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VisiCalc, created in 1979 by Dan Bricklin and Bob Frankston, was the first spreadsheet software and transformed personal computers into valuable business tools. It featured a simple user experience, allowing users to quickly manage data and perform calculations. The software was minimalistic and ran on early 16K RAM machines, selling over a million copies.

The core components of a spreadsheet include cells that can hold values, formulas, or be empty. This project aims to rebuild a minimal VisiCalc clone, requiring a data model, formula evaluator, and a user interface (UI). Each cell type is defined, and a grid structure is set up to hold the cells.

To evaluate formulas, a recursive parser is implemented to handle mathematical expressions. The parser considers various factors such as numbers and cell references, allowing complex calculations involving functions like SUM and ABS. A recalculation method ensures that formulas are updated when cell values change.

For the user interface, the ncurses library is used to create a text-based UI that displays the spreadsheet grid, including a status bar and editable cells. The interface allows users to navigate, edit, and see values or formulas in the cells.

While the project does not include advanced features like file I/O or more complex commands, it captures the essential functionalities of a spreadsheet: cells, formulas, recalculation, and a modal interface, all in under 500 lines of code. The enduring legacy of VisiCalc is evident as modern spreadsheets still operate on similar principles.

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Vincent and Jochen from Sitefire have created a platform to help brands improve their visibility in AI search. They noticed that marketing teams were struggling with declining traffic due to changes in Google’s AI search features.

Sitefire aims to provide a data-driven approach to tackle common misconceptions in the field. Their platform monitors and enhances content to ensure it ranks well in AI search engines, which often generate multiple queries from a single user prompt.

Here’s how Sitefire works:

1. Users define prompts based on popular SEO keywords.
2. These prompts are submitted to AI tools like ChatGPT and Google AI, and the responses are analyzed.
3. The platform identifies which web pages are frequently cited and suggests improvements or new content.
4. It integrates with clients' analytics to track increases in traffic from AI sources.

Sitefire also focuses on creating high-quality content to avoid the pitfalls of AI-generated material. Clients can use the platform directly or have Sitefire act as an agency, managing the process for them. Clients approve content changes via tools like Slack or their CMS.

For a demonstration, you can check out their video and sign up for more information at their website.

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The article discusses the creation of social features in Offpunk, a decentralized social network. The author, Ploum, was inspired by a conversation about utilizing existing tools like blogs and email to create a social network without relying on centralized platforms.

Key features introduced in Offpunk 3.0 include:

1. Share: Users can easily share interesting content via email by simply typing "share" followed by the recipient's email address.
2. Reply: Users can send feedback or thanks to blog authors by typing "reply." Offpunk helps find email addresses on the page and allows users to save these addresses for future use.

Ploum emphasizes that social networks are about how we use existing infrastructure rather than new protocols. He enjoys a simple, personal way of interacting without needing complex platforms or technologies. He has already used these features extensively and encourages others to embrace this approach to social interaction online.

Overall, the author advocates for enjoying lightweight communication methods, staying away from centralized services, and maintaining a personal touch in online interactions.

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Summary of Video Encoding and Decoding with Vulkan Compute Shaders in FFmpeg

Video encoding and decoding has become easier for everyday users thanks to hardware accelerators in devices and open-source codecs. However, professionals handling high-resolution video still face performance challenges, often needing expensive solutions.

FFmpeg utilizes Vulkan Compute shaders to improve video processing on consumer GPUs, allowing for efficient encoding and decoding without relying on specialized hardware. Codecs, which compress video data, present challenges for GPU acceleration due to their mix of parallel and serial processing steps.

Historically, hybrid decoding (using both CPU and GPU) has been slow, leading to a preference for fully GPU-based implementations. Modern GPUs can handle more parallel tasks, making it possible to implement codecs entirely in GPU compute shaders.

FFmpeg is a widely-used open-source tool that facilitates multimedia processing, providing access to hardware acceleration while maintaining robust software codec handling. This allows users to switch between software and hardware decoding seamlessly.

Key codecs discussed include:

• FFv1: A lossless codec that was optimized for GPU use due to its high bandwidth requirements.
• APV: Designed for parallel processing, gaining popularity in professional media.
• ProRes: A standard codec for editing that is not royalty-free, with unofficial FFmpeg support.
• JPEG: A codec with potential for parallel processing, historically significant and adaptable.

Recent updates to FFmpeg have introduced support for several codecs using GPU acceleration. Looking ahead, JPEG2000 and PNG may also benefit from GPU acceleration, but JPEG2000 poses unique challenges due to its complex design.

Vulkan is positioned as a powerful API for both graphics and compute tasks, offering developers flexible optimization options and broad compatibility across devices. FFmpeg leverages Vulkan's capabilities to enhance video processing efficiency, making it a valuable tool in video editing and playback.

In the Illinois primaries, the cryptocurrency industry's super PACs spent $14.2 million, but 90% of that money, or $12.8 million, was ineffective. This money was mostly directed against Democratic candidates who ended up winning their races, like Juliana Stratton for Senate and La Shawn Ford for the House. The PACs significantly opposed Stratton and Ford, spending $10 million and $2.5 million, respectively. Their only wins came when they supported candidates who were likely to win regardless of their contributions. Notably, they opposed Robert Peters, who finished with only 12% of the vote. The PACs still have a lot of funds available, having used less than 6% of their total, indicating a long campaign ahead.

The text discusses a new approach to the $k$-means algorithm, which is traditionally used for organizing data but has not been widely applied in online systems. The authors highlight that current GPU implementations of $k$-means face performance issues due to system limitations, particularly in how data is handled during the assignment and update stages.

To improve this, they introduce "flash-kmeans," a new implementation designed for modern GPUs. Flash-kmeans includes two main innovations:

1. FlashAssign: This combines distance calculations and selection processes to avoid creating large temporary data sets.
2. Sort-inverse update: This method reduces the issues caused by simultaneous data writing, enhancing efficiency.

Additionally, the authors incorporate design strategies to improve system performance. Tests on NVIDIA H200 GPUs show that flash-kmeans is significantly faster, achieving up to 17.9 times quicker results compared to existing methods and outperforming popular libraries by over 200 times.

This text is about optimizing the performance of a Java order-processing application. The author achieved significant improvements in speed and efficiency by addressing common issues in the code, referred to as anti-patterns.

Key Points:

1. Performance Improvements: The app's performance improved from 1,198 milliseconds to 239 milliseconds after optimizations, with orders processed per second increasing from 85,000 to 419,000, and memory usage dropping significantly.

2. Common Anti-Patterns: The author identifies eight performance issues that can occur in Java code:

   • String concatenation in loops: Causes inefficient copying due to string immutability.
   • Stream iteration inside loops: Results in unnecessary multiple scans of the collection.
   • Using String.format() frequently: Slow due to repeated parsing.
   • Autoboxing in hot paths: Creates unnecessary object wrappers for primitives.
   • Using exceptions for control flow: Expensive due to stack trace creation.
   • Too-broad synchronization: Causes bottlenecks by locking entire methods.
   • Recreating reusable objects: Involves costly setup on each call.
   • Virtual thread pinning (JDK 21-23): Blocks the execution of other threads when a virtual thread is synchronized.

3. Profiling and Fixes: The author emphasizes the importance of profiling to identify these issues and suggests fixes, such as using StringBuilder instead of concatenation, avoiding streams inside loops, and using ReentrantLock for better thread management.

4. Cascading Effects: Optimizing these patterns not only improves the performance of a single application but can have significant benefits when scaled across multiple instances in production.

5. Future Parts: The author plans to share more detailed profiling data and automation strategies for identifying performance improvements in upcoming parts of the series.

This summary highlights the critical insights from the text regarding Java performance optimization and the common pitfalls developers should avoid.

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Summary of "Too Much Color"

The author discusses their work on color precision in CSS, specifically for a minifier tool called csskit. They have formed an opinion that when writing colors, three decimal places (3dp) are usually sufficient. For certain color formats, like lab() and lch(), you can even get away with one decimal place (1dp). The author suggests that using excessive decimal precision is unnecessary and wasteful.

Key points include:

1. Color Precision: Generally, 3dp is enough for colors like oklch and oklab, while lab and lch can work with 1dp. For RGB and HSL, using integers or 0dp is acceptable.

2. Measuring Color Difference: The author explains the Delta-E (dE) metric, which quantifies how different two colors appear. A dE value below 2.0 means colors are indistinguishable to most people.

3. Rounding Colors: The author recommends rounding oklch values to three decimal places, as the differences are imperceptible to the human eye. However, rounding to two decimal places can lead to issues in color mixing due to compounding errors.

4. Color Spaces: Different color spaces (like oklch and lab) have different ranges and rounding rules. For instance, oklch components are between 0 and 1, so 3dp is safe, while lab's ranges necessitate 1dp for safe rounding.

5. Alpha Channel: The alpha value should also be considered with 3dp precision to avoid visible errors when blending colors.

6. Browser Handling: Current web browsers do not optimize color precision and store values in a standard format without pre-rounding.

In conclusion, the author determined that a simple static lookup for recommended decimal places based on color space is more efficient than complex computations, leading to a cleaner and more effective minification process in csskit.

Chuck Norris, the famous martial artist and action star known for his role in "Walker, Texas Ranger," has passed away at the age of 86. He died peacefully in Hawaii, surrounded by family. Norris was celebrated not only for his film career but also as a devoted family man.

He was a skilled martial artist with multiple black belts in various disciplines and starred in numerous action films during the late 1970s and 1980s, including "Missing in Action" and "The Delta Force." Norris later found success on television with "Walker, Texas Ranger," which aired from 1993 to 2001.

Born in Oklahoma, Norris served in the Air Force before pursuing a career in martial arts and acting. His legacy includes not only his films but also his influence as a cultural icon, particularly in conservative circles. He is survived by his wife, children, and grandchildren.

The Free Software Foundation (FSF) typically does not initiate copyright infringement lawsuits, but when it does, it seeks to promote freedom. Recently, the FSF was involved in the case Bartz v. Anthropic, a class action lawsuit claiming that Anthropic violated copyright by using works from Library Genesis and Pirate Library Mirror to train large language models (LLMs). A court ruled that training LLMs with these books might be fair use, but the legality of downloading them is still in question.

Instead of going to trial, the parties involved agreed to settle and are now contacting potential copyright holders to offer compensation. The FSF holds copyrights to various works, including a book by Sam Williams and Richard Stallman that was used in the datasets for Anthropic's LLMs. All FSF works are published under free licenses, allowing unrestricted use.

The FSF believes in protecting computing freedom and urges developers like Anthropic to share complete training inputs, models, and source code with users. While the FSF has limited resources and must be selective in its legal actions, it would seek user freedom as compensation if its copyrights were violated in such cases.

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FFmpeg has a filter called drawvg that allows users to render vector graphics on video frames. This feature uses a specific scripting language called VGS (Vector Graphics Script), which is designed to be simple and concise, drawing inspiration from other graphic languages like SVG and PostScript.

Key Points:

• VGS scripts are used to create 2D graphics and can dynamically compute values based on video frame properties.
• The filter can be combined with other FFmpeg filters for various effects, such as animations, transitions, and pixelation.
• Examples include creating progress indicators, using frame metadata to draw shapes, and making custom transitions using masks.
• The displace filter can be used with drawvg to produce wave effects on videos.

The usage of drawvg allows for creative visual enhancements in videos, making it a powerful tool for video editing in FFmpeg.

Summary of SharpAI Aegis Features

SharpAI Aegis is designed for local AI home security and performs well on benchmarks. Here are the main points:

1. Performance Benchmark: The Qwen3.5-9B model runs locally on a MacBook Pro and scores 93.8% on a specific benchmark, which is close to the cloud-based GPT-5.4 model. It operates at 25 tokens per second and requires 13.8 GB of memory.

2. Local vs. Cloud: Running locally has no API costs and ensures complete data privacy. It shows competitive performance compared to cloud models, often having faster response times.

3. HomeSec-Bench: This benchmark evaluates AI performance in real home security scenarios, testing tasks like tool selection, event classification, and multi-turn reasoning. It uses AI-generated images for testing and covers various critical tasks.

4. Key Features: The system can deduplicate events, classify security threats, and maintain context in conversations, making it effective for home security needs.

5. Live Demonstration: Users can watch the benchmark suite in action on Apple Silicon, showcasing the model's capabilities.

Overall, SharpAI Aegis offers a powerful, local AI solution for home security with strong performance metrics and privacy advantages.

The text discusses a program called "Drugwars" created for TI-82/83/83+ calculators. It includes a code snippet that allows players to engage in a simulation game where they buy and sell drugs to pay off debts and make money, while avoiding police encounters.

Key points include:

1. Game Objective: Players aim to pay off a debt to a loan shark and accumulate wealth within a month, while being cautious of police.
2. Gameplay Features: The game features buying and selling drugs (e.g., cocaine, heroin, acid, etc.) with fluctuating prices, and players can interact with rival dealers, police, and other events.
3. Instructions: Players are prompted with choices and can type commands to perform actions like buying or selling drugs.
4. Community Feedback: Several comments discuss issues related to game compatibility with different calculator models, suggestions for improvements, and experiences of playing the game.
5. Code Availability: The code is shared publicly, allowing others to study or modify it for different platforms.

Overall, the text serves as a guide and discussion platform for a nostalgic calculator game, with insights into its programming and community interactions.

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Summary of Building a Reader for the MK4001MTD Hard Drive

Introduction

• The MK4001MTD is the world's smallest hard drive, a 0.85-inch model introduced by Toshiba in 2004, offering 4 GB of storage.
• It was popular in devices like the Nokia N91 but became obsolete as flash storage prices dropped.

History

• The author collected several MK4001MTD drives and aimed to create a reader for them.
• Initial research didn’t yield successful methods for reading data from the drive.

Reverse Engineering

• The author purchased a Nokia N91 to capture the drive's signal and understand its communication protocol.
• They discovered the drive uses a 4-bit SD/MMC interface and transports ATA commands over this interface.
• Using a Raspberry Pi and a Pico microcontroller, they began reverse-engineering the drive's communication and developed firmware to translate USB requests into commands the drive could understand.

Hardware Design

• The hardware involved an RP2040 chip with a design tailored to connect directly to the MK4001MTD.
• The board’s size is larger than the drive due to the complexity of the design.

Results

• The SDIO clock is set to 10 MHz, providing usable but slow performance.
• The project was successful, demonstrating how advanced reasoning capabilities of AI can assist in engineering tasks.

Project Files

• All files related to the project, including source code and design schematics, are available on GitHub for others to study and modify.

References

• The document includes various references to sources about the MK4001MTD drive and its specifications.

Summary of Oregon School Cell Phone Ban

Oregon Governor Tina Kotek visited Estacada High School to assess the impact of her cell phone ban implemented last summer. The policy, which prohibits students from using phones during school hours, has received positive feedback, including from veteran teacher Jeff Mellema, who noted improved classroom discussions and interactions.

The governor aimed to understand how the ban was working, as educators had previously reported that cell phones disrupted learning. Research supports these claims, indicating that phones can distract students even when not in use. Since the ban, all public school districts in Oregon have complied.

During her visit, Kotek observed changes in student interactions and received feedback that the ban has reduced distractions and issues like bullying. However, students raised concerns about difficulties in managing schedules without their phones and suggested allowing phone use during break times.

Estacada School District has seen significant improvements, including a graduation rate jump from 38.5% in 2015 to 92.5% in 2025, attributed to effective teaching policies and student engagement. The district has incorporated parent feedback in its cell phone policy, allowing students to keep phones in backpacks for emergencies.

Kotek expressed gratitude for the positive outcomes and noted that Estacada serves as a model for other districts in Oregon.

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Channels are a new feature in research preview that allows Claude Code to receive events from external sources like Telegram and Discord. To use channels, you need to have Claude Code version 2.1.80 or later and log in to claude.ai, as authentication via console or API key is not supported. Team and Enterprise users must enable this feature explicitly.

Key Points:

• Channels Overview: Channels allow Claude to react to events while you are not at the terminal. They can send and receive messages, functioning like a chat bridge. Events are only processed when the session is active, so for continuous use, you should run Claude in the background.

• Supported Platforms: Currently, Telegram and Discord are the only supported channels. Each is installed as a plugin and requires specific setup steps.

• Installation Steps:

  • For Telegram:

    1. Create a bot using BotFather and get the token.
    2. Install the Telegram plugin in Claude Code.
    3. Configure the plugin with your bot token.
    4. Restart Claude Code with the channel enabled.
    5. Pair your account by messaging the bot.

  • For Discord:

    1. Create a bot in the Discord Developer Portal and get the token.
    2. Enable Message Content Intent in bot settings.
    3. Invite the bot to your server.
    4. Install the Discord plugin in Claude Code.
    5. Configure the plugin with your bot token.
    6. Restart Claude Code with the channel enabled.
    7. Pair your account by messaging the bot.

• Demo Option: The fakechat demo allows you to test the chat functionality locally without needing external services.

• Security Features: Each channel maintains a sender allowlist, meaning only approved IDs can send messages. You can control which channels are active in each session.

• Admin Controls: For Team and Enterprise plans, admins can enable channels in the settings. Users can then opt to use channels in their sessions.

• Research Preview Notes: This feature is still in a research preview phase and may change based on user feedback. During this period, only approved plugins can be used.

For further exploration, you can build your own channels or use features like Remote Control and Scheduled Tasks.

I'm sorry, but I can't access external links or content from specific URLs. However, if you provide me with the text you'd like summarized, I can help with that!

OpenAI has announced its plan to acquire a company called Astral. This acquisition aims to enhance OpenAI's capabilities and expand its offerings. Further details about the deal and its benefits are available on OpenAI's official website.

Summary of Cockpit

Cockpit is a user-friendly web-based interface for managing Linux servers. It allows system administrators to interact directly with the server’s operating system through a web browser.

Key Features:

• Easy installation on various Linux distributions like Debian, Fedora, and RHEL.
• Simplifies tasks such as starting containers, managing storage, configuring networks, and viewing logs.
• Seamlessly switch between the web interface and terminal commands; actions taken in Cockpit can be managed through the terminal and vice versa.
• Supports adding and managing multiple machines that have Cockpit installed and can be accessed via SSH.

For developers, there are resources available for contributing to Cockpit, including documentation, communication channels, and guiding principles.

The n0 team has announced the launch of "noq" (number 0 QUIC), a new general-purpose QUIC implementation that includes support for multipath and NAT traversal. This implementation powers the iroh networking library, which is currently in use since version 0.96.

Background: The n0 team forked the existing QUIC implementation, Quinn, to address specific needs in iroh, such as managing multiple connection paths and congestion control. Over time, the developments in both projects diverged, leading to the decision for a hard fork to allow for more structural changes without burdening Quinn users.

Key Features of noq:

1. QUIC Multipath: This feature allows multiple connection paths (like relay and direct connections) to be recognized by QUIC, improving latency and congestion management.
2. QUIC NAT Traversal: This is a new implementation that handles NAT traversal more effectively, making it reliable across various network conditions.
3. QUIC Address Discovery (QAD): This feature enables identifying public IP addresses securely, improving user privacy.
4. Extended QLog Support: Enhanced logging capabilities for better debugging and insights into QUIC connections.
5. WeakConnectionHandle: A new type of connection handle that can be upgraded without needing to maintain a constant connection, useful for connection management.

noq is already in production with iroh and has been tested for interoperability with other QUIC implementations. The n0 team sees noq as a long-term solution and plans to continue improving NAT traversal and performance optimizations. They invite collaboration with others working on QUIC and related technologies.

For more information or to get involved, users can check the documentation, access the code, or join the conversation on Discord.

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The article discusses a project called "autoresearch" led by Andrej Karpathy, which involves an autonomous coding agent that improves a neural network training script. Initially, the agent could only run one experiment at a time using a single GPU, leading to slow progress. To enhance its efficiency, the team provided the agent with access to a GPU cluster, allowing it to run experiments in parallel.

Key points from the summary:

1. Parallel Experiments: With access to 16 GPUs, the agent was able to conduct about 910 experiments in just 8 hours, significantly speeding up the research process. This approach allowed the agent to test multiple configurations simultaneously, rather than sequentially, which vastly improved efficiency.

2. Phased Approach: The research was divided into five phases:

   • Hyperparameter sweeps: The agent explored various settings, improving its performance from a baseline validation loss of 1.003 to 0.981.
   • Architecture discovery: By testing different model widths simultaneously, the agent found that increasing the model's width significantly enhanced performance.
   • Fine-tuning: Focused on refining the model with various adjustments, achieving a further improvement.
   • Optimizer tuning: The agent made critical adjustments to the optimizer, resulting in small but meaningful performance gains.
   • Diminishing returns: The agent explored further combinations but saw less significant improvements as it approached the best configuration.

3. Emerging Strategies: The agent developed its own strategies to capitalize on the differences between GPU types, utilizing cheaper GPUs for initial testing and more powerful ones for final validation.

4. Cost Efficiency: The entire session of running experiments cost about $300, showing that scaling up with more GPUs not only sped up the research process but also remained economically viable.

5. Setup Instructions: The article provides guidance for others to replicate the setup using SkyPilot, an open-source tool for managing cloud-based GPU jobs.

Overall, the project demonstrates how providing an AI agent with more computational resources can lead to significant advances in research efficiency and effectiveness.

Google is testing a new feature that replaces news headlines in search results with AI-generated ones, which can sometimes change the meaning of the original headlines. This experiment has been noticed by staff at The Verge, where headlines have been altered to sound more like clickbait, potentially misleading readers. Although Google describes this as a small experiment, similar changes were previously implemented in their Google Discover news feed.

Google aims to enhance user engagement by better matching titles to search queries, but this approach raises concerns about the integrity of journalism. Changes to headlines can undermine trust, especially as many news organizations are already struggling. While the altered headlines are not widespread yet, they could signal a shift in how Google prioritizes news content in search results, moving away from the traditional "10 blue links" format that users have relied on.

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Germany has officially adopted the Open Document Format (ODF) as the standard for documents used in public administration through its new digital framework called the Deutschland-Stack. This initiative, announced by the Federal Ministry for Digital and State Modernization, aims to create a unified and interoperable digital environment across all levels of government.

Key points include:

• Standardization: ODF and PDF/UA are now required formats, while proprietary formats are banned from official use.
• Interoperability: The move ensures consistent document handling and compatibility across public institutions.
• Digital Sovereignty: This aligns with Germany's broader strategy to reduce dependency on specific vendors and prioritize open standards, local data storage, and open-source development.
• Support for Democracy: Experts highlight that using open formats is vital for transparent and democratic public administration.
• Future Planning: The Deutschland-Stack will guide digital infrastructure developments until 2028.

This mandate reflects Germany's commitment to digital sovereignty and is part of larger European efforts to enhance digital interoperability and security.

The text discusses a recent request for comment (RfC) on updating Wikipedia's guidelines regarding the use of large language models (LLMs) in article writing. The proposal by Chaotic Enby received strong support, with 44 votes in favor and only two against, indicating a consensus for clearer guidelines.

Key points include:

1. Current State: The existing LLM policy has been vague and has faced criticism for being too broad or too strict. Previous proposals have struggled to gain consensus.

2. Proposed Changes: The new guidelines aim to prohibit the generation of content via LLMs while allowing some limited uses, such as copyediting and translation. They address the rising issues of LLM misuse on Wikipedia and aim to protect editors from unfounded accusations of using LLMs based solely on their writing style.

3. Support and Opposition: Supporters believe the new guidelines are clearer and can serve as a foundation for future policies. Some opponents argue that the guidelines are too strict and do not differentiate between harmful and responsible uses of LLMs.

4. Future Considerations: While the new guidelines are seen as a step forward, some editors express concerns over the potential for a blanket ban on LLMs. There is a desire for ongoing dialogue to refine these policies as LLM technology evolves.

Overall, the discussion highlights the community's efforts to balance the responsible use of technology with the integrity of Wikipedia content.

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Summary of KiCad Version 10.0.0 Release Announcement

The KiCad project has announced the release of Version 10.0.0 on March 20, 2026. This update includes numerous new features, improvements, and hundreds of bug fixes. Users can download the new version from the KiCad website.

Key Highlights:

1. New Features and Improvements:

   • Dark Mode: Support for dark mode on Windows.
   • Customizable Toolbars: Users can now rearrange their toolbars to suit their workflow.
   • Lasso Selection: A new selection method for objects in both PCB and schematic editors.
   • Import Capabilities: Ability to import designs from Allegro, PADS, and gEDA/Lepton PCB.

2. Schematic Editing Enhancements:

   • Support for design variants tracking.
   • New "hop-over" arcs for wire crossings.
   • Added support for jumpers and grouping of schematic components.
   • CSV support for Symbol Editor Pin Table.

3. PCB Design Upgrades:

   • Complete overhaul of track tuning system.
   • Enhanced design blocks feature for managing board layouts.
   • New graphical DRC rule editor for creating custom design rules.

4. Library Improvements:

   • Transition to STEP files for 3D models, reducing install size and improving accuracy.
   • Significant growth in library contributions with thousands of new symbols and models.

5. Contribution and Development:

   • Over 7,600 unique code changes were made by various contributors, marking an increase in development activity.

This release reflects the hard work of the KiCad community, with many contributors enhancing the software through code, bug reports, and library improvements. For more details, users should check the KiCad user forum and the full list of changes.

Voltair, founded by Hayden, Ronan, Avi, and Warren, is developing durable, hybrid drones for inspecting power utility infrastructure. The U.S. has a vast network of aging power lines, many supported by wooden poles, which can cause dangerous wildfires if not properly maintained. Current inspection methods, like foot patrols and helicopters, are inefficient and risky.

Drones are emerging as a better solution, but they face challenges like battery life and regulatory restrictions. Existing drone-in-a-box systems are costly and not scalable. Voltair's innovative approach involves creating rugged, long-range drones that can recharge inductively on inexpensive charging pads placed along power lines. This allows for efficient inspections while minimizing downtime.

The drones can quickly respond to urgent inspection needs and collect large amounts of data, which is then offloaded at charging stations for processing. Although power utilities are their main focus, there are potential applications in various industries like telecom and agriculture.

Voltair has recently secured its first major contract and plans to charge customers per inspection. They welcome feedback, especially from those in the energy sector, to improve their service.

Summary of Clockwise's Transition

Clockwise is entering a new phase as it joins Salesforce. The company has successfully helped major clients like Uber and Netflix by creating over 8 million hours of Focus Time and rescheduling 23 million meetings. However, as part of this transition, Clockwise's services will end on March 27, 2026, and the product will no longer be available.

Key Points:

• End of Service: Clockwise will no longer operate after March 27, 2026. Customers should prepare for the transition.
• Customer Data: All user data will be deleted after the service ends, and Salesforce will not access this data.
• Refunds: Customers will receive prorated refunds for any prepaid subscriptions extending beyond the shutdown date.
• Alternative Tools: Clockwise recommends Reclaim for AI-powered scheduling and offers support for transitioning, including a price match guarantee and dedicated onboarding.
• Customer Support: Support will be available until March 27, 2026, for any questions during the transition.

The team expresses gratitude to the 40,000 organizations that used Clockwise, emphasizing the positive impact made on scheduling and time management.

Teenage brains are not damaged; instead, they are primed for growth and development. This period is a time when young people can thrive and learn new things effectively.

The author reflects on their experiences with cryptocurrencies and AI tools, expressing a preference for waiting until these technologies prove to be reliable and useful. They recall being pressured to invest in cryptocurrencies because of a fear of missing out, which they find unreasonable. The author argues that there's no rush to adopt new tech, as it will still be available later when it’s more established and beneficial. They compare their approach to learning Git, which they adopted when it was widely needed, rather than during its early, unstable days. They emphasize that being an early adopter often doesn't lead to significant advantages and that it's perfectly fine to wait and assess the usefulness of new technologies before diving in.

The text discusses how Linux manages memory, specifically focusing on page faults, the mmap function, and the userfaultfd mechanism, particularly in the context of speeding up Virtual Machine (VM) snapshot restores. Here are the key points summarized:

1. Virtual Memory and Page Faults:

   • Linux processes use virtual addresses rather than directly accessing physical RAM.
   • When a process uses mmap to allocate memory, physical pages are not allocated immediately. They are only allocated when the process first accesses that memory, triggering a page fault.
   • The kernel handles the page fault by allocating the necessary physical page and updating the page table.

2. Types of Memory Mappings:

   • Different mmap calls create various relationships between virtual addresses and physical pages, affecting how memory is managed (e.g., anonymous private, anonymous shared, file-backed private, and file-backed shared).

3. Challenges with VM Snapshots:

   • When restoring a VM from a snapshot, the entire guest memory must be read from disk, leading to long restore times. For example, restoring a 4GB VM can take around 7 seconds due to sequential disk reads.

4. Using userfaultfd for Lazy Restoration:

   • userfaultfd allows userspace programs to handle page faults, enabling on-demand loading of memory pages as they are accessed, rather than all at once.
   • This significantly reduces restore latency because the VM can start running immediately while pages are populated as needed.
   • The handling of page faults involves a series of coordinated events between the guest kernel, host kernel, and userspace handler.

5. Handling Concurrent Faults:

   • Multiple virtual CPUs (vCPUs) may generate simultaneous page faults, which are managed efficiently to prevent data corruption.

6. Trade-offs:

   • On-demand paging has benefits like reduced initial restore time but can introduce complexity and latency under heavy loads or slow storage.
   • The choice between eager copying and on-demand paging depends on workload characteristics and latency requirements.

7. Practical Implementation:

   • Firecracker, a specific VMM, uses userfaultfd to delegate fault handling to an external process, allowing for flexible and efficient page data management.

8. Future Considerations:

   • Strategies such as prefetching and multi-threading handlers could help optimize performance during the initial post-resume burst of page faults.

In summary, understanding Linux's memory management, particularly through mmap and userfaultfd, is crucial for optimizing VM snapshot restorations and improving overall performance in virtualization contexts.

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The text emphasizes the importance of intentionality when using AI Coding Agents to write code. Here are the key points:

1. Intentional Code Writing: As AI tools generate more code, it’s crucial to ensure that the code remains organized and understandable.

2. Self-Documenting Code: Code should be structured so that its purpose is clear without needing extra comments. This helps maintain the codebase over time.

3. Semantic Functions: These are simple, well-defined building blocks that take specific inputs and produce outputs. They should avoid side effects and should be easy to test. The aim is for future developers to understand the code easily.

4. Pragmatic Functions: These are more complex and can contain unique logic. They organize the code but should not be overly complicated. Documentation is important for these functions to clarify unexpected behaviors.

5. Data Models: Data structures should prevent incorrect states and ensure that each field serves a clear purpose. Good naming conventions are essential to avoid confusion.

6. Common Pitfalls: Issues arise when semantic functions become too complex, or when models accumulate unnecessary fields. This can lead to confusion and bugs, so maintaining clarity and focus in both functions and models is crucial.

7. Goal: Following these guidelines leads to scalable codebases, which support faster development and improved software quality.

In summary, the document advocates for clear, intentional coding practices that prioritize simplicity and clarity to create maintainable and scalable code.

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Kitten TTS is an open-source project that offers small text-to-speech models for use on devices. They have just released three new models with different sizes: 80M, 40M, and 14M parameters. The largest model (80M) provides the best sound quality, while the 14M model excels in expressiveness despite being under 25MB. This upgrade supports English speech generation in eight voices (four male and four female).

The models are designed to work on various devices, including Raspberry Pi, low-end smartphones, and more, without needing a GPU. They are optimized for performance with quantization techniques. The aim is to improve on-device AI by providing efficient models for voice applications, with plans for multi-lingual models in the future. Feedback from users is encouraged.

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Aakash and Viswesh are developing a tool called Canary, which uses AI to improve the process of testing code changes. Canary analyzes your codebase to understand changes made in pull requests (PRs) and automatically generates and runs tests that simulate real user interactions. This helps catch potential issues before code is merged.

They noticed that while AI tools help teams code faster, real user behavior wasn't being tested adequately, leading to problems in production. Canary addresses this by reading code changes, understanding their intent, and providing test results directly on the PR, including recordings of what changed.

Additionally, tests created from a PR can be added to ongoing regression tests, and users can generate tests by simply describing what they want to test in everyday language. For example, Canary successfully detected a significant error in an invoicing process for a customer.

The tool involves complex techniques and various testing modalities, making it more effective than single-model AI solutions. To measure its performance, they created QA-Bench v0, a benchmark for code verification, which showed that Canary outperforms other AI models in identifying relevant tests for code changes.

They invite feedback from others in the field of code verification and provide links to their demo and benchmark report for more details.

The text references two articles about a trial involving Afroman, a musician. He is facing legal issues related to a police raid on his home in Ohio. The articles likely cover the details of the lawsuit, the circumstances of the raid, and the trial's outcome, but the specific details are not provided in the summary request.

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There's ongoing work to combine generative user interfaces (UI) and code execution for AI agents. I created a prototype to explore this integration:

• Markdown as a protocol: This approach uses a single stream for text, executable code, and data.
• Streaming execution: Code is executed line by line as it is received.
• Mount() function: The AI agent generates React UIs that allow data to flow freely between the client, server, and language model.

I would love to hear your thoughts!

Ofcom has fined the website 4chan £450,000 because it failed to protect children from accessing online pornography. The fine highlights the importance of keeping children safe from harmful content on the internet.

The text discusses a technical process of analyzing UDP packets transmitted over a network, specifically using the QSGMII protocol. Here's a simplified summary of the key points:

1. UDP and QSGMII Overview: UDP (User Datagram Protocol) is a transport-level protocol used for sending messages over IP networks. QSGMII (Quad Serial Gigabit Media-Independent Interface) is a method that allows four SGMII (Serial Gigabit Media Independent Interface) streams to be sent over a single connection.

2. Setting Up the Experiment: The author, who works on embedded software at Oxide Computer Company, connects a high-speed probe to a network switch to capture raw voltage signals. They configure an oscilloscope to collect data on UDP packets being transmitted.

3. Data Processing: The captured waveform data is exported and decoded using a custom parser. The goal is to understand the signals and extract meaningful information from the raw data.

4. Decoding Process: The author explains how to identify and process the QSGMII signals. They use techniques like looking for specific patterns (comma characters) in the data to synchronize and decode the signal into code groups, which are then organized into separate streams for each port.

5. Extracting Packets: The decoded code groups are further analyzed to extract Ethernet frames. The author identifies key components like source and destination MAC addresses, as well as the Ethernet type.

6. Output and Verification: Finally, the decoded packets are written to .pcap files for analysis with tools like Wireshark. The author confirms that the packets are indeed UDP packets, achieving their goal.

7. Conclusion: The process showcases the journey from low-level signal analysis to extracting high-level packet information, with gratitude expressed to contributors and recommendations for further reading on the subject.

This summary highlights the main objectives and findings of the original text without delving into the complex technical specifics.

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The author woke up to find that after updating MacOS, their Docker containers were no longer accessible due to a problem with custom DNS resolution. They hope Apple will address the issue, and they advise others to avoid updating if they rely on this feature.

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On March 19, 2026, it was announced that OpenAI will acquire Astral, the company behind popular Python projects uv, ruff, and ty. The Astral team will join OpenAI's Codex team, with a commitment to continue supporting their open-source tools.

Key Points:

1. Acquisition Purpose: OpenAI aims to enhance its Codex capabilities by integrating Astral's expertise and tools, particularly focusing on open-source development.

2. Astral Projects:

   • uv: The most significant project, addressing Python's environment management issues. It has gained massive popularity, with over 126 million downloads last month.
   • ruff: A linter and formatter for Python, which improves the coding experience.
   • ty: A fast type checker for Python. While useful, it does not have the same impact as uv.

3. Community Concerns: There are worries about the risks of a single company owning vital pieces of Python infrastructure. Astral's private package registry, pyx, was not mentioned in the acquisition announcements, raising questions about its future.

4. Competitive Landscape: The acquisition may intensify competition between OpenAI and Anthropic, which are both enhancing their coding tools for AI.

5. Investor Influence: Astral's recent funding rounds were not widely known before the acquisition. Investors will now exchange their stakes in Astral for shares in OpenAI, potentially influencing Astral's business decisions.

6. Open Source Commitment: Astral's team has reassured the community about maintaining the quality and openness of their projects, suggesting that if issues arise, the community could fork the projects to keep them alive.

Overall, while the acquisition presents opportunities for growth and innovation in software development, it also raises concerns about the control and future of essential open-source tools in the Python ecosystem.

The FSFE (Free Software Foundation Europe) has lost its payment provider, Nexi, which canceled their contract without notice. This affects over 450 supporters who make recurring donations via credit card or direct debit, halting their payments. The FSFE has informed those affected, but if anyone hasn't received an email, they should reach out to continue their support.

Nexi had previously requested access to sensitive data, including usernames and passwords of supporters, which the FSFE refused. Despite being a customer for 15 years, the FSFE was not notified of a deadline concerning Nexi's request, leading to the cancellation of their contract.

The FSFE is transitioning to a new payment provider, but supporters will need to set up their donations again. They are concerned that some may miss the email notification and stop their financial support, which would impact their work for Free Software.

The FSFE is committed to protecting the privacy of its supporters and has updated their donation pages on their website with the new payment provider. They appreciate continued support during this challenging time.

Microsoft has recently released an update for Windows 11 (versions 25H2 and 24H2) that has caused issues with signing into Microsoft accounts. Users of apps like OneDrive and Office may see misleading "no internet" error messages, even when they are connected online. This problem does not affect businesses using Entra ID for authentication.

Microsoft suggests that the issue may fix itself, but restarting the device while connected to the internet can help. If the device is restarted without an internet connection, the issue might return.

Microsoft is currently working on a solution and plans to release a fix soon. This is part of a pattern of recent troubles with Windows updates, including problems experienced earlier this year.

A start-up called Enhanced is planning a "steroid Olympics" in Las Vegas and will begin selling performance-enhancing peptides. This initiative is supported by Robert F. Kennedy Jr., who aims to deregulate 14 peptides previously restricted by the FDA. Kennedy believes that making these peptides available through ethical suppliers will help move the underground market into pharmacies.

Enhanced plans to offer five peptides initially through an online service, with eight more potentially added later. These peptides claim to improve muscle growth, recovery, and cognitive abilities, but they have been flagged by the FDA for safety risks. The company’s CEO, Maximilian Martin, believes that data from the sporting events will help enhance their brand in the health market.

Kennedy has expressed his personal positive experiences with peptides, while experts warn that using grey market peptides can be risky and may lead to placebo effects rather than real benefits. Enhanced has investors connected to the current administration, including Donald Trump Jr. and billionaire Peter Thiel. The company's founder, Aron D’Souza, sees the event as a way to redefine the rules of performance enhancement in sports. However, interest in the event has waned, with participation dropping from around 100 athletes to about 40.

The article celebrates the 80th anniversary of ENIAC, the first general-purpose digital computer, which was developed by inventors J. Presper Eckert and John W. Mauchly. ENIAC marked a significant milestone in technology, sparking a revolution in computing. The piece highlights its historical importance and the innovations it brought to the field of technology.

Here’s a simplified summary of the update about OpenTTD on Steam and GOG:

• The update addresses changes regarding OpenTTD, especially after Atari's re-release of Transport Tycoon Deluxe.
• There are misconceptions about the situation, but OpenTTD's team clarifies they were not pressured by Atari.
• Atari wants to work with OpenTTD to benefit both the new game release and the existing OpenTTD community.
• New players will need to buy Transport Tycoon Deluxe to access OpenTTD on Steam and GOG, but it will still be available for free on OpenTTD's website.
• Removing OpenTTD from these platforms would disrupt current players, so they chose a compromise instead.
• OpenTTD is based on Transport Tycoon Deluxe, and this collaboration will help keep the project alive.
• Atari has agreed to support OpenTTD's server costs, and the community's donations are appreciated.
• The team asks for respectful feedback about these changes, emphasizing the importance of the Transport Tycoon community for many fans.

Essex Police have temporarily stopped using live facial recognition (LFR) technology after a study revealed it disproportionately targets black individuals compared to other ethnic groups. The Information Commissioner’s Office (ICO) noted potential bias and accuracy risks, prompting the pause.

The study conducted by researchers from the University of Cambridge involved 188 actors and found that while the system correctly identified about half of the individuals on a watchlist, it showed a statistical bias favoring black participants over others. This raised concerns about fairness in law enforcement practices.

Despite the technology leading to over 1,300 arrests in London, critics argue that the risks of bias and inaccuracies in LFR systems are unacceptable. Essex Police stated they would work to improve the technology before resuming its use and would continue to monitor for any bias.

In March 1976, a 20-year-old student began exploring type design while studying commercial art at North Hennepin Community College, near Minneapolis. Initially interested in illustration, the student also took graphic design classes and learned lettering techniques from instructor Lance Kiland.

A project in an advanced lettering class inspired a passion for type design. The student had a background in type from high school, where they worked on a newspaper and yearbook, and began experimenting with different types of lettering. Discovering the magazine U&lc, which showcased beautiful typography, further fueled this interest.

For the final class project, the student designed an original typeface called Uncial Sans, inspired by uncial calligraphy. This experience ignited a lifelong desire to create typefaces, leading to sketches and ideas over the years, even if other graphic design work took precedence. Eventually, about two decades later, the student saw a typeface published, but it all began with that college project in 1976.

Summary of TI-89 Height-Mapped Raycaster

The TI-89 Height-Mapped Raycaster is a custom dungeon crawler game for the TI-89 graphing calculator, based on the FAT Engine from 2002. It uses a 10MHz Motorola 68000 processor and features a 160x100 grayscale display.

Key Features:

• Procedural dungeon generation with various room shapes and staircases.
• Height-mapped raycaster for realistic floor elevations and stairs.
• Textured walls and doors.
• Billboard enemy sprites with transparency effects.
• Bump-to-attack combat system and a blinking minimap.
• Smooth walking on stairs.

Included Tools:

• Standalone height-mapped raycaster demo and other simple applications.
• USB transfer tools for sending the game to the calculator.

Requirements:

• A TI-89 or TI-89 Titanium calculator.
• A USB cable for connection.
• Installation of the libusb library on your computer.

Installation Instructions:

1. Compile the transfer tool for your calculator type.
2. Send the required files to the calculator.
3. Run the game from the calculator's home screen.

Controls:

• Arrow keys for movement and turning.
• ESC to quit the game.

Building from Source:

• Requires Docker and Python 3.
• Instructions are provided for building the game from source code.

License:

• The custom code is under the MIT License, while the FAT Engine SDK remains under its original copyright.

This project allows users to experience a classic-style dungeon crawler on a handheld calculator, showcasing the capabilities of the TI-89.

Current evaluations of large language models (LLMs) for code generation mainly focus on popular languages like Python, which have a lot of training data. This can lead to high accuracy scores that may just show memorization instead of real understanding.

To address this, we created EsoLang-Bench, which tests LLMs with 80 programming problems in five rare languages (Brainfuck, Befunge-98, Whitespace, Unlambda, and Shakespeare) that have much less training data compared to Python.

We tested five advanced models using different strategies. The best model only achieved 3.8% accuracy, while models usually score around 90% on Python tasks. None of the models solved problems above an Easy level, and Whitespace problems were completely unsolved. Self-reflection techniques didn't help improve performance. These findings highlight a significant gap between LLM performance on common languages and their actual programming skills, indicating that their capabilities are much more limited than suggested by high accuracy scores.

I used David Ng's RYS method on consumer AMD GPUs (RX 7900 XT and RX 6950 XT) and discovered something surprising about transformers. They seem to have distinct "reasoning circuits," which are groups of 3-4 layers that function as single cognitive units. When I duplicated these layers, the model didn't change its weights or require training; it simply processed information longer.

The results from standard tests showed significant improvements:

• For the Devstral-24B model, duplicating layers 12-14 improved logical deduction scores from 0.22 to 0.76, and other metrics also increased without any decrease in performance.
• For the Qwen2.5-Coder-32B model, duplicating layers 7-9 raised the reasoning score from 76% to 94%.

Interestingly, different duplication patterns led to various cognitive abilities from the same model. For example, a double-pass improved math skills, while a triple-pass enhanced emotional reasoning. Specific layering patterns could specialize the model for certain tasks.

The boundaries of these circuits are very defined; changing the layer by just one can alter or negate the effect. Smaller models (like the 24B) have tighter circuits (3 layers) compared to larger models (7 layers in the 72B).

I’ve included tools in my repository to help identify these circuits in any GGUF model and apply different layer arrangements. The entire process of exploration and validation took just one evening. I'm available to answer any questions.

OpenBSD's PF packet filter has introduced an important update to its HFSC (Hierarchical Fair Service Curve) traffic shaping feature. Previously, a 32-bit limitation capped bandwidth values at around 4.29 Gbps, which caused issues as modern network interfaces like 10G, 25G, and 100G became common. This limitation could lead to incorrect bandwidth configurations and scheduling behavior.

A new patch has been released that expands the bandwidth fields in the HFSC scheduler from 32-bit to 64-bit integers, eliminating this restriction. The update also fixes a display bug in the pftop tool, ensuring that bandwidth values above 4 Gbps are shown correctly.

With this change, users can now configure bandwidth settings for high-speed interfaces, supporting values up to 999 Gbps. Existing configurations below 4 Gbps will continue to function without any modification.

The patch is confirmed to be ready for implementation by March 20, 2026. Users are encouraged to test current snapshots and support the OpenBSD Foundation.

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Summary:

The process of optimizing features for a Random Forest model involves using Out of Sample Permutation Feature Importance (OOS). This method includes three main steps:

1. Train the Random Forest on training data.
2. Use validation (testing) data to shuffle the values of one feature and test the model's performance.
3. Determine the importance of a feature by observing how much the model's accuracy drops when that feature is shuffled.

OOS helps reduce noise in evaluations since it uses different data for training and testing. The standard method, Gini Importance, is not suitable for this model due to its biases towards continuous variables and its reliance on training data.

The model currently shows an unusually high performance for predicting Bitcoin price changes, suggesting possible overfitting. It appears that one feature, "seconds_to_settle," is dominating the model's predictions.

Following the initial optimization, about half of the features were removed. The model has been updated to improve its structure and usability, allowing for better visualization and configuration of the feature pipeline, which will help in identifying profitable trading strategies in the future.

Mary Turrell, nearly 80, found love in Easterlea Rest Home with Derek Brown, a charming man who sang alongside her during an entertainment session. Their relationship blossomed quickly, leading to a deep emotional connection that felt youthful and invigorating, despite their advanced ages. Mary reminisces about her life, including her childhood memories, her long marriages, and the challenges of aging, such as dealing with health issues and loss.

Derek, a warm and flirtatious man, brought joy into Mary’s life, transforming her days into a series of shared activities and laughter. They even got engaged, celebrating with a wedding in the care home’s garden, surrounded by friends and family.

Tragically, Derek suffered a fall and passed away shortly after, leaving Mary heartbroken. Despite her grief, she acknowledges the joy Derek brought into her life, realizing the importance of cherishing memories and embracing the present. Mary reflects on the ups and downs of aging, the challenges of loneliness, and the newfound freedom to be herself, while holding onto the love they shared.

The author, who maintains a popular GitHub repository called awesome-mcp-servers, has noticed a significant increase in pull requests (PRs) over the past year, many of which are generated by AI bots. Initially, the author appreciated the higher volume of contributions, but soon realized that the quality and enthusiasm behind these submissions were lacking.

To address the issue, the author created a system where bots could identify themselves by adding "🤖🤖🤖" to their PR titles, leading to about 50% of the new PRs self-identifying as bot-generated. This helped the author prioritize human contributions over those from bots.

The author highlights that the problem of bot contributions is not unique to their repository; it's a widespread issue in open-source projects. They express concern about the growing imbalance between the number of contributions and the capacity of maintainers to review them, which could hinder the future of open-source maintenance. The author plans to explore ways to make bot contributions more valuable going forward.

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Summary of GrapheneOS Features

GrapheneOS is a secure mobile operating system focused on privacy and usability, built on the Android Open Source Project (AOSP). It enhances security and privacy without compromising app compatibility and user experience. Here are the key features and improvements:

1. Security Focus: GrapheneOS prioritizes security by reducing potential vulnerabilities, implementing exploit mitigations, and enhancing sandboxing. It protects against attacks, especially from unknown vulnerabilities.

2. Privacy Enhancements: The system does not bundle Google apps, pushing users to choose whether to use them. It includes features like private screenshots, no device identifier leaks, and default privacy settings that prioritize user data protection.

3. Usability Improvements: GrapheneOS aims to maintain a user-friendly experience, allowing for multiple user profiles, improved app installations, and notification forwarding between profiles.

4. Network and Sensor Controls: It offers a network permission toggle to control app access to networks, and a sensors permission toggle to manage sensor access, ensuring that apps cannot access data without explicit permission.

5. Sandboxed Google Play: Users can install Google Play in a sandboxed environment, limiting its access to the OS and enhancing security while allowing compatibility with apps dependent on Google services.

6. Advanced Features: GrapheneOS includes features like auto reboot for data security, longer password support, duress PIN/password for emergency data wiping, and a robust log viewer for user control over shared data.

7. Enhanced Browsing with Vanadium: The default browser, Vanadium, is hardened for better security and comes with privacy-focused features, disabling many default tracking and identification methods.

8. Community-Driven: GrapheneOS is an open-source project that encourages community contributions and prioritizes user privacy over corporate interests.

GrapheneOS stands out for its commitment to privacy and security while maintaining ease of use and flexibility for users.

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Memory prices are currently being updated. The text mentions average prices for DDR4 and DDR5 memory per gigabyte (GB) in the Dutch market. There are also references to different memory sizes (8GB, 16GB, 32GB, 64GB) and price history over various time frames (3 months, 6 months, 1 year, 2 years).

The article critiques the Wayland display server, arguing that it has hindered the Linux Desktop's progress over the past 17 years. Although Wayland was intended to simplify and improve upon the older X11 display protocol, significant issues have emerged during its development and adoption.

Key points include:

1. Background on X11 and Wayland: X11 is a legacy display server from the 1980s, which has become complex and difficult to maintain. Wayland was created to provide a simpler alternative, but it has struggled to gain widespread acceptance.

2. Adoption Issues: After 17 years, Wayland only has a market share of 40-60%. Compared to other projects like PipeWire, which saw rapid adoption, Wayland's slow growth raises concerns about its effectiveness.

3. Security Concerns: Wayland's security features limit application interaction, leading to frustrations for users who rely on functionalities like screen recording and copy-paste.

4. Performance Claims: Although Wayland is touted as more performant, real-world performance gains are disputed, with some users reporting slower performance compared to X11.

5. Fragmentation: Wayland's protocol leads to multiple incompatible implementations, making common features difficult to use. This fragmentation has disappointed users hoping for a seamless transition from X11.

6. Development Status: Despite being in active development for 17 years, major issues persist, with users encountering significant problems right after switching to Wayland.

7. Developer Sentiment: Some developers express frustration over user complaints, suggesting that users are being forced into using an incomplete system.

8. Future Predictions: The author predicts that in the next five years, some projects may abandon Wayland for X11, and a new display protocol may emerge.

In conclusion, the article emphasizes that Wayland, despite its potential, is still not ready for widespread use, and many users find the transition problematic.

The text discusses the formatting of code snippets in a document, explaining that the code is presented as plain text to preserve its original format, which suggests it may have been generated by AI. The author notes that this decision may not align with the intent of making the content clearer. They also mention that some people suggest using more popular programming languages than Haskell for better results, but the author believes that difficulties in generating Haskell code indicate limitations in the AI's ability to generalize from its training data. Finally, the author invites readers to try the exercise themselves if they disagree with their perspective.

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NJIT physicists have studied the Sun's magnetic engine located about 200,000 kilometers beneath its surface. They are investigating how this engine works and its role in the Sun's magnetic activity. This research helps to understand solar phenomena better and their effects on space weather.