Vim is a popular text editor that uses a unique editing style called modal editing. In Normal mode, certain keys help users navigate quickly without needing a mouse. Here are some basic movements in Vim:

• h / l: Move one character left / right.
• k / j: Move one character up / down; jumps to the end of the line if it's shorter.
• w / b: Jump to the beginning of the next / previous word.
• e: Jump to the end of the next word.
• ^ / $: Move to the beginning or end of the current line.

Additionally, the function Function["VimGraph"] allows users to customize movements and patterns, and it supports various graph options.

The author expresses frustration with Nextcloud, a software that combines various services like file storage, calendars, and notes. Despite its appealing features, the user experiences slow performance even on decent hardware. The main issue identified is the large amount of Javascript (15-20 MB) that needs to be loaded, leading to delays in loading pages and apps. For example, the Calendar app alone requires 5.94 MB of Javascript, and the Notes app needs 4.36 MB.

The author notes that while some Javascript is cached, it still results in slow execution during each visit. This situation has led the author to seek alternatives for some features, like using Vikunja for task management, which has a much smaller Javascript footprint (1.5 MB) and performs better. The author acknowledges the complexity of the development process and the challenges faced by teams but emphasizes the negative impact on user experience. They recommend reading Alex Russell's work on web performance for insights into improving website efficiency.

The author discusses the challenges of using pgvector, a PostgreSQL extension for vector search, in a production environment. While pgvector seems appealing as it integrates vector embeddings into Postgres, the reality of running it at scale reveals significant issues that many articles overlook.

Key Points:

1. Limited Real-World Testing: Most discussions about pgvector are based on simple demonstrations, neglecting the complexities of actual production use.

2. Indexing Challenges: pgvector offers two index types (IVFFlat and HNSW), each with drawbacks:

   • IVFFlat: Requires upfront configuration and leads to suboptimal search quality over time without periodic rebuilds.
   • HNSW: Provides better recall but demands significant memory and can create bottlenecks during high write loads.

3. Real-Time Search Difficulties: Immediate searchability after data insertion is hard to achieve. New data can lead to degraded search performance, and maintaining optimal indexing requires complex workarounds.

4. Query Planning Complexity: Effective querying is complicated by the need for filtering and can lead to inefficient performance. There are multiple strategies for filtering, but each has its trade-offs.

5. Operational Reality: Managing vector data alongside metadata adds complexity, especially during index rebuilds, which can be resource-intensive and disruptive.

6. Hybrid Search and Filter Management: Combining vector similarity with traditional search features is not straightforward, requiring additional effort to achieve optimal results.

7. Alternative Solutions: Dedicated vector databases (like Pinecone or Weaviate) offer built-in solutions for these challenges, including better query planning, real-time indexing, and scalability, which might ultimately be more efficient and cost-effective than using pgvector.

In summary, while pgvector can be useful, it poses significant operational challenges that require careful planning and management. For many teams, using a dedicated vector database may be the better choice.

You can find demos at the following link: Linux WASM Demos.

No summary available.

No summary available.

Creating detailed and interactive 3D urban scenes is important but difficult due to the lack of high-quality 3D scans needed for training models. This paper introduces a new method called Skyfall-GS, which combines available satellite images for basic 3D shapes with a diffusion model to generate high-quality details. Skyfall-GS can create large 3D city blocks without needing expensive annotations and allows for real-time exploration. The method uses a step-by-step improvement process to enhance the geometry and textures of the scenes. Tests show that Skyfall-GS produces better and more realistic 3D models than existing methods.

No summary available.

Summary: Offline Math: Converting LaTeX to SVG with MathJax

The text discusses how to convert LaTeX math formulas into SVG format for offline use, particularly when using Pandoc and MathJax. Here are the key points:

• MathJax in Pandoc: Pandoc can prepare LaTeX math for MathJax using the --mathjax option, but this requires an internet connection as it pulls resources from a third-party server.
• Offline Alternatives: To avoid reliance on external servers, users can include their own MathJax library. However, this still won't work if JavaScript is not supported by the device (like some EPUB readers).
• MathML Option: For broader compatibility, users can opt for MathML using Pandoc’s --mathml option, which is supported by most modern browsers.
• SVG Conversion: To create a standalone document with SVGs, you can use an HTML parser like Nokogiri to replace MathJax spans with images. Alternatively, you can use MathJax’s CLI tools or pdflatex for conversion.
• Headless Browsers: Another approach is to use a headless browser (like Puppeteer or jsdom) to run MathJax and then serialize the modified HTML.
• jsdom Experience: The author found jsdom to perform better recently compared to previous attempts, making it a viable option for this task.
• Implementation: A sample command is provided to generate a standalone HTML document that does not require JavaScript or external resources.

The full source code for the implementation is available on GitHub, allowing users to easily convert LaTeX math to SVG for offline use.

The text discusses a new approach to scientific computing called "Geonum," which simplifies mathematical operations by integrating geometric principles into number representation. Here are the key points:

1. Geometric Numbers: Traditional numbers often lose important geometric information when treated as "scalars." Geonum retains angles and dimensions, allowing for more intuitive calculations involving direction and rotation.

2. Dimensional Representation: Instead of stacking coordinates, Geonum uses rotations (π/2 increments) to define dimensions. Each geometric number includes both a length and an angle, with higher dimensions represented by additional angles.

3. Efficiency: Geonum drastically reduces the complexity of operations. While traditional methods may require extensive calculations involving matrices and tensors (leading to slow performance), Geonum maintains constant time operations regardless of dimensionality.

4. Core Operations: Geonum supports various mathematical operations like differentiation, projection, and geometric products—all simplified by its unique structure.

5. Implementation and Performance: Benchmarks show that Geonum operations are significantly faster than traditional approaches, making it practical for high-dimensional calculations.

6. Automatic Calculus: Geonum simplifies calculus operations, allowing differentiation and other complex calculations to be performed easily without the need for traditional limit computations.

7. Usage and Examples: The text provides examples of how to use Geonum in programming, highlighting its ease of use in computing angles and projections in a dimension-free manner.

In summary, Geonum offers a novel way to handle mathematical computations by integrating geometric concepts, resulting in faster and more intuitive calculations across various dimensions.

Summary: Writing an Asciidoc Parser in Rust: Asciidocr

The author created an Asciidoc parser called "asciidocr" using Rust because they wanted a more efficient tool for converting Asciidoc files, as the existing Ruby version (Asciidoctor) required Ruby to be installed on each machine. The author prefers Rust for its performance and the desire to avoid writing in Ruby.

The process involved:

1. Learning Rust: The author sought mentorship to improve their skills.
2. Manual Parsing: Instead of using a pre-existing lexing package, they decided to write the parser from scratch to gain deeper knowledge.
3. Structure: They implemented a token system to scan Asciidoc documents character-by-character, creating a structured representation (Abstract Syntax Graph) of the content.
4. Parsing Logic: A series of match statements were used to determine how to handle different types of tokens during parsing.
5. Output Formats: The parser generates outputs in various formats, primarily targeting HTML and eventually Word documents (docx). It uses a templating system called Tera to create HTML documents.

The author acknowledges that while asciidocr does not cover every feature of Asciidoctor, it provides significant functionality, including directives and a fast conversion process. Future improvements are planned, including expanding language support and enhancing the docx output capabilities.

Overall, the author expresses pride in their work and the efficiency of the tool they've built, highlighting their learning experience with Rust and software development practices.

Summary of the Continual Learning Problem

The continual learning problem focuses on how to update machine learning models without losing previously learned information. The main challenge is to allow models to learn continuously, similar to how humans learn from experiences, without forgetting what they already know.

Key Concepts:

1. Memory Layers: The paper suggests using memory layers as an effective way to enable continual learning. These layers are designed to be high-capacity but only activate a few parameters during each learning step, which helps in learning new information with minimal forgetting.

2. Generalization vs. Integration: Continual learning involves two main challenges:

   • Generalization: Understanding what to learn from new data while ensuring that learned information is applicable in various contexts. For instance, learning facts from sentences while grasping their meanings.
   • Integration: Combining new knowledge with existing knowledge without losing important information, addressing the issue of "catastrophic forgetting."

3. Design Goals: An effective continual learning system should:

   • Update only the necessary parameters when new data is encountered.
   • Handle large volumes of information over time.
   • Adaptively integrate and reorganize knowledge based on new inputs.

4. Current Approaches: The paper discusses various methods for continual learning:

   • In-context Learning: Adapting to information within a limited context but faces challenges with confusion as more data is added.
   • Retrieval-Augmented Generation (RAG): Using a buffer of past experiences to inform current tasks but lacks knowledge compression.
   • Parameter-efficient Fine-tuning (PEFT): Techniques like LoRA enable targeted updates but can be low-capacity.
   • Mixture of Experts (MoE): Adding specialized experts for new tasks, but this approach can become cumbersome.

5. Memory Layer Architecture: Memory layers replace parts of traditional models with a system that retrieves relevant learned information efficiently. This allows for more targeted updates and better retention of knowledge.

6. Sparse Memory Fine-tuning: The authors propose a method that finetunes only the memory slots relevant to new information, minimizing the risk of forgetting. Their experiments show that this approach leads to better performance compared to traditional full finetuning and LoRA.

7. Future Directions: The paper highlights the need for more research on large models and real-world continual learning applications. It calls for better benchmarks and evaluation methods to assess continual learning capabilities.

In conclusion, the authors advocate for memory layers as a promising solution for continual learning, aiming to create models that can learn from user feedback and adapt over time while retaining previous knowledge.

The text explains the concept of the dot product in linear algebra using examples from daily life.

1. Choosing a City: The author discusses how he and his wife evaluated cities to live in by scoring them based on weather and affordability. They initially added these scores but wanted to give more importance to weather. By using weights (e.g., multiplying the weather score by 1.1), they created a weighted sum.

2. Dot Product Definition: The dot product is described as a weighted sum of two vectors. In the city example, scores for weather and affordability are represented as vectors, and the dot product is calculated by multiplying corresponding scores by their weights and then adding the results.

3. Example with Three Cities: The author illustrates that for multiple cities, you still calculate separate dot products for each city's scores, rather than trying to combine all scores into one operation.

4. Lottery Example: The text also provides an example of calculating the expected value of a lottery ticket using the dot product. By weighing prize amounts with their probabilities, the average worth of the ticket is determined.

5. Conclusion: The dot product is highlighted as a simple yet crucial operation in linear algebra, particularly for understanding matrix multiplication, which will be covered in the next chapter.

In summary, the dot product is a method for calculating a weighted sum of two vectors by multiplying and adding their components.

I've been working on a project in Rust to explore its graphics programming features by creating a ray tracer. Initially, I wanted to render a simple 3D scene in the browser, but it has grown into a small renderer that can:

• Run locally or online using wgpu and WebAssembly
• Render meshes with a Bounding Volume Hierarchy (BVH) for faster performance
• Simulate both direct and indirect light for realistic images
• Be easily shared as a free web demo on GitHub Pages

While the project isn't perfect, it's been a great learning experience. I also plan to use Rust for machine learning projects in the future.

You can check out the project on GitHub: GitHub Repository and try the web demo here: Web Demo.

I welcome feedback from anyone with experience in similar projects or with wgpu and ray tracing in Rust.

Summary of llms.py

Overview: llms.py is a lightweight tool that allows users to access various language models (LLMs) offline through a command-line interface (CLI) or a web UI while keeping data private. It supports multiple LLM providers and can be configured easily.

Key Features:

• Lightweight Design: A single Python file with minimal dependencies.
• Multi-Provider Support: Works with providers like OpenAI, Google, Anthropic, and more, allowing users to mix local models with API models.
• Request Management: Automatically routes requests to available providers and retries failed requests.
• Analytics: Built-in UI for monitoring costs and usage.
• Image and Audio Processing: Supports processing images and audio files through compatible models.
• Configuration Management: Easily enable or disable providers and manage settings via a JSON file.

User Interface:

• Features a ChatGPT-like UI with dark mode, cost analysis, and activity logs.
• Allows users to check provider reliability and response times.

Installation:

• Can be installed via pip or Docker.
• Requires setting API keys for different providers.

Usage:

• Users can make queries using simple commands in the CLI.
• Supports various input types including text, images, audio, and documents.
• Customizable chat requests can be defined in JSON format.

Providers Supported: Includes OpenAI, Anthropic, Google, Grok, and more, with detailed API key requirements for each.

Advanced Features:

• Custom parameters for chat requests.
• Ability to run as an HTTP server for OpenAI-compatible requests.
• Docker support for easy deployment and management.

Troubleshooting: Common issues addressed include configuration file problems, provider availability, and API key checks.

Contributions: The project is open for contributions, especially to add new provider support.

Overall, llms.py is a versatile tool for offline access to a variety of language models with a focus on privacy and customization.

KaTeX is a fast and efficient tool for rendering mathematical expressions on websites. Here are the key points:

• Speed: KaTeX renders math quickly without needing to adjust the entire page layout.
• Quality: It uses a layout system based on TeX, known for high-quality math typesetting.
• No Dependencies: It can be easily included in websites without needing extra libraries.
• Server-Side Rendering: KaTeX produces consistent output across different browsers, allowing you to prepare math expressions in advance using Node.js and send them as HTML.

KaTeX performs well even on pages with many mathematical expressions, making it a reliable choice for developers.

The article describes a lengthy and complicated story about road safety improvements on US-26 near Rhododendron, Oregon, which began in the late 1990s due to a high accident rate. Local residents expressed concerns about dangerous driving conditions, particularly the absence of a left turn lane, leading to the Oregon Department of Transportation (ODOT) to consider road widening.

The project faced numerous delays due to regulatory reviews under the National Environmental Policy Act (NEPA) and the National Historic Preservation Act (NHPA), as well as public opposition. A local group, led by Michael P. Jones, raised concerns about potential historical sites, including a rock feature thought to be significant. Despite multiple investigations concluding there was no historical significance, the project faced ongoing legal challenges from Jones and other stakeholders.

By 2008, after years of planning and litigation, construction finally began, but not without complications. The project, which aimed to enhance safety and reduce accidents, was delayed for nearly a decade and resulted in ongoing legal disputes lasting years. Ultimately, the left-turn lane was completed, but the process was marred by significant costs in time, money, and human life due to the delays in implementing safety measures.

A UK company called YASA has developed a new tiny electric motor that is significantly more powerful than Tesla's motors. This new motor weighs just 28 pounds and can produce over 1,000 horsepower, outperforming the previous record holder from YASA by 40%. It can sustain powerful performance continuously, making it suitable for long-term use.

YASA's CEO, Joerg Miska, highlighted that this motor has three times the performance density of current leading motors, which could change the landscape for electric vehicles (EVs). A lighter motor leads to lighter cars, enhancing efficiency, acceleration, and battery range.

YASA already supplies motors for high-end vehicles like Ferrari and Mercedes-AMG, and they hope that as production increases, these efficient motors will become available for more affordable EVs in the future. This innovation shows that advanced performance can come in a compact size.

The article discusses how knowledge workers can protect their careers in the age of AI. It emphasizes the importance of adapting to new technologies and developing skills that AI cannot easily replicate. By focusing on creativity, critical thinking, and emotional intelligence, workers can enhance their value in the job market. The author encourages continuous learning and staying updated with industry trends to remain competitive.

Summary of Oxy: Cloudflare's Next-Generation Proxy Framework

Cloudflare introduced Oxy, a new proxy framework built with the Rust programming language. It supports various Cloudflare projects, including the Zero Trust Gateway and iCloud Private Relay. Oxy is designed to handle large volumes of traffic efficiently and allows for easy customization and extensibility in proxying requests across different communication protocols.

Key Features of Oxy:

1. Proxy Framework: Oxy acts like a traditional proxy server (e.g., NGINX) but offers advanced programmability for traffic handling, including routing and traffic analysis.

2. High-Level Architecture: Users can quickly set up services, like HTTP firewalls, with minimal coding. Oxy simplifies the process of handling requests and responses, allowing developers to focus on core business logic.

3. On-ramps and Off-ramps: Oxy supports various traffic types, enabling flexible input and output methods. It can analyze and manipulate traffic at multiple layers of the OSI model, enhancing application capabilities.

4. Tunneling and Request Handling: Oxy enables efficient tunneling of different traffic types and provides tools for analyzing HTTP requests and responses.

5. TLS Support: Oxy incorporates strong encryption protocols, allowing secure communications and traffic inspection.

6. Extensibility: Developers can use YAML configuration files to customize Oxy’s features and integrate their own code through hooks.

7. Development Approach: Oxy was developed iteratively, reusing components from initial projects to streamline future development.

8. Relation to Pingora: While Oxy and Pingora (another Cloudflare proxy) share some similarities, they serve different purposes; Pingora focuses on handling unconventional traffic configurations, whereas Oxy is a versatile platform for high-performance proxy applications.

In conclusion, Oxy is designed to be a robust and flexible solution for modern network services, aiming to enhance Cloudflare's architecture and improve Internet functionality. Future blog posts will delve deeper into Oxy's technical aspects.

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The blog discusses the repeated suspensions of SSLMate's Google Cloud account, which have occurred three times since 2024 without prior notification. These suspensions disrupt the company's ability to integrate with customers' Google Cloud accounts, forcing a choice between security and usability.

SSLMate uses service accounts to access customer resources securely and easily, based on Google’s documentation. However, the account suspensions have complicated this process, making it difficult to recover access each time.

The author shares experiences with the frustrating recovery process, which includes automated emails and lack of communication from Google about the reasons for suspension. Despite being able to log in again, the suspensions continue to create issues.

The author suggests alternatives to improve integration security, such as using OpenID Connect (OIDC), but finds the setup process unnecessarily complex. They argue that Google should simplify OIDC to encourage secure practices, as current solutions are prone to arbitrary account suspensions.

In conclusion, the author highlights a trade-off in Google Cloud integrations: users can achieve either easy setup, security without long-lived credentials, or protection against suspensions, but not all three at once.

The Arduino Uno Q is a new development board from Arduino, created after Qualcomm acquired the company. It combines features of a computer and a microcontroller, resembling both a Raspberry Pi and an Arduino Uno.

Key Features:

• Runs on a Qualcomm Dragonwing SoC with older Arm A53 CPU cores, 2GB RAM, and 16GB eMMC storage.
• Operates on Debian Linux and includes Arduino's App Lab for programming in Python and C++.
• Offers a single USB-C port for power and connections but lacks multiple ports, making setup potentially cumbersome.

Performance:

• Performance is similar to older Raspberry Pi models, but it has limitations due to its RAM and processing power.
• It consumes more power than traditional Arduino boards, which could limit battery use.

Applications:

• Suitable for robotics and light industrial controls, but not ideal for general use like web browsing or media consumption.
• The software management can be challenging, and integration between the Linux side and microcontroller is not seamless.

Open Source:

• The board's schematics are available, maintaining Arduino's open-source ethos, but there are concerns about Qualcomm's long-term commitment to the project.

Conclusion: The Uno Q is a unique but limited board that might appeal to those already in the Arduino ecosystem. Its value compared to other single-board computers is questionable, and it may not be the best choice for new users or general projects.

The text provides basic information about a list or item related to an individual named Raymond Toy. It shows that the item was last active 280 days ago and has no comments. There is one participant, and options are available to add or remove the item from favorites.

The author shares their journey of rediscovering the joy of self-hosting using FreeBSD, a family of operating systems. After feeling stuck in their previous tech routines, they realized they needed a fresh start. They found FreeBSD to be a great fit for their needs, especially for running multiple applications in containers or virtual machines.

Initially, the author felt lost while setting up their new system, but they were excited to learn. They appreciated FreeBSD's simplicity, good documentation, and long-term compatibility, which made it easy to find solutions to problems. The supportive BSD community also provided helpful responses to their questions.

While the author is uncertain about the long-term future of their current setup, they are enjoying the learning process and having fun. They plan to share more about their experiences in the future.

The article discusses the ecological impact of introducing non-native fish species and the broader consequences of colonial water management practices. Asian carp, imported to the U.S. in the 1970s for pest control, have significantly disrupted local fish populations. This situation reflects a historical pattern of ecological disruption caused by colonial activities, where non-native species were introduced to various regions, often leading to the decline of indigenous species and habitats.

The author highlights several examples, such as the introduction of trout in India and Nile perch in Uganda, where the intentions behind these introductions—whether for sport or pest control—often resulted in ecological destruction. The article connects these historical events to the concept of "high modernism," where state-led projects attempted to control and modify natural environments, often with disastrous consequences.

Key points include how colonial powers exploited water resources and transformed landscapes through large-scale irrigation and infrastructure projects, leading to social and environmental issues. The legacy of these practices continued post-colonization, with developing nations implementing similar projects that mirrored colonial patterns, often exacerbating ecological and social conflicts.

The article concludes by noting that the environmental disruptions caused by these projects have had lasting effects, contributing to a global crisis and suggesting a need for more sustainable approaches to managing water and ecological systems.

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Recantha's Tiny Toolkit Summary

Mike Horne describes a versatile toolkit designed for various tasks, primarily for design work. The toolkit is housed in a Lihit Lab Large Maroon Camo Pen Case and includes several layers of tools and stationery.

Contents Overview:

• Exterior: Pockets for sticky notes.

• Layer 1:

  • Multitool
  • Scissors
  • Swiss Army knife

• Layer 2.1:

  • Chisel-tip black Sharpie
  • Side cutters
  • Scalpel
  • Adjustable spanner
  • Two craft cutters

• Layer 2.2:

  • Small screwdriver handle with bits
  • Wire stripper/cutter
  • Various tweezers
  • Additional tools like a brush and pencils.

• Layer 3:

  • USB cables and splitter
  • Paper clips and bulldog clip
  • microSD to SD adapter
  • More tools including another multitool, a ruler, and a measuring tape.

The toolkit is heavy due to the multitools but very practical. Horne notes some duplication (especially with box cutters) and plans to expand the kit with more tools while reducing duplicates to save space.

FurtherAI is hiring Software Engineers, AI Engineers, and Forward-Deployed Engineers to develop AI Agents for the insurance industry. They have received a $25 million Series A funding led by Andreessen Horowitz and have seen over 10 times revenue growth this year. The team is small and experienced, with many members coming from companies like Apple, Microsoft, and Amazon.

They are looking for strong engineers based in San Francisco who want to take ownership of their work and make a real impact. Interested candidates can contact Sashank (CTO) at [email protected].

Additionally, there is a $10,000 referral bonus for successful hires. For more job details, visit their jobs page here.

A Ukrainian man named Yuriy Igorevich Rybtsov, also known as "MrICQ," has been arrested in Italy and is now in U.S. custody. He was indicted in 2012 for working with a hacking group called Jabber Zeus, which stole millions from U.S. businesses. The group used a modified version of the ZeuS banking trojan to steal banking information, targeting mainly small and mid-sized companies.

Rybtsov played a key role in the cybercrime operation, handling notifications about new victims and helping launder stolen money. The group used tactics like modifying payroll systems to add money mules—people recruited to transfer stolen funds. Rybtsov was extradited to the U.S. after losing an appeal in Italy.

The Jabber Zeus crew was known for their sophisticated methods, including a feature that allowed them to intercept one-time passwords from victims' banks. Their leader, Maksim Yakubets, also led another notorious cybercrime group called Evil Corp, which has been responsible for over $100 million in theft. Rybtsov’s arrest is part of ongoing efforts to combat cybercrime targeting businesses.

Summary of "Machine Logic"

The text discusses the author's experiences and thoughts on type theories and proof systems in mathematics and computer science, particularly focusing on Isabelle and its relationship with dependent types.

1. Dependent Types and Proof Objects: The author reflects on the common question of why Isabelle does not use proof objects, stating that while proof objects are standard in type theories, they are unnecessary and consume space. He explains that type checking can effectively ensure valid proof steps without them.

2. Experience with AUTOMATH: The author recounts his early interactions with N G de Bruijn and the AUTOMATH system, noting that he never used it directly but was inspired by its approach to formalizing mathematical texts.

3. Martin-Löf Type Theory: The author describes his extensive research into Martin-Löf type theory, appreciating its formal approach to program synthesis. However, he grew frustrated with its rigidities and the shift towards intensional equality, which disrupted his work.

4. Formalism Choices: The text highlights the choices researchers face between developing new formalisms or pushing existing ones. The author cites Mike Gordon's successful use of higher-order logic in hardware verification as an example of the latter approach.

5. ALEXANDRIA Project: The author shares his experiences with the ALEXANDRIA project, funded by the European Research Council, which aimed to formalize advanced mathematical results using Isabelle. Despite initial concerns about the limitations of higher-order logic, the team successfully formalized significant theorems.

6. Reflection on Dependent Types: The author concludes by noting that while dependent types have matured and tools like Lean have gained popularity, he remains unconvinced of their necessity, citing potential performance issues and challenges with certain proofs.

Overall, the text emphasizes the author's journey through various type theories, their practical applications, and the evolving landscape of formal verification in mathematics and computer science.

Two recent papers discuss the security of large language models (LLMs) and prompt injection risks.

1. Agents Rule of Two: This paper, published by Meta AI, introduces a "Rule of Two" for AI agents. It suggests that to avoid serious issues from prompt injections, agents should only possess two out of three properties in a session:

   • They can process untrustworthy inputs.
   • They have access to sensitive data or systems.
   • They can change state or communicate externally. If an agent needs all three properties without starting a new session, it should not operate autonomously and requires supervision. This approach aims to clarify the risks associated with prompt injection attacks, which can lead to data theft and other dangers.

2. The Attacker Moves Second: This paper analyzes the effectiveness of 12 defenses against prompt injection and jailbreaking. It finds that these defenses fail against adaptive attacks, which are more sophisticated than static example attacks. The study, conducted by a team from major AI organizations, showed that many defenses could be bypassed with a success rate over 90%. The authors argue for better evaluation methods for defenses and emphasize the need for simple, analyzable defenses. However, the author of the summary expresses skepticism about the development of reliable defenses.

Overall, the "Rule of Two" is recommended as the best current strategy for building secure LLM systems amidst ongoing challenges in prompt injection defenses.

No summary available.

Microsoft has fixed a long-standing issue with the "Update and shut down" feature in Windows 11. With the new Windows 11 25H2 Build 26200.7019 update, selecting "Update and shut down" will correctly power off your PC instead of restarting it. This problem has frustrated many users for years, as it often led to unexpected restarts after updates.

The bug affected both Windows 10 and 11 and was linked to how the operating system processes updates and shutdown instructions. Microsoft identified the root cause and addressed it in an optional update (KB5067036) released in October 2025, with a more permanent fix scheduled for the upcoming November Patch Tuesday.

In summary, users can now trust the "Update and shut down" option to actually shut down their computers, resolving a decades-old bug.

Summary:

Create custom AI chatbots that can be deployed across multiple platforms, like websites and messaging apps (Slack, Discord). These chatbots use a knowledge base to provide accurate information from various sources such as documents, websites, and Google Drive.

Key Features:

• Knowledge Base: Transform documents and data into a chatbot's knowledge base.
• Multi-Format Support: Import content from PDFs, videos, and more.
• Smart Retrieval: Quickly find relevant information with advanced technology.
• Source Citations: Highlight and link to the exact sources of information.
• Channel Deployment: Use one chatbot across different platforms.
• Interactive Conversations: Support multimedia content, code execution, and diagrams.
• Customization: Tailor the chatbot’s appearance and behavior to match your brand.
• Analytics: Track user interactions and engagement.

Integration Options:

• Connect with tools like Google Drive, Notion, and more.
• Use API access for custom integrations.

Open Source: The chatbot is free to use, modify, and self-host, ensuring full control over your data and privacy.

Start building your custom chatbot today with Syllabi, designed for developers and businesses.

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Created a user-friendly Backend as a Service (BaaS) using PostgreSQL and PostGIS. It offers instant APIs, real-time updates, and can be self-hosted with a Docker image. Feedback is appreciated.

We present the Collatz-Weyl Generators, a new type of pseudorandom number generator (PRNG) based on specific mathematical concepts called generalized Collatz mappings and Weyl sequences. These generators have excellent randomness qualities, as shown by their success in rigorous testing for randomness. Key features of the Collatz-Weyl Generators include strong mathematical support, the ability to operate quickly and efficiently, compact code and hardware size, the capacity to create multiple independent streams of numbers, and the potential for use in cryptographic applications.

The author reflects on their journey with Linux and open-source software, emphasizing the influence of UNIX principles. They discuss the importance of perseverance in overcoming personal challenges to grow in their knowledge and skills. The post shares various technical insights and tips related to Linux systems, including:

1. Filesystems: The /dev partition is dynamically created by the kernel at boot, containing essential files like /dev/ttyS for terminal consoles.
2. Initrd vs. Initramfs: Initrd is a compressed filesystem, while initramfs is a compressed cpio archive used during boot.
3. Shell Commands: Aliases are processed differently than commands, and understanding the logical working directory is important for navigation.
4. C Programming: There are distinctions between char and unsigned char arrays, and functions like getchar and putchar handle single characters.
5. System Signals: Terminal commands like Ctrl+C send signals to foreground processes, which can be blocked during certain operations.

The author encourages further exploration of these topics for better understanding and mastery of Linux.

Researchers at the HZDR facility in Dresden have developed a new thin film, just 70 nanometers thick, that can generate terahertz signals using powerful laser pulses. This technology shows potential for creating smaller and more efficient "wireless wired" chips, which could improve telecommunications by minimizing bulky data links.

The text discusses a recent outage at AWS due to a race condition in their DNS management system. AWS's post-mortem highlighted this issue, which occurs in complex systems when multiple processes run simultaneously, leading to unexpected behavior.

To understand this better, the author proposes using a model checker called Spin with the Promela language to simulate the problem. The specific incident involved components like the DNS Planner and DNS Enactors, which work independently in different availability zones.

The DNS Enactor applies new plans and cleans up older, outdated plans. The race condition happened when one Enactor applied a new plan while another one, running slightly behind, applied an older plan, leading to the deletion of necessary DNS entries.

The author plans to model this system in Promela with a DNS Planner and two DNS Enactors to explore how these processes interact and uncover the race condition through systematic testing.

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Serie is a TUI application designed to display commit graphs from Git in a visually appealing way, similar to the command git log --graph --all. It is not a complete Git client and is not intended to replace tools like tig, lazygit, or gitui. Its main goal is to make commit information easy to read.

While many users prefer command-line Git, they often use graphical interfaces to view commit logs. Some find the standard git log --graph output hard to read. Serie aims to simplify this by providing a clearer view.

However, there are some limitations:

• It does not support Sixel, only working with terminals that use iTerm and kitty image protocols.
• Terminal multiplexers and Windows are not supported.

For more information, you can visit the Serie GitHub page.

This project was supported by Patreon contributors. It began as a YouTube video series on web programming. Patrons who contributed at the "C programming" level received several benefits, including:

• Early access to the video series.
• Exclusive content from the programming guide with additional details.
• A weekly forum to ask questions about the week's topics.

After completing the video series, the guide was turned into a "teach yourself programming" book, which patrons could purchase at cost through a publishing partner.

A recent discovery in Android's Carrier Config manager reveals a setting that allows mobile operators to falsely report signal strength as one bar higher than it actually is. This feature, noted as KEY_INFLATE_SIGNAL_STRENGTH_BOOL, isn't documented but is accessible to operators. Both AT&T and Verizon reportedly use this flag. This practice raises concerns about trust, as operators often boast about their network coverage while employing tactics like this and misleading 5G indicators, despite advancements in mobile technology that could eliminate the need for such deception.

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The text discusses the use of git bisect, a Git command that helps identify the commit that introduced a bug in a code repository. The author reflects on the necessity of learning algorithms, specifically binary search, which is applied in this context.

The scenario involves a workplace using a monorepo with numerous commits daily. After tests began failing due to a configuration change in a file, it was challenging to manually find the problematic commit among many recent changes. A teammate used git bisect to efficiently locate the exact commit that caused the issue by selecting a known good and bad commit and performing a binary search.

The process, although time-consuming due to lengthy test runs, successfully identified the faulty commit. Reverting that commit restored the tests to a passing state.

A demo repository illustrates the concept, including example code for a simple addition function that was broken by an accidental string concatenation. The git bisect commands used in the demo show how it systematically checks commit history to find the source of the failure.

In summary, git bisect is a valuable tool for debugging in large codebases, allowing developers to quickly pinpoint the cause of issues.

Royal Mail is launching 3,500 solar-powered postboxes across the UK, marking a major redesign of the traditional red pillar boxes. The new postboxes feature solar panels that power a drawer for depositing small parcels, accommodating items as large as a shoebox. This change comes as Royal Mail faces tough competition from other delivery companies and struggles to meet delivery targets.

After a successful trial in Hertfordshire and Cambridgeshire, the new design will be introduced in cities like Edinburgh, Nottingham, Sheffield, and Manchester. The postboxes will have a solar panel tilted south for better sunlight exposure and include a barcode scanner for tracking parcels. Customers can use the Royal Mail app for proof of posting.

Royal Mail aims to expand its parcel services to keep up with the growing trend of online shopping. Despite these efforts, the company still faces challenges from competitors offering lower delivery prices. The rise of online shopping is changing the postal landscape, with some services, like Denmark's PostNord, even ending traditional letter deliveries.

Summary of Tongyi DeepResearch: A New Era of Open-Source AI Researchers

Tongyi DeepResearch is an advanced open-source AI model, comparable in performance to proprietary models like those from OpenAI. It excels in complex tasks, achieving impressive scores on various benchmarks, including reasoning and information-seeking tasks.

Key Features:

• Innovative Training Methodology: The training process uses fully synthetic data and involves continual pre-training and reinforcement learning. This approach allows the model to learn effectively from diverse data sources.
• Agentic Capabilities: The model can perform complex reasoning and planning, utilizing a structured training pipeline that integrates various training stages seamlessly.
• Rollout Modes: It supports multiple operational modes, including a native reasoning format (ReAct) and a more complex "Heavy Mode" for multi-step tasks, enhancing its problem-solving skills.
• Real-World Applications: Tongyi DeepResearch is already being used in practical applications, such as a navigation assistant and a legal research agent, demonstrating its utility in real-world scenarios.

Limitations:

• The model's context length is currently limited, which may hinder its effectiveness in very complex tasks.
• The scalability of the training process on larger models has yet to be proven.
• Future improvements are needed in the reinforcement learning framework to enhance efficiency.

Overall, Tongyi DeepResearch represents a significant advancement in open-source AI research, with ongoing developments aimed at overcoming its limitations and expanding its capabilities.

Summary: The Importance of Good URL Design

In a recent experience with PrismJS, the author realized how URLs can do more than just link to a page—they can store the state of a web application. This highlights the often-overlooked potential of URLs in state management.

Key Points:

1. URLs as State Containers: URLs can encode application state, making them shareable and bookmarkable. They allow users to return to the exact same state of an application.

2. Benefits of URLs:

   • Shareability: Users can share a link that reflects their current view or configuration.
   • Bookmarkability: Saving a URL captures a specific moment or state.
   • Browser History: Users can navigate back and forth seamlessly.
   • Deep Linking: URLs can direct users to specific application states.

3. Components of a URL:

   • Path Segments: Indicate hierarchical navigation (e.g., /users/123/posts).
   • Query Parameters: Used for filters and options (e.g., ?theme=dark&lang=en).
   • Anchor Fragments: Useful for navigating to specific parts of a page (e.g., #features).

4. Practical Examples: URLs can effectively represent configurations (like PrismJS), highlight code sections (like GitHub), or specify filter criteria (like e-commerce sites).

5. Best Practices:

   • Only include necessary state in URLs.
   • Avoid cluttering URLs with default values.
   • Use clear and consistent naming for URL parameters.
   • Be cautious with sensitive data and avoid complex state that could exceed URL length limits.

6. Implementation: JavaScript and frameworks like React make managing URL states straightforward with tools like URLSearchParams.

7. Closing Thought: Good URLs are not just technical tools; they enhance user experience and maintain context. They remind developers to leverage this powerful feature of the web for better state management.

In essence, effective URL design can significantly improve web applications by making them more user-friendly and resilient.

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Amazon Web Services (AWS) and OpenAI have formed a multi-year partnership worth $38 billion, allowing OpenAI to access AWS's advanced computing infrastructure for its AI projects. This collaboration enables OpenAI to utilize powerful Amazon EC2 UltraServers with extensive NVIDIA GPUs, which can be scaled up to tens of millions of CPUs to meet the growing demand for AI processing power.

The agreement will enhance OpenAI's computing capacity and efficiency, helping to improve services like ChatGPT. AWS brings extensive experience in managing large-scale AI infrastructure securely and reliably. The partnership is expected to be fully deployed by the end of 2026, with potential expansions into 2027 and beyond.

OpenAI's CEO, Sam Altman, emphasized the importance of this partnership in advancing AI technology for wider use. AWS CEO Matt Garman noted that their infrastructure is uniquely suited to support OpenAI's ambitious AI goals. The collaboration builds on previous efforts, including the availability of OpenAI's models on AWS's Amazon Bedrock platform, benefiting numerous organizations across various industries.

Summary: How to Build a Solar Powered Electric Oven

This guide outlines how to create a solar-powered electric oven that can cook efficiently using a small solar panel. The oven can retain heat, allowing cooking even after sunset.

Key Points:

1. Challenges of Electric Cooking: Traditional electric cooking devices consume high power, making them difficult to operate off-grid with solar energy. Storing energy in batteries increases costs and complexity.

2. Adapting to Solar Power: The oven described runs on a 100-watt solar panel and uses thermal insulation and thermal mass to retain heat, allowing it to cook food safely at lower temperatures (around 120°C/248°F).

3. Advantages of the Solar Electric Oven:

   • Can be used indoors since only the solar panel needs to be outside.
   • Offers better insulation compared to traditional solar cookers, making it more energy-efficient.
   • Functions well in cloudy weather, requires minimal attention, and can cook after sunset.

4. Materials Used: The oven is constructed using tiles, cork for insulation, mortar for thermal mass, and a self-made nichrome wire heating element.

5. Building Process:

   • Structure: Create a wooden box to house the oven.
   • Insulation: Apply cork insulation on all sides.
   • Heat Storage: Embed a heating element in a mortar base to store heat.
   • Final Assembly: Seal everything and add a chimney for moisture control.

6. Cooking Guidelines: The oven can cook various foods, but it is essential to monitor temperatures to ensure food safety. Cooking times are longer than regular ovens, averaging two to four hours.

7. Customization: The design can be modified for different cooking needs, such as adjusting the size or insulation thickness.

This solar electric oven is an innovative solution for sustainable cooking, particularly suitable for off-grid living.

Germany is developing the world's largest heat pump, a 162 MW project by MVV Energie and Strabag Umwelttechnik, located at the Grosskraftwerk Mannheim coal power plant. The €200 million project will use water from the Rhine River to generate heat up to 130°C and is set to start construction in mid-2026, aiming for commercial operations by winter 2028.

The system consists of two 82.5 MW modules and will use isobutane as a refrigerant. It will take advantage of the Rhine's summer temperatures of around 25°C and winter temperatures of about 5°C to operate efficiently. MVV Energie has previously built a smaller 20 MW heat pump at the same site in 2023.

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The author shares their experience of preparing for a Halloween event by creating costumes inspired by Belisha beacons, a type of British road safety feature. These beacons, named after a former Minister of Transport, are yellow spheres atop black-and-white striped poles designed to enhance pedestrian visibility.

The costumes involved building light-up beacons using LEDs controlled by simple circuits. Although the author faced challenges with materials and technical issues, they managed to create functional costumes using mostly repurposed items. Despite initial disappointment with the final look, the costumes turned out to be a hit at the event, providing good visibility and a unique twist on Halloween themes. The author reflects on the importance of road safety and the evolution of pedestrian crossings in the UK, highlighting how design and technology have improved pedestrian safety over the years.

In the end, the project was a fun and creative endeavor, even if it strayed from traditional Halloween themes.

Here's a simplified summary of the text:

How-tos & Examples

1. Delaying Specific Endpoints:

   • You can slow down an API by adding a delay to specific endpoints using middlewares.
   • Example command:

     mock serve -p 8000 --base example.com --middleware 'if [ "${MOCK_REQUEST_ENDPOINT}" = "some/endpoint" ]; then sleep 2; fi'
     

   • This makes all requests immediate except for "some/endpoint," which will be delayed by 2 seconds.

2. Using Multiple Languages:

   • You can run an API that responds in different programming languages like JavaScript, Python, and PHP.
   • Example command:

     mock serve -p 3000 --route js --exec 'node ...' --route python --exec 'python3 ...' --route php --exec 'php ...'
     

3. Creating a Stateful API:

   • You can keep track of how many requests your API has received using a temporary file.
   • Example command:

     mock serve -p 3000 --route '/hello' --exec '...'
     

   • Each request to "/hello" will increment and display the request count.

4. CRUD API for User Management:

   • This API allows adding users and fetching user details.
   • You need jq installed for JSON parsing.
   • Example commands:
     • To add a user:

       curl -X POST localhost:3000/user -H 'Content-Type: application/json' -d '{"name":"John Doe","email":"[email protected]"}'
       

     • To get user details:

       curl -v localhost:3000/user/1
       

     • To get all users:

       curl -v localhost:3000/users
       

   • The API stores user data in a temporary directory.

This summary captures the main points and simplifies the technical details for easier understanding.

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MTurk was developed by two small teams at AWS over a year and launched on November 2, 2005. Initially, it took a few days for users to discover it, but soon it became very popular. At that time, AWS had about 100 employees, Amazon had just reached 10,000 employees, and S3 was still in a testing phase while EC2 was just a concept. The text prompts readers to think about what they could create using MTurk and its dedicated workforce.

Scientists are working to uncover the smells of ancient artifacts to help us connect with the past. Museums typically focus on visual and tactile experiences but often ignore the sense of smell, which played a significant role in history. Barbara Huber, an archaeochemist, emphasizes the importance of smell linked to emotions and memory. Recent advances in chemical analysis allow researchers to study ancient scents, such as those from incense and mummification processes.

Huber co-edited a book, "Scents of Arabia," which explores the role of smell in ancient cultures, particularly along the incense trade routes. By analyzing remnants from incense burners and other artifacts, scientists can learn about ancient trade practices and cultural identities. For example, they discovered medicinal uses for incense, indicating that ancient people had knowledge of local healing practices.

Overall, understanding ancient smells not only enriches our knowledge of history but also enhances the experience of learning, making it more immersive and relatable.

Linux gamers on Steam have surpassed the 3% mark for the first time, according to the October 2025 Steam Hardware & Software Survey. This increase coincides with the end of support for Windows 10, leading more users to explore Linux.

Here's a quick breakdown of the operating system usage:

• Windows: 94.84% (down 0.75%)
• Linux: 3.05% (up 0.41%)
• macOS: 2.11% (up 0.34%)

While 3% may seem small, it represents millions of users. Previous estimates in 2022 suggested there were over 4 million active Linux users on Steam, a number likely higher now due to the popularity of the Steam Deck, which runs on SteamOS.

The distribution of Linux systems among users is varied, with SteamOS being the most popular, followed by other distributions like Arch Linux and Linux Mint. The growth in Linux users is expected to continue, especially with potential new hardware like a SteamOS-powered VR kit on the horizon.

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Summary of Born React Native Godot

Born React Native Godot is a framework that allows developers to integrate the Godot Engine into React Native applications. It was developed by Born and Migeran together. Here are the key features and steps to get started:

Key Features:

• Cross-Platform Support: Works on both Android and iOS.
• Stable Performance: Used by millions in Born's applications.
• Engine Control: Start, stop, restart, pause, and resume the Godot Engine as needed.
• Threading: Godot runs on a separate thread, ensuring it does not interfere with the main application.
• API Access: Full access to the Godot API using TypeScript/JavaScript.

Getting Started:

1. Set Up Development Environment: Use ASDF or similar tools to manage dependencies.
2. Export Godot App: Use provided scripts to export your Godot project for Android and iOS.
3. Run on Emulators: Commands available to run the example app on both iOS and Android emulators.
4. Install React Native Godot: Add it to your project using NPM.
5. Initialize Godot: Set up the Godot instance in your app and handle its lifecycle (start, stop, pause, resume).
6. Use Godot API: Access and manipulate Godot objects and properties through the provided API.

Advanced Features:

• Custom Builds: Instructions available for using custom builds of LibGodot.
• Debugging: Detailed steps for debugging both native and Godot code on iOS and Android.

Support & Licensing:

• Born is hiring React Native Engineers.
• Migeran offers professional support for React Native Godot.
• The framework is licensed under MIT.

This framework streamlines the integration of Godot games into mobile applications, making it easier for developers to create engaging experiences.

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The author shares their experience optimizing a custom priority queue in Rust, detailing unexpected performance issues encountered during benchmarking. They found that using a higher optimization level (opt-level=3) resulted in a significant performance drop (up to 123%) compared to a lower level (opt-level=2). The benchmarks were conducted on different CPUs, including a Haswell architecture.

The priority queue is designed to maintain a unique set of elements (pairs of id and distance) using a sorted vector, which required a complex comparison function. The author explains that the way comparisons are handled can drastically affect performance, particularly in binary search operations.

They also discuss the difficulties of benchmarking, noting that results can vary widely, and the complexity of modern CPUs affects performance outcomes. Through various profiling tools and assembly code analysis, they discovered that the use of conditional moves instead of jumps in the assembly code led to slower performance due to increased dependencies.

In summary, the author highlights the challenges of optimizing code, the importance of benchmarking, and the nuances of compiler optimizations, concluding that different techniques can yield vastly different performance results. They suggest that careful consideration of both the algorithm and the underlying architecture is essential for achieving optimal performance.

Summary: Automatically Translating C to Rust

Overview: The transition from older programming languages like C to newer ones such as Rust can improve the reliability of software systems. Automatic translation tools exist but often produce unsafe and unidiomatic Rust code.

Key Points:

1. Challenges of C to Rust Translation:

   • Automatic C-to-Rust translators, like C2Rust, generate code that retains unsafe C features and does not fully utilize Rust's safety mechanisms.
   • This can lead to poor code quality, making it hard for developers to understand and maintain.

2. Improving Translation:

   • The use of static analysis can help enhance the quality of translated code by identifying and replacing unsafe features with safer Rust alternatives.
   • For example, unsafe raw pointers can be replaced with Rust’s ownership model, which improves memory safety.

3. Research Progress:

   • Researchers are working on methods to refine the translated code. Progress has been made in addressing specific unsafe features like scalar pointers, locks, and unions, as well as unidiomatic patterns like output parameters.
   • Techniques involve analyzing the original C code to gather necessary information for safe translation.

4. Future Directions:

   • There’s potential for combining static analysis with large language models (LLMs) to automate and improve translation processes.
   • Ongoing research aims to tackle remaining unsafe features, improve idiomatic usage in the translated code, and ensure correctness through robust verification methods.

5. Conclusion:

   • While automatic translation from C to Rust shows promise for enhancing system reliability, it requires further development to ensure safe and idiomatic code. The field is gaining attention, with significant interest from both the research community and industry.

By addressing these challenges, the migration from C to Rust can lead to safer and more reliable software systems.

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A new study from UBC Okanagan has proven that the idea of the universe being a computer simulation is impossible. Dr. Mir Faizal and his team used advanced logic and physics to show that reality operates in ways that cannot be simulated by any computer. They published their findings in the Journal of Holography Applications in Physics.

The researchers explain that modern physics suggests that space and time are not fundamental but emerge from a deeper realm of pure information. They demonstrated that this information cannot fully describe reality through computation alone, using Gödel’s incompleteness theorem to support their argument.

They argue that some truths exist that cannot be reached through logical steps, meaning a complete understanding of reality requires a type of insight called "non-algorithmic understanding," which goes beyond computational methods. This conclusion indicates that because the fundamental nature of reality is based on non-algorithmic principles, the universe cannot be a simulation.

Overall, this research provides a definitive answer to the simulation hypothesis, which was previously considered untestable and more a philosophical question than a scientific one.

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AI models are known for their frequent use of em-dashes, a trend that has led even human writers to avoid them to prevent being mistaken for AI. Despite various theories, the exact reason for this overuse is unclear.

1. Common Explanations:

   • Some believe AI models use em-dashes because they are common in the training data. However, since people recognize AI for excessive use of em-dashes, this explanation is unconvincing.
   • Another theory is that em-dashes serve as a flexible punctuation mark, allowing models to either continue a thought or introduce a new one. This is also disputed as other punctuation marks provide similar flexibility.

2. Reinforcement Learning with Human Feedback (RLHF):

   • Some suggest that RLHF workers might have a preference for em-dashes, possibly due to their dialect. However, data shows that em-dash usage in African English is actually lower than in other English variants.

3. Training Data:

   • A promising explanation is that the training data for AI models has shifted to include more older print books, which typically contain more em-dashes. This change occurred around 2022 when AI labs began emphasizing high-quality training data.

The conclusion is that the prevalence of em-dashes in AI writing likely stems from the influence of late-1800s and early-1900s literature, which had a higher frequency of this punctuation. Despite speculation, there is still no consensus on the precise cause of this phenomenon.

The text discusses the evolution of how we interact with Large Language Models (LLMs) as they transition from simple chatbots to important parts of complex decision-making systems. Here are the key points:

1. Shift from Prompt Engineering to Context Engineering: The initial method of "prompt engineering," which involved crafting specific prompts to guide LLMs, is being replaced by "context engineering." This involves carefully considering all tokens fed into the LLM to improve its performance and decision-making abilities.

2. Understanding Context Windows: LLMs process language as a series of tokens within a fixed "context window." This window limits the number of tokens the model can consider when generating responses, often leading to challenges in producing accurate or relevant outputs.

3. Chat Framing: To enhance LLMs' usability, developers began training models to recognize conversational structures, which allowed for improved interaction and more relevant responses.

4. In-Context Learning: As LLMs became more sophisticated, they began to "learn" from complex token sequences provided in prompts, leading to better outputs based on provided examples rather than just their training data.

5. Engineering Context for Better Outputs: Instead of simply crafting prompts, users must now construct comprehensive contexts that include relevant information, such as updated statistics or external data, to achieve accurate and timely responses.

6. Design Patterns in Context Engineering: Similar to software engineering, context engineering can utilize design patterns—like RAG (retrieval-augmented generation) and tool calling—to improve the efficiency and effectiveness of LLMs.

7. Multi-Agent Systems: Future applications will likely involve multiple specialized agents working together, each handling different aspects of a task, with clear communication and context-sharing between them.

8. Summary of Best Practices: When using LLMs, it's essential to treat them as analytical tools rather than oracles, manage the entire context window thoughtfully, utilize reusable design patterns, and clearly define interactions between agents.

Overall, the text emphasizes the importance of a structured and systematic approach to using LLMs, highlighting the need for careful context management to achieve optimal results.

The text discusses the evolving value of indoor plumbing versus the Internet over the years, referencing a famous question posed by Robert Gordon in 2000. Initially, in 2000, the author favored indoor plumbing, noting that the Internet had not significantly impacted his daily life. However, by 2005, with greater Internet access through personal computers and broadband, the author began to see the Internet's importance, particularly for online shopping and educational requirements.

By 2010, the Internet's value became undeniable, even in poorer regions like rural China, where people prioritized cell phones and online connectivity over basic amenities like running water. The shift in priorities highlighted that many people globally chose connection over physical comforts.

By 2015, smartphones and Internet access became so prevalent that even those without homes often prioritized them over traditional needs like plumbing. The Internet was now essential for everyday life, influencing work, social interactions, and access to information.

By the end of 2020, the COVID-19 pandemic further underscored the Internet's critical role in daily life, making the question of plumbing versus the Internet obsolete. By 2025, the discussion that once existed was largely forgotten, as society had fully embraced the Internet as a necessity.

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The article discusses the use of primitive data types in Java and the limitations of Java's collections framework. Here are the key points:

1. Java Primitives: Java has eight primitive data types (boolean, byte, char, short, int, float, long, double) that have been around for over 30 years. While these types are widely used, Java does not have native support for primitive collections, meaning you have to wrap them in object equivalents (e.g., Integer for int).

2. Eclipse Collections: To address the lack of primitive collection support in Java, the author and their team created Eclipse Collections, which allows developers to use collections that directly support primitives. This includes various types like List, Set, and Map for all eight primitives.

3. Performance Benefits: Using primitive collections can lead to better performance and memory savings compared to boxed collections. The article suggests that if you need these benefits, Eclipse Collections can provide useful solutions.

4. Functional Interfaces: Eclipse Collections supports functional programming with primitive types, offering various functional interfaces.

5. Future of Java: The article mentions Project Valhalla, which aims to improve Java's handling of generics and primitives, but emphasizes that developers should not wait for future changes and instead use available tools like Eclipse Collections now.

6. Resources: The article provides links to additional blogs, code samples, and a book related to Eclipse Collections to help developers learn more about primitive collection support.

In summary, the author encourages Java developers to utilize Eclipse Collections for better handling of primitive types, rather than waiting for future updates to the Java language.

In July 2025, the U.S. Army signed a major contract worth ten billion dollars with Palantir Technologies. This deal combined seventy-five smaller agreements into one, which was intended to improve efficiency. However, it also meant giving important military responsibilities to a private company. The founder of Palantir, Peter Thiel, has expressed controversial views, claiming that "freedom and democracy are no longer compatible."

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FlightAware, a top flight tracking company, is launching a new flight-tracking map in 2024, which I helped design. The new map is fully vector-based and built in-house, improving airport details and features like terminals and gates. It uses a combination of Natural Earth and OpenStreetMap (OSM) data, with a focus on providing clear overlays for flight tracking and weather.

My role involved advising on data usage at different zoom levels and creating the map's visual styles. While the overall design is similar to the old map, it includes updated colors and focuses on helping users orient themselves while tracking flights and understanding airport situations.

The map omits detailed city information to keep data sizes small and is designed for easy use, including for in-flight displays. You can explore the new map on FlightAware's beta site, featuring enhanced airport views and flight-tracking overlays.

On November 1, 2025, a developer shared their experience using Claude Code, an AI tool, to debug a low-level cryptography implementation called ML-DSA in Go. After struggling with issues where valid signatures were being rejected, the developer decided to let Claude Code take a look. Surprisingly, the AI quickly identified and fixed a complex bug that the developer had overlooked.

In a follow-up experiment, the developer tested Claude again with another set of bugs in the signing process. The AI successfully found and fixed one bug related to incorrect constants, though it struggled with another simpler bug but still provided a useful lead.

The developer noted that using AI tools like Claude Code can save significant debugging time, even if the suggested fixes might need refining afterward. They expressed a desire for better integration of AI tools in debugging processes.

Overall, the experience highlighted the potential of AI in software development, especially in addressing specific coding issues efficiently.

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Tesla has reportedly signed a significant $2.1 billion battery deal with Samsung SDI. However, this deal is not intended for Tesla's cars.

arXiv's computer science (CS) category has updated its rules regarding review articles and position papers. Now, these types of submissions must be accepted by a journal or conference after passing peer review before they can be submitted to arXiv. Authors need to provide proof of this peer review, or their submissions will likely be rejected.

This change is necessary because arXiv has seen a large increase in submissions, especially due to the ease of creating review articles with AI tools. Many of the submissions received are low-quality, lacking substantial discussion.

Previously, arXiv accepted a small number of high-quality review articles and position papers at the discretion of moderators, but the current volume is overwhelming. The goal is to help readers find valuable content while allowing moderators to focus on officially accepted submissions.

To submit a review article or position paper, authors must have it accepted by a peer-reviewed venue first and provide the necessary documentation. If rejected for not having peer review, authors can appeal if their paper has since been accepted elsewhere.

Other arXiv categories may also adjust their practices if they experience a similar increase in low-quality submissions.

OpenBSD 7.8 Highlights

OpenBSD 7.8 has introduced several key updates and improvements, focusing on network security and functionality. Here are the main points:

1. Enhanced Network Performance: OpenBSD improves handling of network traffic by using multiple CPU cores. It now supports up to 8 threads for processing TCP traffic, which increases efficiency but requires multiple connections to fully benefit from this feature.

2. New Graphics Drivers: The update includes support for Qualcomm Snapdragon graphics hardware, enhancing the graphics capabilities of the system.

3. C++ Updates: The C++ libraries have been updated to include new features from C++20, C++23, and C++26, enhancing programming capabilities.

4. Improved Profiling System: A new profiling subsystem allows developers to analyze performance without compromising security, overcoming limitations of the previous system.

5. Network Discovery: A new daemon, lldpd, enables automatic network topology discovery, simplifying the identification of network devices.

6. Raspberry Pi 5 Support: Preliminary support for the Raspberry Pi 5 has been added, allowing it to boot OpenBSD, although some features are still being developed.

7. Emoji Support: The update includes support for emoji rendering in the terminal, thanks to libpng integration.

8. SSH Enhancements: OpenSSH 10.0 features dynamic quality of service adjustments and mandates post-quantum key exchange algorithms for better security.

9. Ongoing Development: Key network daemons are receiving regular updates, focusing on security and functionality, while some others are seeing less active development.

Overall, OpenBSD 7.8 emphasizes enhanced security, performance, and usability for developers and users alike.

Summary: Anki-LLM CLI Toolkit for Flashcards

Anki-LLM is a command-line interface (CLI) tool designed to help you bulk-process and generate Anki flashcards using language models (LLMs). It offers various workflows to streamline tasks like verifying translations, adding vocabulary fields, and generating new cards.

Key Features:

• Batch Processing: You can export your Anki decks to files, process them with AI models, and import the results back into Anki.
• Card Generation: Easily create multiple contextual flashcards for a term with just one command.
• Custom Prompts: Define how the LLM processes your cards using flexible template files.
• Concurrent Processing: Speed up large jobs by making multiple API requests at once.
• Automatic Resume: If processing is interrupted, you can pick up where you left off.

Installation Requirements:

• Node.js (version 18 or higher)
• Anki Desktop must be running along with the AnkiConnect add-on.

Supported Models: The toolkit supports various LLMs from OpenAI and Google Gemini, each with different pricing based on token usage.

Common Commands:

• export <deck>: Exports notes from an Anki deck to a file.
• import <input>: Imports data from a file into an Anki deck.
• process-file <input>: Batch-process notes from a file using an AI.
• process-deck <deck>: Process notes directly from an Anki deck in-place.
• generate <term>: Generate new cards for a specific term.

Use Cases:

1. Verifying Translations: Export your deck, process translations with AI, and import improved translations back into Anki.
2. Adding Key Vocabulary: Analyze sentences to identify important vocabulary and create structured entries in your flashcards.
3. Generating New Cards: Create multiple examples for a vocabulary term and review them before adding to your deck.

Development Setup: To run the tool locally, you can use TypeScript directly, and for testing, link the command globally.

In summary, Anki-LLM is a powerful tool to automate and enhance your Anki flashcard creation and management, making it easier to study efficiently.

We propose a new way to understand calculus that focuses on approximations instead of limits. In this approach, continuity means how well we can approximate a point, and differentiability means how well we can approximate using a straight line. We define errors in these approximations and have rules for combining them, which help us arrive at familiar results in differential calculus. We believe this method is more intuitive for students while still being rigorous. We show its usefulness by deriving key differential rules for trigonometric, hyperbolic, and exponential functions, as well as important concepts like L'Hôpital's Rule, Taylor polynomials, and the Fundamental Theorem of Calculus, all based on approximations.