A distributed computing project leveraging idle CPU time from volunteers' computers has set a new verification record for the Goldbach Conjecture. The project, utilizing a novel grid computing approach, has confirmed the conjecture – which states that every even number greater than 2 can be expressed as the sum of two primes – up to 4 * 10^18 + 7 * 10^13. This surpasses previous verification efforts by a significant margin and demonstrates the potential of harnessing distributed computing power for tackling complex mathematical problems.
OpenAI's Agents SDK now supports Multi-Character Personas (MCP), enabling developers to create agents with distinct personalities and roles within a single environment. This allows for more complex and nuanced interactions between agents, facilitating richer simulations and collaborative problem-solving. The MCP feature provides tools for managing dialogue, assigning actions, and defining individual agent characteristics, all within a streamlined framework. This opens up possibilities for building applications like interactive storytelling, complex game AI, and virtual collaborative workspaces.
Hacker News users discussed the potential of OpenAI's new MCP (Model Predictive Control) feature for the Agents SDK. Several commenters expressed excitement about the possibilities of combining planning and tool use, seeing it as a significant step towards more autonomous agents. Some highlighted the potential for improved efficiency and robustness in complex tasks compared to traditional reinforcement learning approaches. Others questioned the practical scalability and real-world applicability of MCP given computational costs and the need for accurate world models. There was also discussion around the limitations of relying solely on pre-defined tools, with suggestions for incorporating mechanisms for tool discovery or creation. A few users noted the lack of clear examples or benchmarks in the provided documentation, making it difficult to assess the true capabilities of the MCP implementation.
Researchers have developed a computational fabric by integrating a twisted-fiber memory device directly into a single fiber. This fiber, functioning like a transistor, can perform logic operations and store information, enabling the creation of textile-based computing networks. The system utilizes resistive switching in the fiber to represent binary data, and these fibers can be woven into fabrics that perform complex calculations distributed across the textile. This "fiber computer" demonstrates the feasibility of large-scale, flexible, and wearable computing integrated directly into clothing, opening possibilities for applications like distributed sensing, environmental monitoring, and personalized healthcare.
Hacker News users discuss the potential impact of fiber-based computing, expressing excitement about its applications in wearable technology, distributed sensing, and large-scale deployments. Some question the scalability and practicality compared to traditional silicon-based computing, citing concerns about manufacturing complexity and the limited computational power of individual fibers. Others raise the possibility of integrating this technology with existing textile manufacturing processes and exploring new paradigms of computation enabled by its unique properties. A few comments highlight the novelty of physically embedding computation into fabrics and the potential for creating truly "smart" textiles, while acknowledging the early stage of this technology and the need for further research and development. Several users also note the intriguing security and privacy implications of having computation woven into everyday objects.
SheepIt, a distributed render farm utilizing idle processing power from volunteers' computers, has open-sourced its server-side code. This allows anyone to examine, modify, and potentially host their own private SheepIt render farm. Previously closed-source, this release provides transparency and fosters community involvement in the project's future development.
HN commenters generally express enthusiasm for SheepIt's open-sourcing, viewing it as a positive move for the community and a potential boon for smaller studios or individuals needing render resources. Some express curiosity about the underlying technology and its scalability, with questions raised about database choices and handling large numbers of concurrent users. Concerns are voiced regarding potential abuse and the resources required to run a server, alongside a desire for more documentation. A few users share their positive experiences with SheepIt's rendering services, highlighting its ease of use and effectiveness. Others suggest improvements like a more robust client and better integration with existing pipelines. The overall sentiment is one of cautious optimism, acknowledging the project's potential while recognizing the challenges inherent in running a distributed render farm.
The author recounts their teenage experience developing a rudimentary operating system for the Inmos Transputer. Fascinated by parallel processing, they created a system capable of multitasking and inter-process communication using the Transputer's unique link architecture. The OS, written in Occam, featured a kernel, device drivers, and a command-line interface, demonstrating a surprisingly sophisticated understanding of OS principles for a young programmer. Despite its limitations, like a lack of memory protection and a simple scheduler, the project provided valuable learning experiences in systems programming and showcased the potential of the Transputer's parallel processing capabilities.
Hacker News users discussed the blog post about a teen's experience developing a Transputer OS, largely focusing on the impressive nature of the project for someone so young. Several commenters reminisced about their own early programming experiences, often involving simpler systems like the Z80 or 6502. Some discussed the specific challenges of the Transputer architecture, like the difficulty of debugging and the limitations of the Occam language. A few users questioned the true complexity of the OS, suggesting it might be more accurately described as a kernel. Others shared links to resources for learning more about Transputers and Occam. The overall sentiment was one of admiration for the author's initiative and technical skills at a young age.
Polars, known for its fast DataFrame library, is developing Polars Cloud, a platform designed to seamlessly run Polars code anywhere. It aims to abstract away infrastructure complexities, enabling users to execute Polars workloads on various backends like their local machine, a cluster, or serverless environments without code changes. Polars Cloud will feature a unified API, intelligent query planning and optimization, and efficient data transfer. This will allow users to scale their data processing effortlessly, from laptops to massive datasets, all while leveraging Polars' performance advantages. The platform will also incorporate advanced features like data versioning and collaboration tools, fostering better teamwork and reproducibility.
Hacker News users generally expressed excitement about Polars Cloud, praising the project's ambition and the potential of combining Polars' performance with distributed computing. Several commenters highlighted the cleverness of leveraging existing cloud infrastructure like DuckDB and Apache Arrow. Some questioned the business model's viability, particularly regarding competition with established cloud providers and the potential for vendor lock-in. Others raised technical concerns about query planning across distributed systems and the challenges of handling large datasets efficiently. A few users discussed alternative approaches, such as using Dask or Spark with Polars. Overall, the sentiment was positive, with many eager to see how Polars Cloud evolves.
DeepSeek's smallpond extends DuckDB, the popular in-process analytical database, with distributed computing capabilities. It leverages a shared-nothing architecture where each node holds a portion of the data, allowing for parallel processing of queries across a cluster. Smallpond introduces a distributed query planner that optimizes query execution by distributing tasks and aggregating results efficiently. This empowers DuckDB to handle larger-than-memory datasets and significantly improves performance for complex analytical workloads. The project aims to make distributed computing accessible within the familiar DuckDB environment, retaining its ease of use and performance characteristics for larger-scale data analysis.
Hacker News commenters generally expressed excitement about the potential of combining DeepSeek's distributed computing capabilities with DuckDB's analytical power. Some questioned the performance implications and overhead of such a distributed setup, particularly concerning query planning and data transfer. Others raised concerns about the choice of Raft consensus, suggesting alternative distributed consensus algorithms might be more performant. Several users highlighted the value proposition for data lakes, allowing direct querying without complex ETL pipelines. The discussion also touched on the competitive landscape, comparing the approach to existing solutions like Presto and Spark, with some speculating on potential acquisition scenarios. A few commenters shared their positive experiences with DuckDB's speed and ease of use, further reinforcing the appeal of this integration. Finally, there was curiosity around the specifics of DeepSeek's technology and its impact on DuckDB's licensing.
DeepSeek has open-sourced DeepEP, a C++ library designed to accelerate training and inference of Mixture-of-Experts (MoE) models. It focuses on performance optimization through features like efficient routing algorithms, distributed training support, and dynamic load balancing across multiple devices. DeepEP aims to make MoE models more practical for large-scale deployments by reducing training time and inference latency. The library is compatible with various deep learning frameworks and provides a user-friendly API for integrating MoE layers into existing models.
Hacker News users discussed DeepSeek's open-sourcing of DeepEP, a library for Mixture of Experts (MoE) training and inference. Several commenters expressed interest in the project, particularly its potential for democratizing access to MoE models, which are computationally expensive. Some questioned the practicality of running large MoE models on consumer hardware, given their resource requirements. There was also discussion about the library's performance compared to existing solutions and its potential for integration with other frameworks like PyTorch. Some users pointed out the difficulty of effectively utilizing MoE models due to their complexity and the need for specialized hardware, while others were hopeful about the advancements DeepEP could bring to the field. One user highlighted the importance of open-source contributions like this for pushing the boundaries of AI research. Another comment mentioned the potential for conflict of interest due to the library's association with a commercial entity.
This post details how to train a large language model (LLM) comparable to OpenAI's GPT-3 175B parameter model, nicknamed "O1," for under $450. Leveraging SkyPilot, a framework for simplified and cost-effective distributed computing, the process utilizes spot instances across multiple cloud providers to minimize expenses. The guide outlines the steps to prepare the training data, set up the distributed training environment using SkyPilot's managed spot feature, and efficiently train the model with optimized configurations. The resulting model, trained on the Pile dataset, achieves impressive performance at a fraction of the cost typically associated with such large-scale training. The post aims to democratize access to large language model training, enabling researchers and developers with limited resources to experiment and innovate in the field.
HN users generally express excitement about the accessibility and cost-effectiveness of training large language models offered by SkyPilot. Several commenters highlight the potential democratizing effect this has on AI research and development, allowing smaller teams and individuals to experiment with LLMs. Some discuss the implications for cloud computing costs, comparing SkyPilot favorably to other cloud providers. A few raise questions about the reproducibility of the claimed results and the long-term viability of relying on spot instances. Others delve into technical details, like the choice of hardware and the use of pre-trained models as starting points. Overall, the sentiment is positive, with many seeing SkyPilot as a valuable tool for the AI community.
The blog post details how Definite integrated concurrent read/write functionality into DuckDB using Apache Arrow Flight. Previously, DuckDB only supported single-writer, multi-reader access. By leveraging Flight's DoPut and DoGet streams, they enabled multiple clients to simultaneously read and write to a DuckDB database. This involved creating a custom Flight server within DuckDB, utilizing transactions to manage concurrency and ensure data consistency. The post highlights performance improvements achieved through this integration, particularly for analytical workloads involving large datasets, and positions it as a key advancement for interactive data analysis and real-time applications. They open-sourced this integration, making concurrent DuckDB access available to a wider audience.
Hacker News users discussed DuckDB's new concurrent read/write feature via Arrow Flight. Several praised the project's rapid progress and innovative approach. Some questioned the performance implications of using Flight for this purpose, particularly regarding overhead. Others expressed interest in specific use cases, such as combining DuckDB with other data tools and querying across distributed datasets. The potential for improved performance with columnar data compared to row-based systems was also highlighted. A few users sought clarification on technical aspects, like the level of concurrency achieved and how it compares to other databases.
Summary of Comments ( 85 )
https://news.ycombinator.com/item?id=43734583
Hacker News users discuss the computational resources used for the Goldbach conjecture verification, questioning the value and novelty of the achievement. Some commenters express skepticism about the significance of extending the verification limit, arguing that it doesn't contribute significantly to proving the conjecture itself. Others point out the inefficiency of the distributed grid computing approach compared to more optimized single-machine implementations. A few users discuss the specific hardware and software used in the project, including the use of BOINC and GPUs, while others debate the proper way to credit contributors in such distributed projects. Several commenters express concern about the lack of available source code and details on the verification methodology, hindering independent verification and analysis.
The Hacker News post discussing the new world record for verifying Goldbach's Conjecture has a modest number of comments, mostly focusing on the technical aspects of the distributed computing approach used and the nature of the conjecture itself.
Several commenters delve into the specifics of the grid computing system employed. One user questions the efficiency gains of this distributed approach compared to utilizing a single, powerful machine, highlighting potential overheads associated with network communication and data transfer. Another commenter speculates on the possibility of optimizing the verification process further by leveraging SIMD (Single Instruction, Multiple Data) instructions, potentially leading to even faster computation times. There's also a brief discussion regarding the memory requirements of such an endeavor, with one commenter suggesting that RAM limitations wouldn't be a major hurdle.
Another thread of discussion revolves around the mathematical implications of the Goldbach Conjecture and the nature of "proof" versus "verification." One commenter points out that while the project provides further strong evidence supporting the conjecture, it doesn't constitute a mathematical proof. They elaborate on the difference between verifying the conjecture up to a certain limit and proving it for all even numbers greater than 2. Another user concurs, adding that despite the impressive scale of the verification, it remains "an interesting data point, not a mathematical breakthrough."
A few comments address the practicalities of the project. One user asks about the availability of the source code, indicating an interest in examining the implementation details. Another commenter questions the overall value of the project, expressing skepticism about the scientific merit of merely pushing the verification limit higher.
Finally, there are some brief exchanges regarding the history of the Goldbach Conjecture and previous attempts to verify it. One commenter mentions a prior effort using BOINC (Berkeley Open Infrastructure for Network Computing) and inquires about the differences between that project and the one discussed in the article.
In summary, the comments section provides a mix of technical insights into the distributed computing aspect of the project, discussions about the mathematical nature of the Goldbach Conjecture, and some pragmatic questions regarding the project's implementation and significance. While there isn't a single overwhelmingly compelling comment, the collective discussion offers a nuanced perspective on the achievement and its limitations.