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The linked article is about a new technology called "MomBoard" that aims to make it easier for busy parents to stay organized and manage their family's schedules and tasks. MomBoard is a digital assistant that can help with things like managing appointments, tracking chores, and coordinating family activities. The article discusses the features and benefits of this new tool, which is designed to help streamline the daily challenges of modern parenting.

The linked article is about the similarities between the conspiracy theories promoted by Alex Jones' InfoWars and the satirical content published by The Onion. It explores how the line between real and fake news has become increasingly blurred, with both InfoWars and The Onion sometimes being mistaken for credible sources. The article discusses the challenges this creates for media consumers and the broader implications for the spread of misinformation in the digital age.

The linked article is about the U.S. Department of Justice's plan to push Google to sell off its Chrome web browser in order to address the company's alleged monopoly in the search engine market. The article states that the DOJ is preparing to file an antitrust lawsuit against Google, arguing that the company's control over both search and web browsing has stifled competition and harmed consumers. The DOJ's goal is to force Google to divest Chrome, which would weaken the company's grip on the digital advertising market and potentially pave the way for more competition.

I wasn't quite sure if this qualified as "Show HN" given you can't really download it and try it out. However, dang said[0]:

> If it's hardware or something that's not so easy to try out over the internet, find a different way to show how it actually works—a video, for example, or a detailed post with photos.

Hopefully I did that?

Additionally, I've put code and a detailed guide for the netboot computer management setup on GitHub:

https://github.com/kentonv/lanparty

Anyway, if this shouldn't have been Show HN, I apologize!

[0] https://news.ycombinator.com/item?id=22336638

The linked article is about the new "rebeccapurple" CSS color, which was named after the late web designer Rebecca Meyer. The article discusses the historical significance of this color and how it was added to the CSS specification as a way to honor Meyer's contributions to the web development community. The article also provides information on how to use the "rebeccapurple" color in your CSS code and the impact it has had on the industry.

The linked article is about O2's launch of an AI chatbot named "Daisy" that is designed to waste the time of scammers. Daisy is programmed to engage with scammers and keep them occupied, preventing them from targeting vulnerable individuals. The article highlights how Daisy can mimic the behavior of an elderly person, using relevant language and responses to frustrate scammers and discourage them from pursuing their fraudulent activities. This initiative by O2 aims to protect its customers, particularly the elderly, from falling victim to various scams.

The linked article is about the creation of a jungle music video game drum and bass track. The article discusses the process of composing the track, including the use of a range of digital and analog drum samples, basslines, and other sound elements to create the distinctive jungle music style. The article also touches on the history and influence of jungle music, a subgenre of electronic dance music that emerged in the early 1990s in the UK.

The linked article is about the various factors that influence chess playing ability, including natural talent, training, and the role of genetics. It discusses the debate around whether chess is primarily a game of skill or whether innate abilities play a significant role in determining a player's success. The article also touches on the importance of deliberate practice and the potential for technological advancements, such as AI, to impact the future of chess.

The linked article is about the challenges that leading AI companies like OpenAI, Google, and Anthropic are facing in developing more advanced artificial intelligence systems. Despite significant investments and research efforts, these companies are struggling to achieve breakthroughs that would allow them to create AI models that are significantly more capable than the current state-of-the-art language models. The article explores the technical and scientific obstacles that these companies are encountering, as well as the potential implications for the future of AI development.

The linked article is about the life and contributions of Thomas E. Kurtz, the co-creator of the BASIC programming language. It highlights his passing in 2024 at the age of 95 and his significant impact on the field of computer science. The article discusses Kurtz's collaboration with John Kemeny in developing BASIC, which became a widely adopted and influential programming language, particularly in the early days of personal computing. It also touches on Kurtz's role in founding Dartmouth Time-Sharing System and his lifelong commitment to making computing accessible to a broader audience.

The linked article is about the Firehose3D project, which is a free and open-source 3D engine built on top of WebGL. It is designed to be lightweight, easy to use, and suitable for a wide range of 3D web applications, including games, visualizations, and interactive experiences. The article provides an overview of the engine's features and capabilities, as well as information on how to get started with using Firehose3D in your projects.

The linked article is about the disruption of an undersea cable that connects Germany and Finland, which authorities suspect may have been intentionally caused. The incident has highlighted the vulnerability of critical infrastructure and the potential for geopolitical tensions to disrupt communication and energy networks in Europe. The article discusses the investigation into the cause of the disruption and the potential implications for the region's energy and telecommunications sectors.

Hey HN, I’m Hunter the founder of Regatta Storage (https://regattastorage.com). Regatta Storage is a new cloud file system that provides unlimited pay-as-you-go capacity, local-like performance, and automatic synchronization to S3-compatible storage. For example, you can use Regatta to instantly access massive data sets in S3 with Spark, Pytorch, or pandas without paying for large, local disks or waiting for the data to download.

Check out an overview of how the service works here: https://www.youtube.com/watch?v=xh1q5p7E4JY, and you can try it for free at https://regattastorage.com after signing up for an account. We wanted to let you try it without an account, but we figured that “Hacker News shares a file system and S3 bucket” wouldn’t be the best experience for the community.

I built Regatta after spending nearly a decade building and operating at-scale cloud storage at places like Amazon’s Elastic File System (EFS) and Netflix. During my 8 years at EFS, I learned a lot about how teams thought about their storage usage. Users frequently told me that they loved how simple and scalable EFS was, and -- like S3 -- they didn’t have to guess how much capacity they needed up front.

When I got to Netflix, I was surprised that there wasn’t more usage of EFS. If you looked around, it seemed like a natural fit. Every application needed a POSIX file system. Lots of applications had unclear or spikey storage needs. Often, developers wanted their storage to last beyond the lifetime of an individual instance or container. In fact, if you looked across all Netflix applications, some ridiculous amount of money was being spent on empty storage space because each of these local drives had to be overprovisioned for potential usage.

However, in many cases, EFS wasn’t the perfect choice for these workloads. Moving workloads from local disks to NFS often encountered performance issues. Further, applications which treated their local disks as ephemeral would have to manually “clean up” left over data in a persistent storage system.

At this point, I realized that there was a missing solution in the cloud storage market which wasn’t being filled by either block or file storage, and I decided to build Regatta.

Regatta is a pay-as-you-go cloud file system that automatically expands with your application. Because it automatically synchronizes with S3 using native file formats, you can connect it to existing data sets and use recently written file data directly from S3. When data isn’t actively being used, it’s removed from the Regatta cache, so you only pay for the backing S3 storage. Finally, we’re developing a custom file protocol which allows us to achieve local-like performance for small-file workloads and Lustre-like scale-out performance for distributed data jobs.

Under the hood, customers mount a Regatta file system by connecting to our fleet of caching instances over NFSv3 (soon, our custom protocol). Our instances then connect to the customer’s S3 bucket on the backend, and provide sub-millisecond cached-read and write performance. This durable cache allows us to provide a strongly consistent, efficient view of the file system to all connected file clients. We can perform challenging operations (like directory renaming) quickly and durably, while they asynchronously propagate to the S3 bucket.

We’re excited to see users share our vision for Regatta. We have teams who are using us to build totally serverless Jupyter notebook servers for their AI researchers who prefer to upload and share data using the S3 web UI. We have teams who are using us as a distributed caching layer on top of S3 for low-latency access to common files. We have teams who are replacing their thin-provisioned Ceph boot volumes with Regatta for significant savings. We can’t wait to see what other things people will build and we hope you’ll give us a try at regattastorage.com.

We’d love to get any early feedback from the community, ideas for future direction, or experiences in this space. I’ll be in the comments for the next few hours to respond!

The linked article is about the 20th anniversary of the release of Half-Life 2, a landmark first-person shooter game developed by Valve Corporation. The article provides an overview of the game's impact on the gaming industry, its technical innovations, and its lasting influence on the first-person shooter genre. It also highlights the game's memorable characters, story, and environmental design, which contributed to its critical and commercial success.

The linked article is about the origin of the "lake tank" image that has become a popular meme. The question explores the history of this image, which depicts a military tank partially submerged in a lake or river. The article suggests that the image may have originated from a real-world incident or training exercise, though the exact source is unclear. The discussion examines various theories and potential explanations for how this image gained traction and became widely recognized as a meme in internet culture.

The linked article is about reverse engineering iOS 18's inactivity detection system. It delves into the technical details of how the operating system identifies user inactivity and takes appropriate actions, such as automatically locking the device or dimming the screen. The article provides insights into the algorithms and processes involved, offering a deep dive into the inner workings of this crucial iOS feature.

The linked article is about the wood frog, a small amphibian found in North America. It describes the wood frog's unique adaptations, such as its ability to freeze solid during the winter and then thaw out and resume normal activity in the spring. The article also discusses the wood frog's habitat, behavior, and role in the ecosystem.

The linked article is about the technical difficulties faced by viewers during the live streaming of the boxing match between Jake Paul and Mike Tyson on Netflix. The article discusses how the livestream experienced issues with buffering and connectivity, leading to a frustrating viewing experience for many fans. It highlights the challenges of delivering a seamless live sports event through a streaming platform, and the importance of proper infrastructure and planning to ensure a successful broadcast.

The linked article is about analytical anti-aliasing, a technique used in computer graphics to improve the quality of rendered images. It discusses the limitations of traditional anti-aliasing methods and introduces analytical anti-aliasing, which calculates the exact coverage of each pixel by the underlying geometry, resulting in a more accurate and higher-quality image. The article explains the mathematical principles behind analytical anti-aliasing and how it can be implemented in real-time rendering engines, providing a more efficient and effective solution to the problem of aliasing artifacts.

The linked article is about the launch of the Bsky social network, which is a new decentralized social media platform built on the ActivityPub protocol. The post discusses the potential of Bsky to provide an alternative to traditional social media platforms and emphasizes the importance of user control and privacy in the platform's design.

The linked article is about the announcement of Let's Encrypt, a free, automated, and open certificate authority (CA) launched by the Internet Security Research Group (ISRG). Let's Encrypt aims to provide a secure and easy-to-use service for websites to obtain and manage HTTPS certificates, which are essential for securing online communication and protecting user privacy. The article outlines the key features of Let's Encrypt, including its automated and free-of-charge nature, as well as its commitment to transparency and open-source development. The goal of this initiative is to increase the adoption of HTTPS and make the web a more secure place for everyone.

The linked article is about the use of OpenStreetMap (OSM) data to create vector tiles, which are a type of digital map format that can be efficiently served and rendered on the web. The article discusses the advantages of vector tiles over traditional raster tiles, such as smaller file sizes, greater flexibility, and the ability to display maps at any zoom level without loss of quality. It also covers the process of creating and serving vector tiles using tools like Mapbox Vector Tiles (MVT) and Mapbox GL JS.

The linked article is about how the departing editor of Scientific American, Laura Helmuth, has contributed to the degradation of science by promoting a partisan political agenda and biased reporting. The article argues that under Helmuth's leadership, the magazine has increasingly abandoned its commitment to objective and impartial science reporting in favor of pushing a left-leaning narrative on various issues, including climate change, COVID-19, and gender. The article also criticizes Helmuth's efforts to silence dissenting voices and suppress scientific viewpoints that challenge the magazine's preferred narratives.

The linked article is about the potential impact of AI on technical debt. It argues that as AI systems become more complex and integrated into software, the cost of maintaining and updating those systems can increase significantly. The article highlights how AI models can introduce new forms of technical debt, such as model drift and data dependency, which can make it more challenging and expensive to manage over time. It emphasizes the importance of proactively addressing these issues and incorporating AI-specific technical debt management strategies into software development practices.

The linked article is about Epic Games allowing the Internet Archive to distribute the classic first-person shooter Unreal Tournament for free. This decision comes as part of Epic's ongoing effort to make its older games more accessible to the public, enabling players to enjoy these iconic titles without the need for paid ownership.

The linked article is about the Amplify overcharge issue, where users of the Amplify framework were unknowingly being charged for features they didn't use. The article discusses the controversy around Amplify's pricing model, the company's response, and the broader implications for the developer community.

The linked article is about a computer science book that the author believes is worth reading twice. The book, "The Art of Computer Programming" by Donald Knuth, is a classic in the field of computer science. The author discusses the depth and breadth of the book, which covers fundamental algorithms and data structures. He highlights Knuth's unique writing style and the value of reading the book multiple times to truly understand its content. The article suggests that the book is a must-read for anyone interested in computer science and programming.

The linked article is about MIT's new tuition policy for undergraduate students. Starting in the 2024-2025 academic year, MIT will provide full tuition coverage for students from families with incomes below $150,000 annually. This move aims to make MIT more accessible to students from lower and middle-income backgrounds, aligning with the institute's commitment to diversity and inclusion. The policy is expected to benefit around half of MIT's undergraduate population and further enhance the university's efforts to ensure a world-class education is within reach for talented students regardless of their financial circumstances.

The linked article is about the potential success of the Bluesky social media initiative, a decentralized alternative to existing platforms like Twitter. The article discusses the challenges Bluesky has faced, such as delays and skepticism, but suggests that it may have ultimately prevailed by focusing on building a sustainable and community-driven platform. The author suggests that Bluesky's approach of emphasizing transparency, user control, and open-source principles could make it a more attractive option for users dissatisfied with the current state of social media.

Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning.

Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that involve multi-hop reasoning or more advanced domain understanding. Also, it’s impossible to debug it.

To address these limitations, many engineers find themselves adding extra layers like agent-based preprocessing, custom embeddings, reranking mechanisms, and hybrid search strategies. Much like the early days of machine learning when we manually crafted feature vectors to squeeze out marginal gains, building an effective RAG system often becomes an exercise in crafting engineering “hacks.”

Earlier this year, Microsoft seeded the idea of using Knowledge Graphs for RAG and published GraphRAG - i.e. RAG with Knowledge Graphs. We believe that there is an incredible potential in this idea, but existing implementations are naive in the way they create and explore the graph. That’s why we developed Fast GraphRAG with a new algorithmic approach using good old PageRank.

There are two main challenges when building a reliable RAG system:

(1) Data Noise: Real-world data is often messy. Customer support tickets, chat logs, and other conversational data can include a lot of irrelevant information. If you push noisy data into a vector database, you’re likely to get noisy results.

(2) Domain Specialization: For complex use cases, a RAG system must understand the domain-specific context. This requires creating representations that capture not just the words but the deeper relationships and structures within the data.

Our solution builds on these insights by incorporating knowledge graphs into the RAG pipeline. Knowledge graphs store entities and their relationships, and can help structure data in a way that enables more accurate and context-aware information retrieval. 12 years ago Google announced the knowledge graph we all know about [1]. It was a pioneering move. Now we have LLMs, meaning that people can finally do RAG on their own data with tools that can be as powerful as Google’s original idea.

Before we built this, Antonio was at Amazon, while Luca and Yuhang were finishing their PhDs at Oxford. We had been thinking about this problem for years and we always loved the parallel between pagerank and the human memory [2]. We believe that searching for memories is incredibly similar to searching the web.

Here’s how it works:

- Entity and Relationship Extraction: Fast GraphRAG uses LLMs to extract entities and their relationships from your data and stores them in a graph format [3].

- Query Processing: When you make a query, Fast GraphRAG starts by finding the most relevant entities using vector search, then runs a personalized PageRank algorithm to determine the most important “memories” or pieces of information related to the query [4].

- Incremental Updates: Unlike other graph-based RAG systems, Fast GraphRAG natively supports incremental data insertions. This means you can continuously add new data without reprocessing the entire graph.

- Faster: These design choices make our algorithm faster and more affordable to run than other graph-based RAG systems because we eliminate the need for communities and clustering.

Suppose you’re analyzing a book and want to focus on character interactions, locations, and significant events:

  from fast_graphrag import GraphRAG
  
  DOMAIN = "Analyze this story and identify the characters. Focus on how they interact with each other, the locations they explore, and their relationships."
  
  EXAMPLE_QUERIES = [
      "What is the significance of Christmas Eve in A Christmas Carol?",
      "How does the setting of Victorian London contribute to the story's themes?",
      "Describe the chain of events that leads to Scrooge's transformation.",
      "How does Dickens use the different spirits (Past, Present, and Future) to guide Scrooge?",
      "Why does Dickens choose to divide the story into \"staves\" rather than chapters?"
  ]
  
  ENTITY_TYPES = ["Character", "Animal", "Place", "Object", "Activity", "Event"]
  
  grag = GraphRAG(
      working_dir="./book_example",
      domain=DOMAIN,
      example_queries="\n".join(EXAMPLE_QUERIES),
      entity_types=ENTITY_TYPES
  )
  
  with open("./book.txt") as f:
      grag.insert(f.read())
  
  print(grag.query("Who is Scrooge?").response)

This is the kind of infrastructure that GenAI apps need to handle large-scale real-world data. Our goal is to give you this infrastructure so that you can focus on what’s important: building great apps for your users without having to care about manually engineering a retrieval pipeline. In the managed service, we also have a suite of UI tools for you to explore and debug your knowledge graph.

We have a free hosted solution with up to 100 monthly requests. When you’re ready to grow, we have paid plans that scale with you. And of course you can self host our open-source engine.

Give us a spin today at https://circlemind.co and see our code at https://github.com/circlemind-ai/fast-graphrag

We’d love feedback :)

[1] https://blog.google/products/search/introducing-knowledge-gr...

[2] Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the Mind: Predicting Fluency with PageRank. Psychological Science, 18(12), 1069–1076. http://www.jstor.org/stable/40064705

[3] Similarly to Microsoft’s GraphRAG: https://github.com/microsoft/graphrag

[4] Similarly to OSU’s HippoRAG: https://github.com/OSU-NLP-Group/HippoRAG

https://vhs.charm.sh/vhs-4fCicgsbsc7UX0pemOcsMp.gif