The article discusses the author's experience with their open-source project, Spegel, which was created to address issues with Kubernetes image registries. After a high-traffic incident where they struggled due to dependency on GitHub container registries, the author developed Spegel as a solution that minimized operational oversight.

Microsoft expressed interest in Spegel, leading to discussions about collaboration. However, the author later discovered that Microsoft had developed a project called Peerd, which closely resembled Spegel and included code and comments that appeared to be directly taken from it, without proper attribution. Peerd was also released under an MIT license, which allows for such modifications.

This situation has created confusion for users trying to understand the differences between Spegel and Peerd, especially since Microsoft has a strong brand presence. Despite the challenges, the author continued to support and develop Spegel, which has gained significant traction with over 1.7k stars and millions of pulls.

The author reflects on the difficulties faced by independent maintainers when dealing with large corporations and considers potential changes to Spegel's license and funding strategies. They also raise broader concerns about the future of open-source contributions and community support amidst declining investments in the field.

Summary: Why LLM-Powered Programming is More Mech Suit Than Artificial Human

The article discusses how AI tools like Claude Code enhance the capabilities of programmers instead of replacing them. The author likens using these tools to wearing a mech suit that amplifies a developer's skills.

Key Points:

1. Amplification, Not Replacement: AI tools help developers work faster but still require human oversight and control. They boost productivity but do not eliminate the need for skilled programmers.

2. Vigilance is Essential: Developers must remain alert while using AI tools, as they can make unexpected or incorrect decisions. The author experienced issues where the AI generated problematic code, emphasizing the need for constant supervision.

3. Changing Economics of Coding: Traditionally, coding involved significant time spent writing and debugging code. With AI, the time for writing code has drastically decreased, shifting focus to understanding the problem and designing solutions.

4. Experience Matters: Developers with experience are better equipped to recognize when the AI's output is flawed. Newer programmers might struggle to identify mistakes, making real-world experience crucial.

5. Collaboration with AI: The most effective use of AI comes from treating it as a partner. The human provides strategic direction, while the AI offers fast code generation. Successful outcomes depend on the developer’s judgment and expertise.

6. Future Skills: As AI tools evolve, the skills needed in programmers will change. Emphasis will be on architectural thinking and collaboration with AI rather than just raw coding ability.

7. A Positive Outlook: The author encourages embracing AI tools as a means to enhance human capabilities, enabling developers to achieve unprecedented levels of productivity and innovation.

In conclusion, while AI tools transform programming, they require skilled human oversight to ensure effective and sound development practices.

Summary: Operation Babylift and Its Aftermath

Operation Babylift was a U.S. initiative in 1975 to evacuate Vietnamese children during the fall of Saigon, framed as a humanitarian mission to save war orphans. Many of these children, often sick or underweight, were taken from orphanages as their mothers feared for their safety amidst the war. The operation aimed to adopt nearly 20,000 children, but it faced significant challenges, including a tragic plane crash that killed 138 children.

The narrative surrounding the operation portrayed it as an act of charity, but critics argued it was a political maneuver to distract from military defeat. While some adoptions were successful, many children were not true orphans and were unaware of their family backgrounds. As they grew up, many adoptees faced complex feelings about their identities and experiences, often feeling incomplete due to their lost histories.

Adoption agencies prioritized placing children in American homes, often overlooking the wishes of birth parents. Stories of adoptees reveal a range of experiences, from abuse in adoptive families to a deep yearning for connection with their roots. Many adoptees formed communities to share their experiences and seek support.

Today, a significant number of international adoptees, particularly from Vietnam, still face issues such as lack of citizenship and the struggle for recognition of their experiences. Recent movements aim to address the citizenship status of adoptees left out of previous legislation. The stories of those affected by Operation Babylift highlight the complexities of adoption, the impact of war, and ongoing struggles with identity and belonging.

No summary available.

Summary of Inner Loop Agents

Inner loop agents are a new concept where a large language model (LLM) can use tools directly without needing to go back to a client. This simplifies how LLMs operate, allowing them to parse and execute tool calls independently.

1. Current LLMs vs. Inner Loop Agents:

   • Traditional LLMs rely on a client to handle tool calls.
   • Inner loop agents can execute tool calls on their own during their thinking process, improving efficiency.

2. How It Works:

   • The LLM generates text that includes tool calls (e.g., using Google Maps).
   • The software running the LLM (like Ollama or vLLM) processes these calls and handles the responses.

3. Benefits of Inner Loop Agents:

   • They can make tool calls concurrently while processing information, enhancing their capabilities.
   • Models like o3 and o4-mini are specifically trained to use tools effectively.

4. Tool Training:

   • Inner loop agents are trained with tools included in their learning process, improving their ability to use those tools optimally.
   • Current models are limited to a small set of tools but could eventually learn to use a wider range of common tools through proper training.

5. Emergent Tool Use:

   • Future advancements may allow LLMs to adapt to new tools through experience rather than just following instructions.
   • Currently, users can either explain tool usage through detailed descriptions or train models with specific tool knowledge.

6. Agent Communication:

   • Google’s Agent 2 Agent (A2A) protocol allows different agents to interact, which could be important as more specialized agents emerge.

In conclusion, inner loop agents represent a significant step forward in how LLMs can operate, making them more autonomous and effective in using tools. The focus should be on training these models to utilize tools rather than just the technical setup of their operation.

Python is introducing a new feature called t-strings in version 3.14, set to release in late 2025. T-strings aim to improve string processing by providing a safer alternative to the popular f-strings, which can lead to security issues if misused, especially with user input.

Key points about t-strings:

• T-strings are a generalization of f-strings and evaluate to a new type called string.templatelib.Template.
• Unlike f-strings, Template instances must be processed before being converted to a string, helping to prevent issues like SQL injection and cross-site scripting.
• Developers can create custom processing functions, such as one that safely escapes HTML content.
• T-strings allow for more flexible string manipulation, making them similar to JavaScript's tagged templates.

To work with t-strings, developers can access the components of a template, including its strings and values. This allows for advanced processing and customization.

Overall, t-strings will enhance safety and flexibility in Python string handling, and there's hope that the developer community will embrace this feature widely.

No summary available.

No summary available.

Researchers from the University of Waterloo, led by Professor Martin Karsten, have discovered a way to reduce energy consumption in data centers by up to 30% by modifying just 30 lines of Linux code. This improvement addresses an inefficiency, making data centers more energy-efficient.

This project focuses on reverse engineering TikTok's custom Virtual Machine (VM) used for obfuscation and security. Here are the main points:

1. Purpose: The project aims to deobfuscate the webmssdk.js file, which contains TikTok's VM code, and make it more understandable.

2. Deobfuscation Process:

   • The code is heavily obfuscated, using techniques like bracket notation and encoded strings, which makes it hard to read.
   • The project involves replacing obfuscated array accesses with readable dot notation and renaming functions to improve clarity.
   • By running certain scripts, the obfuscated content can be decoded, revealing function names and purposes.

3. Bytecode and VM Functionality:

   • The VM executes bytecode that is encrypted using XOR with a key from the code.
   • The project successfully decrypts this bytecode, allowing analysis of its functionality.

4. Decompiling the VM:

   • The TikTok VM supports advanced features like scopes and exception handling, making it sophisticated.
   • Custom code is developed to interpret the bytecode and document what each function does.

5. Debugging and Testing:

   • Users can replace the original webmssdk.js with the deobfuscated version in their browser for testing.
   • Tools like Tampermonkey are used to run custom scripts, allowing for easier debugging.

6. Making Requests:

   • When making requests to TikTok, certain headers are required (like msToken, X-Bogus, and _signature).
   • The project can generate these headers, enabling actions like posting comments on TikTok.

7. Potential Changes:

   • The TikTok VM frequently updates, which may require ongoing adjustments and new decompilation efforts.

Overall, this project provides tools and methods to understand and interact with TikTok’s obfuscated JavaScript code, focusing on improving accessibility and functionality for users.

The author expresses a strong preference for pipelining in programming languages, which allows for a clearer and more readable way to process data through function calls. Instead of passing arguments in a complicated manner, pipelining lets you chain operations in a straightforward, line-by-line fashion. This method improves readability, makes code easier to edit, and enhances the overall coding experience.

The author contrasts pipelining with traditional function call syntax, arguing that the latter often requires excessive parsing and is less intuitive. They highlight the benefits of pipelining, such as easier code discovery through IDE features, cleaner edits when adding new functions, and improved organization of code.

Examples from languages like Rust and SQL demonstrate how pipelining can simplify complex operations. The author also mentions the Builder Pattern, which aligns well with pipelining for creating complex objects.

Overall, the author finds pipelining to be a neat and effective programming feature that enhances code clarity and maintainability, and they advocate for its wider adoption in languages.

The article discusses how to implement Haskell's lazy infinite lists in Python using generators.

Key points include:

1. Introduction to Lazy Infinite Lists: The author explains a technique learned from Haskell that can also be applied in Python with infinite generators.

2. Creating an Infinite Generator: An example is provided to create an infinite generator for positive integers. The generator is defined recursively, similar to Haskell.

3. Usage of Generators: A function called take is defined to extract the first few elements from the generator, and it successfully retrieves the first 10 integers.

4. Integrating a Taylor Series: The article explains how to create a generator that represents the integral of a Taylor series, demonstrating this with the exponential function.

5. Evaluating the Series: The author shows how to evaluate the Taylor series for the exponential function and compares it to Python's built-in math.exp function, confirming the results are very close.

6. Sine and Cosine Functions: The concept is extended to define generators for the sine and cosine functions using integration.

7. Performance Improvement: The author mentions that Python generators can be slow, but introduces a memoize decorator to speed up the generators by caching results.

8. Bonus Tip: It’s noted that using Python's fractions module can yield exact rational numbers instead of floating-point approximations.

Overall, the article illustrates how to replicate some of Haskell's functionality in Python while also addressing performance issues.

Nerdlog Summary

Nerdlog is a fast, remote-first log viewer designed for simplicity and efficiency. It allows users to view logs from multiple remote machines without needing a central server. Inspired by tools like Graylog and Kibana, Nerdlog focuses on quick log querying and visualization using a timeline histogram.

Key Features:

• Primary Use: Efficiently read system logs (e.g., /var/log/messages) from multiple remote hosts, even with large files.
• Setup: Minimal configuration is required; it connects via SSH to remote nodes and analyzes logs directly there.
• Data Handling: Only small portions of log data are downloaded (up to 250 messages), saving bandwidth as most data is transferred in a compressed format.
• User Interface: Supports a query form for selecting time ranges, log streams, and log patterns. It also includes a timeline histogram for visual insights and a Vim-like command interface for navigation.

Installation:

• Requires Go for building. Users can build and install the binary with a simple command.

Usage:

• The app opens with fields for time range and log streams (specific log files on servers). Users can input multiple log streams and filter logs using patterns.
• The interface includes navigation tools and supports SSH configurations.

Requirements:

• SSH access to remote hosts and a specific version of Gawk are necessary. Rsyslog or similar must be installed for reading system logs.

Navigation and Commands:

• The interface supports various navigation methods, including Vim-like keybindings and command inputs for query management.

For more detailed information, users are encouraged to read the full Nerdlog article, which covers additional concepts, limitations, and implementation details.

Summary of "Things Zig comptime Won’t Do"

Zig's compile-time feature, known as "comptime," is often praised for its capabilities like generics and serialization. However, it has specific limitations designed to maintain clarity and simplicity. Here are the key points:

1. No Host Leakage: Compiled code does not have access to the host machine's architecture, ensuring consistent behavior across different environments.

2. No Dynamic Code Generation: Zig does not allow injecting arbitrary code during compilation, unlike some other languages. Instead, it uses partial evaluation to optimize functions based on known values at compile time.

3. No Domain-Specific Languages (DSLs): Zig does not support custom syntax for code generation. It operates strictly with Zig values, although it allows strings for certain functions.

4. No Runtime Type Information (RTTI): While Zig can generate code based on types at compile time, it does not support dynamic typing like some languages. This means all type information must be resolved during compilation.

5. No New API for Types: Zig allows the creation of new types but does not permit adding methods to them. Any additional functionality must be provided through standalone functions.

6. No Input/Output (IO): There are no IO capabilities at compile time, making the evaluation process safe and reproducible. Any database interactions must be handled through the build system instead.

Overall, while Zig's compile-time features are powerful, the design choices prioritize simplicity and ease of understanding, avoiding the complexity seen in other languages.

Summary of Implemented C Standard Library Functions

The following functions have been reimplemented:

1. strlen(): Counts the number of characters in a string, not including the null terminator.
2. strcpy(): Copies the content from the source string to the destination string.
3. strcat(): Adds the source string to the end of the destination string.
4. strncat(): Adds up to a specified number of characters from the source string to the destination string.
5. strcmp(): Compares two strings and returns 0 if they are the same.
6. strcspn(): Finds the first occurrence of any character from a specified array in a string.
7. strerror(): Provides a description for a given error code.
8. memchr(): Locates the first occurrence of a specified value in a block of memory.
9. strrev(): (Unofficial) Returns the string in reverse order.

The article discusses the experiences of photographer Chris Day who covers NBA games for The Commercial Appeal newspaper. Here are the key points:

• Proximity to Action: Photographers are very close to the court, which sometimes leads to obstructed shots, like capturing referees instead of players.

• Challenges: Day mentions the difficulties of "ref butt" blocking shots and the need to stay seated during games. He also highlights the risk of players and referees accidentally stepping on photographers.

• Equipment: Day uses two Sony cameras and multiple lenses to capture different angles and moments during the game.

• Collaboration: The photography community at games is supportive, with photographers exchanging advice and helping each other.

• Storytelling: Day aims to capture key moments and emotions during the games, focusing on both the action and the reactions of players, coaches, and fans.

• Safety: The number of photographers on the court has decreased for safety reasons, making it less crowded, but still presenting risks during fast-paced action.

Overall, the article gives insight into the challenges and rewards of photographing NBA games from a close perspective.

No summary available.

This research paper examines how deactivating Facebook and Instagram affects users' emotional well-being, based on two large randomized experiments conducted before the 2020 U.S. election.

Key findings include:

• Users who deactivated Facebook for six weeks reported a significant improvement in their happiness and reduced feelings of depression and anxiety compared to those who only deactivated for one week.
• Similarly, users who deactivated Instagram for the same six weeks also saw improvements, though to a lesser extent.
• The positive effects of deactivating Facebook were mainly observed in users over 35 years old, while the Instagram effects were more pronounced in women under 25.

The authors of the study include a diverse group of researchers, some of whom have connections to Meta (Facebook's parent company). The views expressed in the paper are those of the authors and do not necessarily represent the National Bureau of Economic Research.

Summary of Gemma 3 QAT Models Announcement:

Gemma 3 is the latest AI model that offers top performance and can run on high-end consumer GPUs like the NVIDIA RTX 3090. To enhance accessibility, new versions using Quantization-Aware Training (QAT) have been released. This approach reduces memory needs while preserving quality, allowing powerful models to operate on less powerful hardware.

Key points include:

• Performance: Gemma 3 can run on high-end GPUs using BFloat16 (BF16) precision. New quantized versions (using fewer bits) dramatically lower VRAM requirements.
• Quantization: This process decreases the precision of model parameters to save space, similar to compressing an image. The new models can be quantized down to just 4 bits (int4), allowing substantial memory savings.
• VRAM Savings: For example, the Gemma 3 27B model's VRAM requirement drops from 54 GB (BF16) to 14.1 GB (int4), enabling it to run on consumer-grade GPUs.
• Easy Integration: Models are available on platforms like Hugging Face and Kaggle, and can be used with tools like Ollama and MLX for straightforward implementation.

Overall, Gemma 3 aims to make advanced AI technology accessible to more users, enabling them to run powerful models on everyday devices.

Recent neuroscience research has revealed that the brain learns in more complex and varied ways than we previously understood. This new finding changes how we think about memory and learning.

No summary available.

Summary: Turns Codebase into Easy Tutorial with AI

This tutorial teaches you how to create an AI agent that analyzes GitHub repositories and generates beginner-friendly tutorials explaining the code. It uses a framework called Pocket Flow, which is a simple 100-line tool that builds a knowledge base from code and transforms complex programming concepts into easy-to-understand tutorials with clear visuals.

Key Features:

• The AI can generate tutorials by analyzing popular GitHub repositories.
• Examples of projects that can benefit from this include AutoGen Core, FastAPI, and NumPy Core, among others.

Getting Started:

1. Clone the repository and install the necessary dependencies using pip install -r requirements.txt.
2. Set up your AI model credentials in the utils/call_llm.py file.
3. Run the main script to analyze a GitHub repository or a local directory and generate a tutorial.

Commands:

• To analyze a GitHub repo:

  python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*"
  

• To analyze a local directory:

  python main.py --dir /path/to/your/codebase --include "*.py"
  

You can also generate tutorials in different languages. The output will be saved in a specified directory.

For more detailed guidance, check out the YouTube development tutorial.

No summary available.

No summary available.

Hennessey has introduced the Venom F5 Evolution, a supercar that boasts an impressive 2,031 horsepower from its twin-turbocharged V8 engine, making it the most powerful internal combustion car. This model is an upgrade to the existing Venom F5, which has been available for three years.

Key features of the Evolution include:

• Engine: The new "Fury" engine includes advanced components like the largest turbochargers in the world and specially designed aluminum pistons for enhanced performance.
• Performance: It can accelerate from 0 to 200 mph in just 10.3 seconds, outpacing notable competitors.
• Suspension: The car features an adaptive suspension with five driving modes for various conditions, including a special mode for maximum performance.
• Availability: The Evolution package costs $285,000 and can be added to new Venom F5 orders or retrofitted to existing models.

Overall, Hennessey continues to push the limits of automotive power with this new offering.

No summary available.

Summary of DCI Movie Delivery and Encryption

The cinema industry has established its own secure standards for creating and distributing movies, known as the DCI (Digital Cinema Initiatives) specification. This specification covers file formats, encryption, and projection systems. While the DCI specification is publicly available, detailed technical standards from organizations like IEEE and SMPTE must be purchased.

Key Components:

• DCP (Digital Cinema Package): A folder that contains all elements of a movie, such as metadata, subtitles, audio, and video.
• KDM (Key Delivery Message): An XML file that contains cryptographic information to enable movie playback on certified projection systems.

Workflow Overview:

1. A movie is mastered into a DCP, which is then encrypted using a static AES key.
2. The distributor creates a DKDM (Distribution Key Delivery Message) to securely send the AES key to cinemas.
3. Each cinema uses a KDM to decrypt the DCP and play the movie.

Projection System Setup: Projection systems consist of a server, audio processor, and projector. The server controls the DCPs and KDMs, ensuring that the content is stored securely and played correctly. The projector uses a "Media Block" to handle decryption in real time.

DCP Structure: A DCP includes various files for video, audio, and subtitles. The naming convention provides essential information about the movie, such as title, version, aspect ratio, language, and resolution.

Security Measures:

• The encryption key for DCPs is protected and encrypted using the distributor's certificate.
• Projectors must be certified, and only trusted devices can access DCPs.
• Encrypted DCPs include forensic watermarks to trace piracy back to the cinema.

Recent Developments: The DCI has updated its specification to allow playback of DCPs with expired certificates, prompting manufacturers to release necessary software updates.

Overall, the DCI standard prioritizes security and integrity in movie distribution, ensuring that content remains protected from unauthorized access and piracy.

The article by Prof. Ethan Mollick discusses the current state of Artificial Intelligence (AI), particularly focusing on new models like OpenAI's o3 and Google's Gemini 2.5. It highlights the challenges in measuring AI's intelligence and creativity, noting that existing tests, such as the Turing Test, are outdated and not suitable for evaluating AI.

Mollick introduces the concept of "Jagged AGI" to describe AI's uneven abilities; while some models can perform complex tasks exceptionally well, they can also struggle with simpler ones. For instance, the o3 model can generate comprehensive marketing plans and analyze data effectively but may still fail at straightforward brainteasers.

The article emphasizes that even if we consider these new models as a form of AGI, their impact on society will be gradual due to the slow pace of technological integration. However, the unique capabilities of these models could lead to faster adoption of AI technologies in the future.

Ultimately, the author suggests that we are in uncharted territory with AI and its potential to change our lives. Learning to navigate this evolving landscape will be crucial for adapting to future developments in technology.

The text discusses the concept of experimenting as a process. It emphasizes that experimentation involves trying out new ideas and methods to learn and improve. The author, Rémi Forte, presents this topic in a case study format, highlighting key points and insights related to effective experimentation. The focus is on understanding how to approach experiments systematically to achieve better results.

FurtherAI is currently hiring for various positions. They are looking for talented individuals who are interested in working with artificial intelligence technology. The company values innovation and creativity, and they offer opportunities for career growth and development. Interested candidates can find more information on their website about available roles and how to apply.

The article discusses the X-ray motif in chess, focusing on its defensive applications in a follow-up lesson. The X-ray is when one piece influences a square through another piece, which can be used not just for attacks but also for defense.

Key points include:

1. Defensive X-ray Motif: This lesson highlights how players can use X-rays to defend against threats, which is often overlooked in chess literature.

2. Examples of Defensive X-ray: The article provides six positions where players can find hidden defensive moves using the X-ray concept, showing that these tactics can save difficult situations.

3. Importance of Defense: The author emphasizes that recognizing defensive resources is crucial for chess development, as every player will face attacks.

4. Encouragement to Learn: Players are encouraged to practice recognizing X-ray defenses to improve their gameplay and save points.

5. Further Resources: The article suggests revisiting the previous lesson on X-ray attacks and explores additional tactical themes available on the author's blog.

Overall, the article aims to help chess players understand and utilize defensive tactics involving the X-ray motif to enhance their skills.

Summary:

ArXiv is a crucial online repository for scientific research, created by Paul Ginsparg nearly 35 years ago. It allows researchers to share their findings quickly, bypassing the slow peer-review process typical in academic publishing, which is often dominated by profit-driven companies. ArXiv has transformed how scientists communicate and access research, particularly evident during crises like the COVID-19 pandemic.

Despite not being peer-reviewed, submissions are moderated for quality. ArXiv has grown to host over 2.6 million papers, receiving 20,000 new submissions monthly, and has been involved in many significant discoveries. Ginsparg, now 69, remains deeply connected to arXiv, despite challenges like bureaucratic issues and a surge in low-quality submissions.

The platform started as a simple email server and evolved with the internet, becoming essential for various scientific fields. Ginsparg's vision and programming skills were vital in its development. However, he has faced difficulties managing its growth and technical demands, often clashing with library staff at Cornell University, where arXiv is housed.

ArXiv's impact has led to both praise and criticism, including accusations of promoting low-quality research. Ginsparg remains engaged with the platform, finding it an entertaining and dynamic environment, even as he grapples with its ongoing challenges.

Gemini 2.5 is a new language model that can effectively understand the layout of PDF documents. This is a significant improvement over previous models, making it easier to work with PDFs.

At Revision 2025, we showcased a demo called "Signal Carnival" that creatively manipulates the audio and video output of the Commodore 64 (C64). Here are the key points:

1. Concept: The demo involves switching the audio and video cables of the C64, allowing for simultaneous meaningful audio and video output, which is a new approach compared to older methods of mixing audio with video signals.

2. Audio Generation: The demo uses the C64’s VIC chip to produce audio by adjusting waveforms with timers. It creates music by modifying brightness values that affect the screen color, allowing for interesting sound effects even though it uses all available timers.

3. Video Generation: The C64’s SID chip can produce video signals but has limitations due to filtering. The demo takes advantage of the SID's volume register to generate video, despite the blurriness caused by the hardware filter.

4. Effects and Synchronization: The demo employs various visual effects while carefully timing video syncs to ensure they are recognized correctly. It uses an unconventional horizontal frequency to create unique effects.

5. Loading Mechanism: A custom loader is created to manage data transfer without interrupting audio playback. This loader includes advanced features like on-the-fly decoding but is not as versatile as full C64 loaders.

Overall, "Signal Carnival" pushes the boundaries of what can be achieved with the C64 by creatively using its audio and video capabilities.

Training Hot Swap Summary

Training Hot Swap allows you to run PyTorch training code without unloading large model weights from VRAM, which can take a long time to reload. This method keeps your model in VRAM even after your training script ends, making development faster. If a model needs to be reloaded, it happens in the background after the script exits, so it’s ready the next time you run your script.

This is achieved by using a second process that continues to run after your main script exits. It uses Python's eval() to execute your code. You can also use this setup for remote development via a VPN, which is more efficient than IntelliJ's remote SSH interpreter.

A GUI can be integrated with your training scripts to monitor progress and evaluate results. You can submit UI code along with your training code, allowing for quick app launches. For example, an app that displays outputs from the Mistral 7B model takes only 0.32 seconds to become interactive.

To use this method, set the model download location in the model_server.py file and ensure compatibility with the IntelliJ debug server. Replace the traditional model loading code with a function that retrieves the model from a global variable.

How to Run:

1. Start the server: python model_server.py
2. Submit your training code: python client.py ./src ./src/sample_train.py

Important Note: This setup can pose security risks since it executes arbitrary code. Avoid exposing the server directly to the internet.

No summary available.

Summary: The Joy of Linux Theming in the Age of Bootable Containers

The author shares their experience with customizing Linux desktop environments over the years. They initially enjoyed tweaking their setups using various tools and themes, but often faced issues with broken components and decided to stick with default configurations.

Recently, they discovered "bootable containers," a method that allows users to define their operating system through a container file. This approach simplifies customization, making it easy to test new themes and revert changes if something goes wrong. The author appreciates that this method reduces clutter and provides a reliable way to manage system changes.

They also discuss the blurred lines between what constitutes a Linux distribution and a simple containerized project. The author created a container-based project called Blue95, which raised questions about its status as a distribution. They conclude that the definition of a Linux distribution has become less clear due to the rise of container technology, but creating bootable containers can be a joyful and creative process.

Summary:

Jesús Rodrigo Comino, a geographer from Spain, is working to improve soil health in Europe, where 60-70% of soils are unhealthy, costing around €50 billion annually due to degradation. Spain has the highest soil degradation rates in Europe, losing an average of 14.2 tonnes of soil per hectare each year. Healthy soil is vital for agriculture and ecosystems, highlighted in the UN's Sustainable Development Goals.

Comino's research focuses on soil erosion in vineyards, using geographical mapping and artificial intelligence to help farmers adopt sustainable practices. He is involved in the SOILCRATES project, which aims to create experimental sites for better soil management and to raise awareness about soil's importance.

Climate change is also affecting soil health in Spain, with higher temperatures and extreme rainfall causing challenges for vineyards. Comino emphasizes the need for more action to address these issues and improve soil quality.

The text introduces a library called "falsify" for property-based testing in Haskell, inspired by the Python library Hypothesis. It explains the concept of property-based testing, contrasting it with unit testing. In unit testing, specific inputs and expected outputs are checked, while in property-based testing, random inputs are generated, and properties are verified against the function's output.

Key Points:

1. Counter-Examples and Shrinking:

   • The library can find counter-examples (incorrect test results) and "shrink" them to simpler forms for easier debugging. For example, if a property fails with values 38 and 23, shrinking might yield 0 and 1, which are simpler and relevant.

2. Internal Shrinking:

   • Falsify uses internal shrinking, meaning it shrinks generated values without a separate shrinking function. This is more efficient as it works seamlessly across different parts of the code.

3. Sample Generation and Parsing:

   • Falsify generates inputs using a pseudo-random number generator (PRNG) and uses a tree structure for sampling, allowing for better organization and targeted shrinking.

4. Predictability in Shrinking:

   • The design ensures that shrinking behavior is predictable and avoids complications that can arise when values are changed unexpectedly during testing.

5. Selective Functors:

   • The library employs selective functors to manage how generators are used, ensuring that irrelevant parts of the sample tree are not shrunk when they are not needed.

6. Usage and Examples:

   • The text provides examples of how to use the library, including generating properties, using predicates for better failure messages, and specifying ranges for generated values.

7. Compatibility with Other Libraries:

   • Falsify offers compatibility with other testing libraries like QuickCheck and hedgehog through specific combinators, allowing users to integrate it easily into their existing workflows.

In conclusion, falsify aims to improve property-based testing in Haskell by providing a robust and efficient way to generate test cases, shrink counter-examples, and ensure predictable behavior during testing, drawing inspiration from established methods in the field.

Error handling in software is crucial, as poor practices can lead to serious consequences, including financial losses and safety issues. The traditional method for error handling in TypeScript and JavaScript is the try/catch approach, which works for simple situations but struggles with complex applications, especially when dealing with multiple API calls and error types.

Key limitations of the traditional try/catch method include:

1. Type Safety Issues: Errors can be thrown as any value, making it hard to ensure proper handling and increasing the risk of runtime errors.
2. Lack of Type System Integration: There's no way to indicate which functions can throw errors, making it difficult to manage and anticipate errors effectively.

Modern error handling approaches focus on:

• Avoiding unpredictable control flow: Instead of throwing errors, they return them as values.
• Using structures like tuples or monads: These encapsulate success and error states, allowing for better management of errors without traditional try/catch.

Two notable modern approaches are:

1. Go-style Return Tuples: Functions return a tuple containing either the result or an error, promoting error handling at each step.
2. Monadic Style: This uses types like Result<T, E> to represent values that may fail, allowing for functional chaining and better error management.

While the traditional method remains common, especially for simpler applications, the newer methods offer better type safety and clarity. However, they come with increased complexity and learning curves, which might be challenging for new developers.

In summary, the choice of error handling strategy should match the project's complexity and the team's experience, with traditional try/catch being suitable for simpler cases and modern approaches being better for more complex systems.

Summary of Regular Expression Derivatives in Python

Michael Paddon discusses using regular expression derivatives to improve scanner performance and handle Unicode efficiently. The concept, introduced by Janusz Brzozowski in 1964, allows for transforming regular expressions into deterministic finite automata (DFAs).

Key Concepts:

• Regular Expressions (REs): Basic elements include the null string, empty string, symbols, concatenation, Kleene closure, and logical operations.
• Deterministic Finite Automata (DFAs): Defined by states, start state, transitions, accepting states, and error states.

Regular Expression Derivatives:

• Derivatives help simplify regular expressions by showing what's left after processing a symbol.
• The process involves various rules for handling different types of expressions, such as concatenation and alternation.

Benefits:

• The derivation process leads to the creation of states and transitions for a DFA, enabling efficient string scanning.
• For large alphabets, derivatives can be calculated for classes of symbols instead of each symbol individually, which simplifies the process.

Unicode Support:

• The method adapts to handle Unicode by using derivative classes, allowing for broader character set processing.

Regular Vectors:

• A collection of regular expressions can be combined into a single DFA, streamlining the scanning process for sequences of tokens.

This approach enhances the performance of scanners while ensuring they can work with a wide range of character sets.

Quantizing large language models helps reduce their memory usage and computational costs. Traditional methods focus on minimizing errors for each layer individually, but these methods lack strong theoretical support and may not be the best choice. This paper introduces a "linearity theorem" that connects the errors from quantizing each layer to the overall increase in model complexity.

From this, two new techniques are developed:

1. HIGGS: A data-free method for quantization that uses Hadamard rotations and optimal grids, which performs better than previous popular methods.
2. An optimal way to set different quantization levels for each layer under a specific compression limit, using dynamic programming.

The authors show that their approach leads to better accuracy and compression on models like Llama-3.1, 3.2, and Qwen. Additionally, their method can be efficiently run on GPUs, enhancing both data-free and customized quantization for large language models.

Summary of Turing-Drawings

Turing-Drawings is a project that uses randomly generated Turing machines to create images and animations on a 2D canvas. It is a simple demo built with JavaScript and HTML5, available under a modified BSD license. You can try it out at http://maximecb.github.io/Turing-Drawings/. The project generates various patterns, including fractals, movement, chaos, and more. Additionally, there is a blog post that provides a brief description of the project.

In April 2025, mathematicians Peter Sarnak and Noga Alon learned that both were mistaken in a decades-old bet regarding optimal graphs, known as expanders. They initially debated the abundance of these highly interconnected graphs with few edges. Sarnak believed they were rare, while Alon thought random graphs would likely show optimal properties.

Recent research led by Horng-Tzer Yau and his team proved that approximately 69% of regular graphs are indeed optimal expanders, known as Ramanujan graphs. This result utilized concepts from random matrix theory and the universality conjecture, originally proposed by physicist Eugene Wigner.

Their findings not only resolved Sarnak and Alon's bet but also revealed that optimal expander graphs are neither extremely common nor rare. This breakthrough suggests that random graphs can exhibit surprising regularities and further advances in mathematics are anticipated as a result.

This text discusses interesting "movie mistakes" that reveal the behind-the-scenes work in filmmaking. It highlights various films, including "Glory," "Goodfellas," "Aliens," "Duel," "The Dark Knight," and "The Abyss," each containing unnoticed errors that break the cinematic illusion, such as modern items appearing in historical settings or crew reflections in scenes.

The text also focuses on a specific mystery from "Star Wars: Revenge of the Sith," where a ghostly figure briefly appears in a shot. Todd Vaziri, a visual effects artist at Industrial Light & Magic (ILM), investigated this anomaly, discovering it was actually a stunt rigger, not a Force ghost, who accidentally appeared in the scene due to an oversight during post-production. This example emphasizes the handmade nature of film and how imperfections can become part of a movie's history.

Overall, the piece celebrates the artistry of filmmaking while acknowledging that mistakes can provide a glimpse into the creative process.

Summary of JUICYJAM Report

The report discusses a coordinated online harassment and doxxing campaign named JUICYJAM, which has targeted the pro-democracy movement in Thailand since August 2020. Doxxing involves publicly exposing personal information about individuals with harmful intent. The campaign is linked to the Royal Thai Armed Forces and the Royal Thai Police, as revealed by leaked documents in March 2025.

Key Points:

1. Campaign Overview: JUICYJAM uses fake identities on social media, mainly X (formerly Twitter) and Facebook, to harass pro-democracy activists, encouraging their followers to report them to authorities.

2. State Involvement: Confidential documents indicate that the campaign is part of a larger initiative by the Thai military and police to suppress dissent, especially after the military coup in 2014.

3. Impact on Activists: The campaign has resulted in real-life repercussions for activists, including arrests, physical assaults, and judicial harassment. Victims of doxxing have faced severe legal consequences under Thailand's strict lèse-majesté laws against insulting the monarchy.

4. Shortcomings of Social Media Platforms: The report criticizes social media companies for failing to effectively address coordinated doxxing campaigns. Their policies often inadequately account for the context of state-sponsored harassment.

5. Recommendations for Platforms: The authors suggest that social media companies should improve their reporting mechanisms for victims, identify and remove coordinated networks engaging in doxxing, and enhance protections for users in repressive regimes.

In conclusion, the JUICYJAM campaign illustrates the risks faced by pro-democracy activists in Thailand and highlights the challenges in combating state-sponsored online harassment.

The Armatron, a robotic arm toy from the 1980s, significantly influenced modern robotics. Created by Hiroyuki Watanabe at Tomy (now Takara Tomy), the toy was designed to mimic real robotic functions using mechanical components, rather than complex electronics. It featured dual joysticks for controlling its movements and became popular among both children and robotics enthusiasts.

Watanabe was inspired to create the Armatron after seeing a newspaper clipping of a mechanical arm. He aimed to make it more versatile and user-friendly, leading to the final design which could perform multiple movements. The Armatron was not just a toy; it caught the attention of robotics engineers, sparking interest in robotics careers among young users.

Today, the charm of the Armatron continues, as it inspires a new generation of builders and tinkerers who appreciate its mechanical design. As AI technology advances in robotics, the hands-on curiosity fostered by toys like the Armatron remains valuable in engineering and problem-solving.

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The article "Digital Piracy in Times of Covid-19" examines how the COVID-19 pandemic influenced the consumption of cultural goods, like music, films, games, and books. It is based on a survey conducted in January 2022 across 14 countries.

Key findings include:

• The pandemic led to significant shifts in how people consumed cultural content, with a rise in both legal and illegal consumption.
• About 6-8% of individuals engaged in online piracy for the first time during the pandemic, often due to reduced income and increased time at home.
• Adults who faced income losses and worked or studied remotely saw a decline in legal purchases of music and games, while minors showed different trends; illegal consumption was negatively linked to book purchases but positively linked to audiovisual content.
• The study suggests that the pandemic not only caused temporary changes in consumption behavior but may have also led to a more permanent shift towards illegal channels for some users.

Overall, the research highlights the complex relationship between digital piracy and legal sales during the pandemic, showing how economic factors and age differences played a role in consumption patterns.

New research from MIT and Harvard Medical School reveals that a protein called IL-17, which helps the immune system fight infections, also impacts behavior by acting on specific areas of the brain.

• Key Findings:

  • IL-17 affects the amygdala, leading to increased anxiety, and the somatosensory cortex, which promotes sociability.
  • This suggests a close connection between the immune system and brain function.

• Background:

  • IL-17 is involved in the body’s immune response and has been linked to changes in behavior during illness.
  • Studies indicate that IL-17 can regulate neuron activity, influencing feelings and behaviors.

• Implications:

  • Understanding how IL-17 interacts with the brain could help develop new treatments for conditions like autism and depression.
  • The research highlights that immune molecules can also serve as signals that affect mental states, not just physical health.

This research contributes to the understanding of how our immune system can influence our mood and behavior, especially when we are sick.

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PiLiDAR - DIY 360° 3D Panorama Scanner Overview

Core Features:

• LiDAR Integration: Uses LDRobot LD06, LD19, or STL27L with custom driver.
• Data Integrity: Includes CRC package check.
• Visualization: Offers 2D live visualization with export options (numpy, CSV).
• Panorama Creation: Generates a 6K 360° spherical map using fisheye photos and Hugin software, optimizing exposure and color balance.
• 3D Scene Assembly: Combines 2D images into 3D scenes, supports color sampling, and exports in various formats (PCD, PLY, e57).

Hardware Requirements:

• LiDAR Units: LD06, LD19, or STL27L.
• Camera: Raspberry Pi HQ Camera with ArduCam M12 Lens.
• Processing Unit: Raspberry Pi 4.
• Stepper Motor: NEMA17 with A4988 driver.
• Power Supply: Options include 2x 18650 batteries or a 10,000 mAh USB power bank.

System Setup:

• Power Management: Configure GPIO for power button functionality; setup for I2C communication with an accelerometer.
• PWM Control: Enable hardware PWM for smooth operation of motors.
• Panorama Stitching: Install Hugin for photo stitching.

Scanning Process:

• Takes around 2 minutes per scan including setup, shooting, and processing.
• Preliminary results show single scans without registration or post-processing.

Software and Troubleshooting:

• Use Python scripts for GPIO and service management.
• Manage permissions for serial devices and utilize udev rules for ease of access.
• Troubleshoot issues with drivers and performance using suggested solutions.

Additional Features:

• Supports 3D printing for housing and components.
• Offers remote access to Jupyter notebooks for network operations.
• Provides options for USB dump of scan data.

This system is designed for DIY enthusiasts looking to create 3D scans and panoramic images using Raspberry Pi and LiDAR technology.

The author discusses the challenges of finding out if an author has released a new book. They point out that while there are many databases for published books, there aren't any that track new works specifically. To address this, the author experimented with Perplexity AI to create a simple tool that lists books by authors and identifies new releases.

They found the process of using Perplexity straightforward, creating a library called perplexity.el to retrieve book data. However, the results were inconsistent, with the AI sometimes providing different answers for the same query. Despite this, the author was able to track new releases from their favorite authors and even ended up buying several books during their testing.

The author also compared results from Perplexity with those from OpenAI and Gemini, concluding that Perplexity was still useful, even if imperfect. Overall, the author managed to create a tool that helps monitor new book releases, despite the limitations of the AI.

The essay discusses how storytelling can engage young children in learning math and foster curiosity. It highlights that children around the ages of 5 to 8 are often not yet curious about abstract concepts but are very receptive to stories. The author shares an example of how a story about a mathematician helped a young girl named Anna learn about prime numbers effortlessly.

The essay emphasizes the importance of storytelling in making learning enjoyable and relatable for children. It recounts the author's experiences as a camp counselor, where he used imaginative stories to motivate kids to participate in activities and to help them feel included.

The author argues that storytelling can diminish fear of math by normalizing it in daily life, suggesting that incorporating math into bedtime stories can help children develop a more positive attitude toward the subject.

Overall, the piece advocates for more story-driven math content aimed at children, suggesting that this approach can make math less intimidating and more engaging. It calls for improved storytelling skills among educators and content creators to enhance math learning experiences.

A new hormone-free male birth control pill called YCT-529 is currently in clinical trials after showing promising results in animal studies. This drug is designed to prevent pregnancy by stopping sperm production and has been found to be 99% effective in male mice and non-human primates, with no side effects reported. Importantly, fertility was fully restored after stopping the medication.

Currently, men only have two FDA-approved contraceptive options: condoms and vasectomies. In contrast, women have over 20 contraceptive methods available, including oral pills. The development of YCT-529 aims to provide more choices for men and promote shared responsibility in family planning.

The drug has successfully completed phase 1 clinical trials and is now undergoing further safety and efficacy testing. Researchers emphasize the need for more male contraceptive options, especially considering the high rate of unintentional pregnancies.

No summary available.

Summary of "Regex Isn't Hard"

Regex, or regular expressions, is often seen as complicated, but it can be simplified by focusing on a core subset that is easy to understand. Regex is valuable because it allows you to perform complex text operations with minimal code, often much faster than traditional coding methods.

Key Concepts in Regex:

1. Character Sets:

   • Matches specific characters or ranges of characters.
   • Example: [a-z] matches any lowercase letter.

2. Repetition:

   • Operators like ?, *, and + control how many times a character or group can appear.
   • Example: a* matches zero or more 'a's.

3. Groups:

   • Groups are used to create sub-patterns for repetition or extraction.
   • They are useful for substitutions and extracting matched text.

4. Operators:

   • | acts as a logical OR.
   • ^ indicates the start of a string or negation in character sets.
   • $ signifies the end of a string.

Tips:

• Focus on the basics and ignore complex shortcuts that may not be portable across programming languages.
• Stick to the outlined core concepts for better understanding and consistent use across different coding environments.

In conclusion, learning and using regex can greatly enhance your text processing capabilities, and a little knowledge can go a long way.

The article presents a new method for displaying colors called "Oz," which involves directly stimulating individual photoreceptor cells in the human eye. This approach allows for the creation of colors that go beyond the natural human color spectrum by selectively activating M cone cells without affecting neighboring L or S cone cells.

Key points include:

• Oz Principle: The technique enables the precise control of photoreceptor activity, potentially allowing for the display of arbitrary colors.
• Experimental Findings: Experiments showed that when trying to activate only M cones, a new color named "olo" was perceived, described as a highly saturated blue-green.
• Prototype System: The prototype uses advanced imaging and stimulation technology to deliver light to thousands of retinal cells, adjusting for eye movements in real-time.
• Color Matching: Tests confirmed that the colors produced by the Oz system were indeed outside the standard human color gamut, demonstrating its effectiveness.
• Applications: This method may lead to new ways to create and perceive colors in various visual media, enhancing our understanding of color perception.

Overall, the Oz technique represents a significant advancement in color display technology, opening up new possibilities for visual experiences.

The text discusses the concept of state machines in programming, particularly focusing on how to implement them in Rust. Here are the key points simplified:

1. Familiar Patterns: When working on projects, recognizing familiar programming patterns helps speed up understanding and development.

2. State Machines: A state machine is a system that consists of a set of states and transitions between them. For example, a bottle filling machine can be in states like "Waiting," "Filling," and "Done," with specific rules about how it can transition between these states.

3. Defining States and Transitions: Each state has unique responsibilities and does not rely on other states. Transitions between states should be well-defined to prevent invalid state changes.

4. Implementing in Rust:

   • States can be represented using enums, but this approach has limitations, as it doesn't enforce transitions at compile time.
   • Alternatively, structs can be used where each state is a different struct. This allows for compile-time checks but increases code repetition.
   • Using generics can help create a more efficient and clear implementation, where the state is part of the type signature, ensuring valid transitions are enforced at compile time.

5. Error Handling: The design should ensure that error messages about invalid transitions are clear and understandable.

6. Example Implementations: The text provides examples of state machines, such as a bottle filling machine and the Raft consensus algorithm, showcasing how states can transition and how to manage shared values between states.

7. Conclusion: Rust's features allow for expressive and safe state machine representations, despite some challenges in managing transitions and state definitions.

The overall focus is on how to effectively use state machines in programming, particularly in Rust, emphasizing safety, clarity, and efficiency in design.

The text discusses a project to create a basic TCP/IP networking stack for the DEC Professional 380 computer, which runs the PRO/VENIX operating system. The author reflects on their long-standing interest in networking for vintage computers, particularly the Commodore 64, and mentions how others had already developed similar projects.

The DEC Professional series, based on the PDP-11, struggled with compatibility issues and was mainly marketed as office computers. The operating system for the DEC Pro, PRO/VENIX, was a Unix variant that initially lacked networking capabilities. Despite this, the author aims to develop a simple networking solution using a serial connection to allow the DEC Pro to access the modern Internet.

The DEC Pro has two serial ports, and the author plans to use the slower DE-9 port for this project. They want to create a minimal TCP/IP stack that can work with the older system’s limited capabilities and potentially be ported to other low-spec systems in the future. The goal is to make basic networking feasible, even if it means sacrificing speed for compatibility.

No summary available.

Summary of Recursive LLM Prompts:

The concept of recursive LLM prompts involves using language prompts to make an AI model, like GPT-3.5, generate a series of updated prompts, similar to how a recursive function works in programming. Each prompt contains a state that gets modified to approach a final goal, allowing for continuous generation of new prompts.

An example is provided with a Fibonacci sequence prompt that updates variables to calculate the next number. This process can theoretically continue indefinitely without a defined stopping point, as shown in a Python script that automates the interaction with the AI.

Two types of recursion are involved:

1. The recursive prompt, which generates new text prompts when fed to the AI.
2. A recursive Python function that calls itself with the AI's output until a desired condition is satisfied.

The overall aim is to explore how prompts can be structured to effectively guide the AI in generating answers, inspired by concepts from reinforcement learning and historical AI systems.

However, there are challenges, such as the AI sometimes producing incorrect outputs. Ideas for improvement include using reliable fact-checking methods and allowing the model to verify its own answers.

The text also touches on related AI research projects and concepts, including how humans solve math problems and the nuances of different types of recursion. The document ends with instructions for running the prompts and acknowledges contributions from various individuals and communities.

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The text emphasizes that forcing kids to do math is not effective. Instead, parents should foster a love for math by making it fun and engaging. The author shares their approach, which involves stopping math activities if their child shows disinterest and incorporating math into playful, everyday situations. They highlight the importance of curiosity and natural exploration in learning.

The author suggests using games and stories to teach math concepts without pressure. As the child grows, math can be integrated into various activities like games and real-life scenarios, helping them understand math as part of life rather than a chore. The key is to spend quality time with children and balance sharing interests without imposing them. Ultimately, the goal is to nurture a child's eagerness to learn and explore the world, rather than solely focusing on math skills.

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Summary: Demystifying Python Decorators

This article aims to break down Python decorators into understandable parts, making them less intimidating. The author shares their personal journey of learning about decorators, emphasizing that they are simpler than they initially appear.

1. Understanding Closures:

   • Closures are functions defined within other functions that remember the environment in which they were created. They allow a function to access variables from its enclosing scope.
   • The article demonstrates how to use closures to track arguments passed to a custom print function without using global variables.

2. Creating a Decorator:

   • A decorator is a function that takes another function as an argument, adds some functionality to it, and returns a new function.
   • The author walks through building a decorator step-by-step, starting with a function that records arguments passed to it.

3. Making the Decorator Generic:

   • The decorator is modified to work with any function, not just print, by using parameters to accept any function.

4. Handling Multiple Arguments:

   • The decorator is updated to accept any number of arguments using *args and **kwargs, allowing it to function like the built-in print.

5. Returning Values:

   • The inner function in the decorator is updated to return the value from the original function it decorates.

6. Testing the Decorator:

   • The author demonstrates the decorator with both built-in functions like print and user-defined functions, ensuring it works correctly while storing the arguments.

7. Future Parts:

   • The article concludes with a preview of upcoming parts that will further explore decorators, including using the @ syntax and creating more complex decorators.

Overall, the article simplifies the concept of decorators, providing a clear foundation for understanding their purpose and functionality in Python programming.

No summary available.

The Icelandic voting system is set to be used in the upcoming elections on November 30, 2024. It relies on a biproportional apportionment system, similar to those used in Norway and some parts of Switzerland. This system consists of constituencies, each with a mix of constituency seats (CS) and adjustment seats (AS).

Key features include:

1. Divisor Rules: Iceland uses the d'Hondt method for seat allocation, where a party's quotient (the number of votes divided by the number of seats plus one) determines how many seats they receive. This method tends to favor larger parties.

2. Constituency Seats Allocation: Each party starts with zero seats, and the party with the highest quotient receives a seat. This process continues until all constituency seats are filled.

3. Adjustment Seats Allocation: After constituency seats are allocated, the total votes and seats across the country are calculated. Parties that didn't meet a 5% vote threshold are eliminated from this process. Adjustment seats are then distributed based on which parties are most deserving of additional seats, ideally in constituencies where they received the most votes.

4. Critique of the System: The system has some flaws, including failing the monotonicity criterion, which can lead to a party losing seats even if they receive more votes. This has occurred multiple times with the Framsóknarflokkurinn party. The method used in Iceland is not the most mathematically accurate, as it simplifies complex calculations, which can lead to misunderstandings.

5. Suggestions for Improvement: The author suggests increasing the number of adjustment seats to reduce vote inequality and recommends exploring alternative voting methods that are easier to understand, such as scoring systems.

Overall, while the Icelandic voting system has its advantages, there are areas for improvement to make it fairer and more comprehensible for voters.

Summary of Layered Design in Go

This text discusses a method for designing programs in Go, emphasizing a layered architecture. Here are the key points:

1. Understanding Packages: In Go, packages cannot reference each other circularly, which helps maintain clear boundaries and allows for information hiding. This design choice prevents complexity that can arise from circular dependencies.

2. Layered Design Approach: The author shares a structured way to organize code into layers. Packages should be arranged so that lower-level packages do not depend on higher-level ones. This creates a clear hierarchy and makes understanding the codebase easier.

3. Descriptive vs. Prescriptive: The proposed layered design is descriptive of Go’s rules rather than prescriptive. While some design methods suggest strict layering, the author believes flexibility is important and that layers can interact in ways not strictly defined by these rules.

4. Dealing with Circular Dependencies: If circular dependencies arise, the author suggests various strategies to resolve them:

   • Move Functionality: Identify and relocate code causing the circularity to the appropriate package.
   • Create a Shared Package: Extract shared types or functions into a separate package.
   • Use Interfaces: Define interfaces to reduce direct dependencies between packages.
   • Copy Code: As a last resort, duplicate code needed in both packages.
   • Combine Packages: If the circular dependency is too complex, consider merging the packages.

5. Prescriptive Architectures: While other architectural patterns (like MVC) can be applied, they must fit within Go’s layered structure, which disallows circular references.

6. Benefits of Layered Design: This methodology helps keep packages small and focused, making them easier to understand and test. It ensures that each package can be used independently without requiring knowledge of the entire codebase.

7. Practical Application: The author encourages trying this design in new projects to fully grasp its benefits, suggesting it can also facilitate future migrations to microservices if needed.

In essence, the layered design in Go promotes clarity, modularity, and ease of maintenance while adhering to the language's rules regarding package dependencies.

Claude Code is a new command line tool designed to help engineers and researchers at Anthropic integrate AI coding assistance into their workflows. Here are the key points:

1. Overview: Claude Code offers a low-level, customizable interface for coding, making it flexible but potentially challenging for beginners.

2. Setup Customization:

   • Use CLAUDE.md files to document essential project information like commands, code styles, and workflows, which Claude can automatically reference.
   • Optimize these files regularly to enhance Claude’s performance.

3. Tool Management: Claude Code requires permission for actions that modify your system. You can customize which tools are allowed by using commands or editing configuration files.

4. Common Workflows:

   • Explore, Plan, Code, Commit: Research and plan before coding.
   • Test-Driven Development: Write tests first, then implement code to pass those tests.
   • Iterative Design: Use visual references for UI development and refine outputs over multiple iterations.
   • Safe Mode: Use a mode that bypasses permission checks for specific tasks, but be cautious of data loss risks.

5. Interacting with Code: Claude can search through codebases, manage Git operations, and integrate with GitHub to automate tasks like creating pull requests or handling issues.

6. Optimization Tips:

   • Provide specific instructions and visual aids to improve Claude’s performance.
   • Use structured checklists for complex tasks.
   • Use headless mode for automation in non-interactive environments.

7. Multi-Claude Workflows: Running multiple instances of Claude can enhance productivity by allowing parallel tasks, such as one instance coding while another reviews.

8. Community and Collaboration: The tool is built on insights from the user community, encouraging experimentation and sharing best practices.

For detailed guidance, refer to the documentation at claude.ai/code, where you can find additional examples and advanced techniques.

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The board game industry is facing serious issues, primarily due to the mishandling by companies like Final Frontier Games and Cephalofair. Final Frontier recently announced its shutdown, leaving backers without their games after raising substantial funds. Instead of taking responsibility for their mistakes, they blamed external factors like tariffs and distributors.

The author argues that the real problem lies in the companies' poor business practices and decision-making rather than external circumstances. For example, Final Frontier relied on unverified promises from distributors and made production decisions without securing necessary payments upfront. As a result, backers are left without the products they funded.

Cephalofair, known for the popular game Gloomhaven, also attributed its challenges to tariffs, claiming $4 million in costs. However, the author debunks this, explaining that proper tariff classification could significantly reduce their expenses. The panic over tariffs is largely based on misinformation and a lack of understanding of the relevant codes.

Overall, the piece emphasizes that many in the board game industry are not equipped to handle business challenges effectively, leading to a breakdown in trust and accountability. The author calls for better understanding of logistics and tariffs and advocates for more responsible business practices among publishers.

Recent research raises concerns about the impact of artificial intelligence (AI) on human intelligence. As people increasingly rely on AI tools like ChatGPT and Google for information and problem-solving, there is evidence suggesting a decline in cognitive abilities, including critical thinking and memory.

Historically, intelligence has improved across generations (known as the Flynn effect), but recent studies indicate a reversal, with younger individuals showing lower IQ scores and attention spans. Experts warn that offloading cognitive tasks to AI may weaken our mental skills, similar to how neglecting physical exercise can harm our muscles.

Research shows a significant correlation between frequent AI use and reduced critical thinking skills, particularly among younger users. While AI can enhance creativity in some cases, it often leads to less diverse ideas across the population.

Experts emphasize the need to actively engage with AI rather than passively consume information to maintain cognitive health. They advocate for education systems to prioritize critical thinking and intuition, helping individuals learn how to interact with AI thoughtfully. The overarching message is to focus on the effects of AI on our thinking processes, rather than solely on its benefits.

No summary available.

Researchers are exploring new ways to improve large language models (LLMs) by allowing them to process information without relying heavily on language. Traditionally, LLMs convert complex mathematical calculations into words, which can be inefficient and may lead to loss of information.

Recent studies suggest that LLMs can benefit from “thinking” in a mathematical space, called latent space, instead of translating thoughts into words. Two new models, Coconut and a recurrent model developed by Tom Goldstein's team, attempt to do this. Coconut allows reasoning primarily in latent space, which leads to more efficient processing, while Goldstein’s model dynamically adjusts how many computational layers it uses based on the complexity of the task.

Both models show promising results, with Coconut being more efficient than standard models and Goldstein's model achieving better accuracy on various reasoning tasks. However, there are still challenges and limitations, such as Coconut's struggles with elementary math problems and potential issues with reasoning patterns that don’t align with human thinking.

Overall, this research opens up new possibilities for AI reasoning and could lead to significant advancements in how language models function.

No summary available.

You’ve learned how to find great deals, but if you just want to enjoy your favorite animal regardless of price and quality, you can easily browse through 16 different animal groups using the left and right arrows.

Summary of Undercut-f1

Undercut-f1 is an open-source application for live timing of Formula 1 races. It's a text-based user interface (TUI) tool that allows users to view live timing data during races and practice sessions, along with saving this data for later analysis.

Key Features:

• Timing Tower: Displays real-time information for each driver, including sector times, lap times, tire information, and gaps to other drivers.
• Pit Stop Strategy: Shows the pit strategies of all drivers at a glance.
• Race Control Messages: Displays updates on penalties, weather, and other important announcements.
• Driver Tracker: Presents a live map of driver positions on the track.
• Lap Timing History: Allows users to review timing data lap by lap.
• Team Radio Transcription: Users can listen to team radio and get transcriptions of the audio.

Installation Options:

• Install as a .NET tool.
• Run a standalone executable.
• Use a Docker image.

Usage:

• Users can start live sessions or replay pre-recorded data.
• The app features a cursor for navigating data and managing real-time delays to sync with TV broadcasts.
• Configuration is flexible, allowing for adjustments through a JSON file, command line, or environment variables.

Important Notes:

• The tool is inspired by the FastF1 project and isn't officially affiliated with Formula 1.
• It requires the .NET SDK for certain installation methods and has specific audio playback requirements for team radio feature.

Overall, Undercut-f1 is a powerful tool for F1 enthusiasts who want to enhance their viewing experience with detailed, real-time data.

Android phones will soon reboot automatically if they are locked and unused for 3 days. This feature, part of a Google Play Services update, aims to enhance security by making it harder for unauthorized access to personal data. The update, version 25.14, was released on April 14, 2025, and may take time to reach all devices.

The reboot feature is similar to one introduced by Apple, which also helps protect data by ensuring devices are secure when freshly rebooted. When a phone is in this "Before First Unlock" state, it requires a passcode or PIN to access, and data is encrypted, making it difficult for anyone, including law enforcement, to retrieve information.

Overall, this update improves security without requiring user action, as Google can automatically roll out updates through Play Services.

The integral of sec(x) is a well-known concept in calculus but was once a significant mathematical challenge. It was first needed by cartographer Geradus Mercator for his 1569 world map, but he could not find the solution and used an approximation instead. The exact solution was discovered accidentally in 1645, and a formal proof was provided in 1668, nearly a century after Mercator's original work.

The integral of sec(x) is crucial for creating the Mercator map, which preserves angles and is commonly used in navigation. The integral itself is expressed as:

[\int \sec(x) dx = \ln|\sec(x) + \tan(x)| + c]

This integral was highlighted during calculus studies, where students often find integration more challenging than differentiation. Interestingly, the integral was discovered through numerical methods rather than calculus.

The secant function, a basic trigonometric function, is defined as the ratio of the hypotenuse to the adjacent side in a right-angled triangle, and it is the reciprocal of cosine. The Mercator projection, which Mercator developed, stretches a cylindrical map to keep angles constant, making it useful for navigation while distorting areas.

Historically, mathematical tables were used for calculations before calculators became common, and these tables included values for trigonometric functions and logarithms. The discovery of the relationship between the secant integral and logarithmic tables was made by teacher Henry Bond in 1645. This connection was later proven in 1668.

In summary, the integral of sec(x) has a rich history tied to navigation and map-making, illustrating how mathematical discoveries often develop over time through various methods and insights.

The article discusses the problematic rise of botnets, particularly those created by app developers who embed SDKs (Software Development Kits) into their applications. These SDKs allow companies to use users’ network bandwidth for web crawling and other harmful activities, effectively turning unsuspecting users into part of a botnet.

Key points include:

1. Botnets and SDKs: Some companies pay app developers to include SDKs that facilitate network sharing, which can lead to aggressive web crawling and potentially DDoS attacks.

2. Shady Business Model: This practice is seen as unethical. Companies like Infatica provide services that exploit users’ devices for profit, claiming to manage the traffic responsibly but actually contributing to the problem.

3. Impact on Web Traffic: The increase in bot traffic from these practices is straining smaller web services, often making them non-public to avoid being overwhelmed.

4. Accountability of Developers: App developers who include such SDKs should be held responsible for compromising their users' devices and contributing to the botnet issue.

5. Difficulty of Detection: It is challenging for regular users to identify these SDKs and the network traffic they generate, complicating matters for small web server administrators.

In conclusion, the article argues that all forms of web scraping should be considered abusive, and web servers should take measures to block them. The growth of AI and these proxy services is seen as a significant threat to the integrity of the web.

This text is a guide on how to write an efficient matrix multiplication algorithm using Tensor Cores on NVIDIA Tesla T4 GPUs. Here's a simplified summary of the key points:

1. Introduction: The article discusses the author's experience in creating an optimized matrix multiplication kernel in CUDA, aiming for high performance on large matrices using Tensor Cores, which are specialized hardware units for matrix math.

2. The Importance of Tensor Cores: Tensor Cores significantly enhance throughput for matrix operations, which is crucial for applications like AI training and inference. The author notes that understanding how to utilize Tensor Cores effectively is not widely covered online.

3. Performance Goals: The author set out to achieve a kernel performance close to NVIDIA's cuBLAS library, which is considered the gold standard. Initially, their kernel achieved only 8% of cuBLAS performance but was improved to 96% for large matrices.

4. Memory Challenges: The article explains the "memory wall" issue in computing, where the rate of data transfer to the processor has not kept pace with the ability to perform calculations. This affects how fast the GPU can operate.

5. Roofline Model: The roofline model is introduced as a tool to understand performance limits based on memory bandwidth and computational intensity. The balance between these two factors is critical for optimizing algorithms.

6. Kernel Optimization Techniques: The author describes six kernels that use various strategies to maximize performance:

   • Hierarchical Tiling
   • Vectorized Memory Copy
   • Shared Memory Swizzling
   • Asynchronous Memory Copy
   • Tile Dimension Tuning
   • Double Buffering

7. Memory Hierarchy Awareness: Successful kernel design requires careful consideration of memory types—global, L2 cache, and shared memory—each with different speeds and capacities. Efficiently using shared memory can help achieve better performance.

8. Conclusion: The author reflects on what was learned during the project, noting that newer GPUs have features that can simplify optimization efforts. The performance can vary based on matrix sizes, and improvements in hardware support can alleviate some optimization challenges.

Overall, the article serves as a resource for those interested in maximizing the performance of matrix multiplication on GPUs using Tensor Cores, detailing both theoretical background and practical coding techniques.

Peru's ancient irrigation systems transformed arid deserts into fertile farmland, largely due to the cultural practices that supported them. Despite the region's extreme dryness, sophisticated canal networks, dating back over 1400 years, allowed societies like the Moche and Chimu to thrive by efficiently managing water resources.

Today, climate change and modern agricultural practices threaten this delicate balance. The Peruvian government has invested heavily in irrigation infrastructure to draw water from distant melting glaciers, which are rapidly disappearing. Recent projects may provide short-term benefits but risk long-term sustainability as they rely on diminishing glacier water.

Historically, the success of ancient irrigation depended not just on technology but on the cultural knowledge that guided its use. This included flexible and multifunctional designs that adapted to seasonal changes and natural disasters. In contrast, modern attempts to replicate these systems often overlook the cultural context, resulting in inefficiencies.

Understanding and preserving indigenous practices and knowledge could provide valuable insights for building climate-resilient agriculture today. Archaeologists emphasize the importance of incorporating this historical perspective to develop sustainable solutions in the face of ongoing environmental challenges.

Summary:

In his latest post, Tim discusses the sharing of location data by mobile apps and introduces a new method for analyzing app traffic. He has created a guide and a Python notebook that allows anyone to record and analyze mobile app traffic, making the process simpler and faster.

He invites readers to try it themselves, emphasizing the importance of understanding what data apps collect rather than relying on external sources. Users can fill out a form to share their findings, and their contributions will be compiled in a shared spreadsheet.

Tim also shares visualizations of ad tech domain traffic, highlighting that Unity is a leading player in mobile game advertising, while Facebook appears less prominent in his analysis.

The guide includes detailed steps for setting up the tools needed to intercept and analyze traffic, including installing mitmproxy, configuring mobile devices, and filtering data. He encourages experimentation and community involvement in uncovering the data practices of various apps.

Overall, Tim aims to raise awareness about data privacy and the extent of data collection by apps, hoping that more people will explore this topic.

The author wanted to run Xilinx ISE, an older software for FPGAs, in a portable way using Docker. The main challenge was the large size of the installer file (6.1 GB), which made it difficult to manage and share. The author attempted to compress the installer but found that the internal files were already compressed using an unusual method (zip.xz).

After discovering that these files were actually password-protected 7zip archives, the author planned to recompress them to reduce the overall size. However, attempts to do this on their laptop ran into issues with disk space and performance. They then tried using Python to handle the recompression in memory but encountered crashes due to high memory usage.

Despite some files showing minor size reductions, the process was slow and challenging. The author is still exploring better methods to achieve a significant reduction in the installer size, expressing frustration with how Xilinx handles their software, even in aspects like their installer.

Crosswalk buttons in Silicon Valley were recently hacked, causing them to play voices mimicking Elon Musk and Mark Zuckerberg when pressed. Videos from Redwood City, Menlo Park, and Palo Alto showed these voices delivering unusual messages. For example, the Zuckerberg impersonator discussed feelings of discomfort with AI, while the Musk impersonator made strange comments about cancer and offered a Cybertruck to listeners.

Local authorities reported that the tampering affected multiple intersections, prompting them to disable the voice feature on the crosswalks. Palo Alto officials identified 12 impacted intersections, while Redwood City noted four locations. The California Department of Transportation confirmed that some crosswalks under their control were also affected. They have since deactivated the audio and are working to restore normal operations. Officials warned that tampering with city infrastructure is illegal and poses safety risks.

Many key exchange and digital signature methods use the NIST P256 and secp256k1 curves, which offer 128-bit security. However, there is a need for stronger security, which can be achieved with curves like NIST P-521 and others that provide 256-bit security. This paper introduces a new curve called Eccfrog512ck2, which offers 256-bit security and better performance than NIST P-521. Eccfrog512ck2 also includes features to resist side-channel attacks and other vulnerabilities. It provides a 61.5% faster scalar multiplication and a 33.3% faster point generation compared to the NIST P-521 curve.

No summary available.

The UK government attempted to access Apple's end-to-end encryption but faced setbacks when a tribunal denied its request for secrecy regarding national security. The Home Office had allegedly asked Apple to create a backdoor for iCloud, but instead, Apple removed that feature for British users. The case highlights issues with state security and secret courts, which are criticized for lacking transparency in a democratic society.

The article compares these actions to the abuses of power seen under former US President Donald Trump, particularly in suppressing criticism and manipulating security clearances as a form of persecution. The author argues that misuse of security mechanisms by governments, both in the UK and the US, is a significant problem that needs to be addressed legally. The evolving nature of digital security demands greater accountability and recognition of the potential for abuse.

No summary available.

The document discusses the computational complexity of air travel planning, aimed at individuals with a basic understanding of computer science. It explains why planning air travel is a challenging problem and includes original findings on computational complexity along with demonstrations.

Key points include:

1. Air Travel Planning Overview: Travelers often use agents or websites to search for flights, which involve querying databases of flight information maintained by companies like ITA Software. This data includes flight schedules, prices, and seat availability, which are frequently updated.

2. Flight Network: There are over 4,000 airports served by commercial airlines, with a hub-and-spoke system where major airports handle a large percentage of departures. The connectivity between airports is complex, with many potential flight paths.

3. Path Planning Complexity: Finding flight paths between two airports can result in thousands of possible combinations, especially for round trips. The number of potential itineraries grows exponentially, making it impractical to list all options for travelers.

4. Airline Pricing Complexity: The main difficulty in travel planning arises from the complex pricing structures of airlines. Each fare has specific rules and can cover multiple flights, complicating the selection of the best route and price.

5. Challenges for Algorithms: Standard algorithms can find shortest paths in flight networks, but they struggle with the complexity introduced by pricing, making it hard to efficiently find the best routes quickly.

In summary, air travel planning is a complex problem due to the massive number of flight options and intricate pricing rules, making it a unique challenge in computational complexity.

Summary:

The editorial emphasizes the urgent need to address drug-resistant fungal infections, particularly focusing on the dangerous yeast Candida auris, which has seen a rise in infections globally. This fungus is resistant to many treatments, leading to high mortality rates—between 30% and 60% of those infected may die. Annually, around 3.8 million people die from fungal infections, a number that has nearly doubled in the past decade.

Despite the growing threat, fungal infections are often overlooked in discussions about antimicrobial resistance, with only a few antifungal drugs in advanced trials. To combat this issue, the editorial calls for collaborative efforts among researchers, policymakers, and industry to improve treatment options and protect existing medications.

Key challenges include the difficulty of diagnosing fungal infections, especially in low-income regions, and the need for new antifungal drugs that are safe for humans. Additionally, the use of fungicides in agriculture is contributing to the problem, as similar compounds can lead to drug resistance. Policy changes and research into alternative fungicides are needed to safeguard public health.

As climate change may further encourage the emergence of new fungal pathogens, there is an urgent need for improved research and resources to understand and combat these infections effectively.

No summary available.

The text discusses a significant shift in artificial intelligence (AI) toward what is termed the "Era of Experience." This new phase is characterized by AI agents that learn primarily from their own experiences rather than relying heavily on human-generated data.

Key Points:

1. Current Limitations: Traditional AI has progressed through extensive human data training, leading to capable models like large language models (LLMs) that can perform various tasks. However, this approach is reaching its limits, particularly in areas requiring advanced problem-solving, such as mathematics and coding.

2. Need for New Data: To enhance AI capabilities, a new data source is necessary—one that evolves as the AI becomes more competent. This will involve AI agents learning from their interactions with the environment.

3. Characteristics of the Era of Experience:

   • Continuous Learning: AI agents will learn over a long period, adapting based on ongoing experiences.
   • Autonomous Actions: Unlike previous models that primarily processed human dialogue, future agents will interact directly with their environments, using sensors and motor controls.
   • Grounded Rewards: Instead of relying on human feedback, agents will receive rewards based on their actual performance and interactions with the environment, leading to potentially groundbreaking discoveries.

4. Planning and Reasoning: The new agents will develop reasoning capabilities that are not solely based on human thought processes but will instead learn to think and plan based on their experiences, leading to more innovative solutions.

5. Implications: This shift could lead to significant advancements in various fields, such as personalized health and education, and could accelerate scientific discovery. However, it also raises concerns about job displacement and the potential misuse of powerful AI systems.

6. Conclusion: The Era of Experience represents a transformative moment in AI development, moving beyond human data dependence to a system where agents can autonomously learn and adapt, ultimately achieving capabilities that may surpass human intelligence.

No summary available.

The author shares their experience of being laid off from Google, expressing feelings of shock, sadness, and anger. They mention that their termination was unexpected and not performance-related, leaving them feeling mistreated. Despite being involved in significant projects and team activities, they now feel erased from those experiences and responsibilities. They list many opportunities and roles that have been taken away from them, emphasizing the emotional toll of the situation. The author feels devalued and overwhelmed by the sudden change and invites others to reach out, acknowledging they might not respond quickly due to the emotional impact.