Thanks for joining the Hack Days! We had a great time with 300 participants from over 15 countries, including students, researchers, and experienced developers and designers.

Congratulations to the winners:

• 1st Place: VRC Team - Visio Room Connector
• 2nd Place: OpenProject - Hacking Borders: Docs x OpenProject Integration
• 3rd Place: HaXathon - Math in Docs
• People's Choice: Panographix - Tables and graphs in Docs

You can find the full list of projects online.

The Sovereign Workspace, also known as La Suite numérique, is an open-source digital workspace designed for online collaboration. It was developed by French government agencies in collaboration with the Netherlands and Germany.

Our code is fully open source and licensed under MIT. If you want to get involved or learn more, feel free to email us at [email protected] or visit our website.

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Starting in 2026, drivers over 70 in Britain will need to pass an eye test every three years to keep their driving license. This change is part of a new road safety strategy aimed at reducing accidents, as nearly 25% of car drivers killed in 2024 were aged 70 or older.

Currently, UK drivers must be able to read a number plate from 20 meters but do not have to prove their vision or health after obtaining their license. It is up to drivers to inform the DVLA if they feel unfit to drive, which some experts believe is not effective.

Many people support the new eye test requirement, including older drivers who believe it will help ensure safety on the roads. However, some argue that health checks should apply to all drivers, not just those over 70. Experts point out that vision can deteriorate gradually, and regular checks can help prevent accidents.

While the change is seen as a positive step, organizations like Age UK emphasize that eye tests alone may not significantly reduce accidents and that broader support for older drivers who may stop driving is necessary.

Hoot: Scheme on WebAssembly

Hoot is a project that allows you to run Scheme code in web browsers that support WebAssembly (Wasm). It includes a compiler that converts Scheme code to Wasm and a complete set of tools for working with Wasm. Hoot is based on Guile and does not require any extra software to run. It even has a Wasm interpreter for testing code directly within the Guile environment.

Latest Version The most recent version of Hoot is v0.7.0.

Learn More You can find articles and videos about Hoot, including guides on building interactive web pages, using Scheme in the browser, and demonstrations of its capabilities in game development. There are also developer interviews available.

This book provides an accessible introduction to Reinforcement Learning from Human Feedback (RLHF), a key tool in modern machine learning. It starts by explaining the origins of RLHF, drawing from various fields like economics and philosophy. The book covers essential concepts, problem formulation, and data collection methods used in RLHF. It then details the optimization process involved in RLHF, including instruction tuning, reward model training, and different algorithms. Finally, it addresses advanced topics and open research questions in the field.

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Waymo has introduced the Waymo World Model, an advanced simulation tool for autonomous driving that enhances road safety. This model has already helped the Waymo Driver cover nearly 200 million miles autonomously in major U.S. cities and is crucial for training in complex driving scenarios.

The Waymo World Model uses technology from Google DeepMind’s Genie 3 to create realistic 3D environments. It can simulate rare driving situations, like extreme weather or unusual encounters, which are hard to replicate in real life. The model allows engineers to easily modify simulations using simple commands, making it versatile for various driving conditions.

Key features of the Waymo World Model include:

• Emergent Multimodal World Knowledge: It learns from a vast amount of data, allowing it to simulate events that the driving fleet hasn't directly experienced.
• Strong Simulation Controllability: Engineers can control the driving actions, scene layouts, and environmental factors, enabling "what if" scenarios.
• Dashcam Video Conversion: The model can turn real dashcam footage into simulations, increasing realism.
• Efficient Long Simulation: It can simulate longer driving scenarios with less computing power while maintaining quality.

Overall, the Waymo World Model prepares the Waymo Driver for challenging and rare situations, improving safety before these scenarios occur on real roads.

The author reflects on significant changes in software engineering, particularly since December 2025. They emphasize the rise of "automated programming," which allows developers to focus on the creative aspects of building software without the tedious task of writing every line of code. This shift enables engineers to design and architect systems more efficiently.

The author criticizes the over-reliance on frameworks and libraries, which often complicate development rather than simplify it. They argue that many developers opt for these tools out of fear of complexity, leading to a lack of true engineering thinking. Instead of genuinely solving problems, frameworks create new ones and diminish the quality of design.

The key points include:

1. Automated Programming: This new approach reduces manual coding while still requiring deep thought about design and architecture.
2. Critique of Frameworks: Many frameworks overcomplicate software development and create unnecessary burdens for developers.
3. Emphasis on True Engineering: The author believes that software engineering should focus on solving real problems with customized solutions rather than relying on generic tools provided by large companies.

In conclusion, the author urges developers to embrace this new era of automation and take back control of their engineering processes, avoiding the pitfalls of unnecessary complexity and dependency on external frameworks.

CloudBot is an easy-to-use AI employee that you can set up with just one click. It runs on OpenClaw and offers:

• A complete Ubuntu desktop in the cloud
• An AI that can see the screen and control the computer
• 24/7 availability, working while you sleep
• The ability to use your own API keys for AI models
• Pricing starting at $69 per month

The AI can perform tasks like coding, browsing the web, running commands, and managing files, similar to what you would do on a regular desktop. It simplifies the process of setting up an AI agent, making it quick and painless.

You can also watch a demo video here. Feedback from the community is welcome!

The authors have created a set of ten math questions based on their research to evaluate how well current AI systems can answer advanced math problems. These questions haven't been shared before, and while the authors know the answers, they will keep them secret for a little while.

Summary of "Stories From 25 Years of Software Development" by Susam Pal

Susam Pal reflects on his 25 years in software development, sharing personal stories that highlight experiences with programming and collaboration rather than offering lessons. Here are some key points from his journey:

1. Early Curiosity: His journey began in university when he learned about HTML and website creation from an older student, sparking his interest in building websites.

2. Exploring Programming: He experimented with a microprocessor during his studies, leading a friend to adopt a more exploratory approach to learning rather than just aiming for top grades.

3. Technical Support Role: In his first job, he improved a fragile installer for e-banking software, which led him to seek more challenging work that eventually transitioned him to a team developing core architectural components.

4. Growth and Mentorship: He learned about security concepts like MITM (Man in the Middle) attacks and developed confidence in his skills through mentorship from his manager.

5. Debugging Challenges: He faced difficulties with complex code but learned valuable lessons about coding practices and the importance of clear, maintainable code.

6. Innovative Projects: He worked on widget animations for set-top boxes, showcasing how unexpected outcomes can arise from technical limitations and stakeholder reactions.

7. Career Development: Through a pivotal meeting with Dr. Burt Kaliski, he found fulfilling work involving advanced algorithms and databases, leading to significant professional growth.

8. Capture the Flag Contest: In a cybersecurity contest years later, he excelled, reflecting on how experience shifted perceptions of his problem-solving abilities from "smart" to "experienced."

Pal's stories illustrate the evolving nature of learning and collaboration in software development, emphasizing personal growth, mentorship, and the importance of curiosity in the tech field.

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Event Summary: GNU Algol 68 Compiler Presentation at FOSDEM 2026

• Event: Introduction to the GNU Algol 68 Compiler (ga68)
• Track: GCC (GNU Toolchain)
• Date: Saturday
• Time: 13:35 - 14:00 (UTC+1)
• Location: Room UD6.215
• Livestream: Available at ud6215
• Speaker: Jose E. Marchesi

Additional Resources:

• Video recordings available in AV1/WebM (141.6 MB) and MP4 (734.6 MB)
• Subtitle file (VTT)
• Chat rooms for discussion (web and app)

Feel free to join the chat and provide feedback!

Geo joins, which involve combining tables based on spatial relationships, can become very slow as data scales up. They often require comparing every item in one table with every item in another, leading to inefficient performance.

This post introduces a solution using H3 indexing, which organizes the Earth into hexagonal cells. This method allows for faster comparisons by transforming the complex spatial join into a simpler equi-join based on cell IDs.

Key Points:

1. Geo Joins: These are SQL commands that join tables based on spatial criteria like whether shapes intersect.

2. Performance Issues: Traditional geo joins can be slow because they require checking every possible pair of geometries, which grows quadratically with data size.

3. H3 Indexing: H3 allows for partitioning geographic data into manageable cells, enabling efficient spatial queries. It simplifies the join process by first filtering potential matches using cell IDs, then applying the exact spatial check only to those candidates.

4. How It Works: The original spatial join is rewritten to:

   • Generate H3 coverage for both tables.
   • Perform a fast join based on these cell IDs.
   • Deduplicate candidates and finally check for actual intersections.

5. Efficiency Gains: This method significantly reduces the number of expensive spatial checks, allowing for much faster query execution.

6. Trade-offs: While H3 provides an approximation (allowing some false positives), it ensures that true matches are not missed. The appropriate resolution of H3 cells can be adjusted based on the balance between speed and accuracy.

7. Results: Using H3 indexing can reduce query times dramatically. For example, a query that initially took 459 seconds could be executed in as little as 1.1 seconds with H3.

In conclusion, leveraging H3 indexing for geo joins greatly enhances performance by converting complex spatial operations into simpler, faster computations.

This text discusses a project called BreezyBox, which is a lightweight computing solution similar to a Raspberry Pi but without needing a heavy server operating system. It uses a custom fast text mode driver, allowing it to run smooth ANSI text demonstrations.

Key features include:

• Easy installation of apps from any repository without needing approvals, just by publishing compatible ELF files.
• The author experienced a successful installation of multiple apps in one command, likening it to running homebrew on a toaster.

The project has been covered by various platforms, including Hackaday, Hackster.io, and Reddit.

For more information, you can check out the example repositories linked above.

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A solo developer with experience in UI/UX design since 2007 created a new design tool called Vecti. This tool focuses on essential design features like precise grid snapping, a fast canvas renderer, shared asset libraries, and export options, without unnecessary extras like collaboration tools or plugins. Vecti is built in the EU, ensuring compliance with privacy regulations, and offers a free tier with no credit card required. The developer uses minimal analytics but does not track user behavior within the app. They welcome feedback from solo designers or small teams and are open to questions about the tool's features and development. You can learn more at vecti.com.

The author is concerned about memory loss from bike-related concussions and created a solution to keep their important data safe and accessible. They used a method called Shamir secret sharing and age-encryption, packaged it in a way that works in web browsers without needing the internet. The system requires agreement from 5 out of 7 friends to unlock the data if something happens to the author. A demo is available on their website for people to try out.

Summary of Ruliology

Ruliology is a new scientific field that studies the behavior of systems based on simple rules. It focuses on what happens when these rules are applied, exploring the vast "computational universe" of possibilities. Unlike computer science, which deals with practical programs, ruliology examines abstract rules without specific purposes.

The term "ruliology" was created to encapsulate this field, which involves running simple programs and observing their outcomes rigorously. It encompasses both experimentation and theory, starting with specific cases and generalizing findings.

A key concept in ruliology is "computational irreducibility," which means that some behaviors can only be understood through direct computation rather than simplified predictions. This leads to unexpected discoveries, similar to exploring natural history.

Ruliology is foundational for understanding complexity and provides insights into how complex systems can emerge from simple origins. It generates raw material for creating models and technologies and has implications for broader theories about computation.

The Wolfram Language is a crucial tool for conducting ruliology, offering features for visualizing and manipulating rules. Ruliology is an expansive field with many opportunities for research and practical applications, aiming to deepen our understanding of the computational universe.

Heroku is a cloud platform that allows developers to build, run, and manage applications easily. Recently, it has made updates to improve performance and security. These changes include better tools for managing resources and enhanced support for various programming languages. The platform aims to provide a smoother experience for developers and help them deploy their applications more efficiently.

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The article compares two tools for replicating Kafka data across AWS regions: Lenses K2K and Apache Kafka MirrorMaker2 (MM2). This replication is crucial for disaster recovery and multi-region deployments.

Key Points:

• Purpose of Comparison: Organizations need efficient solutions for cross-region data replication that minimize latency, maximize throughput, and simplify operations.
• Operational Complexity of MM2: MM2 is widely used but introduces challenges, especially with offset management, leading to potential issues like message loss or duplication during failovers.
• Advantages of K2K: Lenses K2K was designed to simplify replication while improving performance. It eliminates the complexities associated with MM2.
• Performance Findings:
  • K2K showed 14-32% lower latency and 51-78% faster producer writes compared to MM2.
  • K2K also had 16% higher throughput and significantly better batching efficiency.
  • Both solutions achieved 100% reliability in tests.

Recommendations:

• Choose K2K if: You prioritize lower latency, higher throughput, operational simplicity, or are setting up new replication infrastructure.
• Choose MM2 if: You are already invested in the Kafka Connect ecosystem and can manage its complexities.

Overall, K2K consistently outperforms MM2 across various performance metrics while being easier to operate, making it a strong choice for organizations focused on performance and simplicity.

Here’s a simplified summary of the key points:

1. Establish a Clear Vision: Clearly define your project goals and architecture. Understand which parts of your code require careful planning and testing.

2. Maintain Precise Documentation: Document your project requirements, specifications, and coding standards thoroughly. Use visuals like flowcharts and pseudocode to clarify complex ideas.

3. Build Debug Systems for AI: Create efficient debugging systems that simplify the process for AI to identify and fix issues, reducing the need for complicated commands.

4. Mark Code Review Levels: Identify which parts of your code need more thorough reviews and label them accordingly to ensure important sections receive extra attention.

5. Write High-Level Specifications: Develop comprehensive tests yourself to prevent AI from using shortcuts that could lead to flawed code.

6. Write Interface Tests Separately: Have the AI create tests for expected behaviors without being influenced by the actual code, ensuring the tests remain valid.

7. Use Strict Linting and Formatting Rules: Implement strict coding standards to maintain quality and consistency in your code.

8. Use Context-Specific Prompts: Provide tailored prompts for the AI to help it generate code that meets your specific needs more efficiently.

9. Identify High-Risk Functions: Clearly mark functions with security risks and ensure they are reviewed thoroughly to ensure safety.

10. Reduce Code Complexity: Aim to simplify your code to make it easier to manage and understand, minimizing the potential for errors.

11. Experiment with Prototypes: Use AI to quickly try out different solutions to problems, allowing for exploration without heavy investment.

12. Break Down Tasks: Divide complex tasks into smaller, manageable parts for the AI to handle, ensuring you maintain control over the code and its logic.

By following these guidelines, you can collaborate effectively with AI in software development.

In "Dark Alley Mathematics," a person is threatened by a hooded figure who demands a solution to a mathematical problem involving the probability that three randomly chosen points inside a unit circle will have their circumcircle entirely contained within the unit circle.

To solve this, the individual transforms the Cartesian coordinates of the points into variables representing the circumcircle's center, radius, and angles. They calculate the Jacobian determinant, which involves complex derivatives but ultimately leads to a manageable expression.

Next, the individual sets up the probability integral, which involves integrating over the area of the unit circle and adjusting for the circumcircle's constraints. They separate the problem into geometric and angular components and compute the angular part using trigonometric identities and substitutions.

After working through the integrals, they arrive at a final probability value of ( \frac{25}{24} ). The hooded figure is pleased with the result and allows them to leave, though the individual reflects on the stress of the situation and resolves to avoid dark alleys in the future.

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The author spent five years as a DevSecOps engineer but felt unfulfilled due to monotony, stagnation, and isolation in their work. After realizing they were missing engagement and human interaction, they transitioned to a Solutions Engineer role at Infisical, which combines technical skills with customer interaction.

In this new position, the author enjoys daily challenges, problem-solving with various clients, and building meaningful relationships. They have found that their prior DevOps experience enhances their ability to assist customers effectively. Though the job involves learning new skills and managing stress, it offers a refreshing change from their previous routine.

The author encourages other DevOps engineers who feel stuck or disconnected to consider Solutions Engineering, highlighting the opportunity for continuous learning and collaboration. They express satisfaction in their new role, which allows them to remain technical while engaging with people.

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Benoit Mandelbrot made significant contributions to various fields like linguistics, fluid dynamics, cosmology, and finance, showcasing a broad range of interests. However, this essay argues that at his core, he was driven by a single guiding idea: scaling. This concept, which includes self-similarity, power laws, and fractals, was central to his work across different disciplines. By examining his ideas in mathematics, physics, and economics, the paper shows that despite his diverse interests, there is a consistent intellectual path. Mandelbrot's key insight into understanding both natural and social phenomena comes from the principles of scale invariance in geometry and statistics.

Summary of "PC Floppy Copy Protection: Vault Prolok"

This blog post is the fourth part of a series discussing methods to protect PC floppy disks from copying. It focuses on Vault Corporation's Prolok, a copy protection system developed in the early 1980s.

Key points include:

1. Company Background: Vault Corporation was founded in 1979 and became notable for its Prolok product, which was launched in 1983. They used professional advertising to promote their brand, attracting significant clients like Ashton-Tate.

2. Prolok Technology: Unlike other systems, Prolok's protection was based on intentionally damaging the disk surface, creating a unique "fingerprint." This method allowed software publishers to buy pre-damaged diskettes to protect their programs.

3. Functionality: Software publishers would use a utility, PROLOK.EXE, to encrypt their software onto the Prolok diskettes. The protection was designed to check for the fingerprint; if absent, the software would not run.

4. Variants: Vault later introduced several related products, including Hard Disk Prolok and Filelok, which provided varying levels of data protection.

5. Challenges and Failures: Despite its initial success, Prolok faced issues with compatibility, leading to data loss for some users. Additionally, the system could be bypassed by utilities like Quaid Software's RAMKEY, leading to legal battles over copyright and user rights.

6. Corporate Downfall: Vault's reputation suffered after announcing a version of Prolok that could erase data on failed checks. This led to major clients dropping their products, causing significant damage to Vault's standing in the industry.

7. Legacy: Vault eventually entered bankruptcy but continued to exist for several years before fading from relevance. The post reflects on the historical importance of Prolok and its impact on software protection methods.

This summary captures the essence of the text while simplifying complex details for easier understanding.

Summary of Developer Knowledge API and MCP Server Announcement

On February 4, 2026, Jess Kuras announced the public preview of the Developer Knowledge API and the Model Context Protocol (MCP) server. These new tools aim to provide developers with accurate and up-to-date access to Google’s official documentation, which is essential for building with Google technologies.

Key Features:

• Developer Knowledge API: Acts as a reliable source for Google’s public documentation, allowing developers to search and retrieve information as Markdown.

  • Comprehensive Coverage: Accesses documentation from multiple Google platforms.
  • Search and Retrieve: Enables finding and retrieving documentation pages and snippets.
  • Freshness: Updates documentation within 24 hours of changes.

• MCP Server: An open standard that allows AI assistants to access external data sources safely.

  • Enhances AI tools by providing implementation guidance, troubleshooting help, and comparative analysis.

Getting Started:

1. Generate an API key in your Google Cloud project.
2. Enable the MCP server using the Google Cloud CLI.
3. Configure your tools as per the documentation.

Future Plans: The developers plan to add structured content support and expand the documentation offerings as they move towards general availability.

Developers are encouraged to integrate these tools into their workflows and explore the full documentation for more information.

MirageOS is an event-driven system that doesn't use traditional preemptive threads. Instead, it relies on events (like network packets) to wake up programs, which run until they need to wait for something. This model is simple and scalable, but it can scatter control logic across many small functions, making the code harder to manage. The OCaml Lwt library helps by using a monadic style that simplifies handling events.

In Lwt, threads are represented as types like 'a Lwt.t, which return results when they finish. The return function creates a thread from a value, and the bind function allows chaining operations on threads. An example is given where we can use the sleep function with bind to print a message after sleeping.

Despite its advantages, Lwt has some drawbacks, such as requiring adaptations to existing blocking code and the overhead of creating closures. To make coding easier, Lwt provides syntax extensions, allowing for more natural-looking code.

The text also discusses the potential of using the delimcc library, which implements delimited continuations in OCaml. This allows for more flexible error handling and can be combined with Lwt.

Benchmarks comparing Lwt and fibers (from the Lwt_fiber library) reveal that while fibers can be slower in some instances, they may outperform Lwt in specific cases, especially when yielding less often. The performance differences are generally minor, leading to the conclusion that interoperability with existing code and JavaScript compatibility are more significant factors when choosing between Lwt and fibers for MirageOS.

Overall, while Lwt is robust and compatible with JavaScript, the choice between Lwt and delimcc for new code should consider the nature of the existing codebase and performance requirements.

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Summary of Claude Opus 4.6 Announcement

On February 5, 2026, Claude Opus 4.6 was introduced as an advanced AI model, enhancing its coding abilities and overall performance. Key improvements include:

1. Enhanced Coding Skills: Opus 4.6 can manage more complex coding tasks, plan better, work efficiently with large codebases, and improve code review and debugging.

2. Expanded Functionality: It can handle financial analyses, research, and document creation (spreadsheets and presentations) autonomously in the Cowork environment.

3. Performance Metrics: This model has achieved top scores in various industry evaluations, outpacing previous models and competitors in tasks like coding and reasoning.

4. Long Context Handling: A new feature allows it to manage a 1 million token context, which improves its ability to maintain focus over extended tasks without losing relevant details.

5. Safety Improvements: Opus 4.6 demonstrates a strong safety profile, with low rates of misaligned behavior, and it has undergone extensive safety evaluations to ensure responsible use.

6. New Product Features: The model includes features like agent teams for collaborative tasks, context compaction for long conversations, and adaptive thinking for better task management.

7. Availability: Opus 4.6 is now available on various platforms and retains the same pricing structure as its predecessor.

8. User Feedback: Early testers report significant improvements in the model's reasoning, planning, and ability to execute complex tasks autonomously.

Overall, Claude Opus 4.6 represents a significant leap in AI capabilities, particularly for coding and knowledge work, making it a valuable tool for professionals across various fields.

The text discusses the potential advantages of using FORTH and associative programming languages for transformer architectures in AI, particularly for generating outputs. It suggests that instead of breaking down tasks recursively like humans do, a concatenative approach might be more effective.

The author introduces a programming model that combines two data sets (A and B) using a "sideways-information-passing join" method, which allows for local edits to the program. This model can separate what has been generated from what still needs to be generated, enhancing the efficiency of token generation.

The author conducted experiments to benchmark how different models (Opus and Haiku) perform in creating parity trees using prefix and postfix notations. Results showed that thinking models outperformed non-thinking models, with Opus performing better than Haiku. Postfix notation consistently outperformed prefix notation in generating correct results.

In summary, the exploration suggests that using associative languages and postfix structures can improve the performance of AI models in certain tasks.

Smooth CLI is a new browser designed for AI agents, like Claude Code, to navigate the web easily and effectively. It allows agents to use natural language to specify tasks, simplifying the process and reducing the complexity of traditional web browsing interfaces.

Key features include:

• Natural Language Tasks: Instead of managing low-level actions like clicks and typing, agents can perform tasks by simply stating their goals, such as "Search for flights from NYC to LA."
• Cloud Browsing with IP Address: Smooth CLI can use your IP address while running in the cloud, which helps bypass issues like captchas.
• Faster and Cheaper: Tests show that using Smooth CLI can be 20 times faster and 5 times cheaper than traditional methods.
• Security: The system is designed to minimize security risks by treating web interactions as untrusted, running in a sandboxed environment.

Smooth CLI enables agents to autonomously manage multiple browsers and tasks, making it easier to collect data, fill forms, and interact with web content. It’s free to try, and the developers welcome feedback and ideas from users.

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Thirty-six infants in the UK are suspected of food poisoning after consuming contaminated baby formula. The affected products, made by Nestle and Danone, contained a toxin called cereulide. Symptoms reported include vomiting and diarrhea, but none of the infants are seriously ill.

The UK Health Security Agency (UKHSA) confirmed the babies had consumed the recalled formula but could not definitively link the symptoms to the toxin yet. The affected Danone product has an expiration date of October 31, 2026, and several Nestle products are also involved.

The contamination issue extends beyond the UK, affecting multiple countries. The Food Standards Agency (FSA) identified the toxin in a specific ingredient used in the formula and advised parents to stop using the affected products and consult their doctor if they have concerns. The FSA is working with manufacturers to ensure all contaminated products are removed from sale and to investigate the supply chain to prevent future incidents.

The article discusses concerns about low-quality AI-generated code contributions to the tldraw project. The author, Steve Ruiz, announced a new policy to automatically close such pull requests, which received positive feedback from the community. He reflects on the changing landscape of coding assistance due to AI tools, questioning the value of external contributions when AI can produce code easily.

Ruiz shares personal experiences with contributing to open-source projects, emphasizing the importance of context and engagement in meaningful contributions. He notes that many recent pull requests are poorly aligned with project needs, often generated by AI without adequate understanding of the codebase.

The article concludes that if AI-generated contributions continue to devalue human coding efforts, it might be better to limit external contributions to areas like reporting and discussion, rather than allowing AI-generated code submissions that lack true value.

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Caltech researchers have developed a new technology called Sidewinder, which allows for the construction of long DNA sequences with high accuracy. This innovation addresses a major problem in bioengineering, where creating long DNA strands was previously difficult due to limitations in chemical synthesis. Sidewinder uses a method similar to "page numbers" in books to ensure that short DNA pieces can be correctly assembled into longer sequences, enabling the design of entire genes or genomes.

This technology has vast potential applications in fields like agriculture and medicine, including personalized cancer treatments. The Sidewinder process achieves a very low error rate in DNA assembly, significantly improving upon previous methods. Researchers believe that combining Sidewinder with artificial intelligence could further enhance our ability to design and create complex biological functions.

Overall, Sidewinder represents a significant advancement in the ability to write and engineer DNA, potentially transforming biotechnology and the bioeconomy.

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In an interview, Andy Gavin discussed how the development team for Crash Bandicoot on the original PlayStation improved game graphics with richer environments and better animations compared to other games at that time. Most PlayStation games loaded data from CDs into memory before each level, which limited performance because only part of the level was visible during gameplay.

Crash Bandicoot introduced a new method by breaking levels into smaller sections and using a virtual memory system to load only the necessary parts. This meant the game could show more detail without being limited by RAM capacity, as it efficiently streamed data while playing.

The same principles apply to modern graphics engines and scientific data visualization. Virtual texturing is a technique used to handle large textures that exceed GPU memory. It allows only the parts of a texture needed for what is currently visible to be loaded, reducing memory and bandwidth usage.

The system involves three main components:

1. Addressing - determining where each texture pixel is sampled from.
2. Feedback - tracking which texture pages were accessed and at what resolution.
3. Residency - managing which texture pages are kept in GPU memory.

The process begins with rendering a scene to gather feedback about which texture pages are needed. A page manager then decides which pages to load into memory or evict based on usage patterns.

Virtual texturing has been effectively used in games, notably in id Tech 5, which made it the standard for texture management, resulting in more detailed visuals without repetitive patterns.

In conclusion, virtual texturing is a specialized method for managing large textures effectively, focusing on what is visible at any moment to optimize memory usage and performance. This technique is also relevant in scientific fields where large datasets must be visualized efficiently.

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The blog post discusses error handling in bash scripts, which are commonly used by engineers in production environments. It emphasizes that these scripts can be complex, lengthy, and prone to failure, often leading to serious issues when they break.

Key Points:

1. Bash Exit Codes: Every bash command returns an exit code, with 0 indicating success and any other number indicating an error.

2. Catching Errors:

   • By default, bash continues executing commands even after an error. Using set -e can change this behavior to exit the script upon encountering an error.
   • The if-else statement can be used to handle errors in a more controlled manner, allowing for specific responses to different exit codes.

3. Using Logical Operators: The || operator can run a second command if the first fails, which is useful for simple error handling.

4. Using trap Command: This allows you to catch errors globally in the script without explicitly checking each command.

5. Common Pitfalls:

   • Using pipes can hide errors (e.g., if the first command fails, but the second succeeds, the overall command may still return success). The set -o pipefail option helps prevent this.
   • Undefined variables can cause issues. Using set -u can help catch these errors.

6. Combining Options: You can combine several options for better error handling, such as set -Eeuo pipefail, which provides comprehensive error management.

7. Alerting on Errors: It's crucial to be notified of script failures, especially for cron jobs. This can be done using webhooks for Slack or Teams, or using a tool like Notifox CLI to send alerts via Email or SMS.

Overall, the blog post stresses the importance of proper error handling in bash scripts to maintain system integrity and avoid potential disasters.

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The text outlines a personal journey of adopting AI tools for coding and productivity, emphasizing a step-by-step approach to finding value in these technologies. Here’s a simplified summary of the key points:

1. Initial Phases: Adopting new tools often involves three phases: inefficiency, adequacy, and finally, meaningful discovery. The author had to push through the discomfort of changing their workflow.

2. Stop Using Chatbots: The author advises against relying on chatbots for coding, as their effectiveness can be limited and often requires human correction, leading to inefficiency.

3. Reproducing Work: They experimented with AI agents, initially finding them unhelpful. However, by manually reproducing their work and then using agents, they gained a better understanding of when to use AI effectively.

4. End-of-Day Agents: The author began using agents to work on tasks during the last half-hour of their day, which helped them start the next day with some progress made.

5. Outsource Simple Tasks: They found confidence in letting agents handle straightforward tasks while they focused on more complex work, enhancing overall efficiency.

6. Engineering Solutions: The author emphasizes the importance of improving how agents work by addressing mistakes and providing better tools to ensure correct outcomes.

7. Always Have an Agent Running: The goal is to have an agent working continuously, which helps in managing tasks and maintaining productivity.

In conclusion, the author has successfully integrated AI into their workflow, finding it beneficial while acknowledging the need for a balanced approach. They respect individual choices regarding AI use and aim to share their personal experiences and strategies.

A user on Hacker News (HN) shared their experience using Large Language Models (LLMs) to analyze biotech stocks, which are heavily influenced by events like FDA decisions and clinical trial results. These "catalysts" can dramatically affect stock prices. Understanding them usually requires expertise in biology or medicine, as positive news can still lead to stock drops if the results aren't strong enough or don't significantly change approval chances.

To assess how well LLMs can interpret these catalysts and predict stock movements, the user created a platform called BioTradingArena. This benchmark includes 317 historical biotech events and various data to help predict stock reactions based on pre-release information. The user plans to expand the dataset, which currently covers different company sizes and cancer-related indications.

They acknowledged some limitations, such as the possibility that certain information may already be in the LLMs' training data. Despite the challenges, they've found interesting results, especially using LLMs to analyze qualitative data before applying linear regression for predictions. They invite others to explore their tool and share feedback.

Our team primarily uses Slack, but we lacked access to the Slack MCP and couldn't find a suitable solution. So, we created our own tool called agent-slack CLI.

Key features include:

• Ability to paste Slack URLs
• Efficient in terms of token use
• No setup required (automatically authenticates if you use Slack Desktop)
• Automatically downloads files and snippets
• Can read Slack canvases as markdown

This tool is available under the MIT License.

This project is a tool that helps developers create Android applications using a graphical user interface (GUI). It focuses on using gRPC, which is a method for communication between services, and is designed for systems based on the AArch64 architecture. The goal is to streamline the development process for Android apps.

The author is a software engineer who has taken on a Lead DevOps Engineer role, focusing on creating tools to automate processes. In their free time, they developed Artifact Keeper, a self-hosted artifact registry that supports over 45 package formats. Key features include built-in security scanning, single sign-on (SSO), replication capabilities, and a plugin system for adding new formats.

The motivation behind Artifact Keeper comes from the author's passion for open-source software, stemming from their childhood experiences with limited resources. They aim to offer a fully-featured registry without any hidden fees, contrasting with other platforms that restrict essential features behind paywalls.

The development process utilized AI tools to speed up coding, but the author emphasized that key design decisions were informed by their own expertise. The tech stack includes Rust, PostgreSQL, and various security scanning tools.

Users can try Artifact Keeper by cloning the repository and running it locally. The author encourages feedback on the project and invites discussions about improvements or issues with existing tools like Artifactory or Nexus.

For more information, you can find the live demo and documentation online.

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The plasma effect is a well-known visual effect from the 1980s and 1990s, resembling flowing plasma or aurora patterns. It uses simple mathematics involving sine and cosine functions to create captivating, moving patterns on the screen.

Key points about the plasma effect include:

1. Math Behind It: The plasma effect combines multiple wave patterns using sine and cosine functions, creating complex visuals through interference. It involves generating wave patterns, combining them, animating them over time, and mapping the results to colors.

2. Wave Interference: The effect relies on constructive interference (where waves amplify each other) and destructive interference (where waves cancel each other out) to create its unique flowing appearance.

3. Color Mapping: The output from the sine waves is mapped to colors using techniques that ensure smooth gradients.

4. Modern Enhancements: Newer implementations can add specular highlights, giving the illusion of depth and reflection by analyzing color gradients.

5. Implementation: The effect can be coded in languages like GLSL, and example code is provided for creating this visual in real-time.

Overall, the plasma effect remains a popular demonstration of procedural graphics techniques due to its simplicity and visual appeal.

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Summary of "The Monad called Free"

The text discusses the concept of the "Free" monad in Haskell, which is a way to create monads from functors. The author refers to Free as a "higher order monad" and highlights its unique characteristics.

Key points include:

1. Definition of Free Monad: Free monads are defined as either a pure value or a recursive structure containing functors. The basic structure is:

   data Free f a = Pure a | Free (f (Free f a))
   

2. Functor and Monad Instances: The Free monad has instances for Functor and Monad, enabling it to use Haskell's monadic features.

3. Categories: The author introduces two categories: Hask (the category of types and functions) and Endo (the category of endofunctors). Monads relate to these categories, with natural transformations between functors forming the arrows.

4. General Concepts:

   • Products and sums in Endo are discussed, with examples of how they differ from similar concepts in Hask.
   • The text explores how lists can represent free monads, noting that free monads act similarly to free monoids in the category of endofunctors.

5. Functions and Transformations: The text provides several functions that operate on these structures, drawing parallels between lists and monads, such as singleton and foldMap.

6. HFunctor and HMonad: The author defines classes HFunctor and HMonad to formalize the behavior of functors and monads in Endo.

7. Instance Implementations: The text concludes with the implementation of Free as an instance of HMonad, enabling its usage in a broader context.

Overall, the article presents an in-depth exploration of free monads in Haskell, illustrating their theoretical foundations and practical implementations.

Summary: Owning a $5M Data Center

In a recent blog post, the CTO of comma.ai shares insights on running a private data center, which they have done successfully for years. Here are the key points:

• Benefits of Owning a Data Center:

  • Control: Running your own data center allows you to maintain control over your computing needs and avoid high costs associated with cloud providers.
  • Cost Savings: Owning a data center can be significantly cheaper than using cloud services, especially for consistent computing needs. For comma.ai, they estimate a savings of over $20 million compared to cloud costs.
  • Engineering Challenges: Managing a data center fosters good engineering practices and problem-solving skills, as it requires hands-on knowledge of hardware and infrastructure.

• Key Components of Their Data Center:

  • Power: They use 450kW of power, with significant costs due to high local electricity prices.
  • Cooling: They utilize outside air cooling instead of traditional systems to save on energy.
  • Servers: The center has 600 GPUs in custom-built machines, along with a large storage capacity (around 4PB) using SSDs for speed and reliability.

• Infrastructure Management:

  • They use a combination of software tools to manage storage, compute tasks, and data processing. Their setup includes distributed storage, workload management systems, and a custom experiment tracking service.

• Conclusion: The post encourages others to consider building their own data centers for greater autonomy, efficiency, and cost-effectiveness in managing their computing resources.

The writer expresses enthusiasm for this approach, inviting others to explore similar opportunities.

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Nicholas Carlini, a researcher on the Safeguards team, has developed a new method for supervising language models called "agent teams," where multiple instances of the language model Claude work together without human intervention. He tested this approach by having 16 agents collaboratively create a Rust-based C compiler capable of compiling the Linux kernel. The project involved nearly 2,000 sessions and cost about $20,000, resulting in a 100,000-line compiler that builds Linux 6.9 on various architectures.

Key points from his work include:

1. Autonomous Progress: By implementing a system that allows agents to work in a continuous loop, Carlini ensured they could make progress on tasks without needing constant human input.

2. Parallel Work: Running multiple agents simultaneously allows for specialization and efficiency, as different agents can tackle various aspects of a project at the same time.

3. Testing and Feedback: High-quality tests are crucial for guiding the agents effectively. Carlini focused on creating robust tests and documentation to help the agents orient themselves and track their progress.

4. Challenges: Despite the successes, the project faced limitations, such as the inability to compile certain files independently due to overlapping tasks among agents. Strategies were developed to mitigate these issues.

5. Future Implications: This research pushes the boundaries of what language models can achieve autonomously, raising both excitement and concerns about quality control and safety in software development.

Overall, Carlini's work illustrates the potential for autonomous systems in programming while highlighting the need for careful oversight and testing.

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The article discusses the troubled history of the video game Ultima IX: Ascension, the final installment in the iconic Ultima series. Initially, the Ultima franchise, created by Richard Garriott and published by Origin Systems, was celebrated for its significant contributions to the role-playing game (RPG) genre. However, by the late 1990s, the series faced a decline due to poor design choices and shifts in gaming trends.

Ultima IX was intended to be a return to the series' roots after the disappointing Ultima VIII, but the development was plagued by issues, including inadequate voice acting, a clunky combat system, and a disjointed storyline. The game was criticized for failing to meet the expectations of long-time fans and for feeling like a betrayal of the franchise's legacy.

The development team faced internal conflicts and corporate pressures, leading to a disorganized production. Key decisions, such as simplifying gameplay to attract new players, resulted in a game that failed to resonate with either new or existing fans. After its release in 1999, Ultima IX was widely panned and became a symbol of gaming failures.

The article also reflects on the broader implications of the Ultima series' decline, highlighting how Ultima IX not only marked the end of the franchise but also contributed to the downfall of Origin Systems, which subsequently shifted its focus to MMORPGs like Ultima Online. Richard Garriott's later career is mentioned as having struggled to replicate the success of his earlier work, leading to a sense of nostalgia and disappointment among fans of the series.

In summary, Ultima IX: Ascension is remembered as a significant misstep in gaming history, illustrating the challenges of adapting to changing industry landscapes while remaining true to a beloved franchise.

Summary: Unlocking High-Performance PostgreSQL: Key Memory Optimizations

PostgreSQL performs well in production, but many setups use conservative settings that limit performance. To optimize, you need to understand key memory settings, especially shared_buffers and work_mem.

1. Shared Buffers:

   • This is the memory PostgreSQL uses as a cache for data. Instead of reading directly from disk, data is loaded into shared memory first, which is much faster.
   • The default size is 128MB, which may not be enough for larger workloads. A good starting point is 20-25% of total RAM for dedicated servers, but not over 40% to avoid competition with the OS cache.
   • Check if your shared_buffers setting is effective by monitoring the cache hit ratio. A ratio close to 1 means most reads are from memory.

2. Work Memory:

   • This setting defines the maximum memory for operations like sorting and joining. It can multiply quickly; each query operation can use this memory, especially with parallel processing, which can lead to out-of-memory errors.
   • The default is 4MB, suitable for simple workloads but often too small for complex queries.
   • Measure its effectiveness by looking for signs of disk spills in query plans.
   • For systems with less than 64GB RAM, consider setting work_mem to about 0.25% of total RAM. For larger systems, use a more conservative approach to avoid excessive memory use.

Conclusion: Start with safe default settings and adjust based on real metrics. Focus on tuning for high-impact queries rather than making broad changes.

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Apple has stopped including the old Emacs 22.1 text editor in macOS, starting with version 10.15 ("Catalina") released in October 2019. This change is welcomed by users, as the outdated version often caused confusion and issues.

The reason Apple didn't update Emacs is due to its opposition to the GPL v3 license, while Emacs 22.1 was the last version under the GPL v2.1 license. A former Apple employee noted that it didn’t make sense to support such an old version, especially since users can easily install newer versions themselves.

As a replacement, Apple now includes the mg editor, which is a simpler alternative to editors like vi and nano. Users can set it as their default editor by adding export EDITOR=mg to their shell setup.

This move is similar to Apple's earlier switch from Bash to Zsh in the same macOS version, as both changes reflect Apple's preference for more modern tools.

The article discusses the experiences of female content moderators in India who are exposed to disturbing graphic content while working for global tech companies. Monsumi Murmu, a 26-year-old moderator, describes how she watches up to 800 videos each day, leading to emotional numbness and psychological trauma. Initially, she found the content shocking, but over time, she became desensitized, stating, “In the end, you feel blank.”

The work is categorized as dangerous, with significant psychological risks, including intrusive thoughts and anxiety. Many moderators, particularly women from rural and marginalized backgrounds, face these challenges without adequate support, as companies often provide little to no mental health resources.

Raina Singh, another moderator, shares her experience of being exposed to graphic sexual content, which negatively impacted her personal life and relationships. She felt misled about the nature of her job, which was initially described vaguely.

The article highlights the growing demand for data annotation and moderation in India, driven by cheaper labor costs and improved internet access. However, job advertisements often misrepresent the work, presenting it as safe and easy. Additionally, strict non-disclosure agreements prevent workers from sharing their experiences, further isolating them.

Overall, the piece sheds light on the emotional toll of content moderation work and the lack of support for those in this field, particularly women from disadvantaged communities.

The article discusses how certain communities, particularly rural farm workers in Vietnam, have developed techniques to carry heavy loads, often exceeding their body weight, using tools like springy bamboo poles. This method allows them to walk long distances with reduced effort.

Strength is a significant focus in human activities, with weightlifting being a popular sport for thousands of years. Increasing strength through weightlifting benefits health, mobility, and even mental well-being. It's essential to start with manageable weights and gradually increase them, focusing on proper technique to avoid injuries.

Traditional load-carrying methods are also highlighted, such as those used by market porters in England and women in East Africa, who have developed efficient ways to transport goods over long distances. Sherpas in the Himalayas use a unique carrying technique that combines weight training and cardio, which helps them manage heavy loads effectively.

The article emphasizes the importance of strength training, especially for older adults, to combat muscle loss and maintain health. National health organizations recommend incorporating strength training into regular exercise routines for overall well-being. The experiences of these communities can inspire others to engage in strength training and improve their physical capabilities.

This text introduces various types of engines and propulsion systems. Users can click on different options to learn more about how each engine works. The engines listed include four-stroke, diesel, two-stroke, Wankel, Atkinson, and several others like steam locomotives and Stirling engines.

Summary:

Neural networks generally assume a single function can map all data inputs to outputs. However, in real-world situations, such as clinical trials across different hospitals, each dataset can have unique characteristics that affect outcomes. Standard neural networks often struggle with this hierarchical data because they either average differences or overfit to small datasets.

A proposed solution is to use hypernetworks. These networks adapt to different datasets by generating parameters for another network based on dataset-specific embeddings. This allows the model to recognize and adjust for variations across datasets without needing to retrain from scratch. The key benefits of hypernetworks include:

1. Adaptability: They can infer properties of new datasets quickly with minimal data.
2. Pooling Information: They stabilize predictions by sharing knowledge across datasets.
3. Flexible Architecture: They can model complex hierarchical structures.

The model demonstrates good performance in training on synthetic datasets, achieving comparable accuracy to isolated networks and better stability with fewer data points.

However, there are limitations. Predictions can degrade when faced with out-of-sample datasets, especially if those datasets are noisy or small. This is attributed to the challenges of optimization in neural networks.

Future work will explore Bayesian hierarchical models, which explicitly model uncertainties and may provide more robust predictions. These models could enhance the performance of machine learning when dealing with hierarchical data by offering better generalization and stability.

In summary, while hypernetworks show promise for adapting to hierarchical data and achieving few-shot learning, Bayesian methods could address their current limitations by incorporating uncertainty and more stable extrapolation.

The text discusses the relationship between Large Language Models (LLMs) and compilers, questioning whether LLMs can serve as effective programming tools. The author argues that while LLMs can generate code from natural language prompts, they come with significant risks due to their tendency to produce vague or imprecise outputs, which can lead to unexpected results.

Key points include:

1. Comparison to Compilers: LLMs are likened to compilers in that they translate specifications into executable code, but unlike traditional compilers, they lack clear guarantees about correctness and behavior.

2. Specification Challenges: Writing clear and precise specifications for software is difficult, and LLMs can exploit this by generating code based on vague requests. This can lead to a loss of control over the development process.

3. Functional Correctness: The programming interface provided by LLMs is often underspecified, making it hard to determine if the generated code meets the desired requirements without additional validation.

4. Iterative Development: Using LLMs may shift the approach to software development toward an iterative process of refining vague specifications and examining generated code, rather than a deliberate design process.

5. Importance of Specification: The author emphasizes that the ability to specify requirements clearly will become increasingly important, as LLMs work best with well-defined tasks.

In conclusion, while LLMs can assist in code generation, they require careful specification and verification to ensure that the resulting software meets the intended goals, highlighting the need for developers to maintain control over the development process.

Gigacode is a fun experimental project that combines OpenCode's tools with Claude Code, Codex, and Amp. It is not a separate version of OpenCode, but rather uses its protocol to connect to a server that translates API requests to different agents.

The aim of Gigacode is to quickly switch between coding agents based on the task. Here are the main points about the agents:

• Claude Code is great for executing tasks quickly.
• Codex is best for complex or long tasks.
• OpenCode is ideal for precise edits.

The project relies on the Sandbox Agent SDK, which allows control of Claude Code, Codex, and Amp through a universal HTTP API. It also provides a way for OpenCode to connect with any agent.

The creator appreciates OpenCode for its flexibility and good documentation, which makes projects like Gigacode possible. You can try it out by following the installation instructions provided on their website, and more details can be found on GitHub.

Summary

The article discusses the difficulties encountered while attempting to recreate uncensored PDFs from the recently released Epstein archive by the Department of Justice (DoJ). The release has faced criticism for poor redaction practices, encoding issues, and corruption of files.

Key points include:

1. Encoding Issues: The emails in the archive were badly converted, with some binary attachments included in base64 format, making them difficult to decode.

2. Redaction Problems: The DoJ's redaction process left many documents incomplete and confusing, raising concerns about the effectiveness of their methods.

3. OCR Challenges: Attempts to use Optical Character Recognition (OCR) tools to extract usable text from the PDFs were largely unsuccessful. The choice of the Courier New font made it hard to distinguish between characters like '1' and 'l'.

4. Conversion Attempts: Various methods, including using Adobe Acrobat and Tesseract, were tried to convert the files, but results were inconsistent. The author eventually turned to Amazon Textract, which yielded slightly better output.

5. Call to Action: The author encourages others to attempt to reconstruct the original PDFs from the available encoded content, noting that some attachments might still be recoverable despite the challenges.

Overall, the author highlights the need for better handling of sensitive documents and calls for collaboration to fix the issues found in the Epstein archive.

This text outlines atomic operations available in UNIX-like operating systems that help with thread-safe and multi-process programming without using locks. The key points include:

1. Atomic Operations: These operations are handled by the kernel to improve efficiency and reduce the need for manual locking.

2. File Operations:

   • mv -T <oldsymlink> <newsymlink>: Changes the target of a symlink atomically, useful for code deployment.
   • link(oldpath, newpath): Creates a hard link, useful for inter-process locking.
   • symlink(oldpath, newpath): Creates a symbolic link, similar to hard links but can point to directories.
   • rename(oldpath, newpath): Changes a pathname atomically, enabling inter-process locking.
   • open(pathname, O_CREAT | O_EXCL, 0644): Creates a new file and fails if it already exists.
   • mkdir(dirname, 0755): Creates a new directory with a similar failure mechanism.

3. File Descriptor Operations:

   • fcntl(fd, F_GETLK) and related functions: Allow processes to lock file regions for synchronized access.
   • mmap: Maps a file into memory for shared read/write access across processes.

4. Memory Operations: Atomic operations like __sync_fetch_and_add help manage memory safely without locks.

The author emphasizes the importance of relying on kernel functionality for atomic operations to ensure safety and efficiency in programming. This list is not exhaustive and may be updated in the future.

Summary:

The article introduces a new technique for solving a complex CSS problem known as "shrinkwrap," which involves adjusting the size of an element based on its inner content. This technique uses anchor positioning and scroll-driven animations to dynamically change the element's dimensions. The author presents a detailed breakdown of the method, including:

• The Problem: Traditional methods for shrinkwrapping were ineffective.
• Base Technique: A foundational approach to achieve shrinkwrapping, including code examples and styles for various components.
• Limitations & Requirements: Discusses constraints and prerequisites for using the technique.
• Complex Cases: Solutions for handling nested elements and specific layout scenarios.
• Use Cases: Practical applications for the technique, such as chat bubbles and tooltips.

The article emphasizes the potential of this method to improve web design by creating more flexible and responsive layouts without relying heavily on JavaScript.

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The text discusses the use of ASCII waveforms for testing hardware designs. ASCII waveforms are a way to represent electronic signals in a simple text format, making it easier to analyze and verify the performance of hardware components. This method helps designers ensure their hardware works correctly before final production.

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The Single-Strand Stopper Knot is a basic knot used to prevent the end of a rope or string from slipping through a hole or a guide. It is easy to tie and is useful in various applications, such as fishing or securing items. This knot is simple to learn and can be tied quickly.

Waymo's Chief Safety Officer, Mauricio Peña, revealed that some remote operators assisting their self-driving vehicles are based in the Philippines. These operators provide guidance in challenging driving situations but do not control the vehicles. This admission raised concerns among U.S. lawmakers about potential cybersecurity risks and the qualifications of these overseas operators.

Senators criticized the idea of foreign individuals influencing American vehicles, expressing worries about outdated information and the implications for employment in the U.S. as autonomous vehicles replace taxi jobs.

During a Senate hearing, Waymo and Tesla executives defended their technologies, asserting they are safer than human drivers, despite recent incidents. This included an investigation into a Waymo robotaxi that struck a child near a school, which the company claims would have been more dangerous if a human driver had been involved.

Congress is working on creating uniform safety regulations for self-driving vehicles as they become more common in U.S. cities.

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Summary of the Masked Namespace Vulnerability in Temporal (CVE-2025-14986)

On February 5, 2026, Mav Levin, a security researcher, reported a vulnerability in Temporal's ExecuteMultiOperation endpoint. This vulnerability, known as CVE-2025-14986, involves an identity-binding issue where an outer request is authorized for one namespace, but an inner operation uses a different namespace.

Key Points:

• What is Temporal? Temporal is a platform used by major companies like Netflix and Stripe to ensure reliable code execution even during server failures.

• Nature of the Vulnerability: The vulnerability allows an attacker to exploit the system by passing requests that appear authorized under one namespace (AttackerNS) but reference a different namespace (VictimNS) in the payload.

• Two Main Exploits:

  1. Cross-Tenant Isolation Breach: An attacker could manipulate the system to access data meant for a different tenant by referencing the VictimNS in their request.
  2. "Bring Your Own Policy" Attack: A developer could bypass strict corporate policies by using their personal namespace's more permissive rules in a corporate environment.

• Fix and Remediation: The issue was addressed in Temporal version 1.27, which ensures that the inner namespace must match the outer authorized namespace before processing any requests.

• Timeline of Events: The vulnerability was reported on December 12, 2025, and a patch was released publicly on December 30, 2025.

This vulnerability highlights the importance of careful authorization checks in complex API requests to prevent security breaches.

The Marian R. Byrnes Natural Area in Chicago is a unique landscape that has emerged from former industrial sites, specifically areas covered in slag—a byproduct of steelmaking. Ecologist Alison Anastasio was inspired by her visits to both the Great Alvar in Sweden and the US Steel South Works site in Chicago, where she discovered surprising biodiversity in what seemed like barren land.

In 2018, Anastasio formed a group of urban ecologists called the "Slag Queens" with her colleagues to research the plants growing on slag sites. Their work has revealed that these areas, often seen as wastelands, can actually support diverse ecosystems, including rare species. Their first paper highlighted the ecological value of these sites, arguing that they should be viewed as "novel ecosystems" rather than merely problematic brownfields.

The Slag Queens have conducted surveys to categorize the plant life in different slag environments and discovered significant biodiversity, including rare plants thought to be extinct in Illinois. They advocate for leaving some sites in their current state while selectively introducing native species, as restoring them to their original ecosystems is often unrealistic. Their work is influencing local park management policies and encourages a broader understanding of how to manage postindustrial landscapes effectively.