and searching IRC (Internet Relay Chat) logs. It aims to make exploring and researching public IRC conversations easier by offering full-text search capabilities, advanced filtering options (like by channel, nick, or date), and a user-friendly interface. The project is actively seeking feedback and contributions from the IRC community to improve its features and coverage.
Commenters on Hacker News largely praised IRC Driven for its clean interface and fast search, finding it a useful tool for rediscovering old conversations and information. Some expressed a nostalgic appreciation for IRC and the value of archiving its content. A few suggested potential improvements, such as adding support for more networks, allowing filtering by nick, and offering date range restrictions in search. One commenter noted the difficulty in indexing IRC due to its decentralized and ephemeral nature, commending the creator for tackling the challenge. Others discussed the historical significance of IRC and the potential for such archives to serve as valuable research resources.
Birls.org is a new search engine specifically designed for accessing US veteran records. It offers a streamlined interface to search across multiple government databases and also provides a free, web-based system for submitting Freedom of Information Act (FOIA) requests to the National Archives via fax, simplifying the often cumbersome process of obtaining these records.
HN users generally expressed skepticism and concern about the project's viability and potential security issues. Several commenters questioned the need for faxing FOIA requests, highlighting existing online portals and email options. Others worried about the security implications of handling sensitive veteran data, particularly with a fax-based system. The project's reliance on OCR was also criticized, with users pointing out its inherent inaccuracy. Some questioned the search engine's value proposition, given the existence of established genealogy resources. Finally, the lack of clarity surrounding the project's funding and the developer's qualifications raised concerns about its long-term sustainability and trustworthiness.
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https://news.ycombinator.com/item?id=43680699
Hacker News users discussed Meilisearch's pivot towards an AI-powered hybrid search, expressing skepticism and concern. Several commenters questioned the value proposition, noting that the core competency of a search engine is accurate retrieval, not AI-powered features. Some worried that adding AI features would increase complexity and resource consumption without significantly improving search relevance. Others highlighted potential issues with cost and vendor lock-in with OpenAI's API. There was a general sentiment that focusing on core search functionality and performance would be a more beneficial direction for Meilisearch. A few commenters offered alternative solutions, like using a vector database alongside Meilisearch for semantic search capabilities. The overall tone was cautiously pessimistic, with many expressing disappointment in the shift away from a simple and performant search solution.
The Hacker News thread discussing Meilisearch, a search engine API boasting AI-powered hybrid search, contains several interesting comments. Many users are intrigued by the project, particularly its potential to provide a viable open-source alternative to Algolia and Elasticsearch. However, skepticism is also present, with some questioning the practical implementation of the "AI-powered" features and expressing concerns about scalability and production readiness.
A recurring theme is the comparison to Typesense, another open-source search engine. Several commenters share their experiences with both Meilisearch and Typesense, often highlighting performance differences and ease of use. Some suggest that Meilisearch offers a simpler setup and a more intuitive API, while others argue that Typesense boasts superior performance, particularly for larger datasets. The discussion around indexing speed and resource consumption is particularly noteworthy, with users sharing anecdotal evidence of varying performance across different platforms and dataset sizes.
Another point of discussion revolves around the "AI" aspect of Meilisearch. Some commenters question the specifics of the AI implementation, asking for clarification on the algorithms used and expressing skepticism about the actual impact on search relevance. Others are more optimistic, seeing the AI features as a promising development and expressing interest in learning more about the underlying technology. The thread also touches upon the broader trend of integrating AI into search engines, with some commenters speculating on the future of search and the role of AI in enhancing search relevance and user experience.
The discussion also delves into the practicalities of using Meilisearch in production environments. Concerns are raised about the maturity of the project, potential limitations in terms of scalability, and the availability of community support. Some users inquire about specific features like multi-tenancy and complex filtering capabilities. Others share their experiences with integrating Meilisearch into their own projects, offering insights into the setup process and potential challenges.
Finally, the open-source nature of Meilisearch is a significant point of interest. Many commenters express appreciation for the project's open-source licensing and the potential for community contributions. The discussion also touches on the challenges of maintaining an open-source project, including funding and community engagement. Some users inquire about the project's long-term sustainability and the involvement of the core development team.