The article emphasizes that simply adding Artificial Intelligence (AI) to a poorly designed business process won't improve it; instead, it will just make bad processes faster. Many businesses mistakenly think of AI as a quick fix for inefficiencies, but the reality is that AI is just a tool that accelerates existing workflows.

Key points include:

1. AI is Not a Magic Solution: AI cannot solve fundamental issues in business processes. It does not inherently make organizations smarter; it only speeds up processes.

2. Understanding Unstructured Data: AI excels at handling unstructured data (like emails or vague messages), which often reflects unstructured workflows. These informal processes need to be clearly defined before AI can be effectively applied.

3. Need for Business Process Optimization (BPO): Organizations must first improve and structure their workflows before leveraging AI. This involves identifying triggers, transformations, and outputs within the processes.

4. Difference Between Speed and Intelligence: AI increases the speed of tasks but does not add intelligence or understanding. Human oversight is still required to interpret the results effectively.

5. Focus on Process Improvement: Businesses should prioritize refining their workflows, especially those involving unstructured data, before integrating AI solutions. Understanding and addressing bottlenecks in processes will lead to better outcomes when using AI.

In summary, the article advises against chasing AI hype and encourages companies to first focus on optimizing their processes for successful AI implementation.

CloudFlare has developed its own DNS server called RRDNS to quickly handle DNS requests and defend against attacks. DNS is an old and complex system, and while it manages millions of records, only a small number are LOC (location) records, which specify physical locations.

An example of a LOC record can be found on the website geekatlas.com. Although there are few LOC records, one customer raised a question about theirs, leading to a discovery that RRDNS had the capability to handle LOC requests but lacked the code to convert the format for internal use.

The LOC record format includes required latitude, longitude, and altitude, as well as optional values for size and precision. The data is stored in a specific binary format as described in RFC 1876. The team implemented a parser to fix the issue, and now LOC records are properly served by RRDNS.

CloudFlare's services enhance internet performance and security, supporting various applications and protecting against cyber threats. For more information about their offerings, you can visit their website.

No summary available.

Summary of Hachi: An Image Search Engine

The author is developing a self-hosted search engine called Hachi, aimed at helping users search through their personal data, particularly images. The project recognizes that people generate and store increasing amounts of data across various devices and cloud services. Hachi seeks to simplify this search process by allowing users to authenticate and access their data, regardless of where it's stored.

Currently, Hachi supports image searching, with plans to expand to videos, text, and audio in the future. The author emphasizes the importance of addressing the limitations of existing search engines, which often provide a rigid, unidirectional flow of information without accommodating users' partial knowledge or mis-spellings. Hachi aims to present multiple attributes of resources directly to users, enabling them to refine their searches iteratively.

Key themes of the project include:

1. User-Centric Design: Hachi intends to allow users to provide feedback and refine their queries, improving the search experience based on individual needs.
2. Minimalism: The project aims to have few external dependencies, making it easier for developers to modify and extend the code.
3. Experimentation: The author seeks to explore the fusion of traditional data attributes with machine learning (ML) to enhance search capabilities.
4. Hackability: Hachi is designed for easy modification, allowing developers to customize it according to their needs.

The backend is primarily written in Python, with performance-critical components in Nim and C. The author has developed a meta-indexing engine for storing metadata and a vector index for semantic search, aiming for fast retrieval without duplicating original data.

The project also incorporates face recognition capabilities, allowing users to search for images based on facial attributes. Despite challenges in clustering and recognizing faces accurately, the author reports promising results with a low latency of around 25 ms for face recognition tasks.

The frontend is built using HTML, JavaScript, and Tailwind CSS, focusing on performance and user experience. A Windows app version uses a webview to render the frontend while accessing native APIs.

Overall, the author expresses a desire to continue developing Hachi, exploring features that allow for remote data indexing and ensuring the project remains user-friendly and efficient. The author acknowledges the challenges of funding and sustaining the project while maintaining its core functionalities.

No summary available.

No summary available.

Summary: Build Your Own Router

Introduction: This article discusses how to create a simple Single Page Application (SPA) router using vanilla JavaScript and new browser APIs called URLPattern.

Key Points:

1. What is URLPattern?

   • URLPattern is a tool that helps match and test URLs against specific patterns, making it easier to determine which component to render based on the current URL.

2. Creating a Router:

   • A router configuration can be set up using an array of objects that link URL paths to specific web components.
   • Example paths include /home, /posts, and /about.

3. Rendering Components:

   • The router tests the current URL against the configured paths and renders the appropriate component.
   • A custom web component, MyRouter, manages this logic.

4. Handling Navigation:

   • When a link is clicked, the default navigation behavior is prevented, and the router simulates the navigation by updating the URL and rendering the correct component.
   • The popstate event helps manage browser navigation (back and forward buttons).

5. Server Configuration:

   • The server should be set up to serve a single HTML file for all routes, which allows the SPA to take over routing on the client side.

6. Considerations:

   • Be cautious about exposing your router's configuration to avoid security risks like XSS (Cross-Site Scripting).
   • It's important to keep the router's component list static and private to prevent unauthorized rendering of components.

7. Future Enhancements:

   • Future improvements could include dynamic segments in routes, handling query parameters, and implementing nested routers.

Conclusion: While building your own router can be educational and beneficial for understanding web technologies, it involves handling several complexities that established frameworks have already solved.

Summary of WinApps for Running Windows Applications on GNU/Linux

WinApps allows users to run Windows applications like Microsoft 365 and Adobe Creative Cloud on GNU/Linux systems (KDE Plasma, GNOME, XFCE) as if they were native apps.

How It Works:

• It uses a Windows virtual machine (VM) through Docker, Podman, or libvirt.
• It identifies installed Windows applications and creates shortcuts on the Linux desktop.
• FreeRDP is used to display Windows apps alongside Linux apps.

Key Features:

• Access your Linux home directory from Windows.
• Right-click files in Nautilus to open them in the appropriate Windows app.
• A taskbar widget for easy management of Windows applications.
• Automatically opens Microsoft Office links in the Windows subsystem.

Supported Applications: WinApps supports all Windows applications, excluding kernel-level anti-cheat systems. It scans for applications tested by the community and other .exe files in the Windows Registry.

Installation Steps:

1. Set Up a Windows VM: Use Docker, Podman, or libvirt.
2. Install Required Packages: Depending on your Linux distribution, install necessary packages.
3. Create a Configuration File: A config file is needed to specify Windows credentials and settings.
4. Test FreeRDP Connection: Verify that you can connect to the Windows VM.
5. Run WinApps Installer: Install WinApps to start using it.

Additional Options:

• Users can add their own applications or modify existing ones.
• A WinApps Launcher is available for easier access to applications and VM controls.
• Instructions for installation using Nix are also provided.

Overall, WinApps provides a seamless way to integrate Windows applications into a GNU/Linux environment, enhancing productivity for users who need access to software that is otherwise unavailable on Linux.

No summary available.

The author has created a tool called Magiclip.io to simplify video editing, which often requires many slow and repetitive tools. Magiclip.io combines several editing tasks into one platform, allowing users to quickly handle functions like automatic subtitles, silence removal, AI voice-over, audio enhancement, image upscaling, clip extraction, thumbnail generation, and format conversion for TikTok/Reels. The goal is to make video editing faster and easier by reducing complex workflows to a simple upload-download process. The author is seeking feedback on additional automation tasks, user experience improvements, and potential API features.

The text is about careers but does not provide any specific information or details. It suggests that there might be more to read on the topic.

No summary available.

I created a tool that shows the information your browser shares automatically when you visit websites. You can find the project on GitHub here and try the demo here. Importantly, no data is sent anywhere; everything runs directly in your browser.

Summary of "The CRDT Dictionary: A Field Guide to Conflict-Free Replicated Data Types"

This guide explores Conflict-Free Replicated Data Types (CRDTs), which are data structures that allow for concurrent edits in distributed systems without the need for coordination, ensuring data consistency despite network failures.

Background:

• CRDTs gained attention around 2014 with the distributed database Riak, known for handling concurrent writes.
• The author initially explored CRDTs while working on a failed beer startup, Brewtown, and later revisited the topic in 2025 to understand their current state and applications.

Key Concepts:

1. CRDT Basics:

   • CRDTs allow data to merge automatically, maintaining consistency without needing to agree on changes.
   • Key properties of CRDTs include being commutative, associative, and idempotent.

2. Types of CRDTs:

   • State-based CRDTs (CvRDTs): Send the entire state for merging.
   • Operation-based CRDTs (CmRDTs): Send operations to be applied locally.

3. Common CRDT Types:

   • G-Counter: A counter that only increments.
   • PN-Counter: Supports both increments and decrements.
   • G-Set: A set that only allows additions.
   • 2P-Set: Allows additions and removals but not re-additions.
   • LWW-Element-Set: Uses timestamps to manage adds/removals.
   • OR-Set: Supports adding and removing elements while avoiding data loss from concurrent operations.

4. Advanced CRDTs:

   • RGA (Replicated Growable Array): Handles sequences with unique IDs to manage insertions.
   • MV-Register: Preserves all concurrent values instead of losing them.
   • OR-Map: A map structure that combines OR-Sets and values.
   • Causal CRDTs: Incorporate causal tracking to manage consistency more effectively.

5. Challenges and Considerations:

   • Garbage Collection: As CRDTs accumulate metadata, managing this growth is crucial. Strategies include time-based expiry, coordinated garbage collection, and using version vectors for causal tracking.
   • Performance: Different CRDTs have varying characteristics in terms of space and operation efficiency. The choice depends on specific application needs.

6. Practical Applications:

   • CRDTs are useful in scenarios like collaborative editing, shopping carts, and distributed databases. The right CRDT should be selected based on required operations, tolerance for data loss, and metadata overhead.

In conclusion, while CRDTs provide powerful solutions for distributed data management, they require careful consideration of trade-offs related to consistency, performance, and complexity. The guide serves as a helpful resource for understanding CRDTs and their applications in real-world scenarios.

James A. Garfield, the 20th president of the United States, created a unique proof of the Pythagorean theorem, which he published in 1876 while serving as a congressman. This proof is notable because Garfield is the only U.S. president known to have made an original contribution to mathematics. His proof is considered clever and is included in a collection of 370 proofs of the theorem.

In his proof, Garfield illustrated a right-angled triangle and showed that the areas of certain shapes related to the triangle can be calculated in two different ways, leading to the conclusion that ( a^2 + b^2 = c^2 ), as stated by the Pythagorean theorem.

Summary:

A recent controversy arose at an AI conference after it was discovered that 21% of peer reviews for submissions were entirely written by artificial intelligence (AI). Many academics expressed concerns about the quality of these reviews, noting issues like incorrect citations and vague feedback.

Graham Neubig, an AI researcher, found that some reviews seemed to be produced by AI and sought help to confirm this. A tool from Pangram Labs analyzed nearly 20,000 submissions and found that not only 21% of reviews were fully AI-generated, but over half showed signs of AI use. The analysis also indicated that 1% of the submitted manuscripts were completely AI-generated.

Conference organizers plan to implement automated tools to check for AI violations in submissions and reviews. This situation has raised alarms among researchers, with some finding that AI-generated reviews negatively impacted their work, leading to frustration and concerns about trust in the peer review process.

The text discusses a concept from Gian-Carlo Rota's work, where he argues that every mathematician relies on a small set of "tricks" or techniques that they repeatedly use. The idea is that even renowned mathematicians, like Hilbert, utilize a limited number of strategies in their work.

The conversation includes contributions from various users who interpret Rota's statement in different ways. Some emphasize that each mathematician has unique tricks they master, while others suggest that these tricks are not easily shared or understood by others. Examples of common mathematical techniques mentioned include changing the order of summation in integrals and using specific inequalities.

Overall, the main point is that successful mathematicians have a few key strategies they rely on, and these strategies can vary significantly from one mathematician to another.

Imgur has blocked users in the UK, which has made many links to their images broken. The author, who doesn't actively use Imgur but finds its links still present on the internet, faced frustration when trying to view Minecraft shaders that rely on Imgur-hosted images.

Instead of using a VPN on each device, the author decided to create a network-level solution to bypass the block. They set up a system using their homelab, which includes tools like Traefik for routing and Pi-hole for DNS filtering. The solution involved intercepting requests to Imgur at the DNS level, routing them through a VPN-connected container (Gluetun), and returning the images without requiring individual device configurations.

The setup involves using Nginx to handle proxying within the VPN container and configuring Traefik to route traffic appropriately. The author also integrated the system with NixOS, ensuring that VPN credentials are securely managed.

The result is that any device connected to the network can access Imgur images seamlessly, without speed issues or needing separate VPN clients. While the solution may seem excessive for just accessing Imgur, the author finds it effective and low-maintenance.

Summary: How Stealth Addresses Work in Monero

Alice owns a restaurant and wants to keep her customers' visits private while accepting payments in Monero. She displays two QR codes: one for her public view key and another for her public spend key.

When Bob, a customer, orders food, he uses software to create a random number and generates a unique "stealth address" for his payment. This stealth address is a one-time use address that cannot be traced back to Alice or Bob.

Both Alice and Bob know a shared secret that allows them to confirm the transaction. Alice's software checks the blockchain for payments made to the stealth address, ensuring only she can access the funds because she holds the necessary private keys.

Even though Bob's payment is recorded on the Monero blockchain, the use of ring signatures makes it difficult to identify him as the sender. A third party would only know that Bob is one of several potential senders.

The system relies on complex math (elliptic curve cryptography) to ensure security and privacy, making it nearly impossible to reverse-engineer the keys involved.

No summary available.

Summary of "Confessions of a Software Developer: No More Self-Censorship" by Kerrick Long

Kerrick Long discusses his fears and knowledge gaps as a software developer, revealing that he has avoided publishing his thoughts due to anxiety. He acknowledges that many developers, including himself, often lack fundamental skills.

1. Knowledge Gaps: Long highlights his ignorance of key concepts like polymorphism, which he only recently learned despite writing object-oriented code for years. He admits to forgetting SQL skills due to a lack of practice and recognizes the importance of automated testing, confessing that most of his code lacks tests.

2. Personal Learning Journey: He reflects on his struggles with learning new technologies like C# and .NET, which he initially pursued for work but later abandoned. He also expresses a desire to write more in Ruby, a language he loves but hasn’t used professionally in years.

3. Cyberbullying Experience: Long shares a painful experience of being bullied online after submitting a pull request using AI-generated code, leading to fears about his public presence and employability.

4. Workplace Insights: He argues against creating custom processes in software development, suggesting that established methodologies are more effective. He also critiques remote work, noting that it can hinder collaboration and communication.

5. Moving Forward: With his fears out in the open, Long feels liberated to publish more about his learning journey and encourages others to embrace their knowledge gaps.

Overall, Long's post is a candid reflection on personal growth, the challenges of the tech industry, and the importance of admitting one's limitations in order to learn and improve.

No summary available.

Skald was designed to be a self-hosted solution that maintains user privacy by not sharing data with third parties. It aims to help organizations access advanced AI models without compromising their commitment to data security.

The core components of a Retrieval-Augmented Generation (RAG) setup include:

• A vector database
• A vector embeddings model
• A large language model (LLM)
• Additional components like a reranker and document parser may also be used.

To support local deployments, Skald provides open-source alternatives for these components, allowing users to avoid third-party services. They have chosen Postgres with pgvector for the vector database, Sentence Transformers for embeddings, and users can select their own LLM and reranker.

Skald's performance has been tested in various configurations, yielding promising results. Initial tests with proprietary models showed high accuracy, while local configurations with open-source models performed well but had limitations, especially in handling non-English queries and complex information aggregation.

The overall goal is to refine this setup to improve performance and usability for various applications, particularly for organizations needing AI tools in secure environments. Skald encourages collaboration and feedback from users interested in this technology.

The author wanted to find Anthony Bourdain's content from the old li.st service but couldn't find it. After searching on archive.org and getting help from someone online, they decided to create a site to share that content.

Summary of the Improved Signal App - Molly

Molly is a modified version of the Signal app for Android that offers several enhanced features:

• Fully Open Source: Unlike Signal, Molly has no proprietary components.
• Encrypted Database: It protects your data with passphrase encryption.
• Multi-Device Support: You can link multiple devices to one account.
• Material You Theme: It adapts to your device's color scheme.
• UnifiedPush: It uses an ungoogled notification system.
• Automatic Locking: The app locks itself after a certain period of inactivity.
• RAM Shredding: This feature securely deletes sensitive data.
• Tor Support: It can connect through Tor for added privacy.
• Future Updates: More features will be added over time.

You can download Molly from GitHub and consider making a donation to support its development.

No summary available.

No summary available.

The text discusses a serious issue regarding the security of Monero, a privacy-focused cryptocurrency, due to ongoing deanonymization efforts by a company called Chainalysis. Here are the key points summarized:

1. Chainalysis Operations: Chainalysis is running many compromised Monero nodes worldwide, which can deanonymize users connecting to these nodes. If you use a remote Monero node that isn't your own, you risk exposing your identity.

2. Anonymity Risks: Using these compromised nodes can make your transactions transparent, as they may provide poisoned decoys that undermine Monero's privacy features. This can lead to your real identity being linked to your transactions.

3. Metadata Collection: Chainalysis collects metadata, such as IP addresses and transaction details, which can be used in combination with data from Internet Service Providers (ISPs) to deanonymize users—even if they use Tor or VPNs.

4. Countermeasures:

   • Run Your Own Node: To enhance privacy, users should avoid remote nodes and run their own Monero node.
   • Use Dandelion++: This feature helps obscure the origin of transactions when using your own node.
   • Obfuscate Metadata: Use Tor carefully, mix Monero properly, and prefer decentralized exchanges (DEXes) over centralized exchanges (CEXes) to minimize data collection risks.
   • Avoid Real Identity Links: Do not connect to Tor or any remote node from an IP that is linked to your real-world identity.

5. Example Scenario: The text illustrates a hypothetical user, Joe, who unknowingly uses a poisoned remote node and exposes his transactions. This scenario highlights the risks of using remote nodes and the importance of privacy measures.

In summary, to protect yourself while using Monero, it is essential to run your own node, avoid using remote nodes, and be cautious with any connection that might reveal your real identity.

The article discusses the role of language in human cognition, arguing that language primarily serves as a tool for communication rather than a means for thought. Researchers Evelina Fedorenko, Steven T. Piantadosi, and Edward A. F. Gibson highlight that language has evolved to facilitate sharing knowledge and feelings among people, rather than being essential for thinking itself.

The authors present evidence from neuroscience that shows a distinct separation between language and thought processes. They explain that while language has significantly influenced human culture, it is not necessary for complex thought, including abstract reasoning. Instead, language likely developed alongside our cognitive abilities, enhancing how we share cultural knowledge.

The article also emphasizes the advancements in understanding the brain's language network, which supports both the comprehension and production of language. This network is capable of processing various linguistic inputs, demonstrating that language is optimized for effective communication.

Overall, the authors conclude that while language is integral to cultural transmission and social interaction, it is not required for all forms of thought. They call for further research to explore the relationship between language and cognition, particularly in understanding how thought and language interact.

No summary available.

The text discusses triangles in two types of non-Euclidean geometry: spherical and hyperbolic.

1. Spherical Geometry:

   • In spherical geometry, the interior angles of a triangle add up to more than π (approximately 3.14).
   • The area of a spherical triangle can be calculated by how much the angle sum exceeds π.
   • For example, a triangle can have three right angles if its vertices are positioned at specific points on a sphere.

2. Hyperbolic Geometry:

   • In hyperbolic geometry, the sum of the interior angles is always less than π.
   • The area of a hyperbolic triangle is determined by the difference between π and the angle sum, known as the triangle defect.
   • The smallest possible angle sum is 0, which leads to a maximum triangle area of π.
   • An example of an "improper triangle" is given, where the angles sum to 0, and it has an area of π. This triangle's sides are represented by semicircles that meet at ideal points.

Key Points:

• Spherical triangles have angle sums greater than π; hyperbolic triangles have angle sums less than π.
• Areas in both geometries relate to the angle sums in different ways.
• The hyperbolic triangle example demonstrates unique properties, like infinite perimeter and finite area.

Summary of ABC Programming Language (CWI)

• History: ABC was the main predecessor of Python, developed between 1983 and 1986. The source code can be found at cwi.nl and on Luciano Ramalho's GitHub. The latest updates to the files are from 1991, with some from 1996 and 2021.

• Building: The README file contains instructions for building the software. Currently, the code is designed for 32-bit systems, but there are plans to adapt it for 64-bit systems as well.

• License: ABC does not have an official license, but it is copyrighted by Stichting Mathematisch Centrum from 1988 to 2011. There are intentions to negotiate a license, possibly with MIT.

• Authors: The main contributors include Eddy Boeve, Frank van Dijk, Leo Geurts, Timo Krijnen, Lambert Meertens, Steven Pemberton, and Guido van Rossum.

• References: Key publications include "The ABC Programmer's Handbook" (1990) and an article by Steven Pemberton in IEEE Software (1987). More information can be found on Steven Pemberton's home page and Lambert Meertens' article on the origins of Python.

The article argues against the idea of building datacenters in space, stating it is impractical and inefficient. The author, a former NASA engineer, explains several key reasons why this concept will not work:

1. Power Supply: Space does not provide abundant power. Solar arrays, like those on the International Space Station (ISS), can only support a limited number of GPUs. Nuclear options, such as radioisotope thermal generators, provide insufficient power for modern datacenter needs.

2. Cooling Challenges: Cooling electronics in space is much more complicated than on Earth. Without air for convection, heat management relies on conduction and radiation, making it difficult to keep hardware cool.

3. Radiation Issues: Space electronics face significant radiation exposure, leading to potential damage and performance degradation over time. Current high-performance chips are not designed to withstand this environment.

4. Communication Limitations: Data transfer rates in space are much slower than on Earth, creating a bottleneck for operations.

Overall, the author concludes that while it might be technically possible to create space datacenters, the challenges make it an unwise investment compared to Earth-based solutions.

No summary available.

No summary available.

The author finds traditional presentations boring but enjoys interactive, "choose your own adventure" style books. They've created a tool called "adventure-voter" that combines these two ideas, allowing audiences to choose how a presentation unfolds.

Here's how it works:

1. The presenter starts a server and shares links for the audience to vote and for themselves to present.
2. Audience choices affect the direction of the presentation, making it more engaging.
3. Presentations can be made using simple Markdown, and the tool includes a user-friendly frontend for displaying content.

The author believes this approach will make presentations more enjoyable and memorable. They plan to use this tool for their upcoming presentation at KubeCon in Atlanta, aiming to keep the audience interested. Overall, the goal is to create an engaging experience that stands out from typical presentations.

The pigeon-hole principle, known as "das Schubfachprinzip" in German, is often seen as a remarkable concept in mathematics, especially in combinatorics. It states that if you have more items than containers, at least one container must hold more than one item. However, the traditional explanations of this principle can be confusing and overly specific.

The author argues that a simpler and more general formulation is more effective. Instead of focusing on objects and compartments, a clearer way to express the idea is through averages and maxima, stating that the average of a set of numbers is less than or equal to the maximum. This perspective avoids the clutter of metaphors and leads to a better understanding of the principle's application.

An example illustrating this is the Problem of the German Soccer Lotto, which shows that using the generalized pigeon-hole principle is necessary to solve certain problems effectively. It highlights how specific numbers in the problem create a situation where the principle can be applied easily, but changing those numbers can complicate the solution. Overall, the author encourages looking at the pigeon-hole principle from a more arithmetic and less metaphorical viewpoint to appreciate its true power.

No summary available.

Django 6.0 has introduced a new feature called the background tasks framework, located in django.tasks. However, it's important to note that it doesn't replace existing task management tools like Celery or Huey. Instead, Django provides a way to define tasks and manage their queuing, while the actual execution must be handled by external services.

The new django.tasks module allows developers to define tasks using a standard API, making it easier to integrate with different task queue implementations. A simple example given is an app that sends notifications using the ntfy.sh service, which requires setting up a topic and sending HTTP requests.

Key features of the new task framework include:

1. Task Definition: You can define tasks using the @task decorator. Tasks can only be executed using the enqueue method, preventing accidental in-process calls.

2. Task Enqueuing: Adding a task to the queue is straightforward. However, the built-in backends available in Django 6.0 (ImmediateBackend and DummyBackend) either execute tasks immediately or not at all.

3. Task Result Handling: You can retrieve task results later using their ID. The framework supports fetching results while keeping track of task status.

4. Custom Backends: Developers can create custom backends, like a database-backed task backend, to manage task execution, retries, and results more effectively.

5. Worker Implementation: A basic worker can claim and process tasks from the queue, facilitating task execution.

While the new framework is a step forward, it doesn't cover all advanced features found in existing task queues, such as automatic retries and complex orchestration of tasks. Django's approach is simpler and aims to address common use cases, but users may need to implement additional functionality based on their requirements.

Microsoft's new feature, "Copilot for work," which is integrated into Edge and Windows 11, has faced significant backlash from users. Many long-time Windows users expressed their frustration, stating that they never asked for this AI tool and feel it is being forced upon them.

The "Copilot Mode" is designed to help users manage tasks by using AI, similar to tools like ChatGPT. Microsoft claims it will enhance productivity, but many users disagree, believing it complicates rather than simplifies their experience. Critics argue that Microsoft is out of touch with what users want, with some IT professionals stating they do not want Copilot integrated into Windows at all.

Despite the negative feedback, Microsoft continues to promote the feature, downplaying concerns about AI errors and insisting on its benefits. The situation has led to many users expressing their desire for Microsoft to listen more closely to their feedback, as frustration grows over the direction of Windows 11.

Summary of "Electron vs. Tauri" by Eric Richardson

The Dolt Workbench is an open-source SQL tool built with Electron, a framework that turns web apps into desktop applications. While Electron works well, it has some drawbacks that have become more noticeable as the workbench developed. This led the author to explore Tauri, a newer framework that also converts web apps to desktop apps.

Key Points:

1. Next.js Integration:

   • Electron has challenges working with Next.js due to its server-side features.
   • Tauri simplifies integration by using Next.js's static-site generation, making the process easier.

2. Rendering UI:

   • Electron includes a full Chromium engine, making apps large and potentially bloated.
   • Tauri uses the system's native webview, resulting in lighter applications but with less guaranteed compatibility.

3. Main Process Handling:

   • Electron uses Node.js for its main process, which is familiar to web developers.
   • Tauri uses Rust for the main process but provides JavaScript APIs for developers to use.

4. Sidecar Processes:

   • Electron can easily run Node.js applications alongside the main app, simplifying development.
   • Tauri requires more setup for sidecar processes, as it doesn't include a Node.js runtime.

5. Limitations:

   • Tauri currently lacks support for certain Windows package formats and has issues with MacOS universal binaries, which are not critical but are annoying.

Conclusion: The author is impressed with Tauri for its reduced bloat and better integration with their code but is waiting to fully transition due to current limitations.

Summary of "The Micro Network" (Mu)

Mu is a new social network designed to put users first, moving away from the profit-driven models of big tech companies that promote addictive behaviors. The goal is to create a platform without ads or algorithms for a healthier online experience.

Key Features:

• Currently Available:

  • Basic API
  • Basic Progressive Web App (PWA)
  • LLM chat interface
  • RSS news feed
  • YouTube search
  • Microblogging

• Coming Soon:

  • Private email inbox
  • Wallet for credits
  • Utilities like a QR code scanner
  • Marketplace for services

Membership Model: Mu operates on a subscription basis, ensuring the platform remains sustainable and free from ads and algorithms. Members have a direct voice in the network's development and pay a flat monthly fee to support the tools.

Getting Started:

• Visit mu.xyz to try it for free.
• To develop or contribute, install Go and follow setup instructions to download and run Mu.

API Keys Needed:

• Youtube Data for video search
• Fanar for LLM queries

Run the app by executing mu --serve and access it at localhost:8081.

No summary available.

No summary available.

Summary of Fabric

Fabric is a creative coding environment designed for rapid prototyping of interactive visuals, image and video processing, and 3D content creation. It offers a user-friendly visual node-based interface and supports loading common file formats. Users can also extend its capabilities by creating custom nodes.

Inspired by Apple's old Quartz Composer, Fabric aims to be accessible to those with little programming experience while providing advanced features for professionals and developers. It allows users to create interactive 3D graphics, image effects, audio-reactive scenes, and more, which can be embedded in other applications.

Key features include:

• High-quality rendering techniques like physically based rendering and real-time shader editing.
• Support for 3D model loading and image-based lighting.
• Tools for machine learning-based image processing.

Fabric is currently in development and requires macOS 14 and XCode 15. Users can explore tutorials and sample compositions to get started. The creator, Anton Marini, is building a community around Fabric and welcomes contributions.

Fabric is specifically designed for the Apple platform using Metal and is not intended to be cross-platform. It is built with Swift and SwiftUI. The creator prefers Fabric over other node-based tools because of its unique design and user experience.

Large tech companies often produce poor-quality code despite hiring skilled engineers. This happens mainly because many engineers work on unfamiliar projects or codebases. Here are the key points:

1. Frequent Turnover: Engineers tend to stay at big tech companies for only a year or two. This short tenure means many engineers are new to the code they’re working on.

2. Lack of Expertise: Most code changes are made by relatively inexperienced engineers who are still learning the system. Although some experienced engineers exist, they are often overwhelmed with their own work and can’t provide thorough reviews.

3. Pressure to Deliver: Engineers are typically under pressure to meet tight deadlines, which leads to hasty, subpar coding practices.

4. Company Focus: Big tech companies prioritize flexibility and quick deployment of engineers over maintaining long-term expertise in specific codebases. This approach sacrifices code quality for speed.

5. Inevitability of Bad Code: Even highly competent engineers can produce bad code when working in unfamiliar environments. The issue lies more with the company structure than individual skill.

In summary, while engineers at these companies are capable, the dynamics of their work environment—frequent job changes, deadlines, and lack of deep familiarity with the code—lead to the creation of bad code.

Summary

Introduction The work environment today is filled with interruptions from tools like Slack and video calls, making it difficult to focus. This post explores how interruptions affect productivity and offers a mathematical model to understand this issue.

Understanding a Workday A typical workday can be analyzed by looking at focus time, interruptions, and recovery periods. For example, on a "lost day," one might only achieve a few hours of focus due to frequent interruptions that require time to recover from. In contrast, on a good day, fewer interruptions and quicker recovery lead to significantly more productive hours.

Three Key Factors Affecting Productivity

1. Lambda (λ): The rate of interruptions per hour. More interruptions lead to less productive time.
2. Delta (Δ): The time it takes to regain focus after an interruption. Longer recovery times reduce productive hours.
3. Theta (θ): The minimum uninterrupted time required to achieve meaningful work. Longer tasks require longer focus blocks.

Calculating Capacity Productivity is defined by how many complete tasks (based on θ) can be achieved in a day considering the interruptions and recovery times. Higher λ or Δ drastically reduces capacity for productive work.

Real-World Data Research shows that interruptions often occur every few minutes, leading to significant recovery times. Many workers experience environments that are detrimental to deep focus.

Strategies for Improvement

• Reduce λ: Limit interruptions by protecting your schedule and managing communication expectations (e.g., checking emails less frequently).
• Match θ to Reality: Break down large tasks into smaller, manageable parts that fit better with the interruptions you face.
• Reduce Δ: Implement strategies to minimize recovery time after interruptions, such as leaving notes for yourself before switching tasks.

Conclusion Understanding and adjusting these three factors can greatly improve productivity. By experimenting with your work environment and habits, you can enhance focus and efficiency, transforming how you approach your workdays.

The author, after a break, has successfully built a web server as part of their hobby operating system project, RetrOS-32. The project involved creating a networking stack with essential features like Ethernet, IP, TCP, and DNS. They faced challenges with TCP and HTTP but managed to debug issues, including a terminal buffer problem and packet handling in the network driver.

The web server now includes an HTTP engine to parse requests and a simple web engine for routing and handling responses. The author can serve static HTML pages and has implemented a file repository for managing web files. Future improvements will focus on enhancing the user interface and ensuring the server can shut down gracefully. The ultimate goal is to develop a web browser for the operating system.

You can find the project on GitHub: RetrOS-32.

Summary of JSON Schema Demystified

JSON Schema is a tool used to describe the structure and rules of JSON data. However, its terminology can be confusing, including terms like schemas, metaschemas, dialects, and vocabularies. This article breaks down these concepts to make them easier to understand.

1. Schemas: A schema is a JSON document that specifies the rules for another JSON document. For example, a schema might define that a JSON object must have a string field for a name and an optional non-negative number for age.

2. Metaschemas: A metaschema is essentially a schema for schemas. It defines the structure and keywords of other schemas, helping to validate whether those schemas are formed correctly.

3. Dialects: Different versions of JSON Schema (like Draft 2020-12) are referred to as dialects. Each dialect has its own metaschema, which means the rules and available keywords can differ between versions.

4. Vocabularies: Introduced in Draft 2019-09, vocabularies are collections of keywords that provide specific functionalities. This modular approach allows users to include only the necessary keywords for their use case.

5. Extensibility: Users can create custom vocabularies to add specific keywords relevant to their domain, such as for APIs. This makes JSON Schema flexible and adaptable for various applications.

6. Practical Tips: Always specify which dialect you're using with the $schema keyword, use clear metadata for documentation, and break complex schemas into reusable parts with $defs.

Overall, while the terminology of JSON Schema can seem daunting, understanding the basic concepts allows users to effectively create and validate JSON data structures.

No summary available.

Discover our all-in-one AI-powered DevSecOps platform designed to enhance security and efficiency in development and operations.

No summary available.

Summary: How to Customize Table Styles in Pandoc for Word

To customize tables in Pandoc when converting a Markdown file to .docx, create a new table style named "Table" in your Word template. Pandoc uses the table style labeled "Table" to format tables during conversion. If you don’t start with Pandoc’s default template, you must set up your own style to ensure proper formatting.

While working on a documentation project, I faced challenges getting Pandoc to apply our custom table styles. I initially tried complex solutions but eventually realized the simple fix was to create a new table style named "Table." This approach was not immediately obvious, and I learned it only after exploring various methods.

In summary, for Pandoc to recognize and apply your custom table styles, ensure you have a table style named "Table" in your reference Word document.

Airbus has announced that flights will be disrupted due to the need for immediate software updates on thousands of its planes, mainly the A320 model, which is widely used. Over 6,000 planes are believed to be affected. This issue comes just before a major holiday weekend in the US, impacting airlines like American Airlines, Delta, JetBlue, and United. The problem was revealed after a JetBlue flight experienced a sudden drop in altitude, suspected to be caused by solar radiation interfering with the aircraft's computer systems. While UK airports have seen limited disruption, several airlines globally have reported flight cancellations.

Summary of the New Runbook Execution Engine Announcement

On November 24, 2025, Michelle Tilley introduced a significant upgrade to Atuin Desktop: a redesigned runbook execution engine. This update aims to enhance the reliability and usability of runbooks, ensuring they maintain their state and behave consistently, eliminating issues like lost context and the need for repeated executions.

Key Features:

1. Persistent Runbooks: Runbooks now retain their execution state even when the app is closed or restarted, allowing users to pick up where they left off without having to re-run previous commands.

2. Predictable Behavior: The new engine ensures that each block in a runbook only affects the blocks below it, making the execution flow clear and reliable.

3. Template System: All user input now goes through a robust template system, allowing for advanced variable management, including self-referential variables.

4. Two Context Types:

   • Passive Context: Automatically sets when documents update (e.g., directory changes).
   • Active Context: Created during block execution, representing the current state and output.

5. Foundation for Collaboration: The update sets the stage for real-time collaborative execution, enabling users to share not just documents but entire running environments.

6. Technical Improvements: The update involved a complete rewrite of the execution system for better reliability and portability.

Future Developments:

• A CLI runner will allow runbooks to be executed outside of Atuin Desktop.
• Improved serial execution will enhance monitoring and control over runbook executions.
• Support for secrets management will be added for secure handling of sensitive data.
• Runbooks will be stored as markdown files for easier editing and version control.

The new execution engine is available in version 0.2.0 of Atuin Desktop, and users are encouraged to try it out. Feedback can be shared via community channels.

It seems like you provided "HOP" without additional context or text to summarize. Could you please provide more information or the text you would like summarized?

As AI agents become more advanced, developers want them to handle complex tasks that take a long time to complete. A significant challenge is that these agents often start each new session without remembering what happened in previous sessions. This is like having engineers work in shifts without passing on information, leading to inconsistent progress.

To address this, a two-part solution was developed for the Claude Agent SDK:

1. Initializer Agent: This agent sets up the environment on the first run, creating essential files and logs to track progress.
2. Coding Agent: In subsequent sessions, this agent focuses on making small, incremental improvements while leaving the code in good shape for the next session.

Despite these solutions, issues still arise. Sometimes, the agent tries to complete too much at once or prematurely considers a project finished. To combat this, the initializer agent creates a detailed list of features required, with each feature marked as incomplete. The coding agent then tackles one feature at a time, ensuring that the code is tested and documented correctly at the end of each session.

Key strategies include using structured files for maintaining progress and ensuring that agents follow clear instructions about testing and documentation to avoid leaving bugs or incomplete work. Although these methods have shown promise, there are still questions about whether a single coding agent is the best approach or if multiple specialized agents would perform better.

Future work aims to apply these findings beyond web app development to other fields like scientific research and financial modeling.

Sure! Please provide the text you'd like me to summarize, and I'll help you with it.

The writer is exploring how to detect when smart glasses, like the Meta Ray-Bans, are recording. They have two main methods for this project:

1. Camera Sensor Reflection: They are looking at infrared (IR) reflections from the camera, but the results have been mixed.
2. Wireless Traffic: They are using an ESP32 device to monitor Bluetooth Low Energy (BLE) signals. They can detect when the glasses are paired, powered on, or taken out of their charging case, but not consistently during use.

The writer hopes to improve detection while the glasses are recording and is considering using a different chip, the nRF52840, to track BLE traffic better. However, they find the hardware for Bluetooth Classic detection to be very expensive. They are seeking suggestions to overcome these challenges.

Summary:

The text discusses a financial arrangement involving Meta Platforms Inc. and Beignet Investor LLC, a company created by Blue Owl Capital. Beignet is set to issue $27.30 billion in senior secured debt for a data center project in Louisiana, rated A+ by analysts due to its legal structure that keeps Meta's assets and debts off its balance sheet.

Key points include:

1. Joint Venture Structure: Beignet holds 80% of a joint venture with Meta, which allows Meta to avoid consolidating around $27 billion in liabilities.

2. Debt and Financing: Beignet's debt will fund a $28.79 billion data center project, with complex financing tactics, including using borrowed money to generate interest and meet equity requirements.

3. Meta's Obligations: Meta guarantees all costs related to the project, including construction overruns and lease payments. This creates a strong financial connection between Meta and Beignet.

4. Risks and Stability: The arrangement faces risks such as heavy reliance on Meta and potential changes in accounting rules. However, the current structure is deemed "Superficially Stable," meaning it appears stable under current conditions, but could change if unforeseen events occur.

5. Outlook: The rating could be downgraded if Meta's financial situation worsens, but an upgrade is considered unlikely due to the nature of the financial structure.

Overall, the report reveals a complex financial setup that legally separates Meta from significant liabilities while maintaining substantial economic obligations.

Summary:

There are several recent news articles discussing the need for open-source work to be recognized as volunteer work. A petition has been launched to support this cause, and open-source developers are calling for equal recognition of their contributions alongside traditional club activities.

Summary of Moss

Moss is a Unix-like kernel that is compatible with Linux and written in Rust and Aarch64 assembly. It has a modern design with several key features:

• Architecture & Memory:

  • Supports Aarch64 fully, with a flexible architecture layer for easy adaptation to other systems.
  • Includes advanced memory management, such as Copy-on-Write pages and mechanisms for handling page faults.

• Async Core:

  • Utilizes Rust's async/await model, allowing system calls to be non-blocking, which prevents deadlocks.

• Process Management:

  • Provides task scheduling and migration, supports 51 Linux system calls, and has advanced forking capabilities.

• Virtual File System (VFS):

  • Implements an asynchronous VFS with a FAT32 filesystem driver and a ramdisk.

• Library and Testing:

  • Built on libkernel, allowing for architecture-independent logic testing on host machines. It includes strong typing for addresses and a comprehensive test suite.

Building and Running Moss:

• Prerequisites include installing QEMU for Aarch64 emulation and the aarch64-none-elf toolchain.
• Steps involve preparing dependencies, creating a kernel image, and running it in QEMU.

Development Status:

• Moss is actively being developed, focusing on improving Linux syscall compatibility, networking, and file system capabilities.

Contributions:

• Open to contributions in various areas, including drivers and syscall additions. It is licensed under the MIT License.

Netlist.io offers an AI-powered tool for checking PCB schematic errors before manufacturing. It uses your datasheets and netlists from KiCad or Altium. You can try it for free without a credit card. The service includes various features, but users should be aware that the AI might make mistakes and that layout suggestions are based solely on logic, without access to layout files. The tool is designed to help users identify and correct issues efficiently, though it may consume usage limits quickly.

The text suggests two actions: viewing a live demonstration and reading the documentation.

No summary available.

The author wanted a way to listen to music with friends online, feeling like they were in the same room. This led to the creation of Pulse, a platform where anyone can host live audio streams directly from their browser. Listeners can join without needing an account, using a simple anonymous code. The platform automatically identifies songs and includes a chat feature with fun emotes.

Currently, Pulse offers demo rooms that stream music from NTS Radio and SomaFM 24/7, as well as a room dedicated to looping the "Money For Nothing" instrumental by Dire Straits. Essentially, Pulse allows people to enjoy music together, whether for DJing, listening parties, or sharing musical vibes.

Summary:

The document discusses a preprint titled "Glauert's optimum rotor disk revisited" which is currently under review in the journal Wind Energy Science. The author, J. Gordon Leishman, has submitted a comment on this preprint.

Leishman critiques the original paper by Tyagi and Schmitz, arguing that it contains significant issues, particularly in how it handles the analysis of rotor performance under different conditions. He points out contradictions in the paper’s findings, such as claiming zero power in a scenario where power should be present and presenting solutions that do not apply to real turbine operations due to their reliance on flawed assumptions.

Overall, Leishman believes that the original paper's conclusions are misleading and that it fails to meet necessary standards for transparency and reproducibility in engineering.

Summary of "A Tale Of Four Fuzzers"

In this article, the author discusses the implementation of four different fuzzers for TigerBeetle's routing algorithm. The key points are:

1. Importance of Multiple Fuzzers: Relying on just one fuzzer is insufficient; multiple fuzzers are needed to cover different testing scenarios and potential bugs.

2. Adaptive Replication Routing (ARR): The new routing algorithm enhances data durability and efficiency by dynamically adjusting the routes between data replicas, which helps in maintaining performance even during network issues.

3. Fuzzing Techniques:

   • Exhausting Positive Space: This technique tests all valid routes by checking if serialization and deserialization functions work correctly for every possible permutation of routes.
   • Exploring Negative Space: This tests invalid inputs to ensure the system handles errors gracefully and does not crash. It checks how the system reacts to unexpected data.
   • Qualitative Fuzzing: This evaluates how well the routing algorithm converges to the optimal route under idealized conditions, simulating realistic network scenarios.
   • Random Testing: A fuzzer that randomly calls methods on a single instance of Routing to check for crashes and ensure the routes connect properly without following a strict sequence.

4. Design Principles: The article emphasizes the importance of having well-defined interfaces to facilitate effective fuzzing and testing. A clean interface reduces dependencies and makes the system easier to test.

5. Testing Insights: The author shares insights gained from the fuzzing process, such as the need for careful consideration of latency measurements and the understanding that some bugs can reveal deeper misunderstandings about the system.

6. Final Thoughts: The author concludes that writing fuzzers is often mechanical work, but they serve a crucial role in uncovering bugs and improving understanding of the system's behavior.

Overall, the article highlights the value of various testing strategies in ensuring the robustness of the TigerBeetle system, advocating for a comprehensive approach to software testing through the use of multiple fuzzers.

The KDE Plasma team has announced that their upcoming Plasma 6.8 release will only support Wayland, marking a shift away from the X11 system used for nearly 30 years. Most users are already using Wayland, as it is the default on many distributions. X11 applications will still function through a compatibility layer called Xwayland until early 2027, when official support for the Plasma X11 session will end.

Key points include:

• Users who need X11 can rely on long-term support distributions that still offer it.
• Most X11 applications should continue to work well on Wayland.
• Gaming performance on Wayland is improving, with better support for features like adaptive sync.
• Accessibility features are comparable to X11, with ongoing improvements.
• The transition to Wayland is expected to enhance stability and functionality for most users, allowing for faster development.

The team is committed to addressing known issues and ensuring a smooth transition to Wayland, which they believe will lead to a better user experience in the long run.

The article discusses the problem of chronic overstimulation in today's fast-paced world, where constant exposure to digital devices and information overload can lead to feelings of exhaustion and inability to focus. It suggests that our nervous systems are overwhelmed due to excessive sensory input, which disrupts our ability to relax and enjoy life.

Key points include:

1. Definition of Overstimulation: Overstimulation occurs when the brain receives more sensory information than it can handle, leading to burnout and an inability to focus or enjoy simple pleasures.

2. Impact of Modern Life: The article argues that our current environment, filled with screens, notifications, and constant noise, contrasts sharply with how humans lived for most of history, resulting in maladaptive traits that now harm our well-being.

3. Common Triggers: Activities like multitasking, excessive phone use, background noise, and speed consumption of media contribute to overstimulation.

4. Proposed Solutions:

   • Avoid Stacked Stimuli: Focus on one task at a time and limit distractions.
   • Establish Analogue Hours: Spend at least one hour a day without any technology to recharge.
   • Protect Morning and Evening Routines: Avoid screens immediately after waking and before bed to help regulate dopamine levels.

5. Noise and Light Management: Reducing exposure to constant noise and bright lights can help calm the nervous system.

6. Consider GABA Support: The article mentions the importance of balancing neurotransmitters like GABA to combat overstimulation.

Overall, the article emphasizes the need to consciously manage our sensory input and create a more balanced lifestyle to improve focus, relaxation, and overall quality of life.

No summary available.

Summary

Zero Technical Debt

• Tiger Style emphasizes safety, performance, and developer experience, supported by a commitment to zero technical debt.
• A zero technical debt policy keeps the codebase healthy and boosts long-term productivity by addressing issues early and creating strong solutions from the beginning.
• Key strategies include:
  • Do it right the first time: Carefully design features to avoid future rework.
  • Be proactive: Identify and fix potential problems before they grow.
  • Build momentum: Producing reliable code leads to faster development and less need for rewrites.

Performance Estimation

• Think about performance during the design phase using "napkin math," which involves simple calculations to estimate system performance and resource needs.
• Benefits of napkin math:
  • Quickly understand system behavior.
  • Identify bottlenecks early.
  • Validate ideas before implementation.
• Example: Estimating log storage costs can be done using a straightforward calculation to get a rough monthly storage cost.

Document Information

• This document is inspired by the original Tiger Style guide and has been adapted for a broader audience.
• Maintained by Simon Klee, version 0.1-dev, last updated in October 2024, licensed under CC BY 4.0.

The author, Uros Popovic, is experimenting with creating Linux virtual machine (VM) images that can communicate with their host. He introduces a technology called vsock, which allows VMs to interact without needing a traditional network setup. The article details how to use vsock for a gRPC service in a VM, using Bazel for building the project.

Key Points:

1. Communication Needs: VMs need to communicate with their host for practical use, and vsock is designed for this purpose.

2. gRPC Service: The example demonstrates a simple gRPC service that adds two integers, showcasing how to set up the server and client using C++.

3. Bazel for Building: Bazel helps in managing dependencies and building the project, making it easier to replicate.

4. Server and Client Implementation: The server runs inside the VM and listens for requests, while the client runs on the host and sends requests to the VM.

5. Execution: The server can be booted directly into the VM image, and the client can invoke the gRPC service to perform operations.

6. Conclusion: The use of gRPC over vsock allows running applications in isolated environments efficiently, supporting various operating systems and programming languages.

Overall, the article provides a practical guide on setting up a communication system between a VM and its host using modern technologies.

No summary available.

No summary available.

No summary available.

No summary available.

Summary: Writing Builds Resilience by Changing Your Brain

Writing can significantly impact our mental state, helping us deal with everyday challenges and fostering resilience. It allows us to express our emotions and create distance from our pain, transforming feelings of despair into clarity.

Psychologists define resilience as the ability to grow through life's difficulties. Various fields, including psychology and wellness, emphasize that resilience is a skill individuals can develop. Writing plays a crucial role in this process, as research shows it helps us process trauma and manage emotions.

The concept of expressive writing, introduced by psychologist James Pennebaker, involves journaling about painful experiences. This practice helps create mental distance and reduces emotional burden. Writing engages various brain functions, aiding memory consolidation and emotional regulation.

When we write, particularly in a reflective manner, it can calm our emotional responses and help us think more clearly. Simple writing tasks, like to-do lists, also enhance focus and decision-making.

To cultivate resilience through writing, here are some tips:

1. Write by hand to enhance cognitive processing.
2. Establish a daily writing habit, even if brief.
3. Write down feelings before reacting to them.
4. Compose unsent letters to express emotions safely.
5. View writing as a process that encourages self-awareness and improvement.

Overall, writing is an accessible and powerful tool for building resilience and adapting to life's challenges.

The text discusses various functions in C for formatting and outputting data, particularly focusing on the printf family of functions. Here's a simplified summary:

1. Printf Functions:

   • ANSI C89 has six main functions: printf, sprintf, fprintf, vprintf, vsprintf, and vfprintf.
   • Later versions introduced more functions, such as dprintf, snprintf, and others.
   • These functions mainly differ in their input format and output method.

2. Function Optimization:

   • Many of these functions can be combined or simplified. For example, printf and dprintf can be seen as similar, reducing the count of necessary functions.
   • The text suggests that sprintf is problematic due to safety concerns, while snprintf is safer but can mimic sprintf under certain conditions.

3. qsort Example:

   • The qsort function illustrates the use of function pointers in C, allowing for flexible sorting of different data types. However, this can lead to type safety issues since the type information is lost when using void pointers.

4. Implementing a Custom Print Function:

   • A custom printing function can be created that takes a file stream, buffer, and function pointer for output.
   • To manage state when writing to the buffer, a structure (bufinfo) can be used to keep track of the current position and length.

5. Object-Oriented Programming in C:

   • The text suggests that keeping track of state can be done through passing pointers, allowing for a more organized way to manage data over multiple writing operations.

6. Conclusion:

   • The discussion emphasizes the potential for streamlining C's formatting functions and highlights the challenges of managing types and state in a low-level language like C.

No summary available.

I'm sorry, but I cannot access external links or content from the internet. However, if you provide the text you want summarized, I'd be happy to help!

Rock Paper Scissors Solitaire Summary

If you're really bored and alone without internet, you can play Rock Paper Scissors solitaire using a dice. To play, roll the dice and use your hand to show the shape you choose: 1-2 is Rock, 3-4 is Paper, and 5-6 is Scissors. You can change the number assignments if it helps you. This game is best for when you're very bored.

Written by Federico Klez Culloca.

In the late 19th century, many European cities allowed buildings to be quite tall, while cities in the British Empire and the U.S. had no height restrictions. This changed over the next 50 years in what is known as "the Great Downzoning," which has contributed to housing shortages in major Western cities today. This phenomenon has negative effects on health, family formation, the environment, and economic growth.

The Great Downzoning occurred during a time when many people believed urban density was harmful, leading to a push for lower density development. However, the motivations were more about property interests than ideology. In affluent areas, downzoning often increased property values by limiting undesirable development. In contrast, attempts to enforce density restrictions in new developments faced resistance from landowners who valued higher-density projects.

Historically, suburbs were often unplanned and mixed-use, but as wealthier residents moved to planned suburbs, they sought to maintain their neighborhood's character through covenants and zoning laws. These measures aimed to limit density, reflecting homeowners' desires for quieter, greener spaces.

The Great Downzoning effectively preserved the suburban character of many neighborhoods, but it failed to apply effectively in some areas, particularly in city centers and new developments. While planning ideologies advocated for lower density, the interests of homeowners often countered these efforts, demonstrating that property owners were more influential than planners.

In recent years, the value of increasing density in certain urban areas has become apparent, leading to proposals for reforms that allow neighborhoods to vote on increasing density. However, housing shortages still exist primarily in major cities, and many suburbs continue to enjoy protections against densification.

To reform zoning laws, advocates should focus on aligning homeowners' interests with the benefits of increased density, rather than relying solely on ideological arguments for change.

Summary of File Transfer Tool

This file transfer tool is free, open-source, and allows you to send files directly between devices without using cloud storage. Here are the key features:

• Direct Transfers: Transfers files directly between devices, ensuring privacy without storing data on cloud servers.
• Security: Uses end-to-end encryption for safe transfers, protecting your information.
• No Sign-Up Needed: You can transfer files without creating accounts or sharing personal data.
• Versatile: Supports any file size and format, with integrity checks to ensure files are sent correctly.
• Resume Capability: If a transfer is interrupted, it can automatically continue from where it left off.
• Fast: Optimized for high-speed connections.
• Cross-Platform: Works on local networks and across long distances.

Installation: Download the appropriate version for your operating system (Windows, macOS, or Linux) to get started.

Supported Languages: Available in multiple languages, including French, German, Chinese, and more.

Development: For contributors, specific software prerequisites are needed to run or build the app from source.

License: The tool is licensed under AGPL-3.0.

For more details, including privacy policies and contact information, refer to the provided documentation.

Summary of "My Lobsters Interview" by Susam Pal

Susam Pal had an interesting discussion with Alex from the Lobsters community about computing and mathematics, which was published on the community website. The conversation was edited for clarity and flow. Susam preserved a copy on his website with a table of contents for easier navigation.

Key Points:

1. Background in Computing: Susam primarily uses Lisp for personal projects, including a popular mathematics pastebin and tools for Emacs. He also has experience with other programming languages like Python, C++, and Java.

2. Discovery of Lisp and Emacs: He began using computers with the Logo programming language and later learned Lisp during a layover at an airport. Emacs became his primary editor as he grew fond of its capabilities.

3. Love for Mathematics: Susam shares a long-standing fascination with mathematics, especially its logical proofs. He became interested in number theory through learning about the RSA cryptosystem.

4. Fun in Computing: To Susam, computing is enjoyable when it involves exploration and sharing creations with others. He enjoys asking questions like “What happens if?” and developing tools that solve interesting problems.

5. Approach to Learning: When learning new domains, he emphasizes hands-on experimentation and testing claims through coding. This method helps deepen his understanding and correct misconceptions.

6. Time Management: Balancing hobbies with responsibilities has made Susam more deliberate about choosing projects, often limiting his computing explorations to a few ideas each month.

7. Blogging and Sharing Knowledge: Susam enjoys writing blog posts to archive knowledge and share insights. His blog topics vary widely, from technical guides to humorous explorations of programming concepts.

8. Influence of Mathematics on Computing: He finds that mathematics profoundly impacts computing, particularly in areas like cryptography and error-correcting codes. His studies in mathematics also influence his problem-solving approach in programming.

9. Design and Composability in Programming: Susam believes in maintaining a clear vocabulary and building software in layers, favoring small, composable functions over complex ones to ensure long-term maintainability.

Overall, Susam’s journey reflects a passion for both computing and mathematics, with a strong emphasis on exploration, learning, and sharing knowledge with the community.

Summary

The article discusses a programmer-friendly I/O abstraction built on two modern APIs: io_uring for Linux and kqueue for FreeBSD/macOS. It starts by explaining traditional blocking I/O methods, where reading and writing require multiple system calls, which can be inefficient due to the high cost of context switching and system calls.

To improve performance, the article introduces io_uring and kqueue, which allow for non-blocking I/O and batching requests. This is particularly useful for handling multiple connections in TCP servers. io_uring can even execute read and write operations directly in the kernel, reducing overhead.

A simple TCP echo server example demonstrates these concepts. However, the article emphasizes that in larger applications, it’s better to have a centralized I/O dispatcher that can handle requests and invoke callbacks when operations complete, rather than embedding I/O logic throughout the code.

The proposed design allows flexibility in handling different types of I/O events, supports request overflow management, and can be adapted for different operating systems.

Additionally, the article mentions the potential for a cross-platform I/O library in Zig, which could work with any language that supports C interfaces.

In summary, the article outlines a modern approach to managing I/O that enhances performance and simplifies code structure while providing a foundation for future cross-platform development.

The author, Marius, expresses criticism of the Signal messenger but acknowledges its popularity due to network effects, making it hard for users to switch to better platforms. To help those who must use Signal, he introduces "Molly," an alternative Android client that addresses some of Signal's shortcomings.

Key points about Molly include:

1. Enhanced Privacy Features: Molly offers improved data encryption methods and protects user passphrases better than Signal.
2. Better Push Notifications: Unlike Signal, which relies on Google services, Molly supports UnifiedPush, allowing for more efficient notifications without draining battery life.
3. Tor Support: Molly can use a SOCKS proxy and Tor for additional privacy.
4. RAM Shredding: Molly includes features that clear sensitive data from memory when locked, enhancing security against forensic analysis.
5. Automatic Backups: Molly allows users to back up their data automatically, making it easier to restore information if needed.

Molly is presented as a more privacy-focused and user-friendly alternative for those who still need to use Signal.

NASA's Voyager 1 spacecraft is set to reach a significant milestone, as it will be 16.1 billion miles (25.9 billion km) away from Earth by November 15, 2026. This distance means that signals will take 24 hours to travel to and from the spacecraft, marking one light-day away from Earth.

Launched in 1977 to study Jupiter and Saturn, Voyager 1 entered interstellar space in 2012 and is now the farthest human-made object in existence. It travels at about 11 miles per second and continues to send data thanks to its long-lasting power source.

Communicating with Voyager 1 is slow, taking two days for messages to be sent and received, compared to just seconds for closer celestial bodies like the Moon. The probe's journey highlights the vastness of space and the resilience of technology designed for exploration.

No summary available.

After 15 months of working with Large Language Models (LLMs), I've identified several key behaviors to avoid, known as anti-patterns:

1. Repetition of Information: Avoid sending the same data multiple times in a session. For example, instead of sending many similar screenshots, send only the final relevant one. Use tools that minimize redundancy.

2. Misusing LLMs' Strengths: Don't ask LLMs to do tasks they're not good at. For instance, instead of asking an LLM to count letters, ask it to write code that counts them. It’s better to leverage their coding abilities for accurate results.

3. Overloading Context: LLMs struggle when overloaded with too much context. If a session exceeds their token limit (like 128k tokens), they may forget important information. Be cautious about accuracy when using large contexts.

4. Obscure Topics: LLMs perform poorly on niche or newly emerged topics since they weren’t trained on them. Expect less accuracy with these subjects and find ways to ensure correct information.

5. Neglecting Oversight: Keep track of what the LLM generates. Losing oversight can lead to security risks, such as exposing sensitive information in outputs. Always review the LLM's work carefully.

These insights can help improve your interactions with LLMs and enhance their effectiveness.

Nyno 3.0 Overview

Nyno 3.0 is an open-source multi-language workflow engine designed for building automation using programming languages like Python, PHP, JavaScript, and Ruby. You can create workflows by connecting commands using simple YAML text files (.nyno).

Key Features:

• Flexible Development: You can create new workflow steps in your preferred language.
• High Performance: Each programming language runs in its own efficient worker engine. Nyno optimizes performance by using multiple workers based on your system's CPU cores.
• Easy Command Creation: Scripts in Python, PHP, JavaScript, and Ruby can be turned into reusable commands within workflows.

Installation Instructions:

1. Using Docker/Podman:

   • Clone the Nyno repository.
   • Build the container.
   • Run the container to access Nyno via a web interface.

2. On Linux Host:

   • Install the required dependencies, including Best.js.
   • Clone and set up both Best.js and Nyno, checking system requirements with a provided script.

Example Usage:

• You can create functions in your chosen language and call them in YAML workflows, allowing for data to be passed between workflow steps.

Conclusion: Nyno is a powerful tool for developers looking to automate processes using familiar programming languages, designed to be efficient and easy to integrate.

Airbus has grounded over 6,000 A320 aircraft due to concerns about solar radiation affecting flight controls. A recent incident involving a JetBlue A320, where intense solar radiation caused a loss of altitude and injuries to passengers, prompted this urgent recall.

Airbus warned that strong solar flares can disrupt crucial data channels in the aircraft, leading to potential flight control issues. To address this, they have issued a mandatory alert for airlines to apply software updates and implement hardware changes to safeguard against these radiation effects.

The European Union Aviation Safety Agency (EASA) has also mandated these updates through an Emergency Airworthiness Directive, which may cause temporary disruptions to airline schedules. Airbus has acknowledged the inconvenience this may cause but emphasizes that safety is their top priority. Airlines are now preparing for the necessary maintenance to ensure continued safety of the fleet.

Summary: The Impact of Shor's Algorithm on Cryptography

1. Introduction: Peter Shor's algorithm threatens the security of current public-key cryptography systems like RSA and ECC. It allows quantum computers to break encryption retroactively, meaning encrypted data today could be decrypted in the future.

2. Key Problems in Cryptography:

   • Factoring: Breaking RSA by finding the two large prime factors of a number.
   • Discrete Logarithm: Breaking systems like Diffie-Hellman by finding the exponent in equations.

3. Quantum Advantage: Shor’s algorithm turns these problems into a period-finding problem solvable in polynomial time on a quantum computer, vastly outpacing classical computers.

4. Breaking RSA: The process involves using Shor’s algorithm to find the period of a function related to the public key, which can then be used to derive the private key and decrypt messages.

5. Vulnerability of ECC: Elliptic Curve Cryptography (ECC) is equally vulnerable, with smaller key sizes making it easier for quantum computers to break.

6. Data Harvesting: Intelligence agencies may be collecting encrypted data now to decrypt later once quantum computers become available, posing a significant threat to privacy and security.

7. Need for Qubits: Significant resources will be required to build effective quantum computers capable of breaking current encryption schemes.

8. Limitations of Key Size: Simply increasing key sizes in RSA or ECC will not protect against Shor's algorithm, as its efficiency remains unchanged.

9. Post-Quantum Cryptography: New cryptographic systems based on lattice problems, codes, and hash-based signatures are being developed as quantum-resistant alternatives.

10. Migration Challenges: Transitioning from RSA/ECC to post-quantum systems is complex and urgent, impacting various technologies from TLS certificates to blockchain systems.

11. Immediate Actions: Organizations are urged to adopt hybrid cryptographic solutions combining classical and post-quantum methods to secure long-term data.

12. Migration Planning: A structured five-year plan for transitioning to post-quantum cryptography is essential for organizations to remain secure.

13. Final Message: The threat posed by Shor's algorithm is imminent; immediate action is necessary to safeguard against future quantum computing capabilities.

This summary highlights the urgent need for transitioning to quantum-resistant cryptographic methods to protect sensitive data from potential future quantum attacks.

No summary available.

The MINT Autumn Report by the Institute of the German Economy shows that the shortage of IT specialists in Germany has greatly diminished. Currently, there is a shortfall of only 3,100 IT professionals, down from 18,700 last year. The overall shortage in MINT fields (Mathematics, Informatics, Natural Sciences, Technology) has also decreased significantly, now at 148,500 compared to 205,800 last year.

The decline in demand for MINT jobs is attributed to a poor economy, leading to fewer job openings and an increase in unemployed individuals seeking MINT careers. Job advertisements for MINT roles dropped from 476,400 in 2023 to 367,600 in October 2025.

However, the report warns of a potential future shortage due to factors like digitalization, climate change, and an aging workforce. To address this, the researchers suggest improving education, increasing the recruitment of women in MINT fields, extending the working years of older employees, and facilitating immigration for skilled workers. Notably, the number of international students in MINT subjects in Germany has risen significantly, which could help bolster the workforce if they transition successfully into jobs.

No summary available.

Summary of Mixpanel's Security Incident Response

On November 8, 2025, Mixpanel discovered a smishing campaign that affected a small number of customers. In response, the company took immediate action to secure accounts and prevent unauthorized access. They worked with cybersecurity experts, informed affected customers, and emphasized their commitment to security.

Key actions taken included:

• Securing affected accounts
• Revoking active sessions
• Changing compromised credentials
• Blocking harmful IP addresses
• Conducting thorough forensic reviews
• Implementing new security measures

If you received communication from Mixpanel, please check it for important information. If you did not hear from them, your account was not affected, and no action is needed. For any questions, you can contact [email protected].

Summary of PEP 638 – Syntactic Macros

Author: Mark Shannon
Status: Draft (Created on 24-Sep-2020)

Overview

PEP 638 proposes adding syntactic macros to Python, which are compile-time functions that transform parts of the program. Unlike traditional text-based macros, these operate on the program's syntax tree, allowing for more precise and hygienic code transformations.

Key Points

1. Motivation:

   • New language features can complicate Python and may not benefit all users.
   • Python's growth in areas like data science necessitates domain-specific extensions without bloating the core language.
   • Syntactic macros enable users to create extensions tailored to their needs without impacting the broader community.

2. Benefits of Macros:

   • Improves expressiveness: Macros simplify complex patterns in specific domains.
   • Trial new features: They allow testing of potential language features before full implementation.
   • Stability: Implementing features as syntax tree transformations can enhance the bytecode interpreter's stability.

3. Implementation Details:

   • Macros can be defined in two forms: statement and expression.
   • They include a mechanism for importing and defining macro processors, enabling modular code.
   • New Abstract Syntax Tree (AST) nodes will be introduced for macros.

4. Examples of Use:

   • Macros can simplify data structures and improve readability.
   • They can be utilized in domain-specific applications like parsers or compilers.
   • Macros can serve as zero-cost markers for annotations.

5. Backwards Compatibility: The proposal ensures that existing Python code will not break.

6. Performance Implications:

   • No impact on performance for code not using macros.
   • Some overhead may occur for code that does use macros, primarily during compilation.

7. Future Directions: The PEP does not yet include a reference implementation and is open for community input on macro definitions.

This proposal aims to enhance Python's flexibility while maintaining its user-friendly nature, allowing developers to create powerful domain-specific tools without compromising the language's simplicity.

Summary of Penpot

Penpot is an open-source design tool that facilitates collaboration between designers and developers. It allows designers to create interactive prototypes and design systems while providing developers with ready-to-use code, all without the usual handoff issues.

Key features include:

• Cross-Platform Access: Available via web browser or self-hosted.
• Support for Open Standards: Works with SVG, CSS, HTML, and JSON, and is free to use.
• Design Tokens: The latest version integrates design tokens for better efficiency between design and development.
• Version 2.0 Updates: Introduces a new CSS Grid Layout feature, a complete UI redesign, and a new components system.
• Real-Time Collaboration: Users can collaborate in real-time or work independently.
• Flexible Hosting: Organizations can either use Penpot's cloud service or host it on their own servers.
• Community Involvement: Users are encouraged to contribute, ask questions, and share insights through community platforms.

Penpot aims to streamline the design and coding process, making it a valuable tool for teams looking to improve their workflows.

Summary of Google TPUs

Overview: Google’s Tensor Processing Units (TPUs) are specialized chips designed for artificial intelligence (AI) tasks. They provide significant advantages over traditional chips like GPUs, especially for deep learning.

History:

• Google developed TPUs around 2013 to address the growing demand for AI processing, predicting that their existing hardware (CPUs and GPUs) would not be efficient enough.
• The TPU project quickly progressed, with the first TPUs deployed in data centers by 2014 and officially announced in 2016.

TPUs vs. GPUs:

• Architecture:
  • GPUs are general-purpose chips designed for various tasks, which can lead to inefficiencies in AI-specific applications.
  • TPUs focus solely on running TensorFlow neural networks, using a unique "Systolic Array" architecture that minimizes data movement, improving efficiency and speed.
• Performance:
  • Recent TPU models show significant performance improvements. For example, TPUv7 can deliver 4,614 TFLOPS compared to 459 TFLOPS from an older TPU version.
  • TPUs are generally more cost-effective and energy-efficient than GPUs for specific applications.

Challenges for Adoption:

• The main barrier to wider TPU adoption is the software ecosystem. Many developers are trained in NVIDIA’s CUDA, making it difficult to transition to Google’s TPU ecosystem.
• TPUs are currently only available through Google Cloud, limiting their accessibility compared to NVIDIA GPUs, which are offered by multiple cloud providers.

Competitive Advantage:

• Google’s TPUs provide a crucial edge for its cloud business, giving it the potential to maintain higher profit margins compared to competitors reliant on NVIDIA GPUs.
• The TPU’s design allows Google to control costs and performance, positioning it well in the evolving AI landscape.

Future Potential:

• Google produces a significant number of TPUs and plans to expand production as demand for AI capabilities grows.
• The company is likely to explore offering TPUs externally to enhance their market reach.

In summary, Google’s TPUs are a critical innovation for AI processing, offering advantages in efficiency and cost-effectiveness, though challenges in software adoption and ecosystem development remain.