I was the main contributor to workout.lol, an open-source fitness app that helped users create workout routines. Although it gained popularity (1.4k GitHub stars and 20K monthly visits), the app was sold and eventually abandoned due to licensing issues. I tried to revive it for nine months but received no response.

Determined to help the community, I created a new version called Workout.cool, focusing on a better design and more features without licensing problems. It is completely open-source and offers over 1200 exercises, progress tracking, multilingual support, and the ability to self-host.

I'm not doing this for profit; I just believe in accessible fitness tools. If you’re interested, you can help by starring the GitHub repo, sharing it, suggesting features, or contributing. Together, we can build a great open-source fitness platform.

Website: workout.cool
GitHub: workout-cool

The article discusses the challenges of using homomorphic encryption with Conflict-free Replicated Data Types (CRDTs) in local-first software, particularly when collaborating on sensitive documents.

Key Points:

1. Local-First Software Challenge: When collaborating on a document that needs to remain private, traditional syncing methods can expose the document's content to the server. End-to-end encryption helps, but it requires both parties to be online simultaneously to sync changes.

2. Homomorphic Encryption Solution: This type of encryption allows computations on encrypted data without needing to decrypt it, enabling servers to merge changes without knowing the actual contents.

3. Implementation: The article provides a code example of using homomorphic encryption to create a "last write wins" register, which maintains a single value and handles updates based on timestamps and peer IDs.

4. Limitations of Homomorphic Encryption:

   • Key Size: The encryption keys can be significantly larger than the actual data, creating inefficiencies.
   • Performance: Operations with homomorphically encrypted data are extremely slow compared to unencrypted data.
   • Worst-Case Scenario Execution: The requirement to treat all inputs as worst-case scenarios limits how data can be stored and merged, potentially revealing changes even in encrypted forms.

5. Conclusion: While homomorphic encryption offers a promising way to keep data private during collaborative work, its limitations may hinder usability. The search for secure, efficient solutions for local-first applications continues, with ongoing research and development in the field.

The article emphasizes the need for further innovation to balance security with practicality in local-first software.

Summary:

"Poline" is a micro-library in TypeScript that focuses on creating color palettes. The name combines "polar" and "line," reflecting its core function of drawing lines between anchor points. In Poline, anchors are the points between which colors are created. The number of colors generated depends on the number of points; more points lead to more colors. The placement of these points is controlled by position functions.

No summary available.

No summary available.

The article discusses the performance of AI agents on various tasks, focusing on how their success rates change with task duration. Research by Kwa et al. (2025) suggests that AI agents have a predictable failure rate, leading to a model where their success rates decline exponentially as tasks get longer. This means each AI agent can be characterized by a "half-life," indicating how long it can successfully perform tasks before its success rate drops significantly.

Key findings include:

• The ability of AI agents to complete tasks doubles approximately every 7 months.
• Success rates at different thresholds (like 50% and 80%) show a notable difference in task duration, suggesting that achieving higher reliability takes significantly less time for AI.
• The study uses a constant hazard rate model to explain how task complexity affects performance, indicating that longer tasks involve more subtasks, each with a chance of failure.

The article also raises concerns about the generalizability of these findings, as the tasks used in the study may not represent all real-world scenarios. Additionally, comparisons are made between AI and human performance, noting that humans may perform better over longer tasks than the constant hazard model would predict for AI.

In summary, the constant hazard rate model helps in understanding AI performance over time, predicting success rates, and suggesting areas for further research on AI capabilities and their implications for task management.

Summary of MiniMax-M1 Overview

MiniMax-M1 is a groundbreaking reasoning model that combines a hybrid Mixture-of-Experts (MoE) architecture with a lightning attention mechanism. It has 456 billion parameters, with 45.9 billion activated per token, and can handle a context length of up to 1 million tokens. Compared to DeepSeek R1, it is more efficient, using only 25% of the computational resources for tasks involving 100K tokens.

The model is trained using large-scale reinforcement learning (RL) and excels in complex tasks such as mathematical reasoning and software engineering. It has two versions with different thinking budgets (40K and 80K), and tests show it outperforms other models like DeepSeek-R1 and Qwen3-235B in various benchmark categories.

Evaluation Results: MiniMax-M1's performance is strong across several tasks, including mathematics, coding, and software engineering, with notable scores on benchmarks such as AIME and LiveCodeBench.

Recommendations for Use:

• Inference Settings: Use a temperature of 1.0 and top_p of 0.95 for creative responses.
• System Prompts: Tailor prompts for specific tasks, such as general assistance or web development.

Deployment:

• Download the model from HuggingFace.
• Recommended deployment using vLLM for optimal performance or Transformers for standard deployment.

Function Calling and API: MiniMax-M1 supports function calling and offers a Chatbot and API for developers to utilize its capabilities.

For further details, users can refer to the provided deployment guides or contact the MiniMax team.

The text is a publication from June 9, 2025, authored by Kevin Black, Manuel Y. Galliker, and Sergey Levine. For more information, you can contact them via email at [email protected].

This guide discusses how to improve documentation for AI systems, specifically for Retrieval-Augmented Generation (RAG) systems like Kapa. Here are the main points:

1. Importance of Documentation Quality: Clear and well-structured documentation is crucial because it helps both users and AI systems provide accurate answers. Poor documentation can lead to misunderstandings and incorrect AI responses.

2. How AI Processes Documentation: Kapa retrieves relevant information in a structured way:

   • It uses a retriever to search for content.
   • A vector database stores content for quick retrieval.
   • A generator, or Large Language Model (LLM), formulates responses based on retrieved information.

3. Chunking Content: Content should be divided into smaller, focused sections (chunks) to improve retrieval accuracy. Each chunk should be self-contained and clear to ensure AI can understand it without needing context from other chunks.

4. Optimizing Content for AI:

   • Use standardized semantic HTML for better structure.
   • Avoid PDFs in favor of HTML or Markdown for easier parsing.
   • Simplify page structures to make content crawler-friendly.
   • Ensure clarity in headings and URLs.
   • Provide text descriptions for visual elements.

5. Common Content Design Challenges:

   • Avoid scattering key information across sections, as this can lead to ambiguity.
   • Use consistent terminology to help AI retrieve relevant content.
   • Don’t assume users have prior knowledge; provide all needed information explicitly.
   • Offer text alternatives for visual content to ensure all critical information is accessible.

6. Content Organization: Structure documentation hierarchically so that each section is understandable on its own. Include essential context and details for troubleshooting, using exact error messages to improve searchability.

7. Conclusion: Good documentation for AI is clear, structured, and user-focused. By improving documentation, both human users and AI systems can be better served. Regularly review and revise content based on user interactions to keep it relevant and effective.

Graph search algorithms help find the shortest path on a map represented as a graph. The A* Algorithm is a popular method, along with others like Breadth First Search (BFS) and Dijkstra’s Algorithm. Here are the key points:

1. Graph Representation:

   • A graph consists of locations (nodes) and connections (edges) between them. The algorithms only work on this graph structure and do not consider real-world features like rooms or doors.

2. Types of Algorithms:

   • Breadth First Search (BFS): Explores all directions equally and is useful for finding paths or generating maps.
   • Dijkstra’s Algorithm: Considers movement costs (e.g., different terrains) to find the shortest path.
   • A Algorithm*: Combines the benefits of BFS and Dijkstra’s by using both actual distance from the start and estimated distance to the goal.

3. Pathfinding Process:

   • The algorithms track locations using a "frontier" (the set of locations to explore) and a "came_from" table (to reconstruct paths).
   • Early exit conditions can be applied to stop searching once the goal is found.

4. Heuristic Search:

   • A* uses a heuristic function to estimate distance to the goal, improving efficiency in finding paths.

5. Choosing an Algorithm:

   • Use BFS for equal movement costs and when paths to all locations are needed.
   • Use Dijkstra's for varied movement costs.
   • Use A* for paths to a single goal or when efficiency is needed.

6. Performance Tips:

   • Reduce unnecessary locations in the graph to improve speed.
   • Simpler algorithms run faster, so choose the simplest one that meets your needs.

7. Applications Beyond Maps:

   • These algorithms can be applied to any graph structure, not just geographical maps.

Overall, A* is often the best choice for pathfinding due to its efficiency and ability to find optimal paths when the heuristic is accurate.

Summary of Scrappy: A Tool for Homemade Apps

Overview: Scrappy is a tool designed by John Chang and Pontus Granström for creating simple, personalized apps (called "Scrapps") that you can share with friends and family. Unlike mass-market apps, Scrappy allows users to build software that meets their specific needs in a creative and expressive way.

Key Features:

• User-Friendly Interface: Scrappy has a canvas-like interface similar to design tools like Figma, allowing users to easily drag and drop elements and attach behaviors to them with basic JavaScript.
• Real-Time Collaboration: Multiple users can interact with a Scrapp simultaneously, making it easy to edit and modify apps on the fly.
• Accessibility: Scrappy aims to be approachable for those with basic computer skills, although some knowledge of JavaScript is currently needed to create apps from scratch.

Target Users:

• DIY Enthusiasts: People who enjoy customizing and creating things for personal use.
• Educators and Students: Those looking for simpler ways to learn and teach programming concepts.
• Business Process Improvers: Individuals who want to streamline workflows without extensive programming knowledge.

Examples of Scrapps:

• Arithmetic practice tools for kids.
• Attendee counters for events.
• Chore trackers for roommates.

Advantages Over Traditional Apps:

• Customization: Scrappy apps can be tailored exactly to user needs, providing a personal touch.
• Ease of Use: Users can share and start using Scrapps without needing to set up accounts.
• Fun and Creative: The platform encourages playful exploration and creativity in app creation.

Future Development: The developers plan to enhance Scrappy by making it even easier for non-programmers to use, improving collaboration features, and expanding the types of apps that can be created.

Conclusion: Scrappy aims to democratize software creation, allowing anyone to make and share their own simple apps. It represents a shift away from traditional software reliance towards more personalized, homemade solutions.

The Fall of Civilizations podcast recently released a lengthy episode about the Mongol Empire, prompting interest in modern Mongolia. The country has seen significant improvements in poverty reduction, economic growth, and internet access in the 21st century, but it still struggles with issues like government corruption.

In 2023, Mongolia has a population of about 3.5 million, a fertility rate of 2.7, and a significant decrease in poverty from 11.6% in 2002 to 0.2% in 2022. The GDP per capita has also increased substantially. Despite this progress, corruption and regulatory challenges remain.

Curious about the presence of yurts (traditional homes) in Mongolia, the author used machine learning to count them across the country. They downloaded satellite images and trained a model to identify yurts, eventually annotating over 10,000 images. The project involved complex data handling and model training, leading to the discovery of 172,689 yurts.

Yurts play a crucial role in Mongolia's transition from a nomadic society to a more urbanized one. As many people migrated to cities, they brought their yurts with them, leading to the creation of ger districts—informal settlements that now face infrastructure challenges. The government aims to improve these areas through various development plans, although progress has been slow.

In summary, the exploration of Mongolia highlights its rich history and ongoing development challenges, particularly in urban areas where traditional lifestyles persist alongside modern growth. The author concludes with reflections on Mongolia’s future and the broader issues surrounding urbanization and development.

On June 17, 2025, Honda R&D successfully tested a reusable rocket in Taiki Town, Hokkaido, Japan. The rocket, measuring 6.3 meters long and weighing 900 kg, reached an altitude of 271.4 meters before landing within 37 cm of its target. This test aimed to demonstrate important technologies for rocket reusability, including flight stability and landing capability.

Safety was a priority during the test, with a restricted area of one kilometer established around the launch site. Honda conducted the test with local authorities' cooperation and took precautions to ensure no accidents occurred.

Honda has been exploring space technologies since 2021, focusing on using its core technology to improve people's lives and develop sustainable transportation solutions. Although still in the research phase, Honda aims to achieve the capability for suborbital launches by 2029.

CEO Toshihiro Mibe expressed pride in the progress made and emphasized Honda's commitment to leveraging its technology for meaningful advancements in rocket research.

Large Language Models (LLMs) are effective in solving complex reasoning problems, especially through a method called chain-of-thought (CoT), which involves generating "thinking tokens" before providing answers. While traditional research shows that discrete CoTs improve LLM performance, the benefits of continuous CoTs are not well understood.

This paper presents a proof that a specific type of transformer model using continuous CoTs can efficiently solve the directed graph reachability problem, which is important for various applications. Unlike discrete CoTs, which require many steps to explore options sequentially, the proposed model can simultaneously explore multiple paths. This is done through a technique where each continuous thought vector represents various potential solutions at once.

The researchers conducted experiments to confirm that their theoretical model works effectively in practice, showing that the ability to explore multiple paths arises naturally during training without needing special instructions.

Jiga is changing how manufacturers operate by creating a digital platform. This platform makes it easier and faster to buy manufactured parts directly from suppliers. It automates the process, encourages teamwork, and uses data to improve efficiency.

No summary available.

The author has been developing a tensor library called dsc using C++ and CUDA. Their focus is on creating a simple and clean API that allows users to run small language models (LLMs) locally. Key features of dsc include:

• A core built in C++ with CUDA support.
• A Python API that is similar to PyTorch.
• Capability to load and run models like Qwen from HuggingFace on both CUDA and CPU with minimal changes.
• Built-in observability tools for both Python and C++.

Future plans include adding BF16 support and improving visualization for GPU workloads. The project is in its early stages, and the author welcomes feedback and code reviews from the community. You can find the project on GitHub here.

A developer has created a command-line interface (CLI) for uploading and querying documents using a language model (LLM) that employs search tools instead of relying solely on context. They conducted a demo with the CrossFit 2025 rulebook to illustrate the effectiveness of this method compared to traditional approaches.

The key idea is that LLMs can efficiently retrieve knowledge by searching and refining queries in loops. The CLI simplifies the process, allowing users to upload documents and ask questions like "What are the key findings?" with commands such as:

trieve upload ./document.pdf
trieve ask "What are the key findings?"

Users can customize how the tool behaves, check upload statuses, and receive responses with source references. The tool is free for up to 1,000 document chunks, and the developer is open to adding more features based on user interest. The source code is available on GitHub and via npm. Feedback on the tool and its design is welcomed.

No summary available.

During a long weekend in Germany, the author visited Munich for the first time and compared it to Hamburg, where they have lived for seven years. They noted significant differences between the two cities, shaped by their unique histories. Munich was ruled by the Wittelsbach dynasty, leading to centralized wealth and cultural development, while Hamburg thrived as a free trade city, fostering independence and diversity.

The author highlighted Munich's rich cultural heritage, seen in its impressive architecture, larger churches, and numerous museums, such as the Alte Pinakothek and Glyptothek. They appreciated the cleanliness of the Isar River and the scenic parks but noted some streets lacked trees, making them less pleasant for walking.

Public transportation in Munich was efficient, featuring a tram system, but the author found the city more car-centric than Hamburg. They also observed that Munich felt more crowded due to its higher population density and tourist presence.

Food was a highlight of the trip, with the author enjoying traditional Bavarian dishes and fresh beer. They also discovered a Turkish dessert shop that offered a unique taste experience.

In conclusion, while the author found Munich appealing for its culture and opportunities, they preferred living in Hamburg due to its environment and lifestyle. They expressed interest in returning to explore more of the natural spots and cultural offerings in Munich.

The author has created a new command-line tool called lstr, inspired by the classic Linux tree command. lstr is built in Rust and aims to be fast, minimalist, and interactive, incorporating modern features like:

1. Interactive TUI Mode: Users can navigate directories and open files using keyboard controls.
2. Git Status Integration: It shows the Git status of files and directories in the tree view when using the -G flag.
3. Shell Integration: Users can select a directory and use it to change their current directory in the shell with a simple command.

Additional features include support for file-type icons, file sizes, permissions, and adherence to .gitignore settings. The project is open-source, and the author welcomes feedback. Links to the GitHub repository and Crates.io page are provided for more information.

The author expresses skepticism about claims that artificial general intelligence (AGI) is imminent, particularly in the field of mathematics. While AI can perform well on undergraduate-level math, the author argues that this is not sufficient for AGI, as it lacks the ability to think independently and solve problems at a higher level, such as PhD-level mathematics.

To investigate AI's true capabilities, the author proposes creating a new database of challenging number theory problems, specifically designed for researchers at or above the PhD level. These problems should be complex enough that undergraduates would not have the necessary knowledge to solve them, and should require deeper understanding rather than just pattern recognition.

The goal is to test AI companies on these questions and publicly share the results. The author invites mathematicians to contribute problems and sets a deadline for submissions to facilitate this experiment.

Over the past year, we’ve collaborated with various teams to develop large language model (LLM) agents and found that simplicity is key to successful implementations. Instead of using complicated frameworks, effective agents are built with straightforward, flexible patterns.

Key Concepts:

• Agents vs. Workflows:
  • Agents are systems where LLMs autonomously manage tasks and decisions, often interacting with humans for guidance.
  • Workflows involve predefined steps where LLMs follow specific code paths to complete tasks.

Building Effective Agents:

1. Start Simple: Use the simplest solution first, only increasing complexity when necessary.
2. Consider Use Cases: Choose workflows for predictable tasks and agents for flexible, model-driven tasks.
3. Frameworks: While tools like LangChain and Amazon Bedrock can simplify development, they can also introduce complexity. It’s better to understand the underlying code and start with direct LLM API calls.

Common Patterns for Agents:

• Augmented LLM: Enhance LLMs with tools and memory for better performance.
• Prompt Chaining: Break tasks into sequential steps for higher accuracy.
• Routing: Direct inputs to specialized tasks for better handling.
• Parallelization: Handle tasks simultaneously for efficiency.
• Orchestrator-Workers: A central LLM delegates tasks to others dynamically.
• Evaluator-Optimizer: Use one LLM to generate responses and another to provide feedback for refinement.

When to Use Agents:

• Best for open-ended tasks that require ongoing decision-making. They can handle complex problems but should be rigorously tested to avoid errors.

Best Practices:

1. Simplicity: Keep designs straightforward.
2. Transparency: Clearly outline the agent's decision-making steps.
3. Documentation: Provide comprehensive definitions for tools used by agents.

Applications of Agents:

• Customer Support: Agents can manage conversations and actions, providing effective resolutions.
• Coding: Agents can autonomously solve programming issues and iterate based on feedback.

Conclusion:

Effective LLM agents are built on simplicity and clarity. Start with basic configurations and only add complexity when it clearly improves outcomes. Focus on crafting good interfaces and understanding your tools to ensure reliability and effectiveness.

Summary of S-expressions Specification Document

Overview: This document explains S-expressions, a way to represent data and code in programming, especially in Lisp languages. It introduces a parsing library called S-expr that has unique features for handling strings, comments, and a new format called transposed blocks.

1. Introduction:

• S-expressions are a simple notation for nested lists and code, using parentheses.
• S-expr is a library that parses S-expressions, adding special syntax for strings and comments.

2. Informal Definition:

• An S-expression can be an atom (like a single word) or a list of other S-expressions (like (expr1 expr2 expr3)).
• Lists can be nested for complex structures, e.g., (eq (mul x x) (pow x 2)) shows equality.

3. Strings, Comments, and Transposed Blocks:

• Strings: Single-line strings are in quotes (e.g., "text"). Multi-line strings are enclosed in triple quotes (e.g., """text""").
• Comments: Comments are ignored during parsing and start with a slash (e.g., /comment/ for single-line, /// comment /// for multi-line).
• Transposed Blocks: A unique feature where rows and columns are swapped for better readability. They use asterisks (*) for vertical alignment (e.g., * (eq ...) *). This helps reduce clutter from parentheses.

4. Conclusion:

• The document provides an informal guide to the S-expr format, highlighting its handling of strings, comments, and the innovative transposed blocks. While more complex than traditional Lisp syntax, these features aim to enhance the readability of the code.

The article discusses the impact of large language models (LLMs), like Google Translate, on the fields of translation and programming. Many people fear that LLMs will replace human professionals entirely, but the author argues that these claims lack nuance.

Using Google Translate as an example, the author notes that while machine translation technology has improved significantly since the shift to neural machine translation in 2016, it does not replace the need for human translators. Translators provide context, handle ambiguity, and understand cultural nuances—skills that Google Translate cannot replicate. For instance, translating Norwegian to English requires understanding politeness and cultural context, which a machine might overlook.

The author emphasizes that the job market for translators is actually growing, as many professionals have integrated AI into their workflows to assist rather than replace their work. Similarly, the author likens programmers to translators, as both roles involve interpreting complex human communication for computers. While AI tools can assist programmers, they currently lack the ability to handle context and ambiguity as humans do.

In conclusion, the author believes that while AI tools have their uses, they also come with limitations and ethical concerns. The idea that LLMs will completely replace human professionals is overly simplistic and ignores the complexities of both translation and programming.

This post discusses the Good Regulator Theorem, originally proposed by Conant and Ashby in 1970. It states that for a regulator to effectively manage a system, it must be a model of that system. The theorem emphasizes that a good regulator minimizes uncertainty in outcomes and avoids unnecessary complexity.

The original paper is criticized for being hard to read and using complex terminology. Several resources exist to help understand the theorem better, including summaries by John Wentworth and Daniel L. Scholten.

The post aims to provide a clearer, entry-level explanation of the theorem. It introduces a setup involving three variables: a system variable (S), a regulator variable (R), and an outcome variable (Z). The regulator's role is to influence the outcome based on the system's state, ideally in a way that reduces uncertainty (measured by Shannon entropy).

Key points about good regulators include:

1. They should lead to low entropy in outcomes (i.e., make outcomes predictable).
2. They should not use unnecessary complexity, meaning they should not involve randomness if a simpler deterministic solution exists.

The theorem concludes that if a regulator is effective, its state (R) will be a deterministic function of the system state (S), meaning that knowing S allows for precise predictions about R.

Overall, while the Good Regulator Theorem suggests that a good regulator effectively models the system, the post argues that it should not be solely interpreted in terms of modeling in the conventional sense. It provides a foundation for understanding the relationship between regulators, systems, and outcomes in control theory.

No summary available.

No summary available.

Nathan Figueroa argues that now is an excellent time to learn software development, despite concerns about AI replacing developers. Here are the key points:

1. Importance of Developers: Developers have unique knowledge about their businesses that AI cannot fully replace. Instead of laying off developers, companies should empower them to use AI tools effectively.

2. AI as a Tool: AI can help create applications quickly, but it often generates superficial solutions. Developers still need to understand the underlying problems and communicate effectively with clients to ensure real needs are met.

3. Ongoing Demand for Software: There will always be software problems to solve, making the role of developers crucial. AI can enhance productivity, but human oversight is necessary to ensure quality and accountability.

4. Learning Opportunities: With AI tools available, learning to code has never been easier. Developers should embrace AI and focus on building foundational skills to avoid being misled by superficial solutions.

5. Future of Development: Investing in developer talent remains important, as the demand for skilled professionals will continue. Companies that support their developers will likely succeed in the long run.

Overall, Figueroa emphasizes that embracing AI while improving skills can lead to exciting opportunities in software development.

Summary of OpenSERDES

OpenSERDES is a key component in high-speed communication systems. It transforms parallel data into a single serial data stream for transmission, and then converts it back to parallel form at the receiving end. This process is synchronized with a global CLOCK signal.

Key Details:

• Technology: Built using Skywater OpenPDK 130nm.
• Tools Used: OpenLane and Virtuoso Cadence for design and simulation.
• Design: The Serializer and Deserializer are created in Verilog HDL and synthesized with OpenLane, producing various files for implementation.

Components:

• TX Driver: A chain of CMOS inverters drives the input capacitance of the channel.
• RX Design: Uses a Resistive Feedback inverter for sensing low signals, followed by a CMOS inverter for gain. A D flip-flop (DFF) samples the data with a clock recovered by a Clock Data Recovery (CDR) system.
• Oversampling CDR: Recovers data and clock from the received signal, adjusting the clock frequency based on data transitions.

All relevant design files, including gds, spice, netlist, and Verilog files, are organized in specific folders for further exploration.

Summary: Preparation of Hexanitrogen (N6)

Researchers have successfully created a new form of nitrogen called hexanitrogen (N6), which is considered a potential clean energy-storage material due to its high energy content. This discovery is significant because, until now, only nitrogen gas (N2) was known as a stable form of nitrogen.

The preparation of N6 was achieved at room temperature by reacting chlorine or bromine with silver azide and then trapping the resulting compound in argon at very low temperatures (10 Kelvin). Further tests showed that N6 can also be stable as a film at liquid nitrogen temperature (77 Kelvin).

Various methods, including infrared and ultraviolet-visible spectroscopy, were used to confirm the existence of N6 and its structure. The findings suggest that N6 could lead to new energy-storage technologies, as it decomposes into harmless nitrogen gas, making it environmentally friendly.

Overall, this research expands our understanding of nitrogen compounds and opens up possibilities for developing high-energy materials.

Summary:

On June 17, 2025, Google announced the expansion of its Gemini 2.5 family of models. The Gemini 2.5 Flash and Pro models are now stable and available for developers to use confidently in their applications. Additionally, a new model called Gemini 2.5 Flash-Lite has been introduced in preview; it is the fastest and most cost-effective option in the 2.5 series.

Gemini 2.5 models offer excellent performance for tasks like coding, math, and translation, with improved speed and lower latency compared to previous versions. The Flash-Lite model maintains high-quality performance across various benchmarks and supports multimodal input with a large context length.

Developers can access these models through Google AI Studio and Vertex AI, as well as in the Gemini app. Google looks forward to seeing how developers utilize the new Gemini 2.5 models.

A person was downloading Linux ISO files and noticed that most torrent trackers were down, prompting the idea of reviving a dead tracker. Trackers help users find peers to share files in the BitTorrent system, but if they're not maintained, users face difficulties. To address this, the person purchased a dead tracker domain and set up an anonymous Virtual Private Server (VPS) with tracker software called opentracker.

After starting the tracker, they observed significant traffic, eventually connecting with over 3 million peers and 1.7 million distinct torrents. However, they questioned the legality of hosting a tracker, recognizing that while public-facing trackers are often targeted by copyright enforcers, running a tracker without advertisement might be harder to prosecute. Ultimately, feeling uneasy about potential legal repercussions, they shut down the tracker and deleted the domain, noting that similar domains are still available for registration.

No summary available.

No summary available.

Summary: Why JPEGs Still Rule the Web

JPEGs have been the main format for sharing digital photos online for 30 years. This article by Ernie Smith explores the reasons behind JPEG's enduring popularity. It discusses key concepts like image compression and the discrete cosine transform, which are essential to how JPEGs work. The article highlights that despite the emergence of other formats, JPEG remains widely used due to its effectiveness and compatibility across various platforms.

The text discusses the impact of large language models (LLMs) on the field of programming language design, particularly domain-specific languages (DSLs). Here are the key points:

1. Importance of DSLs: DSLs are crafted to make programming easier for specific tasks, allowing users to focus on problem-solving without dealing with complex syntax.

2. Challenge from LLMs: The rise of LLMs, which can generate code efficiently, raises concerns about the future of DSLs. Developers may prefer using general-purpose languages like Python that LLMs handle well, making DSLs seem less attractive.

3. Performance Issues: LLMs perform significantly better with widely-used languages. For niche or DSLs, their performance drops sharply, which poses a challenge for developers considering DSLs.

4. Future of DSLs: The author expresses concern that the development of DSLs might stagnate as the effort to create them increases alongside the convenience of using LLMs with general languages.

5. Collaborative Directions: The text explores potential ways DSLs can adapt to work with LLMs:

   • Teaching LLMs about DSLs: Using general languages like Python to help LLMs understand DSLs better.
   • Bridging Formal and Informal: Creating DSLs that can integrate informal prompts with formal code, allowing for a smoother workflow.
   • Verified LLM Synthesis: Focusing on specification languages that can verify LLM-generated code, ensuring quality and correctness.

6. Conclusion: While LLMs present challenges for DSL designers, they also create opportunities for innovation. Designers must adapt to this new landscape to maintain diversity in programming languages and prevent stagnation.

Overall, the text emphasizes the need for evolution in programming language design in response to the capabilities and challenges posed by LLMs.

Summary of Time Series Forecasting with Graph Transformers

Time series forecasting is essential for businesses to predict trends and optimize resources. Recent methods have focused on using machine learning and generative models to improve forecasting accuracy, particularly with data structured in graphs.

Key Points:

1. Graph Forecasting: Traditional forecasting methods often analyze time series data in isolation. However, related data (like marketing campaigns or economic indicators) can enhance predictions. Graphs can effectively represent these interconnected data sources.

2. Relational Deep Learning (RDL): This technique helps automatically convert relational database tables into graph structures, facilitating time series forecasting as a graph learning task.

3. Forecasting Framework: The core forecasting framework uses a function to predict future values based on historical data and other conditioning signals. The model incorporates various data types like past time series, date-time encodings, and graph entity encodings.

4. Graph Transformers: These models leverage the graph structure and node features to improve forecasting. They can handle large, real-world graphs by sampling relevant subgraphs.

5. Training the Model: The forecasting model is trained to minimize error between predicted and actual future values, allowing it to learn patterns from past data.

6. Regression vs. Generative Forecasting: Traditional regression methods predict single values but can struggle with complex distributions. Generative forecasting models, like conditional diffusion models, can sample from a distribution, capturing uncertainty and allowing for richer predictions.

7. Results: Generative models often provide better detail and handle rare events more effectively compared to traditional models, while still maintaining similar overall accuracy.

8. Conclusion: Time series forecasting remains a critical area of machine learning. The described approach combines graph structures with advanced forecasting techniques, opening avenues for more accurate predictions.

For those interested in exploring these concepts, resources like PyTorch Geometric offer tools for implementing Graph Neural Networks and Transformers.

The bzip2 crate has switched to a fully Rust-based implementation with the release of version 0.6.0. This change makes the crate faster and easier to use, especially for cross-compilation. Although bzip2 is an older compression algorithm, it is still important for many protocols and libraries.

Key improvements include:

• Performance: The Rust implementation is generally faster than the previous C version for both compression and decompression, with significant speedups noted in various tests.
• Cross-compilation: By removing the C dependency, cross-compilation for platforms like WebAssembly, Windows, and Android is now easier and more reliable.
• No symbol export by default: The Rust implementation does not export symbols, reducing the risk of conflicts with other dependencies.
• Testing: The code can be tested using MIRI, which helps ensure safety and performance.

An audit of the new implementation found a minor bug but no major issues. The changes have resulted in a more efficient bzip2 crate that users can rely on without further concerns.

Summary of Tiger Cloud Products and Services:

• Tiger Cloud offers reliable PostgreSQL cloud solutions for various workloads, including time series and real-time analytics.
• They provide different support options tailored to your needs and budget, including self-managed support.
• Key Features:
  • Time Series: Fast data ingestion and querying.
  • Real-Time Analytics: Quick analytics capabilities on PostgreSQL.
  • AI and Vector: Tools for building AI applications and search capabilities.
• Enterprise Tier: Designed for businesses needing enhanced security, reliability, and support.
• Industries Served: Crypto, industrial IoT, energy, transportation, logistics, and manufacturing.
• Learning Resources: Extensive documentation on optimizing PostgreSQL, tutorials, and benchmarks comparing their services with competitors.
• Community Engagement: Offers blogs, forums, and newsletters for users to stay updated and engaged.

For more information, visit the TigerData website.

Summary of AMD's Van Gogh APU for Steam Deck

Van Gogh is AMD's custom APU designed specifically for the Steam Deck, a handheld gaming console. It combines four Zen 2 CPU cores and RDNA 2 GPU architecture, optimized for low power consumption.

Key points include:

1. Zen 2 Performance: Van Gogh features four Zen 2 cores with a maximum boost clock of 3.5 GHz, offering solid single-thread performance. However, its multithreaded capabilities are limited due to having only one CCX (core complex).

2. Power Efficiency: The APU operates within a tight power budget of 16 watts, allowing flexible power allocation between CPU and GPU based on workload demands.

3. Memory Performance: The Steam Deck uses 16 GB of LPDDR5 RAM, but there are significant concerns regarding memory latency and bandwidth, which perform poorly compared to other setups.

4. Gaming Focus: The integrated RDNA 2 GPU (referred to as AMD Custom GPU 0405) is designed for gaming, providing high memory bandwidth (over 70 GB/s) to support demanding graphics, although it lacks higher clock speeds and advanced features like Infinity Cache found in desktop GPUs.

5. Clock Ramp Behavior: The APU has a slower clock ramp-up time, which may affect responsiveness during gaming.

Overall, while Van Gogh sacrifices some CPU performance for enhanced GPU capabilities, it demonstrates AMD's strength in creating integrated solutions for gaming consoles like the Steam Deck, competing effectively with other architectures in the market.

No summary available.

The author, who has a Computer Science degree and enjoys working on compilers, recently created a simple compiler called "toybasic" for a variant of the BASIC programming language called TinyBASIC. This project was completed over a rainy weekend as a fun challenge.

Toybasic is written in Go and generates Go code from BASIC. The compiler consists of three main stages:

1. Lexer: Converts the source code into tokens (meaningful symbols).
2. Parser: Takes the tokens and builds a structure (syntax tree) that represents the program while checking for errors.
3. Compiler: Transforms the syntax tree into executable Go code.

The author used tools like nex for the lexer and goyacc for the parser, simplifying the development process. The grammar for toybasic was modified from TinyBASIC to exclude certain features like the INPUT statement.

A sample BASIC program demonstrates the functionality, and the author ensured that it works correctly by testing various constructs in the language. This project was both educational and enjoyable, allowing the author to apply theoretical knowledge to a practical task.

The Nintendo Switch 2 was released on June 5, 2025, featuring the game Mario Kart World, which is supposed to showcase the console's 4K HDR capabilities. However, the game has been criticized for its poor HDR implementation, which has been labeled as "fake HDR." This is due to a process where the game was primarily designed for standard dynamic range (SDR) and then had a last-minute tonemap added, resulting in a lack of true HDR quality.

As HDR TVs have become common, many gamers now play on HDR-capable screens. Despite this, Mario Kart World’s HDR output is limited, with peak brightness not exceeding 950 nits, even when the console is set to higher brightness settings. This suggests a failure in the game’s HDR design, which may have been rushed or poorly executed.

The critique is based on thorough testing, where the author, who has experience with HDR game development, analyzed the game’s output and found it limited in both brightness and color range. The game's visuals are predominantly clamped to the SDR color space, missing the vibrant colors and dynamic range HDR can offer.

To improve HDR in games, developers should start with HDR in mind, use wide color gamuts, and implement dynamic tone mapping. Art reviews should be conducted on HDR displays to catch issues early in the development process. The article argues that without significant changes in how HDR is approached, developers risk delivering subpar visual experiences, even in flagship titles like Mario Kart World.

In conclusion, Mario Kart World serves as a reminder that many developers still do not fully embrace HDR, which limits the artistic and visual potential of their games.

The author wanted to honor the people who created the base-60 time system and found out that Cuneiform, an ancient writing system, is included in Unicode.

Summary: JPEG XL and Google's Influence on Image Formats

JPEG, GIF, and PNG are long-standing image formats, but newer alternatives like JPEG XL are struggling for recognition and adoption. JPEG XL, finalized in 2021, offers advanced features such as efficient compression, lossless conversion, and high resolution support. Despite its advantages over existing formats, it lacks browser support, primarily due to Google's influence.

Google developed WebP in 2010, aiming to combine the strengths of JPEG, PNG, and GIF, and quickly pushed for its adoption across browsers. When another format called AVIF emerged, it gained traction due to its connection with the widely supported AV1 video format, although it has limitations compared to JPEG XL.

Google's control over the web through its dominance in search and browser technology allows it to stifle competition. JPEG XL was briefly included in Chromium but later removed, with Google citing lack of interest and benefits compared to existing formats. Critics argue this is a tactic to protect WebP from competition.

Despite the challenges, there are ways to support JPEG XL, like using browsers that enable its features or spreading awareness of its benefits. Advocates believe that pressure on companies like Google and Mozilla could lead to greater support for JPEG XL, helping to promote innovation and competition in image formats.

The article discusses AMD's interconnect technology used in their 2012 "Trinity" Accelerated Processing Unit (APU). Key points include:

1. Infinity Fabric: AMD now uses Infinity Fabric for seamless communication between CPU and GPU, allowing coherent memory access.

2. Trinity Architecture: Trinity combined CPU and integrated GPU (iGPU) capabilities but had a different interconnect design, relying on a Northbridge architecture that wasn't initially meant for tight GPU integration.

3. Interconnect Components: Trinity features a System Request Interface (SRI) and a two-level crossbar (XBAR) for routing requests. The iGPU has its own Graphics Memory Controller (GMC) that manages memory requests separately from the CPU.

4. Memory Links: There are two main links: the "Garlic" link for high-bandwidth iGPU access to DRAM, and the "Onion" link for CPU memory access, which prioritizes cache coherency but has lower bandwidth.

5. Performance: Although the interconnect design has limitations, Trinity performs well under mixed CPU and GPU workloads, maintaining reasonable latency even with heavy GPU demands.

6. Comparison to Intel: At the time, Intel's integrated GPU designs were more efficient due to better memory integration, highlighting AMD's need for improvement in future designs.

In summary, while Trinity's interconnect had its flaws, it was a significant step for AMD in creating powerful integrated graphics solutions.

In the article "Should we design for iffy internet?" by Brian Hicks, the author discusses the importance of considering internet access when designing software, especially in the U.S. as of 2025. Key points include:

1. Internet Access: About 97% of U.S. households have internet access, but many do not have reliable speeds better than 25 Mbps download and 3 Mbps upload.

2. Urban vs. Rural: Internet connectivity is generally better in urban areas compared to rural ones, where speeds can be significantly lower.

3. Data Sources: The article references data from the FCC and the National Center for Education Statistics, indicating that while internet access has improved, many still face challenges with speed and reliability.

4. Student Access: Data from the Department of Education shows that in 2021, around 2.97 million students had only mobile internet access, highlighting ongoing disparities.

5. Design Considerations: Developers should account for users with slow or limited internet connections when creating software. This includes considering the potential latency and data caps for users on mobile networks.

Overall, the author emphasizes the need for developers to understand and accommodate varying internet conditions to better serve all users, particularly those in underserved areas.

The text discusses how Claude Code, an AI tool, appears to be intelligent due to its ability to quickly perform simple tasks repeatedly. It emphasizes that while the underlying model hasn't changed, the speed and autonomy of Claude Code make it feel smarter to users. The author shares their experience using Claude Code and how it efficiently updated project dependencies, leading them to reconsider the value of AI tools. They ponder the potential for even faster task completion if Claude Code could work autonomously with more computing power. The overall message highlights the evolving capabilities of AI and its potential for automating various tasks.

No summary available.

Carnivorous plants, like the Cape sundew, Venus flytrap, and pitcher plants, have been around for millions of years, evolving multiple times to trap and consume insects for nutrients. Despite their fascinating adaptations, these plants remain relatively small and have never grown to sizes depicted in fiction, such as those capable of trapping humans.

These plants use various methods to trap prey. For instance, sundews have sticky leaves, while Venus flytraps snap shut when triggered. Carnivorous plants thrive in nutrient-poor environments, which is why they evolved to attract and digest animals. Larger plants would typically grow in richer soils, where they wouldn't need to consume animals for nutrients.

The history of carnivorous plants dates back over 34 million years, with fossil evidence showing they have adapted to their habitats over time. Their ability to survive and thrive in challenging conditions is a remarkable adaptation, but they are unlikely to grow larger due to their specific environmental needs. In summary, while carnivorous plants are intriguing organisms with unique trapping mechanisms, their size is limited by their habitats and nutritional requirements.

KiCad and Wayland Support Summary

The KiCad development team has addressed user inquiries about the software's compatibility with Wayland, especially as major Linux distributions like Fedora and Ubuntu move away from X11.

Current Status:

• KiCad can run on Wayland, but users will face many issues that affect usability. Key problems include:
  • Window Management Issues: Difficulty in window placement, managing docked panels, coordinating multiple windows, and dragging interface elements.
  • Input Problems: Unreliable cursor movement, unpredictable focus, and issues with input devices.
  • Performance Issues: High CPU/GPU usage, freezes, graphical glitches, and clipboard problems.
  • UI Limitations: Problems with dialog interactions and integrating external tools.

These challenges arise because Wayland lacks features that applications have relied on in other systems, and its varied implementations across desktop environments create additional complications.

Development Approach:

• The KiCad team will not create specific fixes for different window managers and will focus on general improvements while documenting Wayland limitations. They will not support bug reports related to Wayland-specific issues.

User Recommendations:

• For Professionals: Use X11 environments like XFCE or KDE Plasma for a stable experience.
• For Casual Users: KiCad can run on Wayland but expect limitations like window layout issues and occasional crashes.

Looking Ahead:

• The team acknowledges the need for improvements in Wayland and is open to contributions that enhance its compatibility. However, they emphasize maintaining stability and functionality in KiCad.

Conclusion: For now, users needing reliable performance with KiCad should stick to X11 environments. The KiCad team will adapt as Wayland matures.

At Los Angeles General Medical Center, staff are facing the challenge of identifying numerous patients who arrive without identification and are unable to communicate their names due to serious health issues. Each year, about 130,000 individuals visit the emergency room, with some remaining unidentified for weeks or even months.

One specific case involved a man found unconscious in East Hollywood who could not provide any personal information. Hospital social workers gathered details like his height and weight and took his photo, then reached out to the public for help in identifying him. Unfortunately, many unidentified patients remain in the hospital for extended periods because they lack identification and cannot be transferred to other facilities.

These unidentified patients pose a significant problem for the hospital, not only because they cannot receive appropriate care but also due to safety concerns, as staff cannot access crucial medical history. The hospital can legally share limited information to help identify these patients, and they often issue press releases to the media and public.

Despite efforts to identify these patients, about half of the cases remain unresolved after a month. The situation is dire, as some may end up living in the hospital indefinitely, taking up resources that could be used for other critical patients. The hospital's team continues to search for answers while facing the reality that many individuals remain nameless and lost.

Summary: The Hamburger Menu Icon Today

The hamburger menu, recognized as a three-line icon, has become more familiar to users over the past decade. It was originally designed to save space on small screens by hiding navigation options but led to usability issues as users often overlooked it. Despite its increased recognition today, many of the same usability risks remain.

Key Points:

1. Historical Context: Studies from 2015-2016 showed that using hamburger menus decreased user engagement, increased task completion time, and lowered satisfaction. The recommendation was to use visible navigation whenever possible.

2. Current Familiarity: Today, most users recognize the hamburger icon, especially when it follows standard design practices (like being placed in the top-left corner). Research indicates that users can identify it as a menu.

3. Risks of Familiarity: While the hamburger menu is well-known, its design can cause confusion if similar icons are used in the same location, leading users to misinterpret them as navigation menus.

4. Design Recommendations:

   • Use the standard three-line icon.
   • Keep it in the top-left corner.
   • Avoid unnecessary styling or borders.
   • Label it clearly as "Menu."
   • Use subtle animations to guide user expectations.
   • Differentiate it from other line-based icons to prevent confusion.
   • Ensure adequate size, contrast, and visibility.

5. Conclusion: Hamburger menus can be effective but have inherent trade-offs due to hidden navigation. They should be used wisely, following best practices, and should always be tested with users to ensure they meet their needs.

Overall, while the hamburger menu is a recognizable pattern, it requires careful implementation to maintain usability.

The article "Don’t Mock What You Don’t Own" discusses a testing principle in software development, specifically about how to handle third-party dependencies when writing tests.

Key Points:

1. Principle Overview: The principle advises developers to mock only their own objects in tests, rather than third-party objects. This is to avoid complicating tests and making them brittle.

2. Comparison of Mocking Approaches: Using mocks for third-party dependencies can lead to complex and difficult-to-maintain code. The author shares an example of using mocks to test a function that interacts with a Docker registry API, illustrating how nested mocks can make tests unwieldy.

3. Simpler Alternative: Instead of extensive mocking, the author suggests creating a simple wrapper around the third-party library (e.g., an HTTP client) to abstract its functionality. This makes the business logic clearer and the tests easier to write.

4. Improved Testing: By using a wrapper, tests can focus on the business logic rather than the implementation details of the third-party API. This results in tests that are easier to read and maintain.

5. When to Break the Rule: While the principle is beneficial, there are occasions where it may be appropriate to mock third-party objects, especially for simulating specific error conditions.

6. Resources for Further Learning: The author recommends additional readings and resources for those interested in deepening their understanding of testing principles.

Overall, the article emphasizes the importance of keeping business logic clear and testable by avoiding direct dependency on third-party libraries in tests.

No summary available.

Recently, streetlights in various places, including the U.S. and Canada, have been turning purple, raising concerns about safety for drivers and pedestrians. This change stems from cities replacing traditional sodium lamps with LED lights about 15 years ago, which initially changed streetlight color from yellow to white.

Purple streetlights may indicate a problem with the phosphor coating that helps create white light. When this coating peels off, the blue light from the LED underneath shines through, giving a purple hue. This can occur due to heat, vibrations, or other factors affecting the lights.

The presence of purple light can negatively impact vision. In low-light conditions, our eyes rely more on rod cells, which are more sensitive to blue light but struggle with detail and color perception. This means that under purple light, it may be harder for drivers and pedestrians to see objects clearly.

Experts suggest that the issues with purple streetlights shouldn't reflect poorly on all LED technology, as most LEDs are stable and reliable. To stay safe at night under these lights, it's advisable to avoid wearing sunglasses that filter blue light.

Summary of Incant

Incant is a tool that allows you to add magic spells (or functions) to your code using language models. It provides simple ways to safely interact with these models, but be careful not to send any personal or sensitive information, as all input data is shared with external providers.

Here's how it works:

1. Setup: Incant checks your environment for an API key and other variables to make it easy to set up.

2. Selectors: You can create functions to select specific data. For example, you can make a function to find the highest number from a list. The output will always be a number from the provided list, ensuring safety.

3. Filters: You can also create functions to filter data. For instance, you can filter a list of names to get only male names. The order of the original list is maintained, and the output will be a safe subset of the input.

In summary, Incant helps you work with language models in a safe and controlled way, ensuring that your results are reliable and based on the data you provide.

MediaCMS Summary

MediaCMS is an open-source content management system designed for managing and sharing video and media. It is quick to set up and can handle small to medium-sized media portals. Built with Django and React, it includes a REST API.

Key Features:

• Self-hosting for complete data control.
• Supports various media types: video, audio, images, and PDFs.
• Multiple publishing options (public, private, unlisted).
• Role-Based Access Control (RBAC) for user management.
• Advanced media management tools including a video trimmer, playlists, and easy searching.
• Customizable design with responsive themes.
• Supports adaptive video streaming and multiple subtitle formats.
• Offers chunked file uploads for easier content management.
• Extensive documentation and a demo available online.

Use Cases:

• Educational institutions can create ad-free content hubs.
• Organizations can securely share sensitive materials.
• Individuals can build personalized media portals.

Philosophy: MediaCMS aims to provide a quality open-source solution for community portals, focusing on functionality, easy installation, and customization.

Installation and Support: It can be deployed via Docker or a server automation script. Custom installation and support services are available.

Hardware Requirements: For small installations, a minimum of 4GB RAM and 2-4 CPUs are recommended. Larger setups will need more resources.

License: Released under the GNU Affero General Public License v3.0.

Contact: For more information, reach out to [email protected].

WFGY has created a new PDF-based protocol that helps improve large language models (LLMs) by fixing issues like incorrect projections and confusing reasoning patterns. This approach does not require retraining or system calls, as it directly changes how the model thinks when the data is processed.

Key improvements from this method include:

• 42.1% increase in successful reasoning
• 22.4% better alignment with meaning
• 3.6 times more stability in tasks requiring interpretation

The project is open-source, includes formal theories, prompt examples, and reproducible results, and has no additional dependencies. Feedback from experts in model alignment and reasoning would be highly appreciated.

AMD has announced its latest GPU architecture, CDNA 4, which is a small upgrade from CDNA 3. The main goal of CDNA 4 is to enhance performance in matrix multiplication tasks, crucial for machine learning, by using lower precision data types. It retains a similar design to CDNA 3, utilizing a chiplet architecture that has proved successful in AMD's CPUs.

Key features include:

• Compute Unit Adjustments: CDNA 4 focuses on improving matrix multiplication throughput, doubling the performance in some cases. However, Nvidia's GPUs still excel in low precision throughput.
• Larger Local Data Share (LDS): The LDS size increases from 64 KB to 160 KB, allowing more data to be stored close to the execution units, which can improve performance. The bandwidth for reading data from LDS has also doubled.
• System Architecture Enhancements: CDNA 4 uses upgraded DRAM with HBM3E technology, offering greater bandwidth and capacity compared to its predecessor, while also maintaining AMD's advantage over Nvidia's offerings.

Overall, CDNA 4 builds on the strengths of CDNA 3 without major changes, aiming for improved performance in machine learning tasks while maintaining strong capabilities in vector operations. AMD's strategy appears to be focused on refining existing technologies rather than overhauling their architecture.

The Fujifilm X Half camera has sparked mixed reactions online since its release, with some people disappointed by its features relative to its price, while others find it appealing. The author, who has extensive experience with Fujifilm cameras and recently bought this model, believes it could be ideal for families.

The author discusses their frustrations with their current Leica M11, which is difficult for family members, especially children, to use. This led them to search for a more user-friendly camera for their kids. They initially bought inexpensive, no-name cameras for their children, but faced numerous issues, including poor photo quality and complicated usability.

After considering various options, the author concluded that the Fujifilm X Half would be the perfect fit for their family. It is lightweight, easy to use, and has a good battery life. The camera features a simple interface, which allows their children to enjoy photography without much complexity.

However, the author notes some drawbacks, such as unreliable WiFi connectivity and a less-than-ideal sensor quality. Despite the high price of around $700-$850, they believe the camera offers good value for families looking for a straightforward and enjoyable photography experience.

In summary, while the X Half may not satisfy professional photographers, it is well-suited for families and casual users who want a fun and accessible camera.

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3D Gaussian Splatting (3DGS) is a new method for creating realistic 3D scenes, but it is difficult to use on mobile devices like smartphones because of their limited resources. A common solution is to use cloud computing, but directly streaming images from the cloud can be slow and requires too much internet bandwidth.

This paper presents a solution to allow city-scale 3DGS rendering on mobile devices. The main idea is that the number of new visible elements remains fairly constant during normal movement. Therefore, we only send the necessary elements from the cloud to the mobile device. On the cloud side, we use a method to identify these essential elements, and on the mobile device, we speed up the rendering process using a lookup table.

Our system, called Voyager, significantly reduces data transfer by over 100 times and speeds up rendering by up to 8.9 times, all while maintaining similar image quality compared to other methods.

No summary available.

In an interview, Craig Federighi discusses the development of iPadOS and its multitasking features. He shares insights into the challenges and progress made in enhancing the iPad's capabilities. Federighi emphasizes the core qualities of the iPad, highlighting its unique design and functionality that set it apart from other devices. Overall, the conversation reflects on how iPadOS aims to improve user experience and make multitasking more efficient.

Summary:

Astronomers have created an incredibly detailed image of the Sculptor Galaxy, revealing new features using the European Southern Observatory's Very Large Telescope. This image captures thousands of colors, allowing scientists to learn more about the stars, gas, and dust in the galaxy, which is 11 million light-years away.

The research team, led by Enrico Congiu, observed the galaxy for over 50 hours, creating a comprehensive map that helps study star formation and the galaxy's structure. They discovered around 500 planetary nebulae, which can help determine the galaxy's distance, an important factor for further research.

This new map is a significant tool for ongoing studies on how smaller processes impact the larger galaxy, a mystery that researchers aim to explore further.

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Archaeologists recently discovered a well-preserved loaf of ancient bread in Turkey, dating back about 5,000 years to the Bronze Age. This burnt loaf was found during an excavation at the Kulluoba site and had been buried under a dwelling entrance. The bread is notable because such finds are rare; usually, only crumbs are found.

Since May 22, local bakers have been recreating the ancient bread using ancestral wheat, lentils, and bulgur, resulting in a low-gluten and preservative-free product. The recreated bread has been well-received by locals, with one customer expressing excitement to taste it. Discoveries of ancient foods like this are uncommon in archaeology, highlighting the significance of this find.

Welcome to Fossify!

Fossify is a collection of mobile apps that prioritize privacy, are open-source, and do not include ads. It was created as a continuation of the SimpleMobileTools project to provide simple and private technology for everyone.

Get Involved:

• For issues affecting multiple apps, check the general issues section.
• Join discussions or share your ideas in the general discussions area.
• If you want to help with coding or translations, follow the provided link.

Questions or Feedback? Feel free to ask questions or learn more by starting a discussion or emailing [email protected]. Your contributions are welcome! 🌟

Summary of "The Humble Programmer" by Edsger W. Dijkstra

Edsger W. Dijkstra reflects on his early days in the programming profession, which began in 1952, making him one of the first programmers in the Netherlands. He highlights the slow emergence of programming as a respected discipline and recounts a pivotal conversation with his boss that motivated him to pursue programming instead of theoretical physics. He discusses how initial programming efforts were overshadowed by the physical challenges of early computers and how the programmer's role was often underestimated.

Dijkstra notes that as computers became more powerful, programming challenges intensified rather than diminished, leading to a "software crisis." He critiques the design flaws in new generations of computers, which complicated programming tasks, and emphasizes the importance of programming languages in shaping how programmers think.

He reviews historical programming languages like FORTRAN, LISP, and ALGOL60, praising their contributions while also recognizing their limitations. He argues for the need for more effective programming techniques and languages that enhance intellectual manageability while reducing complexity.

Dijkstra envisions a future where programming becomes vastly more efficient and reliable, driven by a societal recognition of the need for better software and economic pressures to improve programming practices. He stresses that this change is technically feasible and could lead to a revolution in programming methodologies.

Finally, he advocates for a humble approach to programming, acknowledging its difficulties and the limitations of the human mind. He encourages programmers to use modest, elegant languages and to understand the complexity of their tasks fully, which will lead to better outcomes in software development.

The text describes a benchmark test comparing two libraries, snapDOM and html2canvas, which capture the same web page element five times to measure their average speed.

Key points include:

• Both libraries will be tested on various elements, including basic text, animations, CSS frameworks, unique typography, shadow DOM, canvas, pseudo-elements, and clip-path shapes.
• Each library will allow users to capture and download the results in different formats (PNG, JPG, WebP).
• The test setup is currently waiting to begin.

Overall, the goal is to determine which library performs better in capturing web elements.

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The text discusses the phenomenon of tetrachromatic vision, which occurs in some women who have a mutation allowing them to have four types of color-detecting cones in their eyes, compared to the usual three. This rare ability may enable them to perceive a wider range of colors—potentially up to 100 million different hues, while most people can distinguish about 1 million. The first known tetrachromat was identified in 1993.

Estimates suggest that 2 to 3 percent of women globally may have this trait, but many may not realize their enhanced color perception. While tetrachromats could excel in tasks requiring color distinction, such as art authentication or skin tone analysis, the modern world is designed for standard color vision, which could make their experience frustrating. Additionally, they might have the ability to see into the infrared spectrum, granting them enhanced night vision.

Overall, while tetrachromacy offers intriguing advantages, it also poses challenges in a world that does not accommodate this unique form of vision.

Picker-to-parts pallet warehouses can be inefficient because traditional layouts require workers to travel long distances and need a lot of labor. This study presents a new layout inspired by computer architecture, dividing the warehouse into three specialized zones: Performance (P), Efficiency (E), and Shared (S). By using discrete-event simulation, the study compares this new layout with traditional ones, like rectangular and Flying-V layouts. The findings show that the new design significantly improves processing speed and reduces labor needs, indicating that layouts based on CPU designs can enhance warehouse efficiency.

The 2025 CNBC Disruptor 50 list highlights companies that are making significant advancements in technology and generating wealth. Anduril, a company focused on defense technology, is ranked first on this year's list due to the growing interest and investment in this sector.

A recent study has found that each person's breathing pattern is unique, similar to fingerprints. This distinctive breathing can not only help identify individuals but also provide insights into their physical and mental health. Researchers monitored the breathing patterns of 97 healthy individuals over 24 hours using a special device. They discovered that these patterns could accurately identify participants and were linked to their body mass index (BMI) and levels of anxiety and depression.

The study, led by Noam Sobel from the Weizmann Institute of Science, used a custom device that tracks airflow through the nostrils. The researchers analyzed 24 different aspects of breathing, such as inhalation and exhalation duration. They found that breathing patterns could reveal health information even in people with low anxiety and depression scores.

This research suggests that analyzing breathing patterns could become a powerful tool in diagnosing health conditions.

The article discusses the importance of building accessible user interfaces (UIs) from a practical perspective, rather than just a moral one. The author, Nolan Lawson, emphasizes several "selfish" benefits of accessibility for developers:

1. Debugging: Accessible UIs make it easier to debug because they are structured clearly (e.g., using tables instead of confusing divs), allowing developers to quickly understand the layout and elements in the code.

2. Naming: Using proper ARIA roles helps clarify what UI components are (like calling a dropdown a "combobox"), which can simplify code and improve readability.

3. Testing: Accessibility improves testability by allowing developers to select elements based on their semantic roles rather than arbitrary classes, leading to more reliable tests.

4. User Experience: Designing accessible UIs benefits power users who prefer keyboard navigation. A UI that supports keyboard shortcuts enhances productivity and user satisfaction.

The author shares personal motivations for advocating accessibility, including a family member's visual impairment, and highlights the widespread accessibility failures on the web. He suggests that building accessible interfaces is not overly difficult and can be beneficial for both users and developers. The post concludes by encouraging developers to embrace accessibility as a practice that ultimately enhances their work.

The author is creating a small library for easily manipulating 2D and 3D objects in a web browser using simple mouse or touch controls. They want it to be user-friendly, like a basic 3D editor, without needing advanced skills like those required for Blender. Since they couldn't find a lightweight solution, they decided to build one using Three.js and R3F, inspired by VR/AR interaction systems. They invite people to try it out and provide feedback.

Summary of the Universal Rules Template for AI Coding Assistants

This template is designed to enhance the effectiveness of AI coding assistants like Cursor, CLINE, RooCode, Windsurf, and GitHub Copilot by providing a consistent framework for coding projects.

Key Benefits:

• Consistent AI Behavior: Establishes clear workflows for planning, implementing, and debugging, ensuring high-quality outputs across different platforms.
• Persistent Project Memory: Utilizes a structured documentation system that keeps essential project information accessible to the AI, improving its understanding of requirements and architecture.
• Cross-Platform Compatibility: Works seamlessly with various coding assistants, adapting to their specific needs.
• Best Practices Enforcement: Integrates software engineering principles into AI instructions, promoting code quality and maintainability.
• Quick Setup: Offers a pre-configured structure that can be customized to fit project needs.

Who Can Use It:

• Developers working on complex projects requiring in-depth AI assistance.
• Teams using multiple coding assistants seeking consistent workflows.
• Individuals wanting structured AI workflows.
• Researchers needing reproducible AI interactions.

Key Features:

• Support for multiple AI platforms ensures consistent rule application.
• A "Memory Bank" provides deep context, enhancing AI performance.
• Encourages adherence to software engineering best practices.
• Optimizes token usage to improve efficiency.

Workflow Overview:

1. Installation: Use provided scripts to set up rules and memory in your project.
2. Customization: Modify project-specific rules and memory as needed.
3. Synchronization: Regularly update rules to keep AI assistants aligned with project changes.
4. Clean-Up: Commands are available for removing rules while preserving project-specific documentation.

Environment Setup:

• Set up a Conda environment and install necessary dependencies to run the tools effectively.

By using this template, development teams can collaborate more effectively with AI, ensuring consistent quality and maintainability in their coding projects.

Summary: A Beginner's Guide to Calculating Oil Storage Tank Occupancy Using Satellite Imagery

TankerTrackers.com aims to provide insights into the oil market by tracking oil tankers, storage changes, and government data. They use satellite imagery for visual confirmation of oil storage levels, which helps traders understand market trends.

The oil market lacks transparency, with many producing countries reluctant to share their production and storage data. By analyzing satellite images alongside tanker data, TankerTrackers can determine if oil is being stored, refined, or transferred.

To calculate the occupancy of oil storage tanks, you need two key measurements:

1. Diameter: Use tools like Google Earth to measure the tank's diameter.
2. Height: This can be trickier, but you can find it on the oil company’s website or through images.

Next, you can estimate how full a tank is by observing shadows. A floating rooftop in storage tanks adjusts with the oil level, creating shadows that indicate how much oil is inside. More shadow means less oil.

By comparing satellite images taken over time, you can see changes in storage levels. For example, images of tanks in Ningbo, China, showed no change in barrel amounts over a month, illustrating how this method works.

This process allows for rough estimates of oil storage levels, providing valuable insights into the oil market.

An Iranian cryptocurrency exchange called Nobitex was hacked for $90 million by a group linked to Israel, known as Gonjeshke Darande. This group claims Nobitex is used for financing terrorism and threatens to release the exchange's internal data and source code, putting remaining user assets at risk.

The hack occurred shortly after a cyberattack on Iran’s state-owned Bank Sepah. Nobitex acknowledged the attack but did not confirm the amount stolen. The stolen funds were tracked to various cryptocurrency wallets, and the group stated that the hack was not financially motivated, but rather a political statement against Iran's regime.

As tensions rise between Iran and Israel, Nobitex now faces a potential credibility crisis, as users who do not withdraw their funds may lose everything.

Summary: Windows 10 Updates After End-Of-Life

This guide explains how to get official updates for Windows 10 after it is retired on October 14, 2025. Some users may not upgrade to Windows 11 due to hardware limitations or personal preference.

Here are your options for continuing to receive updates:

1. Extended Security Updates (ESU): Microsoft will offer paid ESU subscriptions starting November 2025, allowing users to receive updates for Windows 10 until October 2028.

2. Windows 10 IoT Enterprise LTSC 2021: This version will be supported until January 13, 2032. You can clean install it using the ISO available in English or upgrade from existing Windows 10 editions while keeping your files and apps.

3. Windows 11 IoT Enterprise LTSC 2024: Set to be released in 2024, this version has relaxed hardware requirements, allowing installation without TPM or Secure Boot. You can also upgrade from Windows 10 editions while keeping your files and apps.

Installation Steps:

• Use PowerShell to check your system's architecture and language.
• Mount the ISO file and run the setup to install or upgrade. Ensure you choose the option to keep personal files and apps.

Notes:

• LTSC editions do not come with Store apps by default, but upgrades from GAC will retain them.
• IoT editions work on general-purpose devices and are similar to Enterprise editions in function.

If you need assistance, support is available.

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The founder is starting a new project and wants to avoid unnecessary problems. They are asking for honest feedback on what is currently difficult or frustrating in your job, especially regarding how you handle exploratory data tasks. They're interested in understanding your workflows, issues, and frustrations.

Researchers have created a new image of the black hole at the center of our galaxy using an artificial intelligence (AI) model. This model aims to provide more detailed insights by analyzing telescope data that was previously deemed too noisy. While the AI suggests that the black hole is spinning at nearly full speed and has a rotational axis pointing towards Earth, some experts, including Nobel Prize-winning astrophysicist Reinhard Genzel, question the reliability of the results. They point out that the quality of the data used could lead to inaccuracies in the image. Despite the skepticism, the researchers plan to further refine the AI model with better data to improve their findings on the black hole, known as Sagittarius A*.

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I created a Chrome extension called cpdown that helps users copy content from webpages or YouTube subtitles as clean Markdown with just one click or a keyboard shortcut. Key features include:

• Content Extraction: It removes ads and distractions, focusing on the main content using tools like Mozilla's Readability.
• Markdown Conversion: It converts the extracted content into tidy Markdown format.
• Token Count: It shows the token count of the copied text, which is useful for working with language models like ChatGPT.
• YouTube Support: It can copy full YouTube transcripts in Markdown, adding the video title as a header.
• Customizable Options: Users can choose how content is extracted and formatted.

cpdown is free, open-source, and built with modern web technologies. It's available on the Chrome Web Store and on GitHub. Feedback and suggestions are welcome!

A 51-year-old man went to the emergency room with chest pain, and doctors initially diagnosed him with acute coronary syndrome. However, one hospitalist felt something was off and ordered a CT scan, which revealed a life-threatening aortic dissection that others had missed. This case highlights "premature closure," where doctors stick to an initial diagnosis without considering alternatives.

Similarly, in software development, AI coding assistants can provide seemingly perfect solutions that may not address deeper issues. For example, an AI might suggest adding an index to fix a performance problem, but the real issue could be a flawed data model. Relying on AI's first suggestion can lead to missed opportunities for better solutions.

While AI can save time on routine tasks, it can also bias developers towards quick fixes in complex scenarios, leading to technical debt and reduced problem-solving skills. To counter this, developers should:

1. Review AI-generated code critically, just like they would with a peer's work.
2. Always ask for alternative solutions.
3. Balance speed with thorough investigation, especially for complex problems.
4. Understand AI suggestions deeply, as if explaining them to others.
5. Spend time exploring problems before consulting AI.

By practicing these strategies, developers can maintain their critical thinking skills and ensure AI is used as a helpful tool rather than a crutch. The goal is to use AI to enhance productivity while still engaging in thoughtful problem-solving.

Summary of Anthropic's Prompt Engineering Interactive Tutorial

This course teaches you how to create effective prompts for Claude, Anthropic's AI model. By the end of the course, you will be able to:

• Understand what makes a good prompt.
• Identify common mistakes and learn quick fixes.
• Recognize Claude's strengths and weaknesses.
• Develop strong prompts for various tasks.

The course has 9 chapters with exercises and an appendix with advanced techniques. It is designed for a step-by-step learning experience, starting with basic concepts and moving to more complex ideas. You can practice writing prompts and see how changes affect Claude's responses in the "Example Playground" section.

The tutorial uses the Claude 3 Haiku model, which is the simplest version, but more advanced models are available. There is also a user-friendly version on Google Sheets.

Chapters Overview:

1. Basic Prompt Structure
2. Being Clear and Direct
3. Assigning Roles
4. Separating Data from Instructions
5. Formatting Output
6. Precognition (Step by Step Thinking)
7. Using Examples
8. Avoiding Hallucinations
9. Building Complex Prompts (for various industries)

Appendix Topics:

• Chaining Prompts
• Tool Use
• Search & Retrieval

To start, go to Chapter 1: Basic Prompt Structure.

Summary of "Fun with Telnet" by Brandon Rozek

Brandon Rozek shares his experience with Telnet, a network protocol that allows users to connect to remote computers. He highlights a fun Star Wars animation that can be accessed via Telnet by connecting to "towel.blinkenlights.nl" on port 23. Rozek also lists other interesting Telnet connections, including:

• freechess.org (5000): Play chess
• mtrek.com (1701): Space combat game inspired by Star Trek
• fibs.com (4321): Play backgammon
• mapscii.me: Interactive world map
• telehack.com: Arpanet/Usenet simulation with over 60 text-based games

He reminds users to be cautious, as Telnet communications are not secure and can expose sensitive information. Enjoy exploring the unique features of Telnet!

Chawan is a terminal-based web browser built using the Nim programming language. It offers decent CSS rendering, some JavaScript support, and can display inline images using sixel or kitty protocols. Chawan supports various protocols beyond just HTTP and HTTPS, such as FTP, Gopher, and Gemini.

Originally designed as a clone of the w3m browser, Chawan has evolved with a different structure. It loads web pages in separate processes and manages different protocols and file types using external programs. This allows for the possibility of adding custom image format decoders, although there aren't many practical uses for this feature.

You can view a gallery of websites rendered by Chawan at this link.

The author has been developing a project called Canine for about a year. They started it because they were frustrated with the high costs of cloud hosting services like Heroku and Render, which once totaled over $400 a month. They switched to a cheaper option, Hetzner, where a similar service costs only $4 for a 4GB machine.

While Hetzner is affordable, it lacks easy features for DNS and SSL management, team collaboration, and GitHub integration. The author aimed to create a platform similar to Heroku for Hetzner, which turned out to be more challenging than anticipated, but they've made good progress.

Canine simplifies the process of hosting various open-source applications, such as databases and other software. The project is open source and can be found on GitHub, with a cloud-hosted version available online.

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