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The text discusses a well-known compiler-building tutorial called "Let's Build a Compiler" by Jack Crenshaw, published between 1988 and 1995. Despite its age, it continues to be referenced by programmers today. The author reflects on the tutorial's lasting impact and undertakes a project to translate it into Python, targeting modern WebAssembly instead of older technologies.

The tutorial is praised for its clear, conversational style and its step-by-step approach to building a recursive-descent parser. It quickly transitions to generating assembly code, which contrasts with many modern resources that focus heavily on parsing theory before code generation.

The author shares insights from revisiting the tutorial, noting that while it is effective for beginners, it has limitations, particularly regarding type checking and code optimization. The post includes a sample code snippet from the tutorial and encourages readers to explore the new Python implementation. The goal is to help those interested in compilers access the original material without needing outdated technologies.

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Summary:

On December 3, 2025, the author shared their experience trying to manage multiple email accounts more effectively using Emacs and Mu4e. They aimed to create a simpler and less distracting email client, especially since they prefer working offline. Despite some challenges, they learned a lot during the process.

The author uses Emacs to manage their schedule and notes and wanted to integrate email into this system. They chose Mu4e because of its customizable interface, allowing quick access to email searches.

They started by setting up their personal Gmail account and a charity email account, using the Internet Message Access Protocol (IMAP) for email retrieval. They initially tried mbsync but faced issues with Gmail, so they switched to OfflineIMAP, which worked better for them.

To access Gmail through OfflineIMAP, the author set up OAuth 2.0, a modern method of authentication that replaced app passwords. They wrote scripts to manage the OAuth process securely.

They also set up an email helper script to retrieve passwords and tokens without exposing sensitive information. For sending emails, they used msmtp, a simple mail transport agent, configured to use OAuth tokens or app passwords.

Finally, they configured Mu4e to index their emails and customize how they manage emails within Emacs. However, they faced difficulties with their work email, which is managed with Microsoft 365 and does not support third-party clients due to security restrictions.

In summary, the author successfully integrated personal and charity emails into their Emacs workflow but encountered challenges with their work email.

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Summary:

On December 4, 2025, the Television Academy awarded the AV1 video codec with a Technology & Engineering Emmy Award for its significant impact on video content delivery.

In the mid-2010s, the web faced challenges with video codecs that had high costs and licensing issues, mainly relying on H.264. To address this, Mozilla and other tech leaders formed the Alliance for Open Media in 2015 to create a new codec. This led to the development of AV1, launched in 2018, which offers excellent compression as a royalty-free standard. AV1 is now widely used for streaming and has also influenced the AVIF image format.

The Emmy award highlights the importance of open standards and the efforts of the AOM community. Looking forward, AOMedia is developing AV2, which will provide improved compression and efficiency for video content while continuing the mission of making web video accessible to everyone.

Welcome to Bruno's Home! I'm Bruno Simon, a creative web developer. This is my portfolio, where you can explore and learn more about me. Enjoy your visit and please be careful not to break anything!

Mistral AI has launched Devstral 2, a new family of open-source coding models, available in two sizes: Devstral 2 (123 billion parameters) and Devstral Small 2 (24 billion parameters). Both models are designed to be efficient and cost-effective, with Devstral 2 being particularly powerful, achieving a score of 72.2% on the SWE-bench Verified benchmark.

Key features include:

• Cost Efficiency: Devstral 2 is up to 7 times more efficient than other models like Claude Sonnet.
• Open-source: Devstral 2 is released under a modified MIT license, while Devstral Small 2 uses the Apache 2.0 license.
• Versatile Deployment: Devstral Small 2 can run on consumer hardware and supports local deployment and customization.
• Mistral Vibe CLI: This command-line interface allows users to automate coding tasks and interact with their codebase using natural language.

Devstral models are compact yet high-performing, competing well against larger models. They are optimized for production workflows, capable of handling multiple files, tracking dependencies, and correcting errors. Mistral Vibe CLI enhances the coding experience with features like project-aware context and multi-file orchestration.

Devstral 2 is currently free via API, with future pricing set at $0.40 per million tokens for input and $2.00 for output. Devstral Small 2 will be cheaper. Mistral AI encourages users to explore these tools for their projects and provides support through various channels.

In the text, Stephen Ramsay shares his thoughts on "vibe coding," which refers to using AI to generate code quickly and intuitively rather than through traditional programming methods. Ramsay expresses a deep love for programming, viewing it as a fulfilling and enjoyable activity. However, he feels conflicted about vibe coding, which he believes detracts from the joy of understanding and solving problems in programming.

He acknowledges that vibe coding can produce effective and complex systems, challenging the notion that it is inferior to human coding. Ramsay argues that programming languages were designed for human readability and understanding, not just for machine execution. Therefore, he questions why vibe coding uses languages that prioritize human convenience when a more machine-focused approach could be developed, suggesting the idea of a "vibe-oriented programming language" (VOP) that emphasizes ease for AI while simplifying the coding process for humans.

Ramsay reflects on the evolution of programming and expresses both concern and excitement about the future of coding, noting that as technology evolves, traditional programming may become less relevant. He draws parallels between past technological advancements and current shifts in coding practices, suggesting that the landscape of programming is on the brink of significant change.

Summary of Django 6.0 Release Highlights

Django 6.0 has been released, introducing several significant features and improvements to the popular Python web framework. Here are the key updates:

1. Template Partials: Django now supports template partials, which allow developers to define reusable sections of templates. This reduces code duplication and improves maintainability.

2. Tasks Framework: A new built-in Tasks framework enables background task processing, allowing operations like sending emails to run outside the main request-response cycle. Developers can define and enqueue tasks using a simple API.

3. Content Security Policy (CSP) Support: Django 6.0 includes built-in support for CSP, which helps protect against content injection attacks like XSS. Developers can easily configure CSP headers to declare trusted content sources.

4. Email API Updates: The email handling system has been updated to use Python’s modern email API, providing a cleaner and more Unicode-friendly interface for composing and sending emails.

5. Additional Improvements:

   • Keyword Arguments: Email functions now require keyword arguments for optional parameters, enhancing clarity in the code.
   • Automatic Shell Imports: Common utilities are now automatically imported in the shell, improving developer experience.
   • Dynamic Field Refresh: Fields that rely on database-generated values are now refreshed automatically after saving.
   • StringAgg Aggregate: The StringAgg aggregate function is now available on all database backends, allowing easier concatenation of strings.
   • Default Primary Key Type: The default primary key type has changed to BigAutoField, accommodating larger data sets and preventing primary key exhaustion.
   • For Loop Enhancements: A new variable, forloop.length, is available in template loops to simplify length calculations.

These updates aim to enhance security, performance, and usability in Django applications. For developers upgrading from previous versions, tools like django-upgrade can assist in adapting to the new features.

Summary of the Blog Post: Running Linux on a RiscPC - Why is it So Hard?

The author shares their experience of trying to install Debian Linux on a RiscPC, a vintage ARM desktop from 1994. They faced numerous challenges throughout the process, which they document to help others.

Key Points:

1. Why RiscPC?

   • The author has nostalgic feelings for the RiscPC and wants to explore its capabilities with Linux.

2. Choosing Debian:

   • They prefer Debian for its familiarity and wanted to experiment with older versions.

3. Linux Distributions for RiscPC:

   • The author discusses other options like ARMLinux and Slackware but finds them unsuitable for the RiscPC's ARM710 processor.

4. Installation Challenges:

   • The installation process is complicated due to a lack of suitable bootloaders and compatibility issues between Debian versions and the RiscPC hardware.
   • They attempt various methods, including using different Debian versions (Potato and Woody) and manually configuring the system.

5. Custom Solutions:

   • The author creates a custom initrd (initial ramdisk) to include necessary modules and tools, enabling them to get the system to boot.

6. Success:

   • After overcoming many issues, including missing dependencies and configuration problems, the author successfully installs Debian Woody and manages to get the system running.

7. Future Plans:

   • They express intentions to improve the boot process and attempt to install XFree86 for graphical support.

The blog emphasizes the complexity and perseverance involved in reviving an old machine with modern software, reflecting both the challenges and rewards of retro computing.

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Introducing Pebble Index 01: Your External Memory Device

The Pebble Index 01 is a small ring designed to help you capture thoughts and ideas before they slip away. You simply hold the button, speak your idea, and it gets recorded and sent to your phone as a note or reminder.

Key Features:

• Compact Design: The ring is as small as a wedding band and comes in three colors (polished silver, polished gold, matte black) and eight sizes.
• Durable and Private: Made from stainless steel, it's water-resistant and has no recording until you press the button. It requires no internet connection or subscription.
• Long Battery Life: The battery lasts for years with no need to recharge.
• Customizable: You can modify its functions to suit your needs, including controlling smart devices or integrating with various apps.

Usage Scenarios: The Index 01 is perfect for busy moments, like biking or cooking, when you can't easily access your phone. Users can record reminders and notes hands-free.

Pricing and Availability: You can pre-order the Pebble Index 01 for $75, with shipping starting in March 2026. The price will increase to $99 afterward.

Security and Functionality: The device processes recordings locally on your phone, ensuring privacy. You can also listen to recorded messages if needed. It's compatible with both iPhone and Android devices.

Future Enhancements: While its main function is to remember things, there are plans to expand its capabilities with customizable actions and integrations with AI features in the future.

In summary, the Pebble Index 01 is a practical, discreet tool for capturing fleeting thoughts, designed to fit seamlessly into your daily life.

Summary of "10 Years of Let's Encrypt Certificates"

Let's Encrypt, a nonprofit certificate authority, launched its first publicly-trusted certificate on September 14, 2015. Over the past decade, it has grown to become the largest certificate authority globally, issuing billions of certificates and supporting the widespread use of HTTPS encryption on the web.

Key milestones include:

• Issuing its one millionth certificate in March 2016.
• By 2018, issuing a million certificates daily, and reaching a total of one billion certificates by 2020.
• As of late 2025, issuing ten million certificates per day and nearing one billion protected websites.

The organization emphasizes the importance of HTTPS in improving web security, with global encrypted connection rates rising from under 30% to around 80% over five years. In the U.S., this rate is close to 95%.

Let's Encrypt has introduced several features, such as support for internationalized domains and wildcard certificates, while also upgrading its infrastructure to handle increasing demand. The project has received recognition and awards for its contributions to internet security.

The founding sponsors—Mozilla, EFF, Cisco, Akamai, and IdenTrust—played a crucial role in its establishment, with IdenTrust providing essential technical support. Looking ahead, Let's Encrypt aims to continue making the internet safer and more accessible while encouraging community support to sustain its mission.

Summary:

On December 10, 2025, a new initiative was announced where the Model Context Protocol (MCP) is being donated to the Agentic AI Foundation (AAIF), which operates under the Linux Foundation. MCP is an open standard that connects AI applications with external systems and has seen significant adoption, with over 10,000 active servers and integration into major platforms like ChatGPT and Microsoft Copilot.

The donation aims to ensure that MCP remains an open-source, community-driven project. The Linux Foundation, known for supporting important open-source initiatives, will help manage MCP’s development. The AAIF, co-founded by Anthropic, Block, and OpenAI, focuses on the growth of agentic AI in a transparent and collaborative manner.

Key features of MCP include a growing directory of connectors, new capabilities for tool management, and strong community support through official SDKs. The governance of MCP will continue to prioritize community involvement.

This move underscores the commitment to developing a neutral and innovative ecosystem for agentic AI technologies. For more information about MCP and how to get involved, you can visit their website.

Summary: How to Speak at Software Conferences

Dylan Beattie shares advice for aspiring speakers looking to present at software conferences. He emphasizes two key points:

1. Define Your Goals: Understand why you want to speak—whether it’s for promotion, networking, or becoming a professional speaker.

2. Be Realistic: It takes time and effort to become a successful speaker. Be prepared for a long journey; it took him seven years to reach his first international conference.

Year 1: Get Good

• Write a unique talk and refine it through practice at local user groups.
• Seek feedback and improve your presentation skills.

Year 2: Get Seen

• Attend community conferences to network and gain visibility.
• Introduce yourself to other attendees and organizers, and follow up after the event to build connections.

Year 3: Get Accepted

• Submit your polished talks to conferences with open Calls for Papers (CfP).
• Use your network to discover which events are worth attending and what topics are in demand.

Year 4: Get Bored

• After some time, the excitement may fade. Determine what success means for you, and adjust your speaking engagements accordingly.
• Keep your audience in mind—always give your best effort, as their time and investment matter.

Dylan encourages aspiring speakers to reach out for help with abstracts or presentations and to enjoy the process of sharing knowledge.

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Summary: Writing Our Own Cheat Engine - Part 1: Introduction

This series focuses on creating a simple version of Cheat Engine, a tool for modifying single-player games. Cheat Engine helps users debug games and includes features like a memory scanner and disassembler. The series will be educational and intended for private use only, ensuring compliance with software agreements.

We will develop our Cheat Engine using Rust, a language known for its safety and speed, though other languages like Python can also be used. Basic knowledge of C-family languages is recommended.

The tutorial will cover various steps, starting with attaching to a game process. We will learn how to enumerate all processes in Windows, open specific processes, and retrieve their names. Key functions from the Windows API will be utilized, emphasizing the importance of reading documentation to understand permissions and access rights.

The first step in the tutorial involves opening Cheat Engine, selecting a process, and ensuring we have the necessary permissions to interact with it. The ultimate goal is to build a foundation for further Cheat Engine functionalities in subsequent parts of the series.

For code examples and a more in-depth look, users can refer to the GitHub repository associated with the tutorial. The next part will cover "Exact Value Scanning."

Ken Shirriff's blog discusses the Intel 8087 floating-point coprocessor, introduced in 1980, which significantly improved the speed of floating-point calculations—up to 100 times faster than early microprocessors. This advancement benefited applications like AutoCAD and spreadsheets, and its floating-point system is still in use today.

The 8087 chip is complex, containing 40,000 to 75,000 transistors. Shirriff reverse-engineered the chip to understand its functionality, documenting its intricate design. The chip has a "datapath" for calculations, a constant ROM for important values (like π), and eight registers organized in a stack for operations.

Unlike typical processors that directly specify registers, the 8087 uses a stack-based architecture, where operations occur on the top value of the stack. This design aimed to simplify instruction sets and compiler designs, though it didn't fully achieve its goals.

The chip handles floating-point numbers, which consist of a significand and an exponent, using 80-bit registers—larger than the typical 8 or 16-bit registers of its time. The internal structure features static RAM cells that store bits and sophisticated wiring layers that enable its complex functionality.

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The Linux kernel community has been responsible for issuing CVEs (Common Vulnerabilities and Exposures) since becoming a Certificate Numbering Authority (CNA) two years ago. In that time, they have rapidly increased their output of CVEs, ranking third in 2024 and first in 2025. This surge has raised questions about their processes and tracking methods.

The author has given several talks on this topic, discussing the evolution of CVE assignments and how they address issues faced during their first year as a CNA. Much of this work, while crucial, is not directly visible in the Linux kernel development process, except through updates on the linux-cve-announce mailing list.

To clarify their approach, the author plans to write a series of posts detailing how CVEs are reported and assigned within the Linux community. This will include information about their versioning system, tools used for CVE management, and simpler tracking methods compared to the conventional CVE formats. The goal is to share knowledge that may help other open-source projects facing similar challenges.

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The post celebrates the 30th anniversary of WarCraft II: Tides of Darkness, highlighting the fond memories shared by players. Many users reminisced about their experiences with the game, such as sneaking off to play during family gatherings and the creative strategies that emerged from the game's mechanics. Players expressed their deep affection for WarCraft II, recalling how it inspired some to pursue careers in software engineering and sparked a sense of excitement and nostalgia. The game's unique graphics, music, and attention to detail left a lasting impact on players, making it a beloved classic. There was also mention of a source code for the game available online, although it has been taken down. Overall, the post reflects on WarCraft II's significant influence on gaming culture and individual lives.

Summary:

Debugging a Qt WebAssembly application can be challenging, especially with persistent bugs. The easiest way to debug is by using the -g option or setting CMAKE_BUILD_TYPE=Debug. Emscripten automatically includes DWARF symbols in the WebAssembly binaries, which are only fully supported in Chrome with a specific browser extension.

For debugging in other browsers like Safari or Firefox, or if you can't install the extension, you’ll need to create source maps, which will be discussed in a future post.

To enable DWARF debugging, adjust the browser developer tools settings, but you won’t need symlinks, as the full directory paths are embedded in the binaries.

When using Chrome, open the console tools, navigate to your Qt WebAssembly app, and wait for the symbols to load. You can then set breakpoints, reload the page, and debug much like you would in a desktop application.

Overall, building Qt in debug mode with the -g or -g3 arguments preserves the necessary debug symbols for effective debugging.

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The Agentic AI Foundation (AAIF) is a new initiative by companies like Block, Anthropic, and OpenAI, aimed at promoting open source agentic AI. Agentic AI refers to AI systems that can act independently and make decisions with little human input.

The AAIF will provide a neutral space for collaboration on open source AI projects, ensuring no single company dominates the ecosystem. It will support community programs, research, and create standards for different AI systems to work together.

Key points include:

• The AAIF operates on principles of open governance, encouraging diverse contributions and ensuring long-term sustainability of projects.
• Founding projects include Block's "goose," Anthropic's Model Context Protocol (MCP), and OpenAI’s AGENTS.md, all designed to enhance collaboration and integration in AI development.
• The foundation aims to prevent fragmentation in AI development and promote open standards that benefit everyone, rather than concentrating power in a few companies.

The AAIF invites developers, researchers, and anyone interested in open AI to participate, contribute to projects, and join discussions. The goal is to create an open and accessible AI ecosystem, allowing for innovation and collaboration across the industry.

For more information, you can visit the AAIF website.

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This article discusses a new type of supercapacitor made from multiscale curved graphene, which improves energy storage efficiency. Supercapacitors are devices that store energy quickly and deliver high power but often struggle with low energy density, making them less suitable for compact applications.

Key points include:

• Multiscale Graphene: The researchers created a type of graphene that combines small curved structures with disordered areas. This design enhances ion access and improves energy storage capabilities.

• Ion Transport: The unique structure of the graphene allows for better ion transport, which is essential for high performance in supercapacitors.

• High Performance: The new supercapacitors achieved impressive energy densities—99.5 Wh/L with ionic liquids and 49.2 Wh/L with organic electrolytes—along with a high power density of 69.2 kW/L.

• Manufacturing Process: The graphene is produced through a rapid thermal treatment of graphite oxide, which creates the desired curved and tangled structures.

• Comparison to Traditional Materials: Unlike traditional graphite, which has limitations due to its structure, this new multiscale graphene shows significantly enhanced ion insertion rates and overall performance.

Overall, the study demonstrates that optimizing the structure of graphene can lead to supercapacitors that are more efficient and suitable for modern portable electronics.

Summary of Z3 Theorem Prover

Z3 is a theorem prover developed by Microsoft Research, available under the MIT license. It can be built on various platforms using tools like Visual Studio, Makefile, CMake, vcpkg, or Bazel, and supports multiple programming languages.

Key Points:

• Building Z3:

  • For Windows, use Visual Studio with commands for 32-bit or 64-bit builds. It requires Python and C++20 support.
  • On Linux, use make or CMake, with options for different compilers (like GCC or Clang).
  • vcpkg can be used for easy installation, and Bazel is also an option.

• Security Features:

  • When built with Visual Studio, Z3 has built-in security features like Control Flow Guard and Address Space Layout Randomization.

• Dependencies:

  • Minimal; mainly C++ runtime libraries are needed. Python is required for building, and specific toolchains are necessary for some language bindings.

• Language Bindings:

  • Z3 supports several languages including .NET, C, C++, Java, OCaml, Python, Julia, and Smalltalk. Installation commands and flags are provided for building bindings in each language.

• Interface:

  • The default input format is SMTLIB2, and Z3 provides various APIs for different programming languages.

This summary captures the essential information about Z3 and its building and usage guidelines in a simplified manner.

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Summary of MindEval Introduction by Sword Health

On December 9, 2025, Sword Health announced MindEval, a new open-source framework designed to measure the clinical competence of AI in mental health support. With over a billion people needing mental health care, there is a growing demand for effective AI solutions. However, existing evaluations do not adequately assess AI capabilities in providing safe and effective therapy.

MindEval was developed in collaboration with licensed Clinical Psychologists to evaluate AI models through realistic, multi-turn therapy conversations. It automates the assessment of crucial clinical skills, focusing on five key criteria: clinical accuracy, ethical conduct, response assessment, therapeutic relationship, and communication quality. This framework aims to provide a reliable standard for developers and researchers to improve AI in mental health care.

Current AI tools often perform poorly in therapy settings, typically relying on simple knowledge checks rather than assessing dynamic interactions. MindEval addresses this gap by simulating complete therapy sessions and evaluating them against established clinical guidelines.

Initial testing of 12 leading AI models showed they generally struggled, with average scores below 4 out of 6 on clinical quality. The results indicate that simply increasing model size does not guarantee better performance in therapeutic contexts, especially for patients with severe symptoms.

Sword Health is open-sourcing the MindEval framework, including all codes and evaluation data, to promote transparency and enhance the safety and reliability of AI in mental health care.

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Summary of OpenEvolve: Teaching LLMs to Discover Algorithms Through Evolution

OpenEvolve is an open-source tool that helps machines learn to create algorithms by using large language models (LLMs) within an evolutionary framework. Instead of traditional methods like manual coding or exhaustive searching, it allows LLMs to generate and improve programs through a process similar to natural evolution.

Key Features:

• Evolutionary Framework: It uses a quality-diversity search method where candidate programs are generated, evaluated, and organized into a system that promotes diversity and quality.
• Architecture: The system includes five components: a prompt sampler, an LLM ensemble for generating code, an evaluator for testing programs, a program database for organizing successful programs, and a controller to manage the entire process.
• Island-Based Evolution: Multiple isolated populations (islands) evolve independently to encourage diverse exploration and prevent getting stuck on suboptimal solutions.
• MAP-Elites: This technique ensures that a variety of high-quality solutions are preserved and encourages the discovery of diverse algorithms.
• Cascade Evaluation: Programs are evaluated in stages to quickly weed out underperformers before running more intensive tests.

Applications: OpenEvolve has been successfully used in various areas, including:

1. Algorithm Discovery: Significant speed improvements in algorithms were found, such as a 321x speedup through automatic optimization.
2. Circle Packing: It discovered new algorithms that matched state-of-the-art results.
3. GPU Kernel Optimization: It identified optimizations for GPU processing that improved performance while maintaining accuracy.
4. Prompt Optimization: The tool can also enhance prompts for natural language processing tasks, improving accuracy on benchmark tests.

Getting Started: Users can easily implement OpenEvolve with a library or command-line interface, specifying their programs and evaluation criteria. It's available on GitHub and supports complex configurations and parallel evaluations.

In conclusion, OpenEvolve represents a significant advance in using evolutionary strategies to teach machines to develop complex algorithms autonomously.

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AlgoDrill was created to help people who struggle to remember coding patterns after practicing on LeetCode. It offers pattern-based drills using problems from NeetCode 150 and others. Users can rebuild solutions step by step, learn the reasoning behind each step, and focus on specific patterns like sliding window and dynamic programming. The aim is to help users write code confidently and quickly in interviews. The creator is looking for feedback on whether this drill approach improves learning and what aspects of the site may be confusing or lacking.

Summary of Giles' Blog Post on Training a Language Model

Giles discusses his experience training a custom large language model (LLM) using his own hardware, specifically an RTX 3090 GPU. He aims to replicate the training process outlined in Sebastian Raschka's book on building a large language model from scratch.

Key Points:

1. Training Experiment: Giles trained a base model with approximately 163 million parameters over 48 hours using FineWeb datasets. He initially doubted the feasibility of training on his hardware but found it possible with a well-structured approach.

2. Model Configuration: He used parameters similar to those of the GPT-2 model, such as token count and context length, and made decisions on aspects like dropout rates and weight tying based on current best practices.

3. Dataset Selection: He utilized FineWeb datasets, which are curated for quality. He also tried FineWeb-Edu, a version aimed at educational content, to see if it improved model performance.

4. Training Process: The training involved adjusting hyperparameters, managing memory during training, and implementing mixed precision training techniques for efficiency. He noted that using tensor cores significantly improved training speed.

5. Performance Evaluation: After training, the model's performance was evaluated through validation loss on both FineWeb and FineWeb-Edu datasets. While initial results were promising, they did not match the performance of OpenAI's original GPT-2 small model.

6. Further Training: Additional training on FineWeb-Edu showed some improvement but still fell short of OpenAI's results. Giles speculated on the reasons for the performance gap, such as differences in data size, training epochs, and architectural choices.

7. Conclusion: While he successfully trained a functional LLM, Giles concluded that his model was not as effective as OpenAI's. He plans to explore training on more powerful cloud hardware for future experiments.

Overall, the post serves as both a personal account of Giles' journey in AI model training and a resource for others interested in building their own language models.

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Cyril from CTGT has introduced Mentat, an API designed for developers to control the behavior of large language models (LLMs) effectively. This tool allows for real-time adjustments to reasoning and bias without requiring complex fine-tuning or prompt engineering. It is especially useful in regulated industries like finance and media, where accuracy is crucial.

Mentat uses advanced techniques to address issues like hallucinations (incorrect model outputs) and ensures compliance with policies. It modifies model behavior during processing, rather than just filtering outputs, which improves reliability. For instance, it has significantly boosted the accuracy of models in various tests.

CTGT encourages developers to try out the API and provide feedback, as they aim to enhance the performance of AI applications. Users can access the API and a comparison tool without needing to sign up.

The article discusses how technology adoption in law firms varies between large and small firms. While the nature of legal work hasn't changed much, the way it is done has evolved. Lawyers often resist using automation, but there's pressure for firms to adopt technology to stay competitive.

Large firms usually adopt technology to enhance their prestige and future-proof their operations, helping them attract clients. In contrast, smaller firms use technology mainly to reduce administrative tasks and increase client capacity.

Smaller firms tend to have quicker tech adoption processes because they face fewer contractual constraints than large firms, which often have long-term agreements with tech vendors. Additionally, smaller firms typically manage cases with one lawyer, making it easier to streamline workflows.

Despite the benefits, many lawyers remain hesitant to embrace technology due to a reluctance to change traditional practices and billing methods. However, there is a significant opportunity for firms, especially smaller ones, to improve their practices by adopting legal tech.

The text discusses the challenges of benchmarking code due to compiler optimizations. It explains how compilers, like LLVM, can replace complex calculations with simpler formulas to improve performance, which can lead to misleading benchmark results if the computation is optimized away.

There are two main issues:

1. If a computation's result is not used, the compiler may eliminate it entirely.
2. If parameters are known at compile time, the compiler might simplify parts of the computation.

To prevent this, the author suggests using explicit methods to ensure that the code is not optimized away. For example, using a parameter function that allows runtime changes prevents the compiler from treating values as constants. Additionally, computing and printing a result (like a "hash" of the output) ensures that the results are used, preventing optimization.

The key recommendations are:

• Make input parameters adjustable at runtime.
• Always print the results to ensure they are considered in the computations.

By following these practices, you can create more reliable benchmarks without relying on complex anti-optimization tricks.

The author expresses a strong dislike for the idea of a future without death from old age, even though they don't oppose longer lifespans. They argue that death should exist as a limitation that gives life meaning.

Aubrey de Grey suggests that our fear of immortality is a "pro-aging trance," but the author believes this fear reflects our values rather than just coping mechanisms. They share personal experiences showing how limits, like time constraints, help define who we are and encourage us to strive for our goals.

The essay references Bernard Williams, who argues that immortality would lead to boredom and a loss of self. The author agrees, stating that the pursuit of goals, not merely achieving them, is what gives life meaning. Death creates urgency and drives us to take action, as seen in the decline of people after retirement when they stop striving.

The author warns against living solely for longevity without enjoying life, noting that without death, we may lose our sense of purpose and urgency.

Arlan is developing Nia, a tool designed to improve AI coding agents by providing accurate context from real codebases and documentation. Nia helps eliminate errors caused by outdated information that general models rely on. It works by indexing various sources, like GitHub repositories and documentation, to create a searchable database that coding agents can query for relevant information.

Nia uses a combination of search techniques to return precise code snippets and their locations, making it easier for coding agents to generate accurate code. It can serve multiple coding agents and projects simultaneously. Additionally, Nia keeps its indexes updated to avoid quoting outdated documentation.

Nia is available as a paid product with a free tier for individuals. It allows users to connect their repositories and maintain privacy by indexing data locally for private projects. The tool shows promise in various applications, including healthcare and personalizing AI agents based on specific archives.

Arlan is eager to receive feedback from users to improve Nia and understand its limitations better.

This text is about a project called "Anjin Virtual Computer" by Simon J. Gillespie. It includes details about the project repository on GitHub, such as notifications, code, issues, and releases. The most recent release is version 1.0, which was made on December 9. This version supports Bluetooth keyboards and a hands-free mouse, with specific controls for user interactions. There are some errors in loading additional information, and the text also mentions verified signatures for commits.

Adobe is enhancing its use of AI by integrating features from its apps—Photoshop, Express, and Acrobat—into ChatGPT. This allows users to use the chatbot to edit images, modify PDFs, and animate elements by simply asking it to perform tasks like adjusting image properties or merging files.

Users can edit images with Photoshop through ChatGPT by specifying changes, such as blurring backgrounds or applying effects, and can adjust the intensity of these effects with sliders. With Express, users can access design libraries and create themed projects, while Acrobat's features enable tasks like editing text in PDFs.

If users prefer, they can switch back to Adobe's applications to complete tasks that ChatGPT can't handle. These features are available globally on ChatGPT's desktop, web, and iOS apps, with Adobe Express currently the only one available on Android, though support for Photoshop is coming soon.

Adobe has been actively rolling out AI features this year, including new assistants for its apps. As more companies integrate their services into ChatGPT, competition will grow for user preference among similar tools.

The author shares their experience with regular meetings and the importance of taking active notes during these sessions. They emphasize that instead of just writing minutes or using AI summaries, they prefer to jot down key points and insights in their own words. This practice helps them keep track of discussions and actions over time, especially when managing many meetings.

After trying various note-taking tools without complete satisfaction, the author created their own tool specifically designed for regular meetings like 1-1s. They’ve been using it successfully and now want to share it with others. The tool offers a free tier for anyone interested in improving their note management for meetings.

The author expresses frustration with the trend of adding icons to every menu item in software, particularly in Google Sheets and macOS Tahoe. They argue that this approach creates unnecessary visual clutter and makes menus harder to navigate. The author believes that icons should only be used when they genuinely enhance usability, rather than just filling space.

They point out inconsistencies in how icons are used across different menus in macOS, raising questions about the rationale behind their placement. While they acknowledge that some icons can be helpful, such as in Finder, they feel that the overall increase in icons contradicts Apple's previous guidelines, which advised against excessive use of symbols due to potential confusion.

In conclusion, the author is weary of the pervasive use of icons in menus and finds it challenging to advocate for a more thoughtful approach in design.

I created a middleware API called 'Flight Simulator' to prevent my LangChain agent from wasting about $50 in OpenAI credits due to an infinite loop. This tool tests your agent's prompts for issues like loops and private information leaks before you use them. You can check out the code and repository here and see a live demo here. I welcome any feedback on other problems you might have encountered!

Epsilon Overview

Epsilon is a WebAssembly runtime written in Go, with no external dependencies. It fully supports the WebAssembly 2.0 specification and can run on various architectures like amd64 and arm64 without needing CGo. Key features include:

• Ability to embed WebAssembly modules in Go applications.
• An interactive REPL for testing and debugging.

Installation

To install Epsilon, use the command:

go get github.com/ziggy42/epsilon

Quick Start

1. Basic Execution: Load and run a WebAssembly module from a byte array. Here's a simple example:

   package main
   
   import (
       "fmt"
       "os"
       "github.com/ziggy42/epsilon/epsilon"
   )
   
   func main() {
       wasmBytes, _ := os.ReadFile("add.wasm")
       instance, _ := epsilon.NewRuntime().InstantiateModuleFromBytes(wasmBytes)
       result, _ := instance.Invoke("add", int32(5), int32(37))
       fmt.Println(result[0]) // Outputs: 42
   }
   

2. Using Host Functions: You can add custom Go functions to your WebAssembly modules:

   imports := epsilon.NewImportBuilder().
       AddHostFunc("env", "log", func(args ...any) []any {
           fmt.Printf("[WASM Log]: %v\n", args[0])
           return nil
       }).
       Build()
   
   instance, _ := epsilon.NewRuntime().
       InstantiateModuleWithImports(wasmFile, imports)
   

Interactive REPL

Epsilon includes a REPL for testing and debugging. You can run it with:

go run ./cmd/epsilon

Essential Commands:

• LOAD <path|url>: Load a module.
• INVOKE <func> [args...]: Call a function.
• GET <global>: Read a global variable.
• MEM <offset> <len>: Inspect memory.
• LIST: List loaded modules.

Example REPL Session:

$ go run ./cmd/epsilon
>> LOAD https://github.com/mdn/webassembly-examples/raw/refs/heads/main/understanding-text-format/add.wasm
'default' instantiated.
>> INVOKE add 10 32
42

Testing & Benchmarks

• Install WABT to compile WASM code.
• Run unit tests: go test ./epsilon/...
• Run spec tests: go test ./internal/spec_tests/...
• Run benchmarks: go test -bench . ./internal/benchmarks

Contributing and License

For contributions, refer to the CONTRIBUTING.md file. Epsilon is licensed under Apache 2.0. Note that this is not an official Google product and is not eligible for their vulnerability rewards program.

Summary: GitHub Actions Package Manager Issues

GitHub Actions has significant flaws in its package management system, which poses risks to software security. Unlike other established package managers like npm and Cargo, GitHub Actions lacks essential features such as:

1. Lockfiles: GitHub Actions does not have lockfiles that record the exact versions of dependencies, leading to unpredictable changes in workflows without any code modifications.

2. Integrity Verification: There are no integrity checks to ensure that the code being executed matches what was intended. This means users must trust GitHub to provide the correct code.

3. Dependency Visibility: Users cannot see or control transitive dependencies, which are dependencies that come from other actions. This lack of visibility can lead to vulnerabilities.

4. Immutable Releases and SHA Pinning: GitHub has introduced some mitigations, but they only address top-level dependencies and do not protect against changes in transitive dependencies.

5. No Offline Support: Actions must be downloaded from GitHub every time they run, making it impossible to run workflows without network access.

6. Namespace Issues: The naming system allows anyone with a GitHub account to create actions, leading to potential security risks like account takeovers and typosquatting.

Research has shown that a vast majority of GitHub Actions users run unverified code, and there are significant security vulnerabilities in the system. The lack of a lockfile and integrity verification means that users are at risk of executing malicious code without their knowledge.

To improve security, GitHub should implement a lockfile system, integrity hashes, and better visibility into dependencies. However, previous requests for these features have been dismissed. The flaws in GitHub Actions not only affect its users but also impact other platforms that aim to be compatible with it.

As large language models (LLMs) like Gemini gain popularity, more people are posting comments like "I asked Gemini, and Gemini said...". There is a suggestion to discuss whether such comments should be allowed on Hacker News (HN), as they may disrupt genuine human conversations. The author prefers real discussions over LLM-generated text. They are asking for opinions on whether to update the guidelines to discourage posting large LLM responses or to create a new rule against copy-pasting these responses.

No summary available.

The author is feeling frustrated about their job search and the corporate world. They are applying for jobs but struggle to find companies that align with their values, particularly those focused on growth. They feel stuck in a junior position and question the importance of "climbing the ladder" when it seems to benefit only their bosses, not themselves.

They express a desire to work for small companies that aren’t focused on growth, but these are hard to find and often lack the resources for training. The author is confused about what companies value in performance and is disillusioned with the corporate environment, finding no satisfaction in traditional job roles or bonuses.

They believe many software developers are more motivated by personal projects than their jobs, and they wish to focus on work that truly matters to them, like open-source projects. The author acknowledges their feelings might seem negative or self-centered but emphasizes their need for meaningful work over corporate success. They have shared their values on their homepage to connect with like-minded individuals.

Summary of Fate: A Modern Data Client for React and tRPC

Fate is a new data client designed for React applications using tRPC, inspired by Relay and GraphQL. It aims to simplify data fetching and state management with several key features:

1. View Composition: Components specify their data needs through "views", allowing for a single network request per screen, which reduces the number of requests and improves efficiency.

2. Normalized Cache: Fate keeps all fetched data in a normalized cache, making updates easier and preventing stale data issues.

3. Data Masking: It enforces strict data selection, hiding any fields not explicitly requested by components. This reduces overfetching and accidental data coupling.

4. Async React Features: Fate utilizes modern React features like Actions and Suspense for a better user experience.

5. Built-in Lists and Pagination: It supports cursor-based pagination for list components, facilitating infinite scrolling and "load-more" features.

6. Optimistic Updates: The framework allows for immediate UI updates during mutations, with a rollback mechanism if the server request fails.

7. Type Safety: Fate's design promotes type safety across client and server interactions, helping developers avoid errors.

Fate is still in its alpha stage and not ready for production use. Developers can quickly get started with a template or follow manual installation steps. The framework is designed to be simple and predictable, focusing on JavaScript without complex abstractions.

Overall, fate seeks to combine the best aspects of Relay and tRPC, providing a more ergonomic data-fetching experience. It is continually evolving, with plans for future features and improvements.

Summary: A Deep Dive into QEMU's Tiny Code Generator (TCG), Part 1

This blog post explores the internal workings of QEMU's Tiny Code Generator (TCG), which translates target instructions to run on the host machine.

Key Points:

1. Execution Process: The virtual CPU (vCPU) executes instructions using a function called tcg_cpu_exec. This function finds or creates "translated blocks" of code.

2. Code Generation:

   • The tb_gen_code function generates intermediate representation (IR) code from target instructions through gen_intermediate_code, and then translates this IR into machine code using tcg_gen_code.
   • The TCG distinguishes between frontend operations (the generated IR code) and backend operations (the host CPU actions).

3. Intermediate Representation (IR):

   • The gen_intermediate_code function is tailored for different architectures, using architecture-specific translator operations (e.g., for PowerPC on an Intel x86 host).

4. Translation Blocks (TB):

   • Each TB includes a prologue and an epilogue, which help manage execution flow and optimize performance by allowing consecutive TBs to be executed without returning to QEMU’s main code.

5. Disassembly Context:

   • The disassembly context is architecture-specific and is created to record the current CPU state, making TBs context-sensitive.

6. Translating Instructions:

   • The instruction translation is handled by translate_insn, which uses a table of opcode handlers to convert target instructions into IR.

7. Example Translation:

   • The blog provides an example of how PowerPC instructions are translated into TCG IR code, showing the conversion of various operations, such as arithmetic and memory writes.

Overall, the TCG enables efficient execution of code from one architecture on another by generating and translating IR code, optimizing the execution process through structured blocks, and maintaining contextual information for accurate translation.

No summary available.

Summary of Whitespace in Typography

Whitespace, particularly word spacing, is a crucial but often overlooked aspect of typography. It refers to the empty areas that separate letters, words, and lines, playing a significant role in text readability and layout. Here are the key points:

1. Types of Spaces: There are various whitespace characters used in typography, including:

   • Standard Space: The most commonly used space, created by pressing the spacebar.
   • Non-Breaking Space: Prevents a line break at its position, used in languages like Russian.
   • Emspace, Enspace, Thin Space, and Hair Space: These are additional types of spaces that vary in width and are used for specific purposes in typesetting.

2. Historical Context: Word spacing was not used in European languages until the 7th century. In metal type, spaces were physical objects that affected text layout.

3. Modern Usage: In contemporary typography, the width of word spaces is determined by typeface designers. It's essential for maintaining text rhythm and visual balance.

4. Best Practices:

   • Avoid excessive word spacing, as it can disrupt readability and layout.
   • Use specific types of spaces for different purposes, such as thin spaces for dashes or initials.
   • Be cautious of using double spaces, a habit from typewriter usage, which is now considered outdated.

5. Justified Text: When text is justified, word spacing can change to fill the line width evenly. This can lead to inconsistencies, so manual adjustments are often necessary.

6. Software Considerations: Not all software supports advanced spacing options. Professional typesetting tools like InDesign allow for more precise control over whitespace.

By understanding and applying these principles, typographers can enhance the clarity and aesthetic appeal of their work.

The author expresses strong opposition to TLS "Inspection" software, which is meant to allow organizations to monitor encrypted traffic. They argue that this software actually undermines the security that TLS encryption provides by effectively performing a Man In The Middle (MITM) attack, allowing companies to intercept and monitor sensitive communications. This raises serious privacy concerns, as confidential information could be exposed.

The author highlights several issues with TLS Inspection software:

1. Security Risks: It increases the risk of security breaches since the company's MITM private key is more likely to be compromised than the keys of trusted certificate authorities.

2. Administrative Hassles: Managing TLS certificates is complex and varies across different operating systems and applications, making it a logistical nightmare.

3. Normalization of Bad Practices: It leads to a culture where TLS errors are ignored, causing tech staff to overlook important security warnings.

4. Performance Issues: Decrypting and re-encrypting traffic can create bottlenecks and affect system availability.

The author suggests that there are better, less invasive ways to ensure security, such as anomaly detection and Zero Trust architecture. Overall, they call for the industry to abandon TLS Inspection software due to its numerous drawbacks.

No summary available.

The text describes a tool called "fanfa" that allows users to create and share diagrams using Mermaid, a diagramming tool. Key features include:

• Cinematic Animation: Diagrams can be animated for a more engaging presentation.
• Interactive Controls: Users can drag to pan, scroll to zoom, and double-click to reset the view.
• Sharing Options: Users can easily share links to their diagrams.
• Fast Iteration: The tool enables quick modifications and updates to diagrams.

Overall, fanfa.dev enhances the diagramming experience with visual and interactive elements.

The speaker compares the rapid advancements in AI to historical changes in technology, using horses and chess as examples.

• Horses: After the invention of steam engines in the 1700s, horses remained unaffected for 120 years, but by 1950, 90% of horses in the US were gone due to engine improvements.

• Chess: Computer chess improved steadily over 40 years. In 2000, a human grandmaster could win 90% of the time against a computer, but by 2010, the computers were winning 90% of the time.

• AI: Investment in AI is increasing rapidly, doubling each year. The speaker, who worked at Anthropic, noticed that AI (specifically Claude) quickly surpassed human performance in answering questions at work. In just six months, Claude was answering 30,000 questions monthly, while the speaker's team answered 4,000.

The speaker reflects on how quickly AI is replacing human jobs, drawing a parallel to how horses became obsolete. They hope for a longer transition than what horses experienced, but are concerned it may happen much faster.

Summary of "The Joy of Playing Grandia on SEGA Saturn"

The SEGA Saturn is experiencing a revival, with many previously Japanese-only games being translated into English, particularly story-driven RPGs. One standout title is Grandia, released in late 1997, which was initially planned for the Saturn but ended up on PlayStation due to delays. Recently, dedicated fans managed to translate the Saturn version, making it accessible to English-speaking audiences.

Grandia follows the adventures of Justin, a 14-year-old boy eager for exploration, alongside his friend Sue and later Feena. The game is set during an industrial revolution and explores themes of adventure and personal growth as Justin seeks to uncover the mysteries of the lost civilization of Angelou. The narrative feels light-hearted, capturing the essence of childhood adventure.

The game features a unique combat system that emphasizes strategy, allowing players to select actions based on a moving gauge. It also includes an engaging magic system where players learn spells from Mana Eggs and enhance skills through combat.

Graphically, Grandia is impressive for its time, utilizing 3D environments effectively, though it sometimes suffers from frame rate issues. Its sound design, composed by Noriyuki Iwadare, enhances the atmosphere with a fitting soundtrack.

Gameplay alternates between town exploration, where players can interact with NPCs and gather quests, and dungeon exploration, filled with treasures and enemies. The game encourages character development through various experience points systems.

While Grandia has some narrative gaps and technical flaws, it remains a beautifully crafted RPG that resonates with themes of youthful optimism and adventure. It requires a significant time commitment but can evoke nostalgic feelings of childhood exploration and dreams. Overall, it provides a heartfelt experience that encourages players to pursue their dreams, even as they grow older.

Cassette tapes are experiencing a resurgence in popularity, with sales increasing significantly, particularly in the UK and the US. Major artists like Taylor Swift and Billie Eilish are releasing music on cassettes, despite their low sound quality and the inconvenience of using them.

While some view this as a revival of cassettes, it may be more accurate to consider it a rediscovery, especially among younger listeners. Many people appreciate cassettes for their tangible nature and the personal connection they provide to music, in contrast to the intangible experience of digital streaming.

Cassettes also allow for creative expression, such as making mix tapes. They symbolize freedom from corporate control in the music industry, offering a way to connect with music that feels more personal and less commercialized.

In summary, while cassettes won't replace digital streaming, they provide a unique and nostalgic listening experience that stands out in today's digital world.

The article discusses the complexities of choosing ID formats in software systems, particularly as they scale. Here are the key points:

1. Initial Simplicity: Most systems start with auto-increment IDs because they're easy to implement. However, this simplicity can lead to problems as the system grows and evolves.

2. Architectural Commitment: Once an ID format is deployed, it becomes part of the system’s architecture. Changing it later can be painful, especially if IDs are embedded in various parts of the system.

3. When to Change: If a system scales beyond a single database, such as during sharding or multi-region deployments, a more complex ID generation method is required.

4. Common ID Formats:

   • UUIDv4: Simple and unique but lacks ordering and structure.
   • ULID/UUIDv7: Timestamp-based, offering some ordering but can struggle with high concurrency and clock drift.
   • Snowflake: Provides strong ordering and performance but can be rigid and require coordination.

5. OrderlyID: The author created OrderlyID to address the limitations of other formats. It offers structured, time-ordered IDs with support for multi-tenancy and flexibility for future changes.

6. Choosing the Right Format: The choice of ID format should depend on the specific needs of the system, such as the importance of ordering, structure, and future scalability.

7. Surprising Insights: The author learned that adding typed prefixes to IDs and incorporating checksums can significantly improve usability and error handling in real-world applications.

In summary, selecting an appropriate ID format is crucial for maintaining system architecture and avoiding future complications as the system grows.

The text discusses the challenges and risks surrounding EU-US data transfers, particularly concerning the legal frameworks that support them. Key points include:

1. Legal Frameworks: Most EU-US data transfers rely on the "Transatlantic Data Privacy Framework" (TADPF) or "Standard Contract Clauses" (SCCs), which are based on unstable US laws and regulations. This creates a fragile legal situation that could easily collapse.

2. Threats to Legal Stability: The current US administration's legal changes and challenges, especially regarding the independence of the Federal Trade Commission (FTC), could jeopardize the enforcement of data protection laws. A Supreme Court case (Trump v. Slaughter) could further weaken the FTC's independence.

3. Issues with the Data Protection Review Court (DPRC): The DPRC, intended to provide oversight of US government data surveillance, lacks legal establishment and might be deemed unconstitutional if the Supreme Court rules against independent executive bodies.

4. Vulnerability of Executive Orders: The Biden administration's Executive Order 14.086, which supports data transfers, is at risk of being overturned, particularly if Trump returns to power.

5. Future Risks: There are many uncertainties regarding the legal basis for EU-US data transfers, and the text suggests that EU entities should prepare for potential disruptions.

6. Recommendations: The author advises EU governments and organizations to limit data transfers to US providers and consider using European providers to safeguard personal data.

In summary, the legal landscape for EU-US data transfers is precarious, with significant risks on the horizon that could lead to a halt in data flow between the two regions.

Summary of Flow Tutorial

Using Flow Flow introduces new keywords and control structures for asynchronous programming, primarily using Promises and Futures to manage tasks that occur over time.

• Promise: A handle that can set a value once.
• Future: A read-only reference to the value that will be set by a Promise.

Key Functions:

• wait(): Pauses execution until a Future has a value.
• ACTOR: A special function type that can call wait().
• Streams: PromiseStream<> and FutureStream<> manage series of asynchronous messages.

Design Patterns:

• RPC (Remote Procedure Call): Allows servers to handle requests via structured PromiseStreams.
• Cancellation: If an actor's Future reference is dropped, it cancels the actor.

Memory Management:

• Uses Reference Counting: Keeps track of how many references exist for an object to manage its lifespan.
• Arenas: Memory pools for efficient management of buffers.

Potential Issues:

• Reference Cycles: Can prevent objects from being deleted.
• Function Return Types: Functions returning non-Standalone objects may lead to invalid references.

Serialization with Flatbuffers: Flatbuffers facilitate robust serialization, allowing for schema evolution without breaking compatibility.

• File Identifiers: Used for schema validation during deserialization.
• Traits: Define how types are serialized.

ACTOR Return Values: Actors can return a single Future. If you need to return multiple values, use a PromiseStream.

Gotchas with Actor Compiler:

• Avoid using switch statements with wait().
• Be cautious with try/catch statements that don’t include wait().

This guide provides an overview of how to effectively use Flow for asynchronous programming, emphasizing its core constructs, patterns, and memory management strategies.

No summary available.

Horst Gutmann, a software engineer from Graz, Austria, discusses his decision to cancel his O'Reilly subscription after two years. He appreciated the vast selection of technical books and learning resources available, but felt that the $500 annual cost was not worth it since he reads slowly. Additionally, he faced issues with the mobile app's usability, such as poor synchronization and a lack of enjoyable themes compared to other reading apps. He believes it would be cheaper to buy individual books from other platforms where he can keep them permanently. Overall, he is unlikely to renew his subscription.

Welcome to Nova!

Nova is a simple programming language designed for various uses, including:

• Sketching ideas
• Writing documents and notes
• Casual modeling and thinking
• Performing computations without a computer

Programming can often be complex and confusing, but Nova aims to make it easier. It serves as a tool for writing, note-taking, and communicating with both programmers and machines.

We encourage you to explore Nova and see what it can do for you!

If you're interested in learning Nova, you can find resources like:

• Articles on writing Nova
• An online Nova IDE
• Implementations to connect Nova with existing code

You can also join our community on IRC (#nova on Libera) and Discord (#nova on Nouveau).

No summary available.

Summary of AI Window Announcement

AI Window is a new initiative aimed at keeping the internet open and accessible as AI technology evolves. The key features include:

• A fully opt-in experience for users.
• Features that protect user choices.
• Increased transparency about data usage.

The goal is to foster a community of users and developers to shape the future of the web together. Interested individuals can sign up to receive updates and be among the first to try it out.

To join the waitlist, users need to provide their email and agree to the privacy notice. If they haven't previously subscribed to a Mozilla newsletter, they may need to confirm their subscription via email.

A team has developed a bug-finding tool that works particularly well for app backends. Initially, they aimed to tackle technical debt in codebases, which is a common issue exacerbated by AI. They started by creating automated tests to help manage this debt. However, they discovered that their tool was better at identifying bugs in code than writing tests.

The tool analyzes codebases, builds them for local development, and runs numerous tests to uncover bugs and vulnerabilities, even in mature projects. While the scanning process takes several hours, it yields high-quality results that help engineers focus their attention on the most critical issues.

The creators believe that while computing power is cheap, the attention of engineers is valuable. They plan to expand their tool's capabilities for other tasks that improve code quality. They invite others to try their tool, offering a free first scan without needing a credit card, and they also scan open-source repositories upon request.

Here's a simplified summary of the key points from the book list:

1. Science Fiction and Fantasy:

   • 2001: A Space Odyssey: Explores evolution and space travel.
   • Ender's Game: A young boy trains to fight aliens, facing moral dilemmas.
   • The Golden Compass: A girl's journey through parallel worlds.
   • Dune: Political intrigue on a desert planet with valuable resources.

2. Classic Literature:

   • The Good Earth: A family's rise and fall in rural China.
   • Metamorphosis: A man transforms into an insect, exploring alienation.
   • The Grapes of Wrath: A family's struggle during the Great Depression.

3. Philosophy and Social Commentary:

   • Tao Te Ching: Reflects on balance and natural order.
   • 1984: A dystopian world under total surveillance.
   • The Handmaid's Tale: A woman's fight against a repressive society.

4. Contemporary Fiction:

   • The Night Circus: A magical competition between two young illusionists.
   • Never Let Me Go: Explores themes of cloning and humanity.
   • Convenience Store Woman: A woman's life in a convenience store challenges societal norms.

5. Non-Fiction and Essays:

   • Guns, Germs, and Steel: Explains societal development through geography and resources.
   • The Omnivore's Dilemma: Examines food production and consumption choices.
   • How We Got To Now: Discusses innovations and their impacts on society.

Each book touches on significant themes such as identity, society, morality, and human nature, making them noteworthy reads in their respective genres.

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The Joyboard is a balance board peripheral designed for the Atari 2600 video game console, released in 1983. Users stand on the board and lean in different directions to control games, similar to how a joystick works. It was promoted by skier Suzy Chaffee.

The Joyboard uses four directional latches on its bottom to detect movement. A joystick can also be connected to it for games that require one. It has been compared to the Wii Balance Board, which uses more advanced technology.

Only two games were specifically made for the Joyboard: "Mogul Maniac," a skiing game, and "Off Your Rocker," which involved repeating patterns from an on-screen character. Although "Surf's Up," a surfing game, was developed for it, it was never released.

The term "Guru Meditation," associated with errors in the Amiga operating system, originated from developers trying to remain still on the Joyboard during system crashes. A game named "Guru Meditation" was later created as a nod to this history, where players must sit still on the Joyboard to succeed.

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We found that deep neural networks, regardless of their tasks or how they were initialized, tend to group into similar low-dimensional spaces. Our study looked at over 1,100 different models, including various types of neural networks, and showed that they share common patterns in their structure. By analyzing the weight matrices of these models, we discovered that they often use similar subspaces to represent information. This suggests that we might be able to identify these shared structures without needing a lot of data or computing power. These findings could help improve how we reuse models, learn from multiple tasks, combine models, and create more efficient algorithms, which could also lower the environmental impact of training large neural networks.

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

No summary available.

No summary available.

Brent's Encapsulated C Programming Rules: Summary

Brent shares essential guidelines for writing well-structured C programs, emphasizing encapsulation and performance. Here are the key points:

1. Encapsulation: Use header files to hide details of code implementation, allowing users to interact with data structures through functions only, enhancing safety and maintainability.

2. Performance Considerations: While encapsulation may reduce performance, exposing structure members can optimize code. Use const to protect members while still allowing access.

3. Memory Management: Ensure that memory allocation and deallocation are managed by the same module. Clearly indicate ownership of memory to avoid misuse.

4. Avoid void*: Instead of using void*, define specific structures to improve type safety and code clarity.

5. String Handling: Use char* for strings and stick to UTF-8 for compatibility. Avoid complicating string types.

6. Standard Types: Use standard types like uint8_t for byte arrays and include <stdbool.h> for boolean values.

7. Function Design: Write functions that do one task, avoid global/static variables, and prefer inline functions over macros for better readability.

8. Testing: Test functions independently and take advantage of C's straightforward testing capabilities.

9. Warnings as Errors: Treat all compiler warnings as errors and ensure your code adheres to standards.

10. Zero Initialization: Always initialize structs to avoid undefined behavior.

11. Struct Layout: Organize struct members with larger types first to optimize memory alignment.

These rules aim to promote clean, efficient, and maintainable C programming practices.

The text provides several links related to earthquakes and tsunamis. Here are the key points:

1. The first link leads to a page with detailed information about a specific earthquake event.
2. The second link directs to news about weather disasters, including tsunamis.
3. The third link offers a map of recent earthquakes.
4. The fourth link provides information on tsunami warnings and alerts.

These resources are useful for tracking earthquake activity and understanding related tsunami risks.

Summary of NATS JetStream Testing

Background: NATS is a streaming system where producers send messages to streams and consumers receive them. Regular NATS streams can lose messages, but JetStream, a subsystem of NATS, uses a consensus algorithm (Raft) to ensure messages are delivered at least once. It aims to be always available, but the CAP theorem suggests that systems can't be both linearizable and always available. JetStream can tolerate the loss of a few nodes but requires a majority to function properly.

Testing Approach: The testing used the Jepsen library with NATS JetStream to simulate various faults, such as process crashes and network issues, while focusing on a JetStream stream with five replicas. The tests aimed to determine how JetStream behaved under these conditions, particularly regarding message loss.

Key Findings:

1. Total Data Loss in Version 2.10.22: Process crashes could erase a JetStream stream completely, but this was fixed in version 2.10.23.

2. Lost Writes Due to File Corruption: Single-bit errors in JetStream's files led to significant message loss and inconsistent data across nodes.

3. Data Loss from Snapshot Corruption: Corrupting snapshot files could cause the system to mistakenly delete streams, resulting in permanent data loss.

4. Lazy Fsync Policy Issues: NATS does not immediately flush data to disk, leading to potential data loss during power failures or crashes, as acknowledged messages might not be saved.

5. Split-Brain Scenario: A single OS crash can lead to split-brain conditions, where different nodes have different views of the acknowledged data, resulting in further data loss.

Recommendations:

• NATS should consider changing the default data flushing policy to avoid data loss.
• Documentation should clearly outline the risks associated with the current fsync behavior and the limitations of JetStream's availability claims.

Conclusion: While NATS JetStream generally performed well under certain conditions, significant issues remain regarding data loss and availability, especially under fault scenarios. Further testing and improvements are recommended to enhance reliability and consistency.

The author prefers to keep work and personal tasks separate, so they set up a personal virtual machine (VM) on their work laptop using a minimal Ubuntu Server installation. They installed Emacs for note-taking and other tasks, using it as their main interface in full-screen mode.

To do this, they created a simple setup that allows them to run Emacs without a traditional window manager. They can still use other applications, like web browsers, if needed, but primarily rely on Emacs for their work, including browsing the web with text-based browsers or launching graphical ones when necessary.

The author enjoys using Emacs to manage multiple tasks simultaneously, such as IRC, Twitter, and RSS feeds, by creating a convenient window layout. They mention exploring lightweight window managers like Ratpoison and 2wm, which are designed to be minimal and keyboard-driven, enhancing their Emacs experience while maintaining a clean workspace.

In summary, the author effectively uses Emacs as a comprehensive workspace, integrating various applications and maintaining focus on productivity without the distractions of a traditional desktop environment.

On December 6, 2025, at about 21:13 UTC, a cesium beam atomic clock at the NIST Gaithersburg campus failed, causing a time discrepancy of about -10 milliseconds for several internet time servers. The affected servers included time-a-g through time-f-g and ntp-d.nist.gov. During this event, some monitoring systems also failed unexpectedly, and one server (time-a-g) shut down while another (time-f-g) stopped providing time service due to the error.

Currently, the other servers are still operating, as the time offset hasn't reached a level that triggers their automatic failure responses. If the discrepancy increases, the servers may be disabled to maintain accuracy. A backup clock is available on-site, and a replacement of the faulty clock is planned for December 9, 2025, which will require a brief service interruption. Servers at other NIST campuses remain unaffected.

Best,
Jeff Sherman

In a recent project, researchers collected around 10,000 hours of neuro-language data to train models that can decode thoughts into text from noninvasive brain data. This dataset is believed to be the largest of its kind globally. The data collection involved thousands of participants who conversed with a language model (LLM) while wearing headsets in a controlled environment.

Key points include:

1. Data Collection Process: Participants engaged in freeform conversations for two hours, with the aim of maximizing what they said or typed. Initially, structured tasks were used but were replaced with more open conversations over time.

2. Equipment: The researchers designed custom multimodal headsets by combining different brain data measurement technologies, as no existing headsets met their needs adequately.

3. Handling Noise: Early on, noise reduction was crucial, but as the dataset grew to around 4,000-5,000 hours, the amount of data collected outweighed the issues caused by noise, allowing for more flexibility.

4. Scaling Operations: The project ran 20 hours a day, requiring efficient participant booking and management systems. The researchers improved their scheduling and data collection processes significantly over time.

5. Cost Efficiency: They reduced costs per hour of usable data by improving their data format and backend systems, enabling real-time quality checks during data collection.

6. Future Steps: The team is now focused on training models with the collected data and is open to collaborations or hiring for further development.

Overall, the project's success depended on innovative data collection methods, effective participant engagement, and continuous improvements in technology and operations.

CATL, a leading battery manufacturer, plans to launch fully electric ships for ocean navigation within the next three years. Their marine division, active since 2017, has expanded from inland and coastal waters to ocean-going vessels. CATL aims to provide zero-carbon marine transportation and has developed a complete battery replenishment system, including options for battery swapping and cloud-based management.

The company has already supplied batteries for over 900 vessels, including the world's first pure electric ocean-going passenger ship. Recent drops in battery prices, along with advancements in sodium-ion battery technology, are expected to further support the electrification of maritime transport. Studies suggest electric ships could travel distances of up to 5,000 km using current battery technology, making widespread maritime electrification increasingly feasible.

The history of science often highlights the achievements of white men, overshadowing the contributions of women. This phenomenon is referred to as the "Matilda effect," named after Matilda Joslyn Gage, who argued that women inventors were regularly overlooked. Historian Margaret Rossiter popularized the term in 1993, emphasizing the need to recognize women’s contributions to science.

The Time­line series "The Matilda Effect" showcases notable women who have been marginalized in scientific history. One example is Dr. Lise Meitner, a physicist who helped discover nuclear fission but was largely ignored, with her male colleague Otto Hahn receiving the Nobel Prize for their work. Similarly, Alice Augusta Ball, an African American chemist, developed a revolutionary treatment for leprosy but died young, and her contributions were credited to her male professor.

Other women such as Esther Lederberg, Jocelyn Bell Burnell, and Rosalind Franklin also faced similar fates, where their significant discoveries were overshadowed or credited to their male colleagues. These examples reflect a broader trend of women being excluded from recognition and awards in science.

For more insights into the contributions of women in science, Margaret Rossiter's works and the Timeline series provide valuable resources.