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The blog discusses a numerical integration technique called Gaussian quadrature, focusing on Chebyshev-Gauss quadrature. This method is useful for calculating definite integrals when exact solutions aren't available.

Key points include:

1. Gaussian Quadrature: It estimates the integral of a function using evaluations at specific points (nodes). Unlike basic methods that need more points to achieve accuracy, Gaussian quadrature can estimate polynomials of a higher order with fewer nodes.

2. Chebyshev-Gauss Quadrature: This variation uses Chebyshev polynomial roots as nodes, which helps reduce errors at the boundaries of the integration interval. The weights for the nodes are fixed, simplifying calculations.

3. Extending to General Functions: The blog explains how to adapt Chebyshev-Gauss quadrature for arbitrary functions and integration intervals, allowing broader applications of this technique.

4. Interactive Demonstration: The author shares an interactive notebook that lets users experiment with different numbers of nodes to see how it affects integration accuracy.

5. Practical Application: The technique is used in the author's library for modeling sea level change, demonstrating its effectiveness in real-world scenarios.

In summary, Chebyshev-Gauss quadrature is a powerful numerical integration method with advantages in accuracy and efficiency, especially useful in various applications.

In a recent keynote at the AI Engineer World’s Fair, the speaker discussed the rapid advancements in large language models (LLMs) over the past six months. They highlighted that over 30 noteworthy models have been released, making it challenging to evaluate their performance effectively. The speaker humorously used the example of asking models to generate an SVG of a pelican riding a bicycle as a unique evaluation method, emphasizing the complexity of the task.

Key developments mentioned include:

• Model Releases: Significant models like Amazon's Nova, Meta's Llama 3.3 70B, and DeepSeek’s large open weight model were introduced, showcasing improved capabilities and affordability.
• Market Impact: The release of DeepSeek's R1 reasoning model affected NVIDIA's stock valuation significantly.
• Local Models: Models like Mistral Small 3 showed that smaller models can perform comparably to larger ones, making them more accessible.
• Innovations: Anthropic's Claude 3.7 Sonnet and OpenAI's GPT-4.5 had varying levels of success, with Claude being praised for its creative outputs.
• Ethics and Bugs: The speaker discussed notable bugs in models, such as ChatGPT being overly sycophantic, and ethical dilemmas where models might report wrongdoing.

The talk concluded with a discussion on the importance of combining tools and reasoning in AI, as well as the risks associated with security and data privacy. The speaker expressed excitement about their newly developed benchmark for evaluating models but acknowledged the need for ongoing innovation in the field.

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The HTML <blink> and <marquee> tags were popular in the 1990s but are now largely forgotten by newer web developers. The <blink> tag was created as a joke by a Netscape engineer, while the <marquee> tag was introduced by Microsoft as a serious feature to create scrolling text. Both tags were used to draw attention to web content, often by wrapping important messages in both tags to cater to users of different browsers.

In the late 1990s, these tags became common despite their poor design and accessibility issues. Although <blink> is now considered obsolete, the <marquee> tag still works in many modern browsers. However, its use is discouraged in current web development due to better, more accessible alternatives. The article reflects on the quirky history of these tags and emphasizes the importance of creating web content that is accessible to all users.

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The text discusses the author's journey into tech independence, self-hosting, and open-source technology, inspired by PewDiePie's videos about building projects and learning Linux. The author emphasizes the importance of owning a domain and hosting a personal blog to build a long-lasting online presence, rather than frequently switching platforms, which can result in lost content and connections.

The author shares their experience of self-hosting various services, like blogs and newsletters, and highlights the joy of creating and using self-built solutions. They advocate for tech independence, which means not relying on major companies for software needs, and encourage others to start small by acquiring their own domain.

Open-source software is celebrated for its collaborative nature and the community behind it, which provides free resources that anyone can use. The author expresses gratitude for open-source tools they use, such as Listmonk for newsletters and GoatCounter for website analytics.

They also mention various self-hosted applications available for home labs, like document management systems and ad blockers, and encourage experimenting with older hardware instead of buying new expensive servers. Ultimately, the author finds great satisfaction in learning through building personal projects and gaining independence from big tech companies.

The text is about options for fitting an image into different sizes, such as 50x50, 320x240, 512x384, 640x480, 800x600, 1024x768, or a custom size. It also mentions saving the image to the desktop.

The article discusses the reality of using Large Language Models (LLMs) in software engineering, particularly focusing on the concept of "agentic coding." The author shares their experience with LLMs, noting that they can significantly speed up tasks like generating SQL statements. However, they argue that despite the hype, LLMs are not capable of independently completing complex software projects without substantial human guidance.

Key points include:

1. LLMs and Software Engineering: While LLMs can assist in writing code, they often require intense supervision and context management from experienced engineers to be effective.

2. Agentic Coding Hype: The notion that LLMs can autonomously handle software tasks is overblown. The author refers to this approach as overly optimistic and compares it to past technological hype.

3. Importance of Context: The quality of the LLM's output heavily depends on the context provided to it. Poor context leads to mediocre results, and this is a significant challenge in making LLMs truly effective.

4. Current Limitations: The author emphasizes that, while LLMs have potential, they are not yet reliable as standalone coding agents and are best used by skilled engineers who can manage their limitations.

Overall, the article presents a cautious view of LLMs in software development, highlighting the need for human oversight and better context management.

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Summary of Fray: General-Purpose Concurrency Testing

Fray is a tool designed for testing concurrency in Java applications to identify and fix issues like race conditions, exceptions, or deadlocks. It uses advanced techniques such as probabilistic testing and partial order sampling and allows for deterministic replay to help debug specific thread interactions.

Getting Started:

• JUnit 5: Use the @ConcurrencyTest annotation for your tests and add @ExtendWith(FrayTestExtension.class) to your test class.

• Other Frameworks: For other testing frameworks, use FrayInTestLauncher to run your concurrency tests.

• Gradle: Add the Fray plugin to your build.gradle file:

  plugins {
      id("org.pastalab.fray.gradle") version "0.5.1"
  }
  

• Maven: Include the Fray plugin and the fray-junit dependency in your pom.xml:

  <plugin>
      <groupId>org.pastalab.fray.maven</groupId>
      <artifactId>fray-plugins-maven</artifactId>
      <version>0.5.1</version>
      <executions>
          <execution>
              <id>prepare-fray</id>
              <goals>
                  <goal>prepare-fray</goal>
              </goals>
          </execution>
      </executions>
  </plugin>
  
  <dependency>
      <groupId>org.pastalab.fray</groupId>
      <artifactId>fray-junit</artifactId>
      <version>0.5.1</version>
      <scope>test</scope>
  </dependency>
  

Additional Resources:

• Documentation includes a technical report, usage guide, IDE settings, and information on bugs found by Fray.
• Contributions are encouraged, and a guide is available for those interested in helping.

Acknowledgements: The project is partly funded by the National Science Foundation and supported by an Amazon Research Award.

The text seems to list some headings or categories related to a platform or project, including "Main Navigation," "Notes," "About," "Instagram," "GitHub," and "Appearance." However, there is no detailed content provided to summarize.

In essence, these headings suggest sections that might provide information or features related to Instagram and GitHub.

Coventry Very Light Rail is a transport project by Coventry City Council. It focuses on planning, development, and regeneration in the area. The project includes information about vehicles and tracks, as well as road tests. For more details or to sign up for updates, you can contact them via email or visit their website.

Summary of BorgBackup Changes:

BorgBackup is undergoing updates, specifically regarding the append-only support feature. Here are the key points:

1. Error Messages: There have been issues loading some pages, but overall, the project is active on GitHub.

2. Recent Changes:

   • A new change was merged to remove support for append-only repositories, which relied on a server-side component.
   • This feature only worked with SSH repositories in Borg 1.x, but is not applicable for other repository types.

3. Current Repository Types for Borg 2:

   • Different types of repositories include filesystem, SFTP, Rclone (for various cloud providers), S3/B3, and SSH.
   • Only SSH repositories can enforce certain features because they have a server-side component.

4. New Approach for Append-Only:

   • The idea is to manage append-only functionality through storage permissions rather than within Borg itself.
   • This means that Borg would use credentials that restrict delete permissions for creating backups.

5. Community Feedback:

   • Some users have expressed the importance of append-only features, with discussions around the need for proper permissions to prevent data loss.

Overall, BorgBackup is evolving to streamline its features while addressing community concerns about data management and security.

Summary of AI-Assisted Development with Claude

Overview:
This post serves as a guide for developers to effectively use AI in software development, particularly with the tool Claude. You will learn how to boost productivity, manage code quality, and understand the best practices for AI-assisted coding.

Key Learnings:

1. 10x Productivity Boost:
   Achieving high productivity is possible through structured practices that leverage AI's strengths while addressing its weaknesses. Developers who adopt disciplined methods can deploy code significantly faster than those who do not.

2. AI Modes in Development:
   There are three distinct modes for using AI in coding:

   • AI as First-Drafter: AI generates initial code, while you focus on design and architecture.
   • AI as Pair-Programmer: You collaborate with AI, refining its suggestions.
   • AI as Validator: AI reviews your code for bugs and improvements.

3. Importance of Testing:
   Humans must write tests; AI should not be allowed to touch test files. Tests encode human intent and understanding of the code's functionality.

4. Documentation:
   Using a structured document (CLAUDE.md) is essential for maintaining context and guiding AI. This document serves as a single source of truth for your project, detailing coding conventions, architecture decisions, and project-specific guidelines.

5. Git Workflow:
   Use git worktrees to create isolated environments for experimenting with AI-generated code, ensuring your main branch remains clean.

6. Cultural Shift:
   Transparency about using AI is crucial. Tagging commits that involve significant AI assistance helps maintain accountability and clarity in the development process.

7. Boundaries for AI:
   Certain areas, such as test files, database migrations, and security-critical code, should never be modified by AI. Establishing clear rules around AI's role is vital for maintaining code integrity.

8. Future of AI in Development:
   The tools are evolving, and future AI capabilities may include better understanding of codebases and proactive suggestions, but humans will always set the direction.

Action Plan:

• Start by creating a CLAUDE.md for your project.
• Add anchor comments to complex code.
• Experiment with a small AI-assisted feature with clear boundaries.
• Foster a culture of transparency and shared understanding of AI's role in your team.

By following these practices, developers can harness the potential of AI to enhance their coding processes while ensuring quality and maintainability.

Japanese researchers have created a new type of transparent paper using cellulose from plant biomass. This biodegradable material can break down into water and carbon dioxide, making it a potential alternative to plastic containers, which contribute to ocean pollution.

The transparent paper is about 0.7 millimeters thick and allows clear visibility through it. It is strong enough to be shaped into items like cups and straws, comparable in strength to certain plastics. The manufacturing process involves dissolving cellulose powder in a lithium bromide-water solution and heating it to form a gel, which is then shaped and dried.

Tests showed that the paper dissolves in the ocean, mostly breaking down within four months, even at significant depths. While paper packs are common, consumers often prefer clear packaging to see contents. This transparent paper could address that issue, but mass production requires specialized factories. The production cost is expected to be about three times that of regular paper, but carbon emissions can be reduced by half compared to plastic production.

Summary of Radiant AI in Oblivion

Radiant AI was a highly anticipated feature in the RPG The Elder Scrolls IV: Oblivion, designed to create a dynamic game world with over a thousand non-player characters (NPCs) that could act independently based on their individual needs and goals. The promise was that NPCs would have their own daily schedules, engaging in activities like shopping, eating, and exploring without being directly scripted.

Initially announced in 2004 and showcased in a 2005 E3 demo, Radiant AI was touted as a groundbreaking technology that would allow NPCs to make choices and interact in more lifelike ways. However, upon release, many of these ambitious features were either scaled back or not fully realized, leading to disappointment among fans who felt misled by Bethesda’s marketing.

The remastered version of Oblivion has reignited discussions about Radiant AI, highlighting its controversial legacy within the gaming community. The article seeks to clarify misconceptions about Radiant AI, exploring its origins, its actual implementation in Oblivion, and its evolution in later Bethesda games like Fallout 3, Skyrim, and Starfield.

Overall, while Radiant AI aimed to revolutionize NPC behavior in gaming, the reality fell short of expectations, contributing to ongoing debates about game development promises and player experiences.

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Railway has introduced Railpack, a new app building tool that replaces Nixpacks. Railpack was developed after learning from the use of Nixpacks, which served 80% of users well but left 200,000 users facing limitations. Here are the main features of Railpack:

• Granular Versioning: It offers better version control for packages.
• Smaller Builds: Railpack significantly reduces image sizes (38% for Node and 77% for Python), leading to faster deployments.
• Improved Caching: It enhances caching efficiency by directly integrating with BuildKit.

Nixpacks had issues with its versioning system and image sizes, which made it difficult for users to manage dependencies. Railpack addresses these problems by providing a clearer approach to dependency management and building processes. It also offers improved secret management for environment variables.

Railpack works in three steps: analyzing the code, planning the build process, and generating a build graph for efficient image construction. It supports various frameworks and languages, including Node, Python, Go, and popular static site generators. Railpack is currently in beta and is open source, with plans for ongoing support and feature expansion.

The article discusses the importance of program optimization, particularly using the programming language Zig. Here are the key points:

1. Importance of Optimization: Optimizing code leads to faster performance and cost savings. Well-optimized programs can reduce the need for expensive infrastructure.

2. Compiler Trust: While modern compilers are powerful, they can still produce suboptimal code. Developers should provide clear information to compilers and sometimes adjust their code to help achieve better optimization.

3. Low-Level vs. High-Level Languages: Low-level languages, like Zig, allow for more precise control over code, which helps compilers optimize better. High-level languages can introduce overhead due to additional features like garbage collection.

4. Zig's Advantages: Zig’s verbosity allows developers to convey more intent to the compiler, which can lead to more efficient code generation. Zig's features, like non-optional pointers and compile-time execution (comptime), enhance optimization capabilities.

5. Comptime: Zig's comptime allows for code execution at compile time, enabling better optimization without the complex syntax of traditional macros. It can generate constants and optimized structures based on known data at compile time.

6. String Comparison Example: The article illustrates how comptime can improve string comparison efficiency by allowing the compiler to generate optimized assembly code when one string is known at compile time.

7. Conclusion: Zig’s approach to comptime makes writing performant code simpler and more effective compared to other languages with less clean solutions for generics and code generation. The author advocates for an end to language wars, emphasizing that the focus should be on effective coding practices rather than on language superiority.

Overall, the article promotes Zig as a powerful tool for creating optimized software through its unique features.

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Summary: Discovering and Debugging a JDK Race Condition with Fray

The author added integration tests for a tool called Fray and encountered a deadlock issue while testing a piece of code involving a ScheduledThreadPoolExecutor. Initially, they thought the problem was with Fray, but it turned out to be a bug in the Java Development Kit (JDK).

The code creates an executor, schedules a task, and starts a thread to shut down the executor. The deadlock occurs because when the executor is in the SHUTDOWN state, the task may block indefinitely instead of being executed or canceled. This happens due to a race condition where the scheduling and shutdown methods interfere with each other.

Fray helped debug the issue by allowing the author to replay the execution step-by-step, revealing the exact interleaving of threads that caused the bug. This feature made it easier to understand the problem, which is often hard to reproduce with regular debugging tools.

To replicate the bug, the author provided steps for others to run the test with Fray and see the issue in action. They also reported the bug to the JDK team, noting that their patch to trigger the bug was not included in the final report.

Fray proves to be a valuable tool for debugging concurrency issues, especially in complex systems like the JDK.

CDImage Overview

CDImage is a tool designed to burn images onto compact discs.

Background and Acknowledgments The project draws inspiration from two earlier attempts made by users on Instructables, one about 15 years ago and another by a user named [unDEFER]. The author credits their work, particularly in coordinate conversion, as foundational to this project. After some experimentation and creating a user-friendly interface, the author abandoned the project in 2008 due to calibration challenges for different disc types. Recently, they revived the project, updated the code, and made it compatible with modern software.

Building the Tool To build CDImage, you need the Qt 6 library. Users can run commands in the terminal or use Qt Creator. A Windows binary version is also available, though it hasn't been thoroughly tested.

Before You Start It's crucial that your compact disc model is recognized by the software; otherwise, you may face issues. Each disc has unique geometric properties, which affect the image burning process. If your disc isn't listed, guessing its geometry can lead to mistakes and wasted discs.

Usage Instructions

1. Load a high-contrast image file.
2. Adjust the image using mouse controls (move, center, zoom).
3. Create a track by selecting your disc model or entering the geometry manually if not listed.
4. The output will be a large audio track, approximately 800MB, which you can burn to the disc using various software.

Calibration Considerations Calibrating discs involves optimizing multiple criteria, which can be time-consuming. The author suggests methods to improve calibration, including the potential use of AI and image recognition to automate expert feedback.

Further Reading For more insights, the author provides links to the original methods, the "Red Book" standard, and relevant projects for deeper understanding.

Understanding what affects how quickly software is developed is important for teams and organizations, but there is limited large-scale evidence on this topic. This study looks at "cycle time," which measures the duration from when a task is created to when it is completed. Using data from over 55,000 cases across 216 organizations, the researchers analyzed how factors like coding time, task scope, and collaboration impact cycle time, revealing significant variability in results.

They found that while factors such as the number of coding days per week and collaborative efforts do influence cycle time, there is still a lot of unpredictability in the results. This means that a single measurement might not accurately reflect typical performance. The research emphasizes the importance of analyzing complex metrics carefully and warns against using them solely for decision-making. Ultimately, enhancing software delivery speed may require looking at the system as a whole rather than just individual contributions.

A Washington Post tech columnist warns that Meta's Facebook and Instagram apps, along with Yandex, have been secretly collecting user data without effective privacy settings to stop them. Researchers found that these companies bypassed Google's security measures on Android devices. To protect your privacy, consider the following steps:

1. Stop using the Chrome browser. Instead, use browsers like Firefox, Brave, or DuckDuckGo, which block many tracking methods. Safari also offers good privacy features for Apple users, but it's not perfect.

2. Delete any Meta and Yandex apps from your phone, as these apps allow the companies to gather more personal information, such as your location and device details.

3. Even without Meta apps, the company may still track your online activities through other means.

In summary, switching browsers and removing certain apps can help reduce your data privacy risks.

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Summary:

In November 2023, the author took on the role of FreeBSD release engineering lead while continuing to maintain FreeBSD on Amazon EC2. With limited funding and increasing responsibilities, they worried about their ability to effectively manage the FreeBSD/EC2 platform. After discussions, Amazon agreed to sponsor the author's work for a year.

Throughout the sponsorship, the author spent about 50 hours per month on FreeBSD-related tasks, including managing multiple FreeBSD releases (13.4, 14.2, 13.5, and 14.3). They worked on important features for FreeBSD/EC2, including a power driver for AWS Graviton instances and improving device hotplug functionality.

The author also addressed performance issues, notably speeding up boot times for FreeBSD instances by optimizing entropy seeding and adjusting filesystem settings. They added new AMI flavors and cleaned up old images to save storage space.

Despite their significant contributions, the author anticipates having less time for this work in the future as the sponsorship ends, which may delay some planned features. They express gratitude for the opportunity provided by Amazon and pride in their accomplishments.

In June 2025, a new initiative called FAIR (Federated and Independent Repositories) was introduced to improve the WordPress ecosystem. The project arose from concerns about the concentration of power and lack of transparent governance in WordPress.

Key developments included:

1. Community Response: In December, a letter from twenty core contributors called for governance reform, highlighting the need for more options within the ecosystem.
2. Collaboration: Various groups began working together to tackle issues like plugin update services and centralization, leading to a stronger collective effort.
3. FAIR's Creation: FAIR is now a technical project under the Linux Foundation, governed by a community-led Technical Steering Committee. It aims to create a decentralized distribution layer for WordPress without forking the core software.

FAIR is designed to enhance user control over plugin delivery and promote decentralization while maintaining compatibility with existing WordPress systems. It represents a collaborative effort to build better infrastructure and governance for WordPress, emphasizing the importance of community involvement in open-source projects.

For more information, you can visit fair.pm.

President Trump and Elon Musk are engaged in a public feud that began on June 5, involving threats about SpaceX contracts. Musk criticized a budget bill supported by Trump, leading to a social media exchange where Trump suggested canceling government contracts with Musk's companies, including SpaceX. Musk interpreted this as a threat to SpaceX, stating he would start decommissioning the Dragon spacecraft.

The dispute raised concerns that the government might cut SpaceX off from crucial contracts with NASA and the Defense Department, which heavily rely on SpaceX for launch services. Musk later retracted his decommissioning threat after advice from an online user.

NASA remained neutral during the exchanges, emphasizing its commitment to working with industry partners. Additionally, Trump confirmed he withdrew the nomination of Jared Isaacman for NASA administrator, stating he preferred not to appoint a Democrat to the position.

The paper discusses the strengths and weaknesses of Large Reasoning Models (LRMs) in handling complex reasoning tasks. While LRMs, which are advanced versions of Large Language Models (LLMs), show improved reasoning capabilities, their true effectiveness is still not fully understood.

Key Points:

1. Performance Evaluation: Traditional evaluations focus on final answer accuracy, often overlooking how LRMs generate their reasoning. This study uses controlled puzzle environments to analyze the internal reasoning processes of LRMs, allowing for a more comprehensive evaluation.

2. Findings on Complexity:

   • LRMs perform well on simple problems but struggle as complexity increases. They face a significant drop in accuracy beyond a certain complexity level.
   • There are three performance categories based on problem complexity:
     • Low complexity: Standard LLMs outperform LRMs.
     • Medium complexity: LRMs show advantages due to their reasoning capabilities.
     • High complexity: Both models fail, with LRMs unexpectedly reducing their reasoning effort despite having enough resources.

3. Reasoning Patterns: Analysis of reasoning traces showed that:

   • In simple tasks, LRMs find correct solutions quickly but often explore incorrect paths.
   • In moderate tasks, they initially explore incorrect solutions before finding the correct one.
   • In complex tasks, they fail to provide correct solutions altogether.

4. Limitations: LRMs struggle with exact computations and do not consistently apply algorithms effectively. Their reasoning processes are inefficient, leading to wasted computational resources.

5. Implications: These findings raise important questions about the overall reasoning capabilities of LRMs and suggest the need for further research to improve their performance in complex problem-solving scenarios.

In summary, while LRMs represent a significant advancement in reasoning models, they still exhibit notable limitations, especially as problem complexity increases. The study calls for new evaluation methods and deeper understanding of these models' reasoning processes.

Summary: Why are Smokestacks So Tall?

Smokestacks are tall structures used in industrial facilities to manage air pollution. Their height helps disperse harmful emissions from burning fuels, reducing their concentration in populated areas.

1. Purpose: Smokestacks improve air quality by allowing hot, buoyant gases from combustion to rise and spread out, which dilutes pollutants. This helps meet air quality standards set by environmental regulations.

2. Engineering Challenges: Designing smokestacks involves balancing various factors, such as temperature, height, and atmospheric conditions. Engineers use simulations to predict how pollutants disperse and ensure they don't exceed safety limits.

3. Historical Context: Originally, smokestacks improved combustion efficiency in the 19th century. Over time, as pollution regulations became stricter, their role in enhancing air quality became more important.

4. Complexity of Air Pollution: Different environmental factors like wind, temperature, and surrounding structures affect how pollutants behave once released. Engineers must consider all these variables to design effective smokestacks.

5. Conclusion: Smokestacks are crucial for managing air quality in industrial settings. Their design is complicated and costly, but necessary for public health and environmental safety.

No summary available.

No summary available.

Summary of Computational Complexity Post

A significant breakthrough in computational complexity was recently announced by Ryan Williams. He demonstrated that algorithms can be simulated using much less memory than previously thought. Specifically, his result shows that for any algorithm that runs in time t(n), it can be simulated in space t(n) log t(n), a major improvement over the previous best of t(n)/log t(n).

Williams' proof builds on a space-efficient algorithm developed by James Cook and Ian Mertz, which uses clever techniques involving tree evaluations and finite fields. This new finding is applicable to certain types of Turing machines and suggests that it might be possible to further improve the space efficiency of simulations.

The implications of this theorem are significant, as they challenge longstanding assumptions in complexity theory, particularly regarding the relationship between time and space in computation. Future research may explore how to push these results even further to separate complexity classes like P and PSPACE.

Maintaining an Android app can be quite challenging, especially for hobby developers. Here are the key points:

1. Language Compatibility: Kotlin is the preferred language for new Android projects, but older Java projects may face compatibility issues as libraries shift to Kotlin.

2. Frequent Library Changes: Google often makes breaking changes to its libraries, requiring developers to adapt their code frequently. For example, the ExoPlayer library has undergone several incompatible updates.

3. Deprecation of Support: Some libraries and features are deprecated without clear migration paths. For instance, the Google Ads library dropped support for older Android versions without explanation.

4. Evolving UI Guidelines: Android's design guidelines change unpredictably, making it difficult to keep up with new standards.

5. Versioning Confusion: Android uses two versioning schemes, complicating understanding and debugging for developers.

6. Forced Upgrades: Maintaining compatibility often requires upgrading multiple components of the app, which can lead to issues if not managed properly.

In conclusion, if you're considering developing an Android app as a hobby, be prepared for ongoing maintenance challenges and the need to adapt to frequent changes in the platform.

The Federal Aviation Administration (FAA) has announced plans to modernize the U.S. air traffic control (ATC) system, which currently relies on outdated technology like floppy disks and Windows 95. The acting FAA administrator, Chris Rocheleau, emphasized the need to replace these old systems for safety and efficiency. Transportation Secretary Sean Duffy called the upgrade one of the most important infrastructure projects in decades, with bipartisan support.

Upgrading the ATC system is complicated, as some systems must remain operational for safety reasons, and any new systems need to be secure against hacking. The FAA is currently gathering information from companies that can help with this modernization and hopes to complete the project within four years, although experts believe this timeline may be unrealistic. The need for updates has been recognized for many years, and this project aims to address the long-standing issues with the current infrastructure.

The text discusses experiences related to conflict and peace. It highlights how people face challenges and disagreements, but also emphasizes the importance of finding ways to achieve harmony and understanding. The key points focus on the need for communication, empathy, and cooperation to resolve conflicts and promote peaceful relationships.

NASA has decided to cancel its Europa Lander mission, which was designed to explore Jupiter's moon Europa, believed to have an ocean beneath its icy surface. After a decade of development, the lander proved ready for the harsh conditions but faced challenges such as radiation exposure and budget cuts, leading to its cancellation.

However, engineers are now advocating for the lander to be repurposed for a mission to Enceladus, one of Saturn's moons, which also has a subsurface ocean and is more suitable for exploration. Despite the setback, the team hopes the lander can still have its chance to investigate the potential for life beyond Earth.

Dr. Drang recently reviewed Raúl Rojas’s book, The Language of Mathematics: The Stories behind the Symbols, which explores the history and standardization of mathematical symbols. Although the book is older, the English translation is new.

The book consists of nine chapters, each discussing different mathematical symbols, and the sections can be read independently. Dr. Drang found an interesting section on the usage frequency of symbols derived from math papers and textbooks, which had not been analyzed before in this way.

He noted some errors in the frequency table, such as repeated symbols and unclear entries, prompting him to investigate the sources of the data. The analysis was based on the work of Clare M. So and her advisor Stephen M. Watt, who studied a large dataset of papers and textbooks.

Dr. Drang pointed out that some of the symbols in Rojas’s table were misrepresented, and he questioned the inclusion of punctuation marks like commas and periods, which are not true mathematical operators but are used in equations. His exploration highlights the complexities and quirks of mathematical notation and its representation in academic texts.

Summary of Reverse Engineering Cursor's LLM Client

On June 5, 2025, Viraj Mehta, Aaron Hill, and Gabriel Bianconi explored how to enhance Cursor, a coding assistant powered by Large Language Models (LLMs), using a tool called TensorZero. This tool allows engineers to optimize LLM applications by collecting feedback and metrics. They set up TensorZero as a proxy between Cursor and LLM providers to analyze the requests made by Cursor, aiming to improve its performance for individual users.

Key Points:

1. TensorZero Setup: They connected TensorZero to Cursor to observe all LLM interactions, allowing for experimentation with different models and prompts while storing data for analysis.

2. Challenges Faced:

   • Initial Connection Issues: Cursor was unable to connect to TensorZero since it first routed requests through its servers. They resolved this by creating a public endpoint with Ngrok and securing it with Nginx.
   • CORS Issues: They also had to configure Nginx to handle CORS (Cross-Origin Resource Sharing) requests to allow communication between Cursor and TensorZero.

3. Observability Achieved: After overcoming these challenges, they could see detailed interactions within Cursor, including prompts and user information, which could inform further optimizations.

4. Prompt Engineering: The team discussed the effectiveness of prompt engineering, noting how concise prompts could guide the AI in responding accurately and effectively.

5. Model Testing: They have been testing various models using TensorZero without noticeable delays, providing a stable experience.

6. Future Work: The authors plan to release more insights on analyzing AI coding assistant usage and improving Cursor based on individual user patterns.

The blog encourages readers to replicate their setup to analyze their own AI coding assistant usage and stay tuned for future updates.

Sandia National Labs has launched a new supercomputer called SpiNNaker 2, which is designed to mimic brain functions without using traditional GPUs or internal storage. This brain-inspired system, supplied by SpiNNcloud, can simulate between 150 and 180 million neurons and is part of a project funded by the NNSA's Advanced Simulation and Computing program.

The SpiNNaker 2 architecture is based on a design by Steve Furber and features 48 chips per server board, each with specialized cores and memory, allowing for high-speed data processing. The system's memory capacity is significant, with up to 138,240 TB of DRAM available in larger configurations, enabling it to handle complex simulations efficiently.

The supercomputer is integrated with existing high-performance computing systems and relies on SRAM and DRAM for data storage, eliminating the need for traditional disk storage. This setup allows for faster processing speeds and greater energy efficiency, making it particularly suitable for national security tasks and advanced defense applications.

Summary: A Masochist's Guide to Web Development

This guide details the author's journey of creating a web application for a Rubik's cube solver using C code compiled to WebAssembly (WASM) with Emscripten. The process was complex and challenging, but ultimately rewarding.

Key Points:

1. Purpose and Audience: The guide is aimed at C/C++ developers wanting to port their applications to the web, rather than traditional web developers. It's not a beginner's tutorial; readers should be ready for a tough learning experience.

2. WebAssembly: WASM allows for high-performance applications in web browsers, offering near-native speed. It’s a lower-level language designed for efficient web execution.

3. Setup Requirements: To follow along, users need Emscripten and a web server (like darkhttpd). The examples provided are tested on UNIX systems.

4. Hello World Example: The guide begins with a simple C program, showing how to compile it into WASM and run it in a browser. It emphasizes the importance of building libraries for interaction with JavaScript.

5. JavaScript and the DOM: For creating interactive web applications, familiarity with JavaScript and the Document Object Model (DOM) is essential. The guide includes examples of manipulating HTML elements with JavaScript.

6. Building a Library: The author demonstrates how to create a C library and call its functions from JavaScript, discussing details like function naming and exporting.

7. Multithreading: The guide covers how to implement multithreading in web applications using pthreads, along with necessary server configurations to support multithreaded code.

8. Web Workers: It explains how to offload heavy computations to web workers to keep the UI responsive, along with code examples for setting up communication between the main thread and workers.

9. Callback Functions: The guide discusses integrating callback functions into C code, which allows for better interaction between JavaScript and C.

10. Persistent Storage: It explains how to use IndexedDB for storing data persistently in the browser, overcoming the limitations of typical file handling in web environments.

11. Closing Thoughts: The author reflects on the complexity of web development, emphasizing that while Emscripten provides powerful abstractions, these abstractions can "leak," requiring developers to understand underlying web technologies.

Overall, the guide serves as a personal reflection on the author's challenges and learning experiences while developing a web application from C code, providing insights into both technical and conceptual aspects of web development with WASM.

Efficient Matrix Transpose in Mojo: Summary

This blog post explains how to implement a highly efficient matrix transpose operation using the Mojo programming language on the Hopper architecture. The best performance achieved is a bandwidth of 2775.49 GB/s, similar to the 2771.35 GB/s achieved using CUDA on the same hardware.

Key Points:

1. Matrix Transpose Basics: The process involves taking a tile of data, transposing it, and placing it in the correct position in the matrix.

2. Descriptors: Two TMA (Tensor Memory Access) descriptors are created to manage the data's layout in memory.

3. Kernel Implementation:

   • The kernel first loads data into shared memory.
   • It then computes the transpose and stores the result back to global memory.
   • Using shared memory helps achieve higher speeds.

4. Swizzling: This technique rearranges data to enhance memory access patterns. The descriptors and indices are adjusted to implement swizzling.

5. Thread Coarsening: By having each thread process multiple columns, the kernel achieves even higher performance, reaching a bandwidth of 2775.49 GB/s.

Conclusion:

The blog demonstrates how to optimize GPU tasks using Mojo, achieving performance comparable to traditional CUDA methods. The complete code is available on GitHub for further exploration.

No summary available.

The text discusses gradient noise, specifically focusing on Perlin noise, which is widely used in creative fields like visual effects and video games. It emphasizes the importance of understanding gradient noise starting from 1D and progressing to higher dimensions, using GPU-based implementations in WebGL2/GLSL.

Key Points:

1. Gradient Noise: A tool for creating visually interesting patterns, often implemented in creative work.
2. Hashing Function: A deterministic method is needed to generate pseudo-random values based on integer coordinates. A specific hashing function, lowbias32, is used for this purpose.
3. Dimensional Expansion: The text explains how to extend the noise generation from 1D to 2D and 3D, including methods for bilinear and trilinear interpolation.
4. Fractal Brownian Motion (fBm): This technique combines multiple octaves of noise to create more complex patterns by adjusting frequency and amplitude.
5. Derivatives: The importance of calculating derivatives of noise functions is highlighted for applications like lighting and terrain generation.
6. Future Directions: The text suggests exploring alternative noise methods like OpenSimplex and techniques such as domain warping for advanced effects.

Overall, the article serves as a comprehensive guide to understanding and implementing gradient noise in various dimensions, with a focus on practical applications and deeper mathematical principles.

The main goal of cognitive modeling is to create models that can predict human behavior and explain the mental processes behind it. Traditional neural network models often predict well but do not clearly explain how they work. This study looks at using large language models (LLMs) as cognitive models that can both predict behavior accurately and provide understandable explanations. The researchers used reinforcement learning to help these models generate clear reasoning for why people make risky choices. Their results show that this method produces good explanations and also makes accurate predictions about human decisions.

No summary available.

At FlightAware, our software must adapt to various unexpected situations in aviation data, which is often messy and inconsistent. Here are some common misconceptions about aviation that can lead to errors:

1. Flights:

   • Flights can depart from places other than gates and may leave multiple times or have different flight numbers.
   • Flight schedules can change, and a flight number on a ticket may not be the same as what's used by pilots or air traffic control.
   • Flights can have overlapping flight numbers, especially among flights from the same airline.

2. Airports:

   • Airports can have multiple codes and may not always have a fixed location.
   • Not everything with an IATA or ICAO code is necessarily an airport.

3. Airlines:

   • Airlines may share codes or have different codes for the same flight; they might also assign flight numbers to flights they do not operate.

4. Navigation:

   • Waypoint names can overlap, and not all flight information is as accurate as it should be.

5. Transponders and ADS-B:

   • ADS-B messages can come from various sources and may not always be accurate.
   • Transponders can malfunction, and flight information can be incorrectly transmitted.

These misconceptions highlight the complexities our flight tracking engine, Hyperfeed, must navigate to ensure reliable data for our users.

AI Sprite Generator Overview

The AI Sprite Generator allows users to create professional game animation sprites quickly and easily. Here are the key points:

• How It Works:

  1. Upload your character image or describe it with text.
  2. Choose from a variety of animation types.
  3. Download high-quality, ready-to-use sprites.

• Features:

  • AI-powered generation for smooth animations.
  • Supports various actions like walking, jumping, and attacking.
  • Offers multiple art styles, from retro to modern.
  • Allows custom action training for unique gameplay needs.

• Target Users:

  • Indie developers can create sprites without hiring artists.
  • Game studios can save time and costs by generating character sets quickly.
  • Artists can enhance their workflow with AI-generated animations.

• Training Options:

  • Users can train their own action models for free with as few as 5 samples.
  • Options to keep models private or share them for revenue.

• Pricing:

  • Pay-as-you-go model with no subscriptions. Credits can be purchased, and they never expire.
  • Different credit packs available for various user needs, with one-time payments.

• Additional Support:

  • Customized AI solutions for game studios to enhance productivity.

• FAQs: The platform provides answers to common questions regarding file formats, commercial use, generation time, and refunds.

Overall, the AI Sprite Generator is a powerful tool for anyone involved in game development, offering an efficient way to create and manage animation sprites.

The text is asking whether to use a carousel, suggesting a decision needs to be made about it. It includes navigation options labeled "next" and "prev," indicating there may be more information or choices available.

Odyc.js is a simple JavaScript library that allows anyone to create video games, even if they don't have any programming experience. It provides tools to help you design and build your own games easily. You can also explore a gallery of games made using Odyc.js.

Summary of Monaspace:

Monaspace is a new superfamily of monospaced fonts designed specifically for coding. It includes five different fonts and allows for customization along three axes: weight, width, and slant. This type system enhances the way code is displayed, making it more expressive and easier to read.

Historically, monospaced fonts have limitations due to their uniform width, which can create uneven text density. Monaspace introduces a feature called "texture healing," which improves the visual appearance of code by adjusting the spacing between characters for better readability while still conforming to a monospaced grid.

Monaspace also supports ligatures for various programming languages, allowing users to enable specific styles based on their coding needs. It aims to create a better coding experience by allowing developers to mix and match fonts seamlessly and improving how code suggestions are visually represented.

Overall, Monaspace combines functionality and aesthetics to enhance the coding experience for developers.

No summary available.

No summary available.

Summary:

Kir Shatrov explores Claude Code, a tool by Anthropic, to understand its inner workings and why it's slower and costlier than alternatives like Cursor. He uses a tool called mitmproxy to capture Claude Code's prompts.

When testing Claude Code with his blog repository, he finds it analyzes user input to decide if it's a new topic, extracts titles, and formats responses as JSON. It also has access to the project’s Git status and directory structure, enhancing its ability to provide relevant information.

For example, when asked to describe his project, Claude Code generated a summary of his personal website/blog built with Next.js, React, and Tailwind CSS. This process took 40 seconds and cost $0.11.

Shatrov also tested writing a Bash script to fetch the top story from Hacker News, but encountered security restrictions preventing access to external URLs. Despite this, Claude Code provided a workaround by outlining steps to create the script.

The tool operates with a safety policy that assesses potential risks when executing Bash commands, which can slow down the process due to required confirmations.

Overall, Claude Code offers a secure and user-friendly experience, albeit at a higher cost and slower speed compared to other tools. Shatrov prefers its UX, though he notes that for simpler tasks, cheaper options like GPT-3.5-turbo could be more efficient.

Summary: CEO Convicted of $1 Billion Health Care Fraud

On June 3, 2025, Gary Cox, the CEO of Power Mobility Doctor Rx, LLC, was found guilty of a massive fraud scheme that defrauded Medicare and other health programs of over $1 billion. Cox and his associates created fake doctors' orders for unnecessary medical items, targeting Medicare beneficiaries through misleading advertisements and calls. They used an online platform to connect pharmacies and medical suppliers with telemedicine companies that paid doctors to sign orders without proper evaluations.

The scheme involved illegal kickbacks, leading to more than $360 million in payments from Medicare. Cox faces serious charges, including conspiracy and health care fraud, with potential prison sentences totaling up to 20 years. Authorities emphasized their commitment to fighting health care fraud and protecting taxpayer money. A sentencing date will be determined later.

Summary: Too Many Open Files

The author encountered an error while running tests on a Rust project, specifically: "Too many open files." This error occurs when a program exceeds the allowed number of open file descriptors (fd) in Unix-like systems.

Key Points:

1. File Descriptors Explained: File descriptors are integers that the operating system uses to identify open files and other resources like directories, pipes, sockets, and devices. Every Unix process starts with three default file descriptors for input, output, and error.

2. Limits on Open Files:

   • Each operating system sets limits on how many file descriptors can be open at once to prevent resource hogging.
   • On macOS, the maximum number of open file descriptors system-wide is 245,760, while a single process can open up to 122,880.
   • Each shell has a "soft" limit (default 256), which can be raised by the user.

3. Diagnosing the Problem: The author suspected that the test command was reaching the soft limit of 256 file descriptors. To investigate, they created a monitoring script to track the number of open file descriptors while running the tests.

4. Finding a Solution: The monitoring revealed that failures began when the number of open file descriptors reached 237. The author then increased the soft limit to 8,192 using the ulimit command, which resolved the error.

5. Learning Outcome: This experience taught the author about file descriptors and how to troubleshoot the "Too many open files" error, which can occur in various projects.

In conclusion, understanding file descriptors and their limits is crucial for managing resources effectively in programming.

The attention mechanism in Transformers is key for effective sequence modeling, but it has issues with high compute and memory demands. Linear attention and state-space models improve efficiency by allowing for faster processing and lower memory use. However, these models still rely on a fixed-size hidden state, which limits their ability to represent context.

This paper introduces log-linear attention, which combines the efficiency of linear attention with the flexibility of softmax attention. Instead of a fixed hidden state, log-linear attention uses a growing set of hidden states, allowing for better context modeling. It maintains efficient computation that scales log-linearly with sequence length.

Log-linear attention can enhance existing linear attention models. The paper demonstrates this by applying it to two recent architectures, Mamba-2 and Gated DeltaNet, showing that they perform well compared to their linear variants.

The author discusses their experience creating a program for job interviews at the NRAO, which involves calculating scan times using different programming languages: Haskell, Common Lisp, and Smalltalk. They find that Haskell's syntax and structure resonate with their preferred coding style, making it more enjoyable and clearer compared to the other languages.

In Haskell, they appreciate how the code feels modular and expressive, whereas they struggle with the complexity and awkwardness of Lisp and Smalltalk. The author reflects on their preference for Haskell, noting that while it may not be objectively better than other languages, it aligns better with their coding approach. They also acknowledge their reliance on Haskell's compiler for catching errors, contrasting it with their experiences in Lisp and Smalltalk, where mistakes often go unnoticed until later in the coding process.

Ultimately, the author recognizes that programming is an evolving journey, filled with questions about language features, dependency management, and personal preferences in coding style. They express a desire to explore new tools like Autotest, which could improve testing and development processes.

No summary available.

The article discusses the advantages of open source large language models (LLMs) over closed source models for common tasks. As businesses often use closed models like GPT and Claude, they may overlook significant cost savings and performance improvements offered by open source alternatives.

While closed source models excel in complex tasks, many everyday tasks like summarization, classification, and data extraction don't need such advanced capabilities. Open source models can perform equally well or better for these tasks, often at a lower cost.

The article categorizes models into "frontier" (the most capable but expensive) and "workhorse" models (suitable for routine tasks). It highlights how workhorse open source models can deliver better performance-to-cost ratios, especially when used through batch processing.

A comparison chart is provided, showing that many open source models outperform their closed source counterparts while being more cost-effective. For example, models like Qwen3 offer significant savings and competitive performance.

In conclusion, businesses should consider switching to open source LLMs to maximize performance and minimize costs, particularly for routine tasks. The article encourages organizations to explore these alternatives for better efficiency.

Summary:

Dreamstar is a new luxury overnight train startup aiming to revive the elegance of train travel between Los Angeles and San Francisco, reminiscent of the 1940s. Co-founders Joshua Dominic and Thomas Eastmond want to create a service offering all-bedroom accommodations, gourmet dining, and high-end hospitality. Their inspiration stems from their disappointments with current American travel options compared to experiences in Europe and Asia.

The startup plans to use tracks from Union Pacific, which has minimal overnight freight traffic, to operate its services. Dreamstar aims to provide a limited-stop, overnight journey designed for maximum comfort, claiming to cut carbon dioxide emissions by 75% compared to flying.

While ticket prices are still being finalized, they intend to be competitive with similar travel options, and tickets will be sold online. The train will offer four classes of private cabins, lounges, dining areas, and even a spa.

Dreamstar is currently working on design and engineering phases, with plans to start construction of the train cars and to launch the service before the 2028 Olympics in Los Angeles. They are seeking suppliers and have received some investment support while focusing on building an efficient and sustainable business model.

Summary: Curating Shell History

Simon Tatham suggests turning off your shell history file by using the command unset HISTFILE in your .bashrc. This way, you can access commands within a single shell session but won't keep records between sessions. Tatham prefers this method because it helps him focus on only the successful commands, avoiding clutter from failed attempts.

In contrast, the author of this summary prefers to keep a large shell history, as they find it useful for recalling how to perform tasks. However, they agree with Tatham that it's unnecessary to save all failed commands, which can lead to confusion.

To manage their history better, the author created a function called smite that allows them to easily delete unwanted commands from their history. This function uses a tool called fzf to display history entries, enabling the user to select and remove multiple commands at once.

Overall, the author encourages others to think about how they use their shell history and consider ways to improve it, such as removing unnecessary entries to keep it tidy.

No summary available.

Meta is turning private AI chats into public content without many users realizing it. The Mozilla community is urging Meta to stop this practice until better privacy protections are established. They want AI interactions to be private by default, require informed consent for any public sharing, and ensure transparency about how many users have unintentionally shared private information. Additionally, they call for a simple opt-out system for all Meta platforms to prevent user data from being used in AI training and for users to be notified if their conversations become public, with the option to delete their content permanently. People deserve to know when they're speaking publicly, especially if they think it's private. If you agree, you can support the demand for Meta to change its policies.

Anysphere has raised $900 million in funding, valuing the company at $9.9 billion. This investment will support the development of Cursor, which has achieved over $500 million in annual recurring revenue (ARR) and is used by many major companies, including NVIDIA, Uber, and Adobe. Anysphere aims to advance AI coding research with this funding and is excited about the progress made so far.

The ProPublica report reveals serious flaws in an AI system used by the Trump administration to evaluate contracts for the Department of Veterans Affairs (VA). This system was created to identify contracts that could be canceled, particularly those not directly supporting patient care. However, experts found that the AI made many errors due to unclear instructions and outdated technology.

Key issues included:

• The AI flagged essential contracts, such as those for internet services, as cancelable, misinterpreting vague guidelines.
• It inaccurately assessed contract values and did not analyze complete contract texts, leading to misleading conclusions.
• The prompt given to the AI contained irrelevant information, which confused its decision-making process.

Sahil Lavingia, the developer of the AI code, admitted to mistakes and cited time constraints and lack of proper resources as contributing factors. While the VA defended the use of AI for efficiency, experts criticized its approach, emphasizing that many flagged services were necessary for effective patient care and that relying on AI could overlook crucial details.

Overall, the report underscores the risks of using AI without sufficient context or understanding in critical government functions, especially regarding veterans' health services.

Gary Marcus discusses a new paper from Apple that reveals significant weaknesses in large language models (LLMs). The paper builds on previous arguments that LLMs can perform well only within the data they were trained on, often failing when faced with tasks outside that range, such as solving the Tower of Hanoi puzzle. Despite advances in "reasoning models" intended to improve LLMs, they still struggle with basic logical tasks and do not reliably produce correct answers.

Marcus emphasizes that while LLMs have their uses, they cannot replace traditional algorithms for complex tasks. The expectation that LLMs will lead to true artificial general intelligence (AGI) is unrealistic, as they continue to exhibit limitations that human intelligence does not share. The discussion highlights the need for a combination of human adaptability and computational reliability in the pursuit of AGI. Marcus concludes that while LLMs have potential, their current capabilities are not sufficient for significant societal transformation.

The Federal Trade Commission (FTC) is looking to identify and address regulations that limit competition. In collaboration with Authors Alliance and EFF, they highlighted an overlooked issue: the triennial exemptions to Section 1201 of the Digital Millennium Copyright Act (DMCA), which are complicated and restrict fair use of copyrighted works.

Copyrights give creators exclusive rights, but fair use is essential for public activities like commentary, research, and innovation. Section 1201 makes it illegal to bypass software locks on copyrighted materials, which can hinder fair use. Although Congress intended for a system to allow exemptions through a triennial process, this has become burdensome and ineffective, creating barriers for those seeking to use copyrighted material lawfully.

The FTC cannot change laws directly, but they hope their findings will highlight the negative impact of Section 1201 and encourage Congress to either repeal or reform it. They also suggest that the upcoming 2026 review process by the Library of Congress should be improved to better support innovation and fair use.

No summary available.

No summary available.

Summary: The Impossible Predicament of the Death Newts

Doug Muir discusses the Rough-Skinned Newt (Taricha granulosa), a highly toxic amphibian found in the Pacific Northwest of North America. Its poison can kill multiple adult humans, but it is not venomous, meaning it doesn't bite or sting. Newts and common garter snakes (Thamnophis sirtalis) are in an evolutionary arms race: as garter snakes develop resistance to the newt's potent toxin, the newts must increase their toxicity in response.

This relationship comes with costs for both species. Newts must consume more food to support the bacteria that produce the toxin, while snakes may experience negative side effects from evolving toxin resistance, although these effects are not yet fully understood.

Interestingly, garter snakes prefer to eat toxic newts over other prey because they can store the toxin in their livers, making themselves less appealing to their own predators. This dynamic leads to a continuous cycle where more toxic newts are favored by snakes, while less toxic ones are consumed.

The newts face multiple challenges: they must remain toxic without developing warning colors (which would attract more predators), and they cannot revert to being less toxic without risking increased predation. The situation exemplifies the complexities of predator-prey relationships in nature.

Despite the detailed exploration, many mysteries about this interaction remain, including variations in toxicity among newt populations in different regions.

Summary: Setting Up a Cute Framework Project with CMake

The Cute Framework is a C/C++ framework for creating 2D games, and this guide explains how to set up a project using CMake with Swift for game logic.

Prerequisites:

• Install the latest version of Swift (preferably Swift 6 or later).
• Install CMake (version 4.0 or later).
• Install Ninja for building Swift projects.

Project Structure:

1. Create a new project directory (e.g., MyCuteGame) and set up the following structure:
   • src/ for source files
   • include/ for header files
   • CMakeLists.txt for project configuration
   • src/main.swift for your main Swift file
   • include/shim.h for C header interoperability
   • include/module.modulemap for the C module map

CMake Configuration:

• The CMakeLists.txt file specifies project details, collects Swift source files, sets up the Cute Framework as a dependency, and creates an executable named MyCuteGame.

Swift Interoperability:

• The shim.h file includes the Cute Framework header to allow Swift access to its functions.
• The module.modulemap file defines how Swift imports the C header.

Writing Swift Code:

• In src/main.swift, you can write the game logic using the Cute Framework, such as creating a window and displaying a sprite.

Building the Project:

1. Create a build directory and configure the project using CMake:

   mkdir build
   cd build
   cmake -G Ninja ..
   cmake --build .
   

2. Run the executable with ./MyCuteGame to launch your application.

You have now set up a Cute Framework project using CMake and Swift and can start developing your game. For more resources, check the documentation and join the Cute Framework Discord for support.

Summary of OAuth 2.1 Provider Framework for Cloudflare Workers

This is a new TypeScript library for implementing the OAuth 2.1 protocol with PKCE support on Cloudflare Workers. As it is in beta (March 2025), the API may change.

Key Benefits:

• Simplifies API endpoint authorization by wrapping your Worker code.
• Handles token management automatically.
• Provides authenticated user details without requiring additional checks.
• Flexible user and UI management, allowing integration with any framework.
• Stores only hashes of secrets for security.

Usage Overview:

• The library acts as an entry point for HTTP requests, checking access tokens for authorized API calls.
• You can define API routes and handlers, with options for single or multi-handler setups.
• It requires an OAuth UI implementation for user authorization and provides endpoints for token exchange and dynamic client registration.
• It supports optional features like scopes and client registration settings.

Handling Requests:

• The library provides a default handler for requests without valid tokens and an API handler for authorized requests.
• The API can return user information and handle various endpoints.

Token Management:

• Tokens are stored securely, with sensitive information encrypted and only hashes stored.
• A token exchange callback can be configured for additional processing during token issuance or refresh.

Error Handling:

• Custom error responses can be defined, and the library logs errors by default.

Security Features:

• Ensures secrets are hashed and props are encrypted.
• Implements a mechanism for handling refresh tokens to avoid issues with token invalidation.

Development Notes:

• The library was developed using AI assistance, with thorough reviews for security and adherence to standards.

Overall, this library aims to streamline OAuth 2.1 implementation on Cloudflare Workers while prioritizing security and flexibility.

The website is checking your browser for security reasons. If you own the website, there’s a link available for you to address any issues.

The text lists different sounds: "None," "Deep Bowl Strike," "Crystal Bowl Ping," and "Wood Click."

The author expresses a mix of shame and humility about creating a search library called SearchArray, which adds full-text search capabilities to Pandas. They compared its performance to Elasticsearch using the BEIR benchmarks, specifically on the MSMarco Passage Retrieval dataset.

The results showed that SearchArray performed worse in every category:

• NDCG@10 scores were similar, but Elasticsearch had higher search and indexing throughput.
• SearchArray only does BM25 scoring and returns all document scores, while Elasticsearch uses an optimized method called Weak-AND (WAND) to efficiently find the top results without unnecessary calculations.

SearchArray lacks postings lists, instead using a positional index to facilitate phrase matching. This design choice means it can be slower because it computes term frequencies and BM25 scores for all documents rather than selectively optimizing for the top results.

The author notes that while SearchArray is useful for small datasets and prototyping, it’s not suitable for building large-scale search systems. They appreciate the complexity and efficiency of established search engines like Elasticsearch and encourage respect for the engineers behind them.

The text concludes with technical details on how to integrate SearchArray with BEIR and a call to join a course on applying LLMs to search applications.

On June 5, 2025, OpenAI's COO, Brad Lightcap, addressed concerns about a lawsuit filed by The New York Times, which demands that OpenAI retain all user data indefinitely. OpenAI argues that this demand conflicts with their commitment to user privacy and is an overreach. They are appealing the court order to protect user data.

Key points include:

1. User Control: OpenAI provides tools for users to manage their data, including options to opt-out and delete chat history.
2. Lawsuit Background: The New York Times is suing OpenAI and wants to keep all user content, claiming it might help their case.
3. Data Impact: The lawsuit affects users of ChatGPT Free, Plus, Pro, and Team subscriptions, but not ChatGPT Enterprise or those using Zero Data Retention agreements.
4. Legal Compliance: OpenAI is required to keep some data due to the court order, but it is stored securely and accessed only by a limited legal team.
5. Future Updates: OpenAI promises to keep users informed about any changes related to this issue and reassures that data retention policies remain in place.

Overall, OpenAI is actively challenging the demand to protect user privacy while complying with legal obligations.

Py-PGlite Summary

Py-PGlite is a tool for testing with PostgreSQL in Python that requires no setup, Docker, or configuration files. You can install it using pip install py-pglite.

Key Features:

• Zero Configuration: No installation or server setup needed.
• Instant Testing: Automatically creates fresh databases for each test.
• Framework Support: Works seamlessly with SQLAlchemy, Django, and FastAPI.
• Full PostgreSQL Features: Supports advanced features like JSON and arrays.

Quick Start Examples:

1. SQLAlchemy: Automatically creates tables and allows easy user management.
2. Django: Auto-configures models, making it easy to test.
3. Raw SQL: Provides direct access to PostgreSQL features for fast execution.

Installation Options:

• General: pip install py-pglite
• With SQLAlchemy: pip install py-pglite[sqlalchemy]
• With Django: pip install py-pglite[django]
• All features: pip install py-pglite[all]

Advanced Features:

• Custom configuration options for flexibility.
• Performance testing for bulk inserts.
• Easy isolation for different frameworks in tests.

Community Feedback:

Users appreciate the simplicity and speed of Py-PGlite, noting it drastically reduces setup time for PostgreSQL testing.

In summary, Py-PGlite simplifies PostgreSQL testing for Python developers, providing powerful features without the usual complexity.

The article "Unfit for Work" by Chana Joffe-Walt discusses the significant increase in disability claims in the U.S. over the past 30 years, with 14 million Americans now receiving government disability checks. Despite advancements in medicine and anti-discrimination laws, the number of people on disability has grown, often overlooked in discussions about the economy and social safety nets.

Key points include:

1. Disability and Employment: Many individuals on disability do not work and are not counted as unemployed, leading to a hidden aspect of the economy. The majority of those on disability payments are not able to return to work.

2. Conditions for Disability: The criteria for determining disability can be subjective, leading to cases where similar health conditions yield different outcomes regarding disability status.

3. Rural Impact: In places like Hale County, Alabama, nearly 25% of working-age adults are on disability, often due to chronic pain or other conditions that can complicate job performance.

4. Economic Shift: The decline of jobs that require only a high school diploma has led many workers, especially older ones, to seek disability as a form of financial support instead of retraining for new jobs.

5. Children on Disability: The number of children receiving disability benefits has increased significantly, often leading to a conflict where families rely on these payments for survival, which can discourage progress in the child’s education.

6. System Challenges: The disability program is not designed as a welfare replacement but has become one, with few individuals returning to work after receiving benefits. The costs associated with these programs are rising, raising concerns about their sustainability.

7. The Disability-Industrial Complex: A network of lawyers and firms has emerged to help people navigate the disability application process, often incentivizing more people to claim benefits.

Overall, the article highlights the complex relationship between disability, economic shifts, and social support systems in America, raising questions about the future of these programs and their impact on individuals and the economy.

New research from the University of British Columbia shows that the ideal amount of sleep varies by culture, challenging the idea that everyone needs the same amount. The study analyzed sleep data from nearly 5,000 people across 20 countries and found significant differences in average sleep duration. For instance, people in Japan averaged just over six hours of sleep, while those in France averaged nearly eight hours.

The research indicated that people who sleep in line with their country's cultural norms tend to have better health, and surprisingly, there was no evidence that shorter sleepers had worse health outcomes. However, it was noted that people in all countries were sleeping at least one hour less than what their culture deemed optimal.

Overall, the findings suggest that sleep recommendations should consider cultural differences to improve public health outcomes.

The text discusses the challenges of using Software as a Service (SaaS) products and how they can lead to vendor lock-in. Here are the key points summarized:

1. Integration Costs: Integrating SaaS products involves hidden costs, not just monetary, but also in terms of time and effort. These include:

   • Discovery Tax: Researching what the service offers, its compatibility, pricing, and documentation can be time-consuming and subjective.
   • Sign-Up Tax: Signing up often involves unclear pricing models, potential extra costs, and restrictions on usage, even before using the service.
   • Integration Tax: Actual integration requires reading documentation, installing libraries, and addressing undocumented edge cases, which can be frustrating.
   • Local Development Tax: Testing services locally can be complicated, needing special configurations and sometimes requiring access to cloud resources.
   • Production Tax: Managing the service in a production environment involves ensuring reliability, handling API keys, and troubleshooting differences between local and production setups.

2. Vendor Lock-In: Once integrated, switching to a different service can require significant code rewrites, making it hard to change providers.

3. Recommendation: Instead of piecemeal integrations, it's suggested to choose a comprehensive platform (like Cloudflare or Supabase) that offers multiple services under one umbrella. This minimizes integration issues, reduces complexity, and improves workflow, allowing developers to focus more on writing software rather than managing multiple services.

In conclusion, while SaaS can simplify certain tasks, it often introduces complexity and dependencies that can hinder development flow. Integrated platforms can help mitigate these issues.

Summary of Local Network Access Proposal

Overview:
This proposal, from the Chrome Secure Web and Network team, aims to address security issues related to public websites accessing a user's local network. It is still in the design phase and has not been approved for implementation in Chrome.

Problem:
Public websites can exploit vulnerabilities in local devices (like printers) using a user's browser, which can lead to attacks without the user's knowledge. This proposal seeks to prevent such unauthorized access.

Goals:

1. Enhance Security: Stop websites from exploiting local devices.
2. User Control: Require user permission for websites to access local network devices.

Key Features of Proposed Solution:

• Permission Requirement: Websites must request permission from users to access local network devices. If permission is not granted, access is blocked.
• Local Network Request Definition: A local network request is defined as any request that crosses from a public address space to a more private one.
• Fetch Integration: A new parameter, targetAddressSpace, will be added to the fetch API to help identify the nature of requests (local, loopback, etc.).

Use Cases:

1. Unexpected Requests: Users who do not expect a website to connect to their local network can deny access, enhancing security.
2. Device Setup: Websites from manufacturers can assist users in setting up local devices while still requiring user permission.

Challenges Addressed:

• The proposal avoids breaking existing services that rely on local network access.
• It does not solve the HTTPS issue for local network devices but aims to manage permissions effectively.

Security and Privacy Considerations:

• Users must explicitly grant permission for local network access, reducing the risk of unauthorized connections.
• The proposal aims to ensure clarity for users regarding permissions, unlike previous methods that allowed more hidden access.

Stakeholder Feedback:
The proposal has garnered positive feedback from organizations like Mozilla and WebKit, indicating broad support for the direction it is taking.

This summary highlights the key points of the local network access proposal, focusing on its purpose, main features, and intended benefits for user security and control.

The Wendelstein 7-X research facility in Greifswald has achieved a new record in nuclear fusion, marking a significant step toward commercial fusion energy. On May 22, 2025, the facility set a world record for the "triple product," a key measure in fusion, during a 43-second plasma discharge. The triple product combines the plasma's particle density, ion temperature, and energy confinement time.

During this record-setting experiment, about 90 fuel pellets were injected into the reactor while powerful microwaves heated the plasma to over 20 million degrees Celsius. A new pellet injector, developed in the U.S., was used to optimize the heating and fuel injection process.

This achievement demonstrates the potential of the Wendelstein 7-X and shows that it can maintain long plasma durations, which is crucial for future fusion power plants. While traditional tokamak reactors have achieved higher peak triple product values, Wendelstein 7-X is now leading in longer plasma durations, essential for practical energy production.

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Summary of "Semi-Sync Meetings: Stop Wasting Our Time" by Luke Bechtel

Meetings often fail because only one person talks at a time, leading to disengagement and unclear outcomes. This traditional format wastes time and talent, as many team members don’t get to contribute meaningfully. Simply using AI note-taking apps won’t solve these issues; the structure of meetings needs to change.

To improve meetings, Bechtel suggests a "Semi-Synchronous" approach, which involves two phases:

1. Semi-Sync Phase (10-15 minutes): Participants work silently on a shared document to add ideas, updates, and comments without interruptions.
2. Sync Phase (15-20 minutes): The team discusses the most important topics identified during the silent phase, focusing on decisions rather than reiterating what was already written.

This method encourages equal participation, reduces hierarchy bias, and enables better idea generation. Studies show that it can lead to more ideas and better decision-making compared to traditional meetings.

Key benefits include:

• Parallel Thinking: Multiple people contribute simultaneously, enhancing creativity.
• Equal Voice: Written contributions allow everyone to share ideas without being overshadowed by louder voices.
• Real-Time Documentation: Decisions are recorded immediately, reducing reliance on memory.

To implement this approach, teams should set clear expectations, choose the right tools for collaboration, and start with one meeting type to test the new format. The goal is to save time, clarify ownership of tasks, and allow quieter team members to participate more fully.

Overall, Bechtel urges teams to adopt this method to make meetings more productive and engaging.

No summary available.

Tim Heffernan, a water quality expert at Wirecutter, shares his experience and insights about water filtration. He received multiple inquiries from readers concerned about tap water safety, prompting him to explain why he doesn’t use a filter at home. After testing his water repeatedly, he found it to be clean, leading him to conclude that many U.S. water supplies are safe and don’t require filtration.

He highlights three key points:

1. Understanding Water Quality: Water utilities are mandated to meet safety standards and test for contaminants, providing annual Consumer Confidence Reports (CCR) to inform the public. Homeowners can also test their water for peace of mind.

2. Need for Filters: Although fears about contaminants like PFAS (forever chemicals) are common, many readings show that most tap water is safe. For example, recent studies indicate that PFAS levels in public water supplies are often below enforceable limits.

3. Limitations of Filters: No water filter is perfect; each type has its drawbacks, such as high costs, inefficiency, or maintenance issues. Filters may not always be necessary, and it’s important to know what’s in your water before deciding to use one.

Heffernan advises readers to consult their utility’s CCR for detailed water quality information and consider testing their water if they have concerns. Ultimately, he emphasizes that understanding water quality empowers individuals to make informed decisions.

The text discusses "Strictly Necessary Cookies," which are essential for the website to work properly. These cookies help with security, prevent fraud, and allow purchases. You can block these cookies in your browser, but doing so may cause some parts of the site to not work correctly.

The article discusses how much energy the brain uses during thinking. Research by neuroscientist Sharna Jamadar and her team shows that the brain consumes only about 5% more energy during focused tasks compared to when it is at rest. This indicates that even when we feel mentally exhausted, our brains are primarily engaged in maintaining bodily functions rather than just thinking.

The brain, which is only about 2% of our body weight, uses around 20% of our energy, making it very energy-intensive. It relies on a molecule called ATP, created from glucose and oxygen, to power its functions. Most of the brain's energy goes towards basic maintenance tasks, including regulating vital physiological systems.

The article also highlights that our brains evolved in energy-scarce environments, leading to a tendency to conserve energy. Thus, the fatigue we feel after mental effort is a result of this evolutionary adaptation rather than a lack of energy. Overall, the brain operates efficiently, balancing the need for complex thinking with the constraints of energy availability.

Recent research from the Peter Doherty Institute in Melbourne has made significant progress towards finding a cure for HIV. Scientists discovered a method to make the HIV virus visible within white blood cells, which has been a major obstacle in treating the disease. The virus can hide in these cells, creating a reservoir that can reactivate, making it difficult for the immune system and medications to eliminate it.

The researchers used mRNA technology, similar to that used in COVID-19 vaccines, to develop a new lipid nanoparticle (LNP X) that can deliver mRNA directly into the cells where HIV is hiding. This mRNA prompts the cells to reveal the virus. Initial results were promising, leading to excitement among the research team, although further studies are needed to confirm if this method will enable the immune system to effectively eradicate the virus.

There are nearly 40 million people living with HIV globally, and current treatments require lifelong medication. While this discovery offers hope, it is still in the early stages, and additional testing in animals and humans will be necessary before it can be widely applied. Experts believe this breakthrough could also have implications for other diseases, including certain cancers. However, questions remain about the best way to eliminate the virus once it is revealed.

No summary available.

No summary available.

The paper titled "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity" examines Large Reasoning Models (LRMs), which are advanced language models designed to showcase their thinking processes before answering questions. While these models perform well on reasoning tasks, their abilities and limitations are not fully understood.

Current evaluations mainly focus on the accuracy of final answers, often overlooking how these models actually reason through problems. This study uses controlled puzzle environments to explore these issues, analyzing both the final answers and the models' internal reasoning. The research findings reveal that LRMs struggle with complex problems and that their reasoning efforts can decrease unexpectedly as complexity increases.

The study identifies three performance levels:

1. Low-complexity tasks where standard models outperform LRMs.
2. Medium-complexity tasks where LRMs show advantages due to extra reasoning.
3. High-complexity tasks where both types of models fail.

LRMs also show limitations in performing exact calculations and reasoning inconsistently. Overall, the research highlights both the strengths and weaknesses of LRMs, raising important questions about their true reasoning abilities.

The text discusses a new tool called "ask-human mcp," designed to improve AI performance by reducing errors and confusion. Here are the key points:

• Problem: AI can make mistakes, such as providing incorrect information or being overly confident in its wrong assumptions, leading to wasted time on debugging.

• Solution: The ask-human mcp server allows the AI to "ask for help" instead of guessing. When the AI is unsure, it can send a question to a designated markdown file instead of hallucinating an answer.

• How It Works:

  1. The AI asks a question.
  2. The question is recorded in a markdown file.
  3. A user provides the correct answer in the file.
  4. The AI continues working with the correct information.

• Benefits:

  • Easy installation with a simple command.
  • Requires no configuration and works across different platforms.
  • Provides instant feedback and supports multiple AI agents.
  • Keeps a history of questions and answers for better debugging.

• Setup: Installation is quick, and the necessary commands are provided for users to get started.

Overall, ask-human mcp aims to enhance AI reliability and efficiency by allowing it to seek guidance when needed.

Five years ago, the author transitioned from using R to Python for data analysis, specifically using the pandas library. Despite being a fan of R's tidyverse, the author still finds pandas difficult to use and struggles to explain its drawbacks to Python users who see it as a powerful tool.

The text presents a comparison of performing a simple purchase analysis in both R and Python. In R, the analysis is straightforward and involves reading data, grouping by country, summing amounts, and applying filters, all with a smooth and clear syntax. In contrast, the same tasks in Python with pandas become more complex and cumbersome, requiring additional steps to achieve similar outcomes.

Key frustrations with pandas highlighted include:

1. Confusing method names and functionalities (e.g., .filter() doesn't filter values).
2. Different methods for grouped and non-grouped data frames.
3. Lack of convenience functions, leading to more repetitive coding.
4. Frequent issues with data being moved into the index, requiring extra steps to manage.

Ultimately, the author emphasizes the challenges faced by R users when switching to pandas, advocating for understanding and compassion from Python users regarding these frustrations.