Summary of "Introducing tmux-rs" by Collin Richards

On July 3, 2025, Collin Richards announced a personal project where he has successfully rewritten the tmux codebase from C to Rust. After six months of work, the entire codebase, which increased from about 67,000 lines of C to 81,000 lines of Rust, is now fully in Rust, albeit using unsafe code.

Key Points:

1. Project Motivation: The project is a hobby, similar to gardening, and there isn't a specific reason for the conversion other than personal interest.

2. Initial Approach: Collin started with a tool called C2Rust to automate the conversion but found the generated code to be unmanageable. He ultimately decided to manually translate the C code into Rust.

3. Development Process:

   • Understanding the build process was crucial; he adapted the existing autotools setup to work with Rust.
   • He initially translated files one at a time but later switched to translating one function at a time to facilitate easier debugging.
   • Bugs were encountered during the translation, often due to mismatches between C and Rust types or incorrect function prototypes.

4. Technical Challenges:

   • Raw pointers in Rust were used due to the limitations of directly using Rust references in the translated code.
   • He addressed the use of C's goto statement using Rust constructs like labeled blocks and loops.
   • He reimplemented a parser originally built with yacc using the lalrpop crate.

5. Tools Used: His workflow included using Vim with custom macros for efficiency and experimenting with AI tools, although he found they didn't significantly speed up the process.

6. Future Goals: Although the project is now complete at version 0.0.1, Collin aims to improve the code further by transitioning to safe Rust and addressing existing bugs.

The project is open for others interested in Rust and tmux, inviting feedback and collaboration via GitHub Discussions.

No summary available.

Pennybase Overview

Pennybase is a lightweight Backend-as-a-Service (BaaS) similar to Firebase and Supabase. It is built with less than 1000 lines of Go code and has no external dependencies. Here are its main features:

• File Storage: Uses CSV files to store data, keeping track of versions for each record.
• REST API: Provides a simple API for data access and manipulation, returning JSON responses.
• Authentication: Supports session cookies and Basic Auth for user authentication.
• Permissions: Implements role-based access control (RBAC) and ownership-based permissions.
• Real-time Updates: Offers real-time updates using Server-Sent Events (SSE).
• Schema Validation: Validates data types and formats for records.
• Template Rendering: Uses Go templates for serving dynamic HTML content.

How It Works

Data is stored in easily readable CSV files, with each update creating a new version of records. An in-memory index is used for quick access to the latest versions. The CSV structure includes columns for record ID, version number, and data fields.

Users and permissions are managed through special CSV files:

• _users.csv: Contains user credentials and roles.
• _permissions.csv: Defines access rules for different resources.

REST API Endpoints

Pennybase provides endpoints for:

• Listing and retrieving records.
• Creating, updating, and deleting records (with appropriate permissions).
• Streaming real-time events.

Static Assets and Templates

Pennybase can serve static files (HTML, CSS, JS) and render HTML templates, allowing dynamic content generation based on user and resource data.

Extensibility with Hooks

Pennybase supports extending its functionality through a hook system that allows custom actions during data modifications.

Contributions

The project encourages contributions, focusing on keeping the codebase small and clear. It is open-source under the MIT license.

No summary available.

Locality of Behaviour (LoB)

Locality of Behaviour is the idea that a programmer should be able to understand what a piece of code does by looking at that specific code alone. This principle aims to make code more straightforward and maintainable.

Key Points:

1. Definition: LoB means that the behavior of a code unit should be clear without needing to examine other parts of the code.

2. Examples:

   • In htmx, a button's behavior is clear from its tag: <button hx-get="/clicked">Click Me</button>. This satisfies LoB.
   • In jQuery, the button’s behavior is hidden in multiple places, making it hard to understand:

     $("#d1").on("click", function(){
       $.ajax({
         /* AJAX options... */
       });
     });
     

   This shows poor LoB.

3. Implementation vs. Invocation:

   • There's a difference between showing how something works (implementation) and showing how to use it (invocation). Good functions hide the complex details but still make their use clear.

4. Conflicts with Other Principles:

   • LoB can sometimes clash with other principles like:
     • DRY (Don’t Repeat Yourself): While avoiding redundancy is important, it can lead to less obvious behavior if used excessively.
     • SoC (Separation of Concerns): Separating different aspects of code can obscure behavior, making it hard to see how changes in one area affect another.

5. Conclusion: While LoB is important for making code easier to maintain, it should be balanced with other principles. Striving for LoB can lead to better software quality and sustainability.

Summary: Spending Too Much Money on a Coding Agent

The author discusses their experience using various coding models, particularly Cursor and Claude Sonnet, for programming tasks. Initially, they found these models useful but became frustrated with their inefficiencies under tight deadlines. They then switched to a new model called o3, which, despite being slower, produced better results.

The author and their co-founder decided to spend $1,000 a month on o3 due to its effectiveness, even though it felt excessive. They discovered that o3 and similar models excelled in several areas, such as using tools effectively, avoiding unnecessary complications, and providing valuable research assistance.

Despite concerns about costs, they found that spending on high-quality coding agents could be justified, especially as prices for these models dropped recently. They outlined strategies to maximize the value from these models, including early error detection, using straightforward technologies, refining coding rules, and enhancing development scripts.

Ultimately, the author believes that using multiple coding agents can significantly improve productivity and allow developers to focus on more complex tasks rather than routine coding. The evolution of these tools suggests a shift towards using them collaboratively for better coding practices.

The main idea is to create a simple Hacker News (HN) profile that allows you to personalize your feed with minimal effort. By rating 30 posts once, you can establish a permanent homepage that reflects your preferences. The tool aims to test how much personal information is needed for better results when using language models (LLMs) and to explore the balance between data input and user effort. The creator is sharing this tool for feedback and to connect with others interested in enhancing LLM workflows with personal context.

Neural networks are everywhere today, powering technologies like language models, speech recognition, and robotics. At their core, neural networks consist of basic components that can be adjusted through a method called differentiable programming. This introduction is designed for beginners, like Alice, who are exploring this complex field. It covers the basics of optimizing functions using automatic differentiation and explains common designs for processing sequences, graphs, texts, and audio. The focus is on providing an easy-to-understand overview of key techniques, including convolutional, attentional, and recurrent blocks. The goal is to help readers understand both the theory and practical coding aspects (using tools like PyTorch and JAX), enabling them to grasp advanced models like large language models and multimodal systems.

It seems that there is no text provided for me to summarize. Please provide the text you would like summarized, and I'll be happy to help!

Summary: Parallelizing SHA256 Calculation on FPGA

P. Trujillo developed an SHA-256 hash calculator on an FPGA, which computes the hash of a string in 68 clock cycles. However, the initial design was underutilized because it produced only one hash at a time. To improve performance, the new design includes multiple hash calculators that run in parallel.

Key improvements made to the design include:

• Storing the K matrix (pre-computed values) at a higher level, allowing all calculators to access it without duplication.
• Initializing the W matrix values in parallel, which removes the need for a complex interface.

The new module is called sha256_core_pif. A coordinating module, SHA256_manager, was added to manage inputs and outputs for the hash calculators. The system is used for a hash cracker application that tests possible strings against a given SHA-256 hash.

The project runs on a Litefury board connected to a Raspberry Pi 5 and uses a clock speed of 62.5 MHz to meet timing requirements, integrating 12 parallel hash calculators. A Python driver was created to interface with the FPGA, allowing it to read and write data as needed.

The project demonstrates the potential of FPGAs in accelerating cryptographic tasks, which is expected to grow in importance in the fields of cryptography and cybersecurity. All project files are available on GitHub, and the author invites others involved in cryptography to explore FPGA solutions.

Summary of AI Evals FAQs

This text addresses common questions about AI evaluations, particularly for AI applications, based on insights from a course taught by Hamel Husain and Shreya Shankar.

1. RAG Usage: "RAG" (Retrieval-Augmented Generation) is still valuable. The claim that "RAG is dead" refers to specific naive uses in coding applications, not the concept itself. Effective retrieval strategies are key for AI performance.

2. Model Selection: Using the same model for both tasks and evaluations can work, especially for binary classification. Prioritize understanding where your model fails through error analysis before switching models.

3. Custom Annotation Tools: Building a custom annotation tool is highly recommended as it enhances workflow speed and efficiency. Off-the-shelf tools may not meet specific needs.

4. Binary vs. Likert Evaluations: Binary evaluations (pass/fail) are often more effective than 1-5 rating scales, as they lead to clearer, quicker decision-making.

5. Debugging Multi-Turn Conversations: Start debugging by assessing overall success and identifying the first failure point. Simplifying the context can streamline error identification.

6. Automated Evaluators: Focus on building automated evaluators for persistent issues rather than every failure. Simple checks are often more cost-effective.

7. Annotation Staffing: For most small to medium organizations, one expert ("benevolent dictator") is sufficient for quality decisions.

8. Evaluation Gaps: Existing tools may not cover all evaluation needs, especially for error analysis and custom metrics. Be prepared to fill these gaps yourself.

9. Synthetic Data Generation: Use a structured approach to generate synthetic data, focusing on dimensions that reflect potential failure modes.

10. Evaluating Diverse Queries: Base evaluation strategies on observed failure patterns rather than trying to classify every query type in advance.

11. Chunk Size for Document Processing: Choose chunk sizes based on task type—larger chunks for fixed-output tasks and smaller chunks for expansive-output tasks to improve reasoning.

12. Evaluating RAG Systems: Use traditional information retrieval metrics for the retrieval component and error analysis for the generation component.

13. Custom Interfaces for Review: Build annotation tools that enhance clarity and speed, allowing for intuitive reviews and efficient navigation.

14. Budgeting for Evaluations: Treat evaluation as an integral part of development, focusing on error analysis rather than just automated checks.

15. Error Analysis Importance: Conduct thorough error analysis to identify unique failure modes and guide the development of relevant evaluation metrics.

16. Guardrails vs. Evaluators: Guardrails prevent immediate errors in real-time, while evaluators assess quality and performance post-response.

17. Minimum Viable Evaluation Setup: Start with error analysis, using a single expert to guide quality. Utilize notebooks for efficient trace review and analysis.

18. Evaluating Agentic Workflows: Assess overall task success first, then analyze individual steps to identify failures.

19. Vendor Selection: Choose evaluation vendors based on support and fit for your needs, as features are often similar.

20. CI/CD vs. Production Evaluation: CI tests are small and focused on known issues, while production evaluations sample live data and may require more complex assessments.

Overall, the emphasis is on practical strategies for improving AI evaluations, highlighting the importance of understanding failures and tailoring approaches to specific applications.

Summary: Importance of Context Management in AI NPCs

In developing my AI NPC project, I encountered challenges with managing AI context. This aspect is often overlooked but is crucial. To address the growing context issue, I implemented a solution involving frequent summarization and self-growing systems. This allows the AI to remember important details about interactions, creating a more immersive experience where each AI can learn and observe uniquely.

However, as the context increases, its effectiveness decreases, slowing down the AI's response time. I've found that too much context can overwhelm the model, leading it to ignore relevant information. To combat this, I developed a system to keep the AI's context cleaner, preventing it from becoming confused by unrelated messages.

I believe in maintaining a clean context by not overloading the AI with unnecessary information, such as error messages or irrelevant tool use details. This approach improves the AI's performance and keeps it focused.

The term 'Context Engineering' has emerged as a new concept in AI development, which I see as an evolution from 'Prompt Engineering.' While I enjoy working on this for fun, effective context management is essential for creating truly interactive NPCs. It's important to balance context with relevant prompts to enhance the AI's capabilities.

Kyber is developing a next-generation document platform for businesses, using AI to improve regulatory document workflows. Their solution helps insurance claims organizations reduce template usage by 80%, cut drafting time by 65%, and speed up communication by five times.

Key achievements over the past eight months include:

• A 20x increase in revenue and profitability.
• Securing multi-year contracts worth six and seven figures with major insurance companies.
• Forming partnerships with leading software firms like Guidewire and Snapsheet.

Kyber is seeking skilled Enterprise Business Development Representatives (BDRs) to help grow their customer base. Responsibilities include:

• Identifying and connecting with key decision-makers.
• Executing outbound strategies like cold calls and emails.
• Managing leads and collaborating with the growth team to convert them into sales.

Ideal candidates should have a strong work ethic, excellent communication skills, resourcefulness, and a team-oriented mindset.

New hires will spend their first week learning about the insurance industry and Kyber's offerings, with goals to articulate the company’s unique advantages and secure initial meetings with potential buyers within the first month.

Kyber values innovation, customer focus, craftsmanship, high standards, and teamwork. They offer competitive compensation, including uncapped commissions and comprehensive health insurance.

Candidates are encouraged to apply with a referral to stand out in the application process.

The Arista 7130 MetaWatch network application has demonstrated that direct-attach copper cables are faster than both single-mode (SMF) and multi-mode (MMF) fiber cables, which have similar performance. In tests, two machines were connected to an Arista device using 10G Ethernet, and a million packets were pinged back and forth to measure latency.

Key findings include:

• Direct-attach copper cables showed lower latency compared to fiber cables.
• The average latency for copper was about 4.60 ns per meter, while both SMF and MMF fibers had latencies around 4.96 ns per meter.
• Copper cables have a maximum reach of 7-10 meters, whereas MMF can reach 300 meters and SMF can reach up to 10 kilometers.

The results indicate that for short distances, using copper cables can significantly reduce latency, making them preferable when speed is critical.

The text is about a piece titled "Head in the Clouds" by Jared Marcel Pollen, published on June 23, 2025, in a section about books. It invites readers to share their comments via email to a specified address.

The Minor Planet Electronic Circular provides updates about minor planets and related astronomical events. For detailed information, you can visit the link provided.

The article discusses the whole-genome sequencing of an ancient Egyptian man from the Old Kingdom, specifically from a site called Nuwayrat, dated between 2855 and 2570 BCE. This individual was buried in a ceramic pot inside a rock-cut tomb, which helped preserve his DNA.

Key findings include:

• The man's ancestry primarily reflects North African Neolithic origins, but about 20% of his genetic makeup is linked to the eastern Fertile Crescent, including areas like Mesopotamia.
• This suggests that ancient Egypt had connections with neighboring regions not just through trade and culture but also through human migration.
• The study contributes to understanding the genetic diversity of early Egyptians, challenging the idea that their population was solely of local origin.

The individual was likely of higher social status, with evidence suggesting he led a physically demanding life despite his elite burial. Genetic predictions indicate he had brown eyes and skin that ranged from dark to black. Additionally, his diet was consistent with typical ancient Egyptian fare, including terrestrial animal protein and plants.

Overall, this research provides valuable insights into the genetic history and connections of ancient Egyptians with surrounding regions during a pivotal time in their civilization.

The text discusses how to solve certain interview questions using XOR, a logical operator that works on bits. Here are the key points:

1. XOR Basics: XOR (denoted as ^) returns 0 if both bits are the same and 1 if they are different. It can be applied to various types of values, not just booleans.

2. Key Properties of XOR:

   • XOR with 0: (x \oplus 0 = x)
   • XOR of the same number: (x \oplus x = 0)
   • Commutative: (x \oplus y = y \oplus x)
   • Duplicate cancellation: A sequence of XOR operations can cancel out duplicates.

3. Applications:

   • In-Place Swapping: You can swap two variables using three XOR operations.
   • Finding a Missing Number: Given an array of numbers from 1 to n with one missing, you can find the missing number by XORing all numbers from 1 to n with the numbers in the array.
   • Finding a Duplicate Number: The same XOR method can be used to find a duplicated number in an array of n+1 integers containing numbers from 1 to n.
   • Finding Two Missing/Duplicated Numbers: The process can be extended to find two missing or duplicated numbers by analyzing bit differences from the XOR result.

4. Limitations: The XOR trick works well for one or two missing/duplicated numbers but fails for more than two.

5. Final Thoughts: While using XOR in interviews may not be ideal due to its specialized nature, the properties of XOR provide elegant solutions to these problems. The author appreciates the beauty and utility of XOR in solving these challenges.

The author shares their experience with a pair of Android earbuds that feature ChatGPT integration. After purchasing the earbuds for 245 euros, they discover various functionalities, including AI features like translations and music apps. However, they find issues with audio quality and the usability of the small screen.

While exploring the device, they find that it directly communicates with OpenAI, indicating it contains an OpenAI API key, which raises security concerns. The author attempts to hack the device and discovers vulnerabilities, such as the ability to access chat histories and bind devices using guessable IMEI numbers. They report these issues to the company, IKKO, which eventually updates the app and device to improve security but still leaves some vulnerabilities unaddressed.

The author expresses frustration over the ongoing security flaws and the lack of a professional response from the company. They conclude by stating that despite the updates, certain exploits remain possible, and they invite others to address these issues.

No summary available.

The author argues that writing code has never been the main bottleneck in software engineering. Instead, challenges like code reviews, mentoring, testing, and communication slow down the process more than actually coding does. With the rise of AI tools like LLMs, which can generate code quickly, there's a misconception that coding is now the bottleneck.

However, while LLMs make it easier to produce code, they increase the complexity of understanding, testing, and trusting that code. As more code is generated, the pressure on reviewers and maintainers grows, especially when they struggle to understand the generated code or when it deviates from established practices.

The author emphasizes that the true challenge remains understanding the code, not writing it. Even though LLMs speed up code production, the effort required for reasoning about the code and ensuring quality remains high. Collaboration and shared understanding are still crucial, but faster code generation can lead to assumptions about quality, putting additional stress on teams.

In summary, while the cost of writing code has decreased, the need for thorough review and understanding has not changed, making it the real bottleneck in software development.

Summary: "How Doom Didn't Kill the Amiga"

The author recounts their journey with the Amiga computer, starting with their desire for an Amiga 500 after seeing it in 1988. The Amiga stood out for its superior graphics and sound compared to competitors like the Atari ST and PC, making it a popular choice among gamers and creatives in the early 90s.

Despite the rise of the PC market and the emergence of games like Doom, the author argues that Doom wasn't the reason for the Amiga's decline. Instead, the Amiga's architecture, which excelled in 2D graphics and multitasking, struggled to compete with the rapidly advancing PC hardware, especially as CPUs became cheaper and more powerful.

As PCs became more affordable and capable of running complex games, the Amiga's limitations became apparent. Commodore's failure to innovate and release a competitive new model contributed to the decline. The Amiga's unique features, like pre-emptive multitasking, were no longer enough to keep it relevant.

The author reflects on their loyalty to the Amiga, even as they eventually transitioned to PCs for gaming and everyday tasks. Despite the Amiga's decline, they still cherish their experiences with it and recognize its impact on the computing landscape. Ultimately, the Amiga's downfall was not just due to a single factor, like Doom, but rather a combination of technological advancements in PCs and missteps by Commodore.

No summary available.

This text provides a guide on how to view the Moon's daily phases using a simple ASCII art representation. It includes information on a specific date (July 3, 2025) when the Moon's illumination is 58.40% and it is in the First Quarter phase.

Key points include:

• Users can cycle through the Moon's phases day by day.
• The text contains a JavaScript code that uses the SunCalc library to calculate the Moon's phases based on a given date.
• It defines functions to get the Moon's phase, display it, and change the date to see different phases.
• The Moon phases are categorized into different names based on illumination percentages, ranging from New Moon to Waning Crescent.

Overall, the text serves as a technical description for a program that tracks and displays the Moon's phases.

I'm unable to access external links or specific content from them. However, if you provide the main points or sections of the research paper, I can help you summarize that information in a clear and concise way. Please share the details you would like summarized!

Gmailtail Summary

Gmailtail is a command-line tool for monitoring Gmail messages and outputting them in JSON format, ideal for automation and integration with other tools. It offers:

• Real-time Monitoring: Continuously checks for new emails.
• Flexible Filtering: Filter emails by sender, subject, labels, and attachments.
• Checkpoint Support: Can resume monitoring from where it left off.
• Multiple Output Formats: Outputs data in JSON, JSON Lines, or compact formats.
• Easy Setup: Uses YAML configuration files for complex setups.
• Gmail Search Support: Utilizes Gmail's search syntax.
• Simple Authentication: Supports OAuth2 and service accounts.

Quick Start:

1. Install using uv:
   • Run the installation script.
   • Clone the project and set it up.
2. Set up Google API credentials in the Google Cloud Console.
3. Run Gmailtail with your credentials.

Usage Examples:

• Monitor all new emails or filter by specific sender, subject, or labels.
• Use various output formats and include email bodies or attachments.
• Manage checkpoints to resume or reset monitoring.

Advanced Usage:

• Integrate with jq to extract specific data or perform advanced filtering.
• Create alerts for urgent emails or summarize daily email activity.

Command Line Options:

• Specify authentication files, filtering queries, output formats, and monitoring behaviors.

Use Cases:

• Monitor alerts, automate workflows, analyze data, integrate with other tools, or back up important emails.

Configuration:

• Create a gmailtail.yaml file for detailed settings on authentication, filters, output formatting, and monitoring behavior.

Authentication:

• For personal use, set up with OAuth2; for server automation, use a service account.

Development:

• Clone the repository, install dependencies, run tests, and maintain code quality with formatting and linting tools.

License: MIT License.

No summary available.

This project is about creating an advanced glassmorphism effect using CSS. The creator spent months experimenting to improve the effect while ensuring it works well across different web browsers.

To avoid issues with color bleeding and blurring, they use ::before and ::after pseudo-elements to layer different properties. The key layers include:

• Adjustable blur, brightness, and saturation (using backdrop-filter)
• A subtle translucent texture
• A faux 3D bevel effect created with box-shadows

Glassmorphism can be resource-intensive, so it's best used for accents rather than large areas on desktop screens. It should work on recent versions of Chrome, Safari, and Firefox, and the creator welcomes feedback on any bugs. This is also a preview of a glass SCSS/component library they are developing.

No summary available.

The text mentions posts related to interviewing at Jane Street and information about their internship program.

The article titled "Conversations with a Hit Man" by David Howard, published in The Atavist Magazine, recounts a meeting between a retired FBI agent, Myron Fuller, and a convicted hit man, Larry Thompson, in a Louisiana prison. Fuller, who has been haunted by a past murder case involving Thompson, seeks to confront his regrets and better understand how his FBI career was impacted by the case.

Key points include:

• Fuller and journalist David Howard visit Thompson, who is serving an 80-year sentence for his crimes, including a robbery and multiple murders for hire.
• Thompson's friendly demeanor surprises both men, contrasting with their expectations of a more menacing encounter.
• Fuller aims to confront the lingering guilt over a murder he believes he could have prevented, involving a woman named Maria Marshall, whose husband hired Thompson to kill her.
• Both men share similar backgrounds, having grown up in poverty in the South, which shaped their lives in different ways.
• Fuller hopes that by revisiting this chapter of his life, he can find some closure and clarity regarding the mistakes of his past.

The article explores themes of regret, redemption, and the complexities of human relationships, even among adversaries.

This text highlights ten features of the D programming language that the author appreciates. Here’s a simplified summary of the key points:

1. Automatic Constructors: D generates constructors for structs automatically, simplifying the creation of Plain Old Data types.

2. Design by Contract: D allows functions to define conditions for input and output, ensuring that parameters are valid and return values are correct.

3. Dollar Operator: This operator provides a shorthand for referencing the length of arrays, making code cleaner.

4. Compile Time Function Execution (CTFE): D can execute functions at compile time, improving efficiency and allowing for cleaner code.

5. Built-in Unit Tests: D facilitates easy integration of unit tests within the same file as the code, encouraging developers to write tests.

6. Exhaustive Switch Statements: D's switch statements can automatically handle missing cases, helping catch errors during development.

7. Parenthesis Omission: D allows omitting parentheses in certain function calls, enhancing code readability.

8. Uniform Function Call Syntax (UFCS): This feature lets functions be called as if they were methods of their first parameter, improving code clarity.

9. Scoped & Selective Imports: D allows limiting imports to specific parts of the code, reducing clutter and enhancing comprehension.

10. Built-in Documentation Generator: D provides tools to create documentation easily, making it easier to maintain and understand code.

The author concludes that these features contribute to D's unique and effective programming environment, making it easier for developers to write and manage their code.

Summary:

The ICEBlock app, which allows users to anonymously report sightings of U.S. Immigration and Customs Enforcement (ICE) agents, has quickly gained popularity and reached the top of the Apple App Store rankings after being criticized by U.S. Attorney General Pam Bondi. The app has around 20,000 users, mainly in Los Angeles, where ICE raids are frequent. It enables users to share sightings within a 5-mile radius and provides notifications about nearby ICE activity. Importantly, the app does not collect or store user data.

The article explains the differences between ordinary functions and arrow functions in JavaScript, highlighting their syntax, behavior, and when to use each type.

Key Points:

1. Function Types:

   • Function Declarations: Named functions that are hoisted (available before their declaration) and can be used anywhere in the scope.
   • Function Expressions: Functions defined within an expression, either anonymous or named, that are not hoisted. They must be defined before being called.
   • Arrow Functions: A more concise way to write functions, always anonymous, and do not have their own this, arguments, or super.

2. Syntax Differences:

   • Arrow functions use => instead of the function keyword and can be shorter when the body has one expression (no curly braces or return needed).

3. Behavioral Differences:

   • Arrow functions do not bind their own this, which can lead to issues when using them as methods on objects.
   • They cannot be used as constructors (with new) and cannot use the yield keyword, making them incompatible with generator functions.

4. When to Use Each:

   • Use arrow functions for concise, anonymous callbacks, especially in array methods like .map().
   • Use function declarations or expressions when you need access to this or want to define functions before using them (for readability).

Conclusion:

Choosing between function types depends on your specific needs regarding this context, readability, and whether you are working with generators. Arrow functions are generally preferred for their brevity and simplicity in most cases, while traditional functions offer more flexibility in some scenarios.

A programmer recently expressed anxiety about their career choice due to layoffs and the rise of AI, questioning whether they made a mistake. The author, who has 28 years of experience in tech, understands these fears but remains optimistic about the future of coding careers.

Key points include:

1. Industry Fluctuations: The tech industry experiences cycles of hiring and layoffs, causing stress and feelings of inadequacy among professionals.

2. AI as an Opportunity: While there's concern that AI might replace junior engineers, the author believes AI will instead handle repetitive tasks, allowing humans to focus on creativity and innovation.

3. Shared Knowledge: Contributions to platforms like GitHub build a collective intelligence that benefits everyone, enhancing collaboration and innovation.

4. Thriving in the Future: Programmers should:

   • Master context to improve AI interactions.
   • Guide AI in problem-solving rather than just using it as a tool.
   • Maintain curiosity and creativity to avoid over-reliance on AI.

The author reassures that being a programmer is not a mistake. Instead, the profession is evolving, and demand for problem-solvers who can leverage AI will grow. Embracing this change offers exciting opportunities for the future.

Simon’s problem involves finding a hidden pattern in a special type of function. This problem is significant because it was one of the first to show that quantum computers can solve certain tasks much faster than classical computers. In this study, researchers used two IBM Quantum processors to show that their quantum algorithm could solve a version of Simon’s problem more quickly when the hidden pattern had specific limitations. They achieved a notable speedup with up to 58 qubits, although it was not as high as the ideal case. The speedup improved when they used techniques to protect against errors and to reduce measurement mistakes. This research demonstrates a clear advantage of quantum computing for solving specific problems.

No summary available.

Starting this month, Microsoft Authenticator will stop supporting the autofill password function as part of a shift towards using passkeys instead of traditional passwords. Last month, Microsoft also prevented users from saving new passwords in the app. By August 2025, all your saved passwords will be removed, and you'll need to use passkeys—like a PIN, fingerprint, or facial recognition.

Digital security expert Attila Tomaschek explains that passkeys are safer than passwords because they require both a public and a private key for authentication, reducing risks like phishing and identity theft. Using the same password across multiple accounts can be risky, making users vulnerable to scams.

Microsoft Authenticator helps you log into your accounts securely with biometric options, and while passwords can still be stored in Microsoft Edge, it's recommended to transition to passkeys.

A passkey is a secure credential created using biometric data or a PIN, stored only on your device, which eliminates the hassle of remembering passwords. To set up a passkey in Microsoft Authenticator, open the app, tap on your account, and follow the prompts after logging in with your existing credentials.

Summary:

Sony and AMD are collaborating on a project called "Project Amethyst," which will influence the development of AMD's next-generation graphics technology, known as RDNA 5. Mark Cerny from PlayStation has confirmed his involvement in this project, working on significant aspects of RDNA 5, which may eventually be referred to as UDNA.

The partnership is already showing promising results, particularly in creating an upscaling algorithm for gaming, set to launch with the PlayStation 5 Pro in 2026. Both companies are working on improving gaming hardware and software together, which should benefit both PC and PlayStation gamers. Overall, this collaboration aims to enhance gaming experiences and hardware performance for future consoles and systems.

Recent research shows that audible sound can influence gene activity in mouse cells, potentially leading to medical advancements. A study at Kyoto University found that exposing mouse muscle precursor cells to sound waves for different durations altered the activity of over 100 genes. Many of these genes are involved in cell attachment and movement.

Specifically, the sound increased the attachment sites of cells to surrounding tissues, likely by activating an enzyme called focal adhesion kinase (FAK). Additionally, the sound reduced the development of fat cells, leading to a decrease in fat accumulation by 13 to 15%.

Audible sound is noninvasive and easier to produce than ultrasound, making it a promising tool for treating various conditions, including obesity and cancer. Researchers aim to test these methods in living mice and eventually in humans within the next five to ten years.

Summary of Caching Libraries in Go

This article discusses the development of caching libraries in the Go programming language, focusing on recent advancements and the state of caching as of 2023.

Key Types of Caches

• On-Heap Caches: Store data in the heap and are affected by garbage collection (GC) but offer better eviction policies and features.
• Off-Heap Caches: Allocate memory outside the heap, reducing GC pauses and overhead but often have poor eviction policies and require costly data conversions.

Historical Context

• Initially, Go lacked sophisticated caching solutions, mainly offering basic mutex-protected maps with LRU or LFU eviction strategies.
• Ristretto: Launched in 2019, it became popular for its high throughput and innovative features like variable entry costs. However, it faced criticism for its eviction policy and design flaws, leading to a decline in users.

New Caching Libraries

• Theine (2023): Introduced adaptive W-TinyLFU, improving hit rates across various workloads, but had limitations in scaling and memory efficiency.
• Otter v1 (2023): Aimed for high performance and low memory overhead, but had design flaws and lacked key features like cache stampede protection.
• Sturdyc (2024): First to implement advanced features like loading and refreshing but suffered from poor eviction and expiration policies.
• Otter v2: The latest version focused on flexibility, high throughput, and excellent hit rates, drawing inspiration from the Caffeine library in Java.

Conclusion

The article provides an overview of several caching libraries in Go, highlighting their advantages and disadvantages to aid developers in selecting the right library for their projects.

A recent study from Karolinska Institutet shows that gene therapy can successfully restore hearing in deaf patients with genetic mutations. The study involved ten patients aged 1 to 24 who had hearing issues due to a defect in the OTOF gene, which affects how sound signals are transmitted to the brain.

The treatment used a specially designed virus to deliver a working version of the OTOF gene into the inner ear through a single injection. Most patients experienced significant hearing improvement within a month, with average sound perception increasing from 106 decibels to 52. Younger patients, especially those aged 5 to 8, showed the best results, with one seven-year-old girl nearly regaining full hearing.

The therapy was found to be safe, with only mild side effects reported. Researchers plan to expand their work to target other genes related to deafness in future studies.

No summary available.

James Ball created an AI-powered 24/7 political podcast called "The Torment Nexus" after being inspired by a joke from cartoonist Zach Weinersmith. The idea originated from a comic that humorously suggested a podcast discussing polling numbers endlessly. Despite initially seeming like a silly concept, Ball realized that advancements in AI made it possible to bring this idea to life.

Using ChatGPT, he designed a podcast where two AI characters, "Alex" and "Blake," discuss Donald Trump's approval ratings and current headlines. Although the process was complex and fraught with issues, including the AI making mistakes and needing guidance, Ball managed to set up the podcast for a low monthly cost.

The resulting podcast is described as tedious and repetitive, yet strangely engaging. Ball noted that it reflects the absurdity of technology and its rapid evolution from joke to reality. While the final product is intentionally terrible, it serves as a commentary on the capabilities of AI and how easily it can generate content, even if it’s not particularly meaningful.

Donald Abdallah reached out to Gail to share his appreciation for Peter's talk on Zen Buddhism and Quakerism. He has been practicing Zen for about 15 years and is interested in submitting an article to the Friends Journal about famous scientists who are Quakers. He mentions two scientists he admires: Arthur Stanley Eddington, a Quaker active in education, and George Ellis, a co-author with Stephen Hawking who has received notable awards. Abdallah has a background in Radiation Oncology and holds a Master's degree in Information Systems from Friends University. He asks if non-Quakers can submit articles for consideration.

The author has created a service called 'Recivo' that simplifies receiving emails through an API. Users can generate a new email address to receive emails, which can be accessed via a REST API endpoint. This endpoint provides essential information like the subject, content, sender, and attachments of the emails. Users can also set up notifications for new emails using webhooks.

The author envisions using Recivo for triggering AI agents through email or for easy document uploads to other software by forwarding emails. They were inspired by the convenience of forwarding invoices to accounting software.

When visiting the Recivo website, users get a temporary email address and instructions to test the API. However, to create a permanent email address, they need to log in.

The author is seeking feedback on additional use cases for this "programmatic" mailbox.

This text tells the story of Imagebin, a personal image hosting software created by a programmer named Max, who was a school friend of the author. The author has been maintaining Imagebin since its inception around 2010 but is the only user. Originally open to public uploads, it is now password-protected.

The author reflects on Max's programming style while rewriting Imagebin in Go, contrasting it with his own more complex code. Max's original PHP code is praised for its simplicity and effectiveness, while the author's Go version is longer and more complicated due to structured design and error handling.

Through this comparison, the author realizes that Max’s straightforward approach, which he once viewed as naive, is actually a strength that contributes to the longevity and reliability of the software. In the end, the author decides to stick with Max's original PHP code rather than using the newly written Go version, acknowledging the value of simplicity in programming.

Summary:

When creating links on a webpage, use clear and meaningful text that informs users about what to expect without getting into the technical details. Avoid phrases like "click here" or instructions about how to access content. Instead, use straightforward phrases that describe the link's purpose.

For example, instead of saying, "To download Amaya, click here," you could say, "Get Amaya!" while providing additional context, such as "Tell me more about Amaya: W3C's free editor/browser for creating HTML, SVG, and MathML documents."

The W3C QA Tips offer brief advice for web developers and designers, but they are not official specifications. For more information and tips, you can explore the Tips Index.

The text discusses Apple's new user-interface design called Liquid Glass, which will be implemented across its platforms. The author, Riccardo Mori, expresses frustration with the redesign, feeling that it lacks clarity and consistency. He critiques how navigation elements like tab bars and sidebars are meant to be transparent, which he argues actually distracts from content. He points out contradictions in Apple's guidelines that suggest both blurring and separating content from navigation elements.

Mori also critiques the increased use of white space, which he believes unnecessarily reduces the amount of visible information and complicates navigation. He is particularly concerned about the simplification of app icons, noting that they have become bland and less representative of their functions over time.

He argues that Apple's current design philosophy prioritizes aesthetics over functionality, leading to a user experience that lacks personality and clarity. Additionally, he highlights that Apple’s guidelines now impose restrictions on third-party developers, limiting their creative freedom and enforcing a uniform look across applications that may not serve their unique branding.

Overall, Mori feels that the Liquid Glass design changes do not enhance the user experience and instead create confusion and restrict creativity within the developer community.

Summary: A List is a Monad (Part 1)

This post discusses the concept of monads in functional programming, emphasizing that they can be seen as containers or contexts for values. Monads allow functions to be reused across different situations without rewriting the control flow logic.

There are two main types of monads:

1. Monads as Results: These hold already computed values with additional context, like a list or an optional value.
2. Monads as Recipes: These represent computations that haven't happened yet, like C#'s Task<T>, which holds a promise of future values.

The post focuses on the "Results" category, particularly using lists to illustrate the mechanics of two key operations: Map and flatMap.

• Map applies a function to each item in a list, transforming it without needing to manually iterate through the list. This allows for cleaner, more declarative code compared to traditional procedural programming.

• flatMap is used to chain computations that return monadic values, preventing the creation of nested monads.

The post also introduces the Maybe monad, which can hold a computed value or indicate absence. It simplifies the process of checking for null values by incorporating those checks into its operations.

For a type to be a true monad, it must implement two operations (Unit and flatMap) and follow three laws (Left Identity, Right Identity, and Associativity) that ensure consistent behavior when chaining operations.

The overall goal is to show how monads can simplify code by managing control flow and value presence, paving the way for more advanced monadic concepts in future discussions.

No summary available.

The text explains the role of strictly necessary cookies on a website. These cookies are essential for the website to work properly, including keeping it secure and allowing purchases. While you can block these cookies in your browser, doing so may cause some parts of the site to not work correctly.

Physicists at the Facility for Rare Isotope Beams (FRIB) in Michigan are making progress in understanding how stars create heavy elements, specifically those heavier than iron. By simulating the conditions found in stars, researchers are studying the "intermediate neutron-capture process" (i-process), which is believed to be a third method of forming heavy elements, alongside the known s-process and r-process.

The s-process occurs slowly in red giant stars, while the r-process happens quickly in neutron stars. The i-process is thought to occur in environments that are neutron-rich but do not fit neatly into either category. Scientists at FRIB are conducting experiments that involve accelerating atomic nuclei and smashing them to create unstable isotopes, which helps in determining how these heavy elements are formed.

As they gather data, the researchers are comparing their findings with the elemental compositions observed in ancient stars to validate their models. They aim to discover the origins of precious metals like gold and platinum, which are still not fully understood. The research is expected to yield significant insights into the formation of heavy elements over the next five to ten years.

The article discusses how AI coding assistants can pose security risks and highlights a project by Sharon Brizinov, who scanned GitHub for "Oops Commits" to find leaked secrets. Here are the main points:

1. GitHub's Commit System: When developers delete commits on GitHub, the commits are not actually removed; GitHub retains a record of them. This means that sensitive information can still be accessed even after it appears to be deleted.

2. Research Findings: Brizinov discovered that he could access these deleted commits by using GitHub's Event API and the GitHub Archive, which logs all public commit events. He found secrets worth $25,000 in bug bounties by analyzing these deleted commits.

3. New Tool: Together with Truffle Security, he developed an open-source tool called the Force Push Scanner that helps users detect secrets in these hidden commits.

4. Impact of Leaked Secrets: The research revealed that many sensitive credentials, including GitHub Personal Access Tokens and AWS secrets, were leaked through deleted commits, posing significant security risks.

5. Case Study: One notable incident involved a leaked GitHub PAT that granted administrative access to all repositories of Istio, an important open-source project. This could have led to severe supply-chain attacks, but the issue was quickly addressed once reported.

6. Conclusion: The research emphasizes that once a secret is committed to a repository, it should be deemed compromised, and immediate action should be taken to revoke it, regardless of whether it has been deleted from the visible commit history.

The article serves as a warning about the persistent nature of data on platforms like GitHub and the importance of managing sensitive information carefully.

This text discusses the author's visit to the Escher museum and draws parallels between Escher's artistic concepts and their work in computer science, specifically with a project called Replicated Data eXchange (RDX).

Key Points:

1. Escher's Influence: The author finds that Escher’s art, known for its mathematical intricacies, offers insights into their development of the RDX data format and the accompanying librdx library.

2. RDX Overview: RDX is a comprehensive data format that combines elements like document formatting, binary serialization, and data synchronization, all while maintaining a minimalistic codebase.

3. Development Process: The author emphasizes a methodical, step-by-step approach to programming, likening it to building with bricks. They explain how developing tools with tools previously created leads to efficient testing and building of the project.

4. Tuples: The text highlights the importance of tuples in RDX, which serve various functions, from representing data pairs to placeholders for deleted data. The complexity in using tuples reflects the intricate interactions within the system.

5. System Organization: A well-structured codebase is essential for maintaining order as it grows. The author notes that implementing clear rules and naming conventions helps prevent chaos in the code.

6. Focus on Key Parameters: The author advises separating important features from less critical ones to simplify the development process. By identifying key parameters early on, other aspects of the code can be derived more easily.

7. Experience and Expertise: The text concludes by emphasizing the value of experience in programming. The ability to navigate complex situations distinguishes different levels of expertise among developers.

Overall, the author illustrates how art and structured programming practices can inform and enhance the development process.

The author discusses a tweet by Bhavye Khetan where he boasts about deceiving venture capitalists (VCs) to secure meetings. The author finds this behavior typical and points out a response by Rob Bailey, who calls Khetan a hypocrite. Bailey argues that Khetan shouldn't complain about VCs not taking cold outreach since he tricked them. The author critiques this reasoning, stating that Khetan is being judged for contradicting what "people" say, not for his own beliefs.

The author notes a common online pattern where individuals are accused of hypocrisy based on the views of a larger group they are associated with, rather than their own statements. They highlight that this creates a toxic environment where people are viewed as representatives of their groups instead of as individuals with unique opinions. The author questions if society has become too tribal, prioritizing group identity over individual perspectives.

Summary of k-XORSAT Filters Library

Overview: The k-XORSAT filters library allows users to create and query compressed set-membership filters, which are similar to Bloom filters but cannot have items added after creation. This makes them 'static' filters, which use memory more efficiently, making them suitable for large datasets or applications with many users.

Key Features:

• k-XORSAT filters have optimal memory usage compared to Bloom filters.
• They offer fast query speeds, especially with low false positive rates.
• Items cannot be added after the filter is built.

Dependencies: The library requires pthreads and standard C math libraries, as well as some git submodules. To set up, clone the repository using:

git clone --recursive [email protected]:NationalSecurityAgency/XORSATFilter.git

Installation: To install the library, run make in the root directory to create libxorsatfilter.a.

Usage:

1. Create a Builder: Allocate a builder using:

   XORSATFilterBuilder *xsfb = XORSATFilterBuilderAlloc(0, 0);
   

   The first argument is the expected number of elements (0 if unknown), and the second is the metadata size.

2. Add Elements: Add elements and their metadata with:

   XORSATFilterBuilderAddElement(xsfb, pElement, nElementBytes, pMetaData);
   

   You can also add the absence of an element using:

   XORSATFilterBuilderAddAbsence(xsfb, pElement, nElementBytes);
   

3. Finalize the Querier: Create a querier from the builder:

   XORSATFilterQuerier *xsfq = XORSATFilterBuilderFinalize(xsfb, XORSATFilterFastParameters, nThreads);
   

   Choose parameters for filter efficiency and the number of threads.

4. Query the Filter: Use the querier to check for elements:

   uint8_t ret = XORSATFilterQuery(xsfq, pElement, nElementBytes);
   

5. Retrieve Metadata: Access stored metadata with:

   uint8_t *pMetaData_retrieved = XORSATFilterRetrieveMetadata(xsfq, pElement, nElementBytes);
   

6. Serialize and Deserialize: Save and load the querier using serialization functions.

7. Cleanup: Free resources with:

   XORSATFilterBuilderFree(xsfb);
   XORSATFilterQuerierFree(xsfq);
   

Testing: A sample test is provided to demonstrate building a k-XORSAT dictionary and querying it. Run it with:

$ make test/test && test/test

Further Information: For more details, refer to the included papers on SAT Filters and XORSAT Filters.

License: This work is exempt from copyright and is provided under the CC0 1.0 Universal license. There are no warranties, and users agree to indemnify the U.S. Government from any claims arising from use.

Summary: Finding Your Voice

Researchers at the University of California, Davis have developed a neural brain implant that allows paralyzed patients to communicate almost instantly by translating brain signals into speech sounds. This new technology moves beyond traditional systems that convert brain activity into text, which often faced delays and limited vocabulary.

The implant uses 256 microelectrodes to capture detailed neural activity from the brain area responsible for speech. An AI algorithm then decodes these signals and synthesizes them into speech, enabling users to express themselves more freely, including pitch modulation and singing.

In tests, a participant named T15, who suffers from ALS, showed significant improvement in speech intelligibility compared to his unaided speech. However, while the system has demonstrated potential, it is not yet reliable enough for everyday conversations. Researchers believe that using more electrodes in future designs could enhance performance, and clinical trials are planned to further develop this promising technology.

A recent study presented at EAN 2025 by Dr. Abidemi Otaiku found that frequent nightmares may lead to faster biological aging and a significantly higher risk of premature death. The research involved 4,196 adults and revealed that those who experience weekly nightmares have over three times the chance of dying before age 75 compared to those without nightmares.

The study used various methods to measure biological aging and found that accelerated aging explained about 39% of the link between nightmares and early death. This suggests that the stress and sleep disruption from nightmares could harm cellular aging.

The findings emphasize the need to recognize and treat nightmares as a serious health issue. Healthcare providers should screen patients with frequent nightmares for sleep disorders and mental health problems and consider treatments like cognitive behavioral therapy. More research is needed to see if treating nightmares can slow aging and increase lifespan, but the current evidence supports addressing nightmares in healthcare to improve overall health and longevity.

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Summary of WebAssembly Troubles Part 4: Microwasm

This article is the final part of a series discussing issues with WebAssembly (Wasm) and potential solutions. The author appreciates Wasm but aims to improve it by addressing its design flaws.

Key Points:

• Microwasm Introduction: Microwasm is a new format compatible with Wasm, designed for better efficiency in both runtime consumption and compiler production. It is implemented in the Microwasm branch of Lightbeam.

• Goals of Microwasm:

  1. Simplify the conversion process between compiler intermediate representation, Wasm, and native code.
  2. Maintain Wasm's safety and determinism guarantees.
  3. Ensure effective information transfer from compilers to runtimes.
  4. Optimize performance while consuming Microwasm streams.
  5. Enable streaming conversions from Wasm to Microwasm without waiting for entire functions.

• Differences from WebAssembly:

  • Microwasm does not use local variables; it uses stack-based arguments.
  • It has a simpler control flow structure without hierarchical blocks.
  • Return from functions is handled differently, resembling continuation-passing style.

• Code Quality Improvement: Microwasm generates cleaner and more efficient assembly code compared to traditional Wasm, reducing unnecessary complexity.

• Comparison with WebAssembly: Microwasm aims to simplify code generation without sacrificing the performance or safety of existing WebAssembly. Implementing improvements directly into WebAssembly is challenging due to limitations in existing engines like V8 (Chrome) and IonMonkey (Firefox).

• Outlook: The author hopes that creating a separate format will allow for more experimental changes that could eventually enhance WebAssembly itself.

This article concludes the series without a follow-up, encouraging readers to continue exploring related topics.

Summary of Poudriere Inside FreeBSD VNETJail

This guide explains how to set up Poudriere, a package building tool, within a FreeBSD jail instead of a Bhyve VM. Here are the key steps involved in the setup:

1. Host Setup:

   • Install FreeBSD using a raw system image for quicker setup.
   • Configure network settings in /etc/rc.conf.
   • Set up device rules in /etc/devfs.rules.
   • Create a jail configuration file in /etc/jail.conf.d/joudriere.conf to define jail properties and permissions.

2. Jail Setup:

   • Create necessary directories and fetch the FreeBSD base system.
   • Start the jail and enable services like SSH and Nginx.
   • Configure package management settings and directories for Poudriere.

3. Poudriere Configuration:

   • Fetch the FreeBSD release and ports tree.
   • Set up a bulk build job, targeting a specific package (e.g., devel/cmake).

4. Testing:

   • Run the bulk build job to ensure everything is working correctly.

This setup allows leveraging the benefits of FreeBSD's jail system for package building with Poudriere.

No summary available.

No summary available.

Six months ago, a project to completely rewrite SQLite, called "Project Limbo," was announced. This project has attracted a community of over 115 contributors and is now named "Turso," which represents the next version of SQLite. Turso aims to match SQLite's reliability through modern testing techniques, including a partnership with Antithesis, an autonomous testing platform. They are so confident in their testing that they will pay $1,000 for any missed data corruption bugs.

The need for a rewrite arises from limitations in SQLite, such as issues with concurrent writes, real-time data handling, and support for non-relational data. These challenges have become more apparent with the rise of AI applications.

Turso introduces key features like an asynchronous interface for better performance, especially in web environments, and native vector search capabilities for AI and machine learning. The alpha version supports basic SQLite operations and aims to provide a reliable foundation for future development. Early adopters, such as Spice.ai, are already seeing performance benefits from using Turso.

The team emphasizes the importance of community contributions and invites those interested to join their Discord. They also acknowledge their partners, including Antithesis and Blacksmith, for their support in achieving stability and reliability in this alpha release. Overall, Turso is positioned as a modern evolution of SQLite, designed to meet the demands of contemporary applications.

The Mixture of Experts (MoE) approach in Large Language Models allows for a larger model with better learning capabilities while keeping execution costs low, as only a few parameters are activated for each input. However, some experts are used much more frequently than others, causing inefficiencies when running on multiple devices. To address this, we developed the Mixture of Grouped Experts (MoGE), which groups experts to ensure a more balanced workload. This design helps distribute the computational load evenly when executed on different devices, improving performance during model inference.

We created the Pangu Pro MoE model, which has 72 billion parameters, with 16 billion activated for each token. This model is optimized for specific Ascend NPUs and has shown improved expert load balancing and efficient execution in both training and inference. Our tests reveal that Pangu Pro MoE achieves an inference speed of 1148 tokens per second per card, with potential improvements to 1528 tokens per second using acceleration techniques. This performance surpasses other models with 32 billion and 72 billion parameters. Overall, Pangu Pro MoE provides an excellent cost-to-performance ratio and demonstrates effective parallel training capabilities, making it competitive against other leading models.

No summary available.

Security Research Labs is part of the Allurity group.

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No summary available.

No summary available.

Fakespot, a tool for detecting fake online reviews, officially shut down on July 1, 2025, after nine years of operation. Founded in 2016 by Saoud Khalifah, Fakespot was created to help shoppers identify unreliable reviews, particularly on Amazon, where studies showed 43% of top-selling products had misleading feedback.

Despite its success and a $7 million funding round, Fakespot was acquired by Mozilla in 2023, which later decided to discontinue it due to sustainability challenges. Mozilla integrated Fakespot's technology into its Firefox browser but ultimately chose to refocus on core features and AI innovations, leading to the closure.

Many users expressed disappointment over the loss of the service, highlighting the ongoing need for reliable review analysis. In response to this gap, a new tool called TrueStar is being developed to provide similar services with a focus on sustainability.

In summary, Fakespot's closure marks the end of a significant resource for online shoppers, but there are emerging alternatives being developed.

No summary available.

No summary available.

In a Discord chat, I mentioned I was having a normal day reading RFC 3986, a technical document. A friend joked about me having a "linking argument" and revealed they only knew RFC 2119 by number. This RFC explains important terms for engineers, which I often use for my work.

Being playful, I suggested I wanted to hire a voice actor to dramatically read RFC 2119 in a sarcastic way. I found a voice actor, Andrew Winson, who did an amazing job with the task.

The White House has launched a website called White House Wire, designed to provide supporters with what it calls "real news" in one place, similar to a link blog. Curious about the type of links it features, the author used a programming tool called Quadratic to analyze the site. They wrote a script to collect external links from the White House Wire and wanted to explore which sites are most frequently linked and analyze the headlines.

To gather data over time, the author used another tool, val.town, to schedule daily scraping of the website and store the HTML for analysis. After refining their method to focus on relevant links, they created visual representations of the data.

After a month and a half of collecting information, they found that the top linked domains included YouTube, Fox News, and Breitbart, among others. They also created a word cloud highlighting frequently used terms in the headlines, with "trump" being the most common. The author maintains a spreadsheet that updates automatically with new data and visualizations.

Summary of Large Language Models (LLMs) Size Overview (2025)

This document provides factual information about the sizes and developments of large language models (LLMs) from 2019 to 2025, focusing on their base models rather than chatbot versions.

Key Points:

1. History of Model Sizes:

   • GPT-2 (2019): Ranged from 137 million to 1.61 billion parameters.
   • GPT-3 (2020): 175 billion parameters, trained on around 400 billion tokens.
   • Llama Models: Include various sizes from 7B to 65B parameters, with Llama-3.1 having 405 billion parameters trained on a vast dataset of 3.67 trillion tokens.

2. Trends in Model Development:

   • The release of models like Llama-3.1 marked a turning point, leading to larger models with more capabilities.
   • New models, such as Deepseek V3 (671 billion parameters), represent significant advancements, allowing broader access to high-performance LLMs.

3. Mixture of Experts (MoE) Models:

   • Several recent models utilize MoE architectures, which allow for more parameters to be activated for specific tasks, making them efficient and powerful.
   • Examples include Databricks DBRX and MiniMax, with sizes ranging from 132B to 456B parameters.

4. Current Landscape:

   • There are now many large models available for public use, with a growing trend towards models that are multi-modal (handling various types of data) and multi-lingual.
   • However, there are concerns regarding the quality and integrity of training data, especially regarding copyrighted material.

5. Conclusion:

   • The landscape of LLMs has evolved significantly, with a shift towards larger models and MoE architectures. Many models now incorporate elements of AI assistance, which might affect their original purpose as text continuation engines. More research is needed to explore different approaches beyond just developing AI chatbots.

No summary available.

A jury in San Jose, California, has ordered Google to pay $314.6 million to Android smartphone users in the state. They found that Google improperly transferred data from users' phones while the devices were idle, which burdened users for the company's benefit. The lawsuit was filed in 2019 by attorneys representing around 14 million Californians, who claimed that Google's data collection affected their cellular data usage without permission. Google plans to appeal the decision, arguing that users consented to these data transfers through their terms of service and that no harm was done to users. Another similar lawsuit against Google will go to trial in April 2026.

Introducing CoMaps: A New Navigation App!

On July 3, 2025, we proudly launched CoMaps, now available on Google Play Store, Apple App Store, and F-Droid!

Key Features of CoMaps:

• Offline Navigation: Plan your trips without needing the internet.
• Battery Efficient: Designed to save your device's battery life.
• Privacy First: No tracking, no data collection, and no personal identification.
• Free & Ad-Free: Enjoy the app completely free of charge, with no advertisements.

Why Choose CoMaps?

• Community-Driven: Developed as an open-source app with public input.
• Transparent Process: All decisions are shared openly with users.
• Non-Profit Focus: Our goal is to benefit the community, not to make money.

Get CoMaps Now! Available on Google Play Store, Apple App Store, and F-Droid. Explore your journey with the support of the community!

Some of my friends were laid off and are looking for jobs. We discovered that LinkedIn doesn't have an option to view jobs only at companies where you have connections. To solve this, I created a website called jobsbyreferral.com. The site uses a service from RapidAPI, which costs money, so I'm considering whether to invest more time into it and possibly charge a fee to cover expenses.

In a recent session featuring software engineer Alberto Fortin, he shared his experiences with AI and large language models (LLMs) after 15 years in the field. Initially excited about the potential of LLMs to improve his workflow, Alberto faced challenges when rebuilding his infrastructure. He wrote a blog post highlighting the differences between the hype surrounding AI and the actual results in production environments.

Alberto conducted follow-up tests on newer models like Claude Opus 4 to see if improvements were made. He emphasized the need for engineers to balance realistic expectations with understanding where LLMs truly add value and where they still have limitations.

Key takeaways from his experience include:

1. Quality Concerns: He was surprised by the poor quality of some AI outputs, finding them messy and full of errors, which complicated maintenance of the codebase.

2. Overhyped Productivity: While LLMs can enhance coding speed, expectations were inflated, leading to disappointment when reality fell short.

3. Taking Charge: Alberto stressed the importance of being in control as a developer. He prefers using LLMs as assistants rather than relying on them for major features, which he now handles personally.

4. Practical Advice: He encouraged experienced developers to trust their skills and use AI as a tool to enhance their knowledge, while remaining cautious about its current capabilities.

In conclusion, Alberto advocates for a balanced approach to using AI in software development, recognizing its potential while acknowledging that we are not fully there yet.

A decade ago, the scientific publisher Nature started tracking the contributions of researchers from various institutions to papers in 145 respected journals. The first Nature Index was released in 2016, showing that the Chinese Academy of Science was ranked first. However, American and European institutions were prominent in the top ten. Harvard was ranked second, with Stanford and MIT in fifth and sixth places. The French National Centre for Scientific Research and the German Max Planck Society were third and fourth, while Oxford and Cambridge ranked ninth and tenth. The seventh and eighth places were held by the Helmholtz Association and the University of Tokyo, respectively.

A new study from the National Cancer Institute (NCI) found that air pollution is linked to genetic mutations in lung cancer tumors of non-smokers. This research analyzed lung tumors from 871 non-smokers across four continents and discovered that those living in highly polluted areas had more genetic mutations compared to those in cleaner environments. The study is part of a larger project on lung cancer in non-smokers led by Maria Teresa Landi.

Lung cancer is the leading cause of cancer deaths in the U.S., with a significant percentage of cases occurring in non-smokers. The study highlights air pollution as a major public health concern, especially as climate change worsens conditions like wildfires and extreme heat. It found that air pollution causes more mutations than exposure to secondhand smoke.

The research also noted geographic differences in mutations linked to specific pollutants, such as aristolochic acid in Taiwan, which is associated with traditional medicine. The study emphasizes the need for more research into the health effects of air pollution, especially as funding for such studies is being cut.

Experts warn that ongoing fossil fuel use not only leads to increased air pollution but also raises healthcare costs and health risks for millions of Americans. The findings call attention to the urgent need to address air quality and its impact on health.

At the Money Museum in Chicago, there is a transparent cube said to hold $1 million in $1 bills. Curious about the accuracy of this claim, the author attempted to count the stacks of bills but struggled to keep track. They realized a simple tool for counting items in photos didn’t exist, so they created "Dot Counter," an easy-to-use app that allows users to count items by clicking on images.

Upon analyzing the cube, they discovered it likely contains about $1.55 million, which is $550,400 more than claimed. The author suggests that while the cube may technically contain $1 million, it also has extra cash, similar to getting free items that weren't ordered.

The text speculates that the cube could be hollow or just a decorative shell, with the outer stacks alone worth over $530,000. The author concludes that the cube might hold $1 million or more but emphasizes the importance of verifying such claims. They have created a tool to help others count accurately, encouraging readers to question what they see.

Mathematical software has often been developed in "packages" that rely on C++ and use header files for their interfaces. This system can be cumbersome and slow. To improve this, C++20 introduced a "module" system that allows packages to export their code in a more efficient way. This summary explores how to convert large C++ mathematical software packages to this new module system, using a finite element library with 800,000 lines of code as an example. The author presents a method to create both header-based and module-based interfaces from the same code. While converting to modules requires some effort, it can reduce compile time for the library itself, even though the effects on downstream projects are unclear. The author concludes with ideas for gradually converting the entire mathematical software ecosystem to this new system over the coming years.

Summary:

This analysis explores the ratings of nootropics—substances claimed to enhance cognitive abilities—gathered from a recommendation system. The author defines nootropics broadly, including medications, plants, and even activities like sleep and exercise, which can significantly impact cognitive performance.

Key Insights:

1. Recommendation System: The author developed a system where users rate nootropics they've tried. This system collected data from nearly 2,000 participants, resulting in over 36,000 ratings.

2. Rating Scale: Ratings range from 0 (useless) to 10 (life-changing), based on participants' experiences. The analysis considers biases such as self-reporting and the placebo effect.

3. Lifestyle Interventions: Sleep and exercise (especially weightlifting) received very high ratings, often outperforming well-known nootropics like Adderall and Modafinil. This suggests lifestyle changes are critical for cognitive enhancement.

4. Peptides: Nootropics like Semax and Cerebrolysin, although underutilized, showed promising ratings and low risk profiles, but are not widely known outside Russia.

5. Common Nootropics: Many popular nootropics (e.g., Piracetam, Ashwagandha) received mediocre ratings compared to lifestyle interventions and certain lesser-known substances.

6. Tianeptine: This antidepressant was rated higher than other medications, raising questions about its effectiveness and perception.

7. Zembrin: Contrary to previous surveys suggesting it was highly effective, this analysis found Zembrin's ratings comparable to normal Kanna, indicating it may not be as special as once thought.

8. Cautions: The analysis emphasizes the importance of understanding risks associated with nootropics, especially less-studied substances, and reminds readers to approach self-reported data cautiously.

In conclusion, the results highlight the significant impact of lifestyle choices on cognitive performance and suggest that many well-known nootropics may not be as effective as commonly believed.

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Chatbot Flow Editor Summary

The Chatbot Flow Editor is a visual tool for designing chatbot conversations that runs in your web browser. You can create, test, and export conversation flows as JSON files.

Quick Start:

1. Install: Use npm to add it to your project:
   • npm install --save-dev @enumura/chatbot-flow-editor
2. Launch: Start the editor with:
   • npx @enumura/chatbot-flow-editor
   • This opens the editor at http://localhost:3001.

Usage Instructions:

• Create Nodes: Click "Add Node" to add conversation points.
• Edit Content: Select a node to change its details.
• Test Flow: Use the chat preview to simulate interactions.
• Export JSON: Download your conversation flows in JSON format.
• Import Flows: Load existing JSON files to edit them.

Development Workflow:

1. Install the editor in your project.
2. Design your conversation flows.
3. Export the flow as JSON for use in your chatbot.

JSON Structure Example: When exported, a flow looks like this:

[
  {
    "id": 1,
    "title": "Welcome to our support!",
    "options": [
      { "label": "Technical Support", "nextId": 2 },
      { "label": "Billing Questions", "nextId": 3 }
    ]
  }
]

Integration: You can use the exported JSON in your chatbot application by importing it and navigating through conversation nodes based on user choices.

Requirements: You need Node.js version 20.0.0 or higher.

Development: For those looking to contribute, clone the repository and follow the setup instructions.

License: The project is licensed under the MIT License.

For further details, refer to the documentation and support sections.

Graphics Features Summary:

• Quick 2D rendering for efficient graphics display.
• Automatic grouping of images (sprite batching) for better performance.
• Various blending options for combining images.
• Ability to draw basic shapes and images.
• Support for bitmap fonts using a TrueType font (TTF) loader.

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No summary available.

The text provides information about the website "utk.claranguyen.me."

Key points include:

• Appearance: Users can choose between a light or dark theme for the site.
• Language: The default language is UK English, but users can select their preferred language (US English or Japanese) for displayed content. Changes will need a page refresh to take effect.
• About: The site details include the domain name (claranguyen.me), the site version (3.0.1), and the last update date (August 18, 2019).

Summary of BackSparsity is Cool

Sparse attention models, like Native Sparse Attention (NSA), are becoming efficient alternatives to traditional attention mechanisms in large language models (LLMs). These models are faster and can handle longer contexts without significant performance loss. The study explores the attention patterns in these sparse models, providing insights into their architecture and performance.

Key Points:

1. Challenges with Traditional Attention: Traditional attention mechanisms scale poorly with longer sequences because they require computations that grow quadratically with sequence length. Most queries only need to focus on a small subset of tokens, making full attention inefficient.

2. Sparse Attention Approaches:

   • Native Sparse Attention (NSA) and Mixture of Block Attention (MoBA) are two recent models designed to incorporate sparsity directly into the training process, improving both efficiency and performance.
   • NSA uses a multi-branch design, allowing each query to attend to compressed, selected, and local contexts simultaneously, improving both speed and expressivity.

3. Implementation and Efficiency:

   • The NSA model is implemented with hardware in mind to optimize memory access and computational efficiency. It manages attention computations dynamically to reduce unnecessary operations.
   • The model demonstrates significant speedups over traditional attention mechanisms during both training and inference.

4. Attention Patterns:

   • The study provides the first detailed analysis of attention maps in sparse models, revealing different patterns compared to dense attention.
   • Attention sinks are identified, where certain tokens receive disproportionately high attention scores, often used to reduce computation for less relevant tokens.

5. Performance Evaluation:

   • NSA outperforms dense attention models on several benchmarks, especially in long-context tasks.
   • The model's design allows it to generalize better over longer contexts, maintaining performance even as sequence lengths increase.

6. Future Directions: The findings encourage further exploration of the mechanisms behind sparse attention and suggest potential improvements in model architecture for better efficiency and performance.

In summary, sparse attention models like NSA present a promising evolution in the design of LLMs, effectively balancing computational efficiency with the ability to process long contexts.

Summary of Bionode Project by Steven K. Roberts

The Bionode is a portable mobile lab on wheels, designed by Steven K. Roberts, featuring advanced technology for various applications. It includes 14TB of storage, video and audio production tools, sensors, AI capabilities, and solar power, all built into a hand truck for easy transport.

Roberts, known as the first "digital nomad," has a history of integrating computers into mobile setups, starting with his bicycle, Winnebiko, in 1983. His latest creation, Bionode, is a compact system that encapsulates essential technology for information management and production.

The design follows a mini-rack standard, making it more modern and space-efficient, and incorporates a robust power supply and storage solutions. Bionode features multiple computers, including a powerful PC for AI tasks, various Raspberry Pi units, and a network management system, all organized for optimal airflow and performance.

Roberts aims to use Bionode to simplify his information management while keeping his extensive digital archives secure and accessible. The project reflects his passion for technology and serves as a versatile toolkit for his work in media and digitization.

The team tested a tool called Cluely and found that a simple way to catch interview cheaters is by using "hallucination traps." These are questions that sound believable but are actually silly or impossible to answer correctly. Vibe created an app to show this idea, which can be found at beatcluely.com.

They shared examples of prompts that trick even advanced models like o4-mini-high into giving incorrect answers, despite their ability to search the internet. You can find these examples on ChatGPT's website. Additionally, the code for the app is available on GitHub.

Summary

Security researchers have found serious vulnerabilities in Azure's built-in roles and API configurations that could jeopardize enterprise networks.

1. Over-Privileged Roles: Some Azure roles grant more permissions than intended. For example, roles like the Managed Applications Reader allow users to read all Azure resources instead of just specific ones. This can lead to unauthorized access to sensitive information.

2. API Vulnerability: A flaw in the Azure API allows attackers with read-only permissions to leak VPN pre-shared keys. These keys are crucial for connecting to internal networks, enabling attackers to access cloud resources.

3. Attack Chain: By exploiting these vulnerabilities, a weak user can gain access to both cloud and on-premises resources. Attackers can steal credentials, discover sensitive data, and plan further attacks.

4. Mitigation Strategies:

   • Audit Roles: Organizations should review and avoid using the over-privileged roles.
   • Limit Scope: Assign roles with restricted access to only necessary resources.
   • Create Custom Roles: Instead of using built-in roles, custom roles with specific permissions should be created.

5. Vendor Response: Microsoft acknowledged the VPN vulnerability as significant and fixed it, but deemed the over-privileged roles issue to be of low severity, only updating the documentation instead of rectifying the roles.

6. Key Takeaway: Organizations must be vigilant and proactive in managing permissions and roles in Azure, as relying solely on cloud providers can lead to security oversights.

For further security enhancements, organizations can seek specialized help or services to bolster their Azure environments.

The IRS has open-sourced most of its Direct File tax tool, which they had been developing for a few years. However, they are no longer working on it. I have decided to continue the project and prepare it for the next tax season. I also made much of the work on Direct File available online for anyone to read. You can find it at this link.

No summary available.