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Summary of "Images over DNS"

The text discusses the limitations of storing data in DNS TXT records. Contrary to the common belief that a TXT record can only hold 255 bytes, the actual limit depends on the size of the DNS payload. For UDP, this is around 1232 bytes, but for TCP, it can go up to 64KB.

The author aims to demonstrate serving large TXT records over TCP using Google Public DNS's JSON API. While it works mostly well, there are complications with handling binary data in JSON. To optimize space, the author suggests using raw binary data instead of encoding methods like Base64.

To query this data, users can utilize the dig command and a Perl script to convert the output back into binary format. The author notes that while Google's JSON resolver can handle large TXT records, local DNS resolvers might struggle, and suggests using Google's DNS servers for better results.

The author finds this method interesting for security reasons, as it allows for the tunneling of large payloads over DNS, which could be unexpected in environments with DNS filtering. The technique uses a low time-to-live (TTL) for DNS records to prevent excessive caching.

Finally, the server for this process is a custom-built DNS server in Go, with assistance from AI for some coding aspects, though the author claims most of the work is theirs.

Apple is introducing a new cooling technology for its iPhones, using water vapor to manage heat. This innovation aligns with similar efforts from Samsung and Google. While the new iPhone's color may catch your eye, it's the advanced cooling system that stands out as a significant feature.

The text discusses a potential issue with Zig's new std.Io.Reader functionality. When writing a function to read from a Reader and output to stdout, the size of the buffer used can lead to undefined behavior.

The author illustrates this with an example where using a small buffer of 64 bytes causes the code to fail in debug mode and enter an endless loop in release mode. This happens because different readers and writers may have specific buffer size requirements, which can lead to unexpected failures, especially when the nature of the reader is not clear.

The author argues that this is not just a documentation issue, as readers can have varying requirements that are hard to determine, especially when they depend on external factors like HTTP response headers. They emphasize the challenge of determining the right buffer size, as changing input data can lead to different outcomes, making it difficult to catch these errors early.

In conclusion, the author is concerned that the requirement for buffer sizes is not sufficiently clear and poses a serious problem for developers.

The article discusses the capabilities of Claude Code, an AI coding agent from Anthropic, particularly its effectiveness in interactive theorem proving (ITP). The author finds it surprising that this AI can perform complex proof steps independently, though it still requires guidance from a human "project manager."

Key points include:

1. Interactive Theorem Proving (ITP): ITP tools like Lean are powerful for formal verification but are challenging to use, even for experts. They require significant cognitive effort and are often time-consuming.

2. AI's Role: Claude Code can assist with ITP by breaking down tasks and managing proof processes, but it makes various mistakes that require human oversight. The author describes their experience using Claude Code to formalize a research paper, noting that while the AI could handle many tasks, it often needed guidance and took longer than doing it manually.

3. AI Limitations: The author highlights the AI's tendency to get stuck on errors and its occasional inability to recognize conceptual mistakes. They suggest that better tools and features could enhance the AI's performance.

4. Future of Formal Methods: The author believes that AI could eventually make theorem proving more accessible, reducing the need for expert intervention. They express optimism about a future where formal methods become more automated and efficient, making them more widely adopted.

Overall, the article emphasizes the potential of AI in formal methods while acknowledging the current limitations and the need for human oversight.

The website uses a system called Anubis to protect against bots that scrape content. Anubis employs a Proof-of-Work method, similar to one used to reduce email spam, to make scraping more difficult and costly. This solution is temporary while developers work on better ways to identify automated browsers.

To access the site, users must enable modern JavaScript, as the current anti-bot measures require it. If you have plugins that block JavaScript, like JShelter, you need to disable them to proceed.

Supply chain attacks involve harmful updates that can infiltrate open source code used in many applications. To ensure that Obsidian is secure and private for users, we take several steps:

1. Minimize Dependencies: We limit our reliance on third-party code, keeping our dependencies low compared to similar apps. This reduces the risk of attacks.

2. Custom Development: Features like Bases and Canvas are built from scratch rather than using pre-made libraries, giving us full control over the code.

3. Reimplementation: For small functions, we create our own implementations, and for medium-sized modules, we fork them to keep them within our codebase. For larger libraries, we use stable, version-locked files and only update them when necessary.

4. Controlled Shipping: Only a few essential packages are included in the app for users; many others are used only during development.

5. Strict Version Control: All dependencies are pinned to specific versions, ensuring consistent installations and a clear audit trail. We avoid running post-install scripts to prevent unauthorized code execution.

6. Careful Upgrading: When we do update dependencies, we thoroughly review changes, check for any new sub-dependencies, and conduct extensive testing before committing the new versions.

7. Delayed Releases: We take our time with upgrades, allowing a buffer period during which the community can identify any issues with new versions.

By following these practices, we significantly reduce the risk of supply chain attacks and enhance the security of Obsidian. For more information on our security measures, you can visit our security page and audits.

MapSCII Summary

MapSCII is a tool that allows you to view maps in your console using Braille and ASCII characters. It runs on Node.js and is compatible with xterm terminals. You can connect to it by using the command telnet mapscii.me.

Key Features:

• Use your mouse to navigate and zoom in/out.
• Find points of interest near any location.
• Customize map styles with Mapbox support.
• Connect to any vector tile server or use the provided OSM2VectorTiles.
• Work offline with local VectorTile/MBTiles.
• Works on most Linux and macOS terminals.
• Built entirely with JavaScript.

How to Use:

• To run locally, install Node.js and start with npx mapscii.
• For installation, use npm: npm install -g mapscii (might need sudo on macOS).
• Alternatively, install with Snap on supported Linux systems: sudo snap install mapscii.

Controls:

• Use arrow keys to scroll.
• Press 'a' or 'z' to zoom.
• Press 'c' to toggle block character mode.
• Press 'q' to exit.
• If your terminal supports it, use the mouse to drag and zoom.

Development Notes:

• Includes various libraries for handling map data and rendering.
• Future updates may include features like GeoJSON support and enhanced styling.

Licenses:

• Map data is licensed under the Open Data Commons Open Database License (ODbL).
• Cartography and documentation are under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA).

MapSCII is acknowledged for its contributions from various developers in the open-source community.

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The African clawed frog, once used in pregnancy tests, has lived in Wales for about 50 years after escaping from research facilities in the 1960s. These frogs were originally kept in the UK following a discovery by biologist Lancelot Hogben, who found that they could indicate pregnancy by producing eggs when injected with a woman's urine. The frogs established breeding populations in areas like Bridgend and Vale of Glamorgan, with the first sightings in the 1960s and a well-established colony by the 1970s.

Researchers tracked about 2,000 frogs between 1981 and 2010. However, due to concerns about a deadly fungus affecting native frogs, the Welsh Government initiated an eradication plan in 2008. Unfortunately, severe winters in 2009 and 2010 likely led to the frogs' decline, as ponds dried up and food became scarce. By 2010, only one frog was captured during extensive trapping efforts, and no sightings have been reported since 2014.

Today, it is believed that the African clawed frogs have been eradicated from Wales, although there could be a few surviving individuals. Overall, there have been no significant conservation concerns regarding their presence during their time in Wales.

The UK has signed a £1.5 billion ($2 billion) defense deal with the US tech company Palantir, aimed at boosting investment in British defense technology. This agreement was announced during a visit by US President Donald Trump and is part of a broader commitment from several American tech companies, which the UK government claims will total £31 billion ($42 billion) for AI and tech infrastructure.

Palantir plans to establish its European headquarters for defense in the UK, which is expected to create around 350 new jobs. The partnership will focus on developing AI and software to enhance military decision-making and targeting, building on technologies tested in Ukraine.

UK Defense Secretary John Healey stated that this collaboration will advance innovation in the UK military and support local defense tech companies. Palantir CEO Alex Karp emphasized the investment's importance in reinforcing the UK's military strength and its role as a leading defense partner. The contract aligns with the UK's Strategic Defence Review, which aims to integrate various data sources for improved military planning.

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FocusStream is a tool designed to help you learn without distractions. Instead of getting lost in YouTube's recommendations, you can enter a topic and receive only relevant videos. It's free and simple to use. The creator invites feedback and encourages you to try it at focusstream.media. A demo video is also available for viewing.

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Summary of LLM-Deflate: Extracting LLMs Into Datasets

Large Language Models (LLMs) store vast amounts of knowledge from their training data within their parameters. This knowledge can be extracted to create structured datasets that represent what the models have learned. The author, Greg Diamos, has successfully developed a method to reverse this compression process, generating valuable training datasets from popular open-source models.

Key Points:

1. Synthetic Data Generation: This method builds on advancements in creating synthetic data for LLMs. Notable examples include:

   • Stanford Alpaca: Used a small set of human-written examples to generate 52,000 instruction-following tasks, demonstrating cost-effective model training.
   • NVIDIA Nemotron: Employed a sophisticated pipeline to generate over 100,000 synthetic conversations with high quality.

2. Knowledge Extraction Process: The approach involves systematically exploring a model's knowledge to extract both factual information and reasoning patterns. This is done using a structured topic exploration method.

3. Implementation Challenges: The process is computationally intensive, requiring efficient inference infrastructure to manage costs effectively. Key challenges include prompt design, maintaining balance in topic exploration, and ensuring quality in generated examples.

4. Results: The methodology was applied to three models (Qwen3-Coder, GPT-OSS, and Llama 3), resulting in over 10,000 structured training examples for each, showcasing how different models handle various topics.

5. Practical Applications: The extracted datasets can be used for:

   • Evaluating model capabilities.
   • Fine-tuning other models.
   • Supplementing training data in areas with limited resources.
   • Debugging models by identifying gaps in knowledge.

6. Future Directions: The technique opens up possibilities for cross-model knowledge transfer, tracking knowledge evolution in models, and enhancing model interpretability.

In conclusion, LLM decompression allows for the effective extraction of knowledge from trained models, creating structured datasets that can significantly enhance various aspects of machine learning practice. As inference costs decrease, this method is expected to become a standard tool in the field.

Ig® Nobel Prize Winners Summary

The Ig Nobel Prizes celebrate unusual scientific achievements that first amuse people and then make them think. The awards are held annually, and the most recent ceremony was in September 2025 at Boston University.

Highlights from the 2025 Ig Nobel Prizes:

• Literature Prize: Dr. William B. Bean, for documenting his fingernail growth over 35 years.
• Psychology Prize: Marcin Zajenkowski and Gilles Gignac, for studying how telling people they are intelligent affects narcissists.
• Nutrition Prize: A team studied lizards' preferences for certain types of pizza.
• Pediatrics Prize: Researchers found that a mother’s garlic consumption alters the taste of her breast milk.
• Biology Prize: A group tested if cows painted with stripes could avoid fly bites.
• Chemistry Prize: Scientists explored if eating Teflon could increase satiety without adding calories.
• Peace Prize: A study showed that drinking alcohol might help people speak foreign languages better.
• Engineering Design Prize: Researchers analyzed how smelly shoes impact shoe rack designs.

Key Points from Previous Years:

• The 2024 ceremony featured a prize for using pigeons to guide missiles and another for plants mimicking plastic plants.
• In 2023, winners included those who studied why scientists lick rocks and the swimming abilities of dead trout.
• The 2022 awards recognized research on heart rate synchronization in romantic partners and the effects of ice cream during chemotherapy.

The Ig Nobel Prizes not only entertain but also highlight the creativity and quirks of scientific inquiry.

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Summary of Token Exfiltration Campaign via GitHub Actions Workflows

Recently, there was an attack where hackers added malicious code to GitHub Actions workflows, aiming to steal PyPI publishing tokens. However, PyPI was not breached, and the attackers did not publish any packages. They targeted various repositories with PyPI tokens stored as GitHub secrets, successfully stealing some tokens but not using them.

All affected tokens have been invalidated, and project maintainers were informed via email from [email protected].

Timeline of Events:

• On September 5th, a GitGuardian employee reported a malicious workflow attempting to steal PyPI tokens.
• An email detailing further findings from GitGuardian was initially missed due to spam filtering, causing a delay in response until September 10th.
• Many project maintainers quickly addressed the issue by reversing changes or deleting affected workflows and rotating their tokens.
• On September 15th, maintainers were updated that their tokens were invalidated and advised to use Trusted Publishers for more security.

Recommendations for Users:

1. Use Trusted Publishers instead of long-lived tokens for GitHub Actions to enhance security. Trusted Publishers use short-lived tokens that are safer.
2. Check your account's security history for any unusual activity.

While Trusted Publisher tokens can still be stolen, they greatly reduce the risk of compromise.

Thanks to GitGuardian for their support in this investigation and to all who contribute to improving security in the Python community.

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The text discusses creating a version of Redis, a popular data structure store, using the Zig programming language. The focus is on building it from the ground up, which involves implementing Redis's features and functionality in Zig.

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The article discusses the performance costs associated with system calls in Linux, particularly on the x86-64 architecture. System calls allow user programs to interact with the operating system, but they are often expensive operations in terms of performance.

Key Points:

1. What are System Calls?

   • System calls are routines in the kernel that provide services to user space, such as opening files or creating processes. They require switching from user mode to kernel mode, which is inherently costly.

2. Cost of System Calls:

   • The direct overhead includes the time taken to execute kernel code during the entry and exit of the system call.
   • The indirect overhead arises from CPU disruptions, such as flushing its instruction pipeline and resetting branch predictors, leading to a performance drop when returning to user space.

3. Microarchitectural Effects:

   • When a system call is made, the CPU must finish executing user instructions before switching to kernel mode, leaving the instruction pipeline empty. This results in a slow start when returning to user space.
   • Additionally, clearing the branch history buffer and untraining the return stack buffer during system calls can lead to increased branch mispredictions and slower execution.

4. Mitigation Strategies:

   • Developers can use techniques to reduce the frequency of system calls, such as:
     • Using virtual dynamic shared objects (vDSO) to execute certain calls directly in user space.
     • Caching values obtained from system calls.
     • Optimizing I/O operations by using techniques like buffered I/O or batching requests with tools like io_uring.
     • Offloading work to the kernel with eBPF, which allows running code directly in the kernel to avoid system calls.

5. Conclusion:

   • While system calls are essential for interacting with the operating system, their performance costs can be significant. Understanding these costs and employing optimization strategies can lead to faster software development and execution.

Overall, minimizing system calls and their associated costs is crucial for improving application performance.

AI Agents are being integrated into SaaS platforms like Notion, which recently introduced AI Agents in its Notion 3.0 update. These agents can perform tasks like creating documents, updating databases, and managing workflows. Users can customize these agents to automate tasks such as gathering feedback and updating records.

However, this new technology comes with risks. Simon Willison identified a "lethal trifecta" problem where the combination of AI agents, access to tools, and long-term memory can create security vulnerabilities. AI Agents can autonomously execute actions, which can lead to unauthorized access and misuse of sensitive data.

A specific vulnerability was found in Notion's web search tool, which allows attackers to craft queries that could extract data from a user’s Notion account. An example exploit involved creating a deceptive PDF that tricks the AI agent into querying confidential data and sending it to an external malicious server.

This exploit showcases that even advanced AI models can be manipulated, highlighting the increased security risks associated with integrating AI into SaaS platforms. The integration of various data sources can further expand these vulnerabilities, potentially leading to significant data breaches.

The creator of Math2Tex, a tool designed to convert handwritten or printed math content into LaTeX or plain text, shares their experience as a PhD student. They found it tedious to manually type complex math formulas from notes and textbooks, often making errors. Existing tools struggled with handwriting and mixed content, prompting them to create Math2Tex.

Math2Tex is a simple web app where users upload images of their notes or PDFs, and it quickly converts the content into clean LaTeX code or text, providing a real-time preview for easy copying. Unlike general AI tools, Math2Tex is faster and more specialized for academic content, making it 3-5 times quicker and more reliable for complex equations.

The tool uses a custom-trained OCR engine and is free to use. It's still in early development, so user feedback is encouraged to improve its performance.

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Summary of Evals in 2025

In 2025, the focus of AI model evaluation is shifting from merely assessing intelligence to ensuring that models are practical and efficient tools for users. Current uses of large language models (LLMs) include roles like coding assistants and administrative support.

To build effective assistants, models should handle ambiguity, create step-by-step plans, stay on task, adapt to new information, and minimize false information. Evaluating these capabilities requires a comprehensive approach:

1. Specific Capabilities Testing: Individual skills can be assessed separately, but this may introduce bias. Important datasets for knowledge evaluation include MMLU (and its updated versions) and GPQA. Math evaluation has evolved with datasets like GSM8K and AIME, while coding abilities are tested with benchmarks such as HumanEval and LiveCodeBench.

2. Long Context Management: Models must manage long discussions without losing track. New evaluations like NIAH and RULER help assess this capability.

3. Instruction Following: Models are evaluated on their ability to follow specific formatting and content instructions using datasets like IFEval.

4. Tool-Calling: Evaluations like TauBench and ToolBench focus on models’ abilities to utilize external tools effectively.

5. Integrated Assistant Tasks: Real-world scenarios like GAIA and DABStep require models to combine various skills, reflecting how they perform as assistants.

6. Dynamic Decision-Making: Game-based evaluations (like ARC-AGI) test adaptability, reasoning, and memory management in changing environments.

7. Forecasting: New tasks involve predicting future events, which is inherently challenging and not easily manipulated.

Overall, the evaluation landscape is moving toward assessing how well models can integrate multiple skills to solve real-world problems. Future recommendations for evaluators include using diverse benchmarks that measure both core capabilities and real-world performance in a comprehensive manner.

Cachey Summary

Cachey is a high-performance read-through cache designed for object storage, particularly for caching immutable blobs. It uses a simple HTTP API and combines memory and disk caching, making it efficient for handling requests.

Key Features:

• Works with S3-compatible backends and has a unique /fetch API.
• Requests are mapped to fixed 16 MiB pages, optimizing data retrieval.
• Can handle multiple requests for the same data concurrently and makes additional requests to reduce latency.
• Supports up to two bucket attempts for data retrieval.

API Details:

• To fetch data, use the request format HEAD|GET /fetch/{kind}/{object}.
• The kind identifies the bucket set, while the object is the S3 object key.
• A required Range header specifies the byte range to fetch.

Response Structure:

• Responses follow standard HTTP semantics, indicating whether content was partially served or not found.
• Includes headers like Content-Range, Content-Length, and a custom C0-Status for tracking the source of cached data.

Monitoring:

• Use GET /stats for throughput statistics and GET /metrics for detailed metrics in Prometheus format.

Command Line Options:

• Docker images are available for easy deployment.
• Users can configure memory limits, disk paths, caching options, and TLS settings through command line options.

Development Support:

• A justfile provides commands for development tasks, and additional resources are available for collaborative coding.

A long-sealed envelope containing a message from Tomáš Garrigue Masaryk, the first president of Czechoslovakia, was revealed during a live broadcast on September 19, 2025. The message, which had been kept secret for nearly a century, began with “It is the end, but I’m not afraid.” Masaryk, who served as president from 1918 until 1935, dictated these words to his son Jan while he was seriously ill in 1934, three years before his death in 1937.

The envelope was handed to the National Archives in 2005 with a request not to open it for 20 years. Inside, experts found five pages of handwritten notes in Czech and English. Masaryk reflected on his illness, his funeral, and the political challenges facing Czechoslovakia at the time, including comments on the German minority in the country. Historians noted that this was a critical period in Europe, with the rise of Hitler in Germany.

Mark Cornwall, a history professor, highlighted Masaryk's significance in Czech history, comparing him to American figures like George Washington or Abraham Lincoln. The unveiling of the letter sparked interest in Masaryk's enduring legacy.

Large Language Model (LLM) agents will likely become the main users of data systems in the future. These agents analyze and manipulate data using a process called agentic speculation, which involves exploring and finding solutions to tasks. However, the high volume and inefficiencies of this process can create problems for current data systems. To address this, data systems need to evolve to better support these agentic tasks. The authors propose focusing on key characteristics of agentic speculation—like scale, variety, redundancy, and control—to create new research opportunities. This includes developing new ways to query data, improve query processing, and create better memory storage for agents.

Summary of "Compiling with Continuations" Review

"Compiling with Continuations" by Andrew Appel is a book that explores advanced concepts in Standard ML and the practical use of continuations in programming languages. Although it's informative, the book lacks exercises, making it difficult to study systematically. It presents many details without clear guidance, which can be overwhelming.

Key points include:

• MiniML Language: Discusses a version of Standard ML used in the SML/NJ compiler, which is intended to represent elaborated programs.
• Lexing and Parsing: Describes how tokens are created and how the abstract syntax tree (AST) is formed using tools like lex and yacc.
• Evaluation and CPS: Focuses on compiling ASTs into Continuation-Passing Style (CPS) language, which is crucial for optimizing and analyzing programs.
• Closure Conversion and Register Spilling: Explains techniques for managing closures and optimizing register use, though it lacks depth on register assignment.
• Optimizations: Covers various optimization techniques in CPS, including inlining and hoisting, but with limited practical examples.
• Virtual Machine and Runtime: Introduces a virtual machine design and discusses garbage collection, but has inaccuracies regarding linking and runtime details.

In conclusion, while the book reveals valuable insights into Standard ML and continuations, it falls short in practical application and clarity. The CPS language hasn't gained widespread popularity, and many of its ideas may not be necessary for modern compiler design. Overall, the reviewer feels that engaging with the book was not a productive use of time.

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Summary

• Node20 Deprecation: Node20 will no longer be supported after April 2026. GitHub Actions will start using Node24 by default in March 2026.

• Testing Node24: You can test Node24 now by setting an environment variable in your workflow.

• OS Compatibility: Node24 does not support macOS versions 13.4 and below and lacks support for ARM32 architecture.

• Actions Maintenance:

  • For maintainers: Update your actions to use Node24.
  • For users: Update your workflows to use actions that are compatible with Node24.

For more details on OS support and runner architectures, please check the documentation.

Researchers from KAUST have developed micro-LEDs that can generate random numbers at an impressive speed of 9.375 Gbit/s. Random number generation is crucial for data security, such as creating encryption keys, and for computer simulations in fields like finance and weather forecasting.

These micro-LEDs are compact, cost-effective, and use less power compared to other technologies. They work by utilizing the random intensity fluctuations of light emitted from the LEDs, which allows them to produce more bits of random data per sampling cycle than previous methods.

To ensure the reliability of the random numbers generated, the team conducted tests based on standards from the U.S. National Institute of Standards and Technology (NIST), and all tested micro-LEDs passed these rigorous evaluations.

Looking ahead, the researchers plan to enhance their system by creating 2D arrays of micro-LEDs for faster generation and aiming to develop a fully integrated random number generator chip.

The author created AvoSmash to help people easily make professional-looking AI videos by combining various AI tools for images, videos, and audio into one platform. The goal is to simplify the process and automate tasks, making it easier for users. They invite others to try it out with free credits and share their feedback.

RMCP: R Model Context Protocol Server Summary

RMCP is a powerful Model Context Protocol (MCP) server version 0.3.7 that provides 40 statistical analysis tools across 9 categories. It allows AI assistants and applications to perform advanced statistical modeling, econometric analysis, machine learning, and data science tasks through natural conversation.

Key Features:

• Statistical Analysis Tools: Includes tools for regression, time series analysis, data transformation, statistical testing, and more.
• Machine Learning: Implements clustering, decision trees, and random forests for predictive modeling.
• Data Visualization: Offers scatter plots, histograms, box plots, and correlation heatmaps that display results directly in conversation.
• Natural Language Processing: Users can build statistical formulas using natural language, simplifying the interaction process.

Quick Start:

1. Install RMCP using pip install rmcp.
2. Check for required R packages with rmcp check-r-packages.
3. Start the server with rmcp start.

Real-World Applications:

• Business Analysis: Analyzes sales data to determine marketing effectiveness.
• Economic Research: Tests economic theories like Okun's Law using GDP and unemployment data.
• Data Science: Builds customer churn prediction models.

Installation Requirements:

• Python 3.9+ and R 4.0+ with necessary R packages are required for full functionality.

User Interaction:

Users can query the system in natural language, and the AI assistant (like Claude) responds with analytical insights, visualizations, and error recovery suggestions.

Conclusion:

RMCP is designed for seamless integration with AI assistants, enabling immediate statistical analysis and visualization without complex setup or file management. It is suitable for businesses, researchers, and data scientists looking to leverage data insights effectively.

Dynamo AI is developing secure and scalable AI systems for businesses. Their platform aims to help companies safely implement advanced AI models while ensuring reliability and trust.

They are looking for a Senior Kubernetes Engineer to guide enterprise customers through the onboarding process, from initial contact to full deployment. This role involves deploying Kubernetes-based Dynamo AI clusters in customer environments and acting as a technical link between the product and the customer's systems.

Key responsibilities include:

• Understanding customer needs and designing effective deployment strategies.
• Improving core infrastructure for better performance and security.
• Creating automated and scalable deployment models.
• Collaborating with teams to design features that enhance AI system security.
• Building strong customer relationships as a trusted expert.

Candidates should have:

• Experience in fast-paced environments and a strong focus on customer success.
• Expertise in Kubernetes, Helm, and cloud platforms like AWS, Azure, or GCP.
• At least 4 years in solution architecture, DevOps, or cloud engineering, with 2 years of customer-facing experience.
• Familiarity with security systems and strong communication skills.

This position requires U.S. government security clearance or citizenship and some in-office work in San Francisco or New York.

Summary of Key Points:

1. Historical Significance of September 20th:

   • Two pivotal events in computing history occurred on September 20th: the execution of the first FORTRAN program in 1954 and the patenting of the RSA algorithm in 1983.

2. FORTRAN (1954):

   • FORTRAN was the first high-level programming language that demonstrated that high-level abstractions could achieve performance comparable to low-level assembly language.
   • The IBM 704 computer, which ran the first FORTRAN program, was advanced for its time, executing 40,000 instructions per second.
   • Early FORTRAN allowed programmers to focus on high-level logic rather than low-level machine details, significantly enhancing productivity.
   • Despite initial skepticism regarding the performance of compilers, FORTRAN’s optimization techniques led to substantial performance improvements.

3. RSA Algorithm (1983):

   • RSA solved the problem of secure communication between parties who have never met, using mathematical principles that make certain operations easy while others are hard to reverse.
   • Its security relies on the difficulty of factoring large numbers, making it a cornerstone of internet security.
   • RSA utilizes concepts from number theory and modular arithmetic, demonstrating the importance of mathematical foundations in creating secure systems.

4. Common Themes:

   • Both FORTRAN and RSA faced initial skepticism but proved their worth through performance enhancements and real-world applications.
   • They addressed significant challenges in computing, making technology more accessible and secure.
   • The enduring legacy of both technologies highlights the importance of understanding foundational mathematics in driving innovation and solving complex problems.

5. Enduring Impact:

   • FORTRAN is still used in high-performance computing today, while RSA remains vital for internet security, despite emerging threats from quantum computing.
   • These technologies exemplify how advancements often arise from rigorous mathematical principles rather than mere marketing hype.

The history of artificial intelligence (AI) is linked to the development of technology since ancient times, starting with tools like the abacus. Formal AI research began after World War II with the symbolic approach, which aimed to mimic human reasoning but faced many challenges and stagnated by the early 2000s.

A new approach, machine learning, emerged, focusing on algorithms that could learn from data. This gained momentum in the 2000s due to increased data availability and advances in computing power, particularly with the use of graphic processing units (GPUs). A significant breakthrough occurred in 2011 when a team developed a neural network that dramatically improved image recognition, leading to the current applications of AI in social media and e-commerce.

In 2015, OpenAI was founded, and by 2020, it released ChatGPT, which quickly gained popularity, highlighting the rapid growth of generative AI. However, this rise has also led to confusion about what AI truly is, with various products being labeled as "AI" without clear definitions. Experts Arvind Narayanan and Sayash Kapoor argue that the hype around AI often oversimplifies its capabilities and risks, particularly distinguishing between generative AI (which creates content) and predictive AI (which forecasts outcomes).

The authors express concern about the overhyping of AI and the misconceptions it generates, particularly through popular media and literature. They emphasize the importance of understanding AI's limitations and the potential social consequences of its misuse. Many prominent figures in tech, like Ray Kurzweil and Yuval Harari, often make grand predictions about AI's future, sometimes without a solid understanding of the technology.

Overall, while machine learning has real potential to benefit various fields, the narrative around AI often leans towards exaggeration, potentially distracting from its more practical applications and the genuine challenges it poses. The authors call for a more grounded understanding of AI, cautioning against buying into overly optimistic or sensationalized views that could lead to misguided expectations and decisions.

The text discusses the relationship between effective code review skills and the successful use of AI coding tools like Claude Code, Codex, and Copilot. Here are the key points:

1. Code Review Skills: Being good at reviewing code helps you use AI tools effectively. AI can generate code but lacks the judgment of experienced software engineers, which can lead to poor design choices.

2. Examples of AI Limitations: The author shares personal experiences where AI tools attempted complex solutions unnecessarily. By understanding the codebase, the author was able to simplify these approaches and save time.

3. Mistakes in Code Review: A common mistake in code reviews is focusing only on the written code without considering if it's the right solution for the problem. Good reviews should also look at the code's structure and context to find simpler, more effective solutions.

4. Approaches to Code Review: Being overly critical (nitpicky) or too lenient (rubber-stamping) can hinder effective collaboration with AI tools. A balanced approach is necessary to guide AI away from poor architectural decisions.

5. Understanding AI: Using AI effectively is complex and requires an understanding of its capabilities. While some believe that fully embracing AI will lead to better results, the author argues that AI still needs significant supervision.

6. Future of AI in Coding: The author reflects on the progress of AI coding tools over time and suggests that their capabilities might grow similarly to a human engineer gaining experience.

In summary, good code review skills are essential for effectively leveraging AI coding tools, as they help avoid poor design decisions and promote simpler, more efficient solutions.

A nonprofit called Hack Club, which helps teach coding to teenagers, faced a sudden and steep price increase from Slack, their communication tool. After nearly 11 years on a nonprofit plan, Slack demanded an extra $50,000 this week and $200,000 annually, threatening to deactivate their account and delete all messages if they didn’t comply. This short notice caused significant disruption for Hack Club, forcing them to scramble to update their systems and migrate data.

Fortunately, after their situation gained attention on social media, Slack’s CEO reached out to offer a better solution. Hack Club is now planning to switch to Mattermost and emphasizes the importance of owning your data, especially for small organizations. They encourage others to consider similar precautions.

Summary of "Show the Physics"

This book showcases 99 engaging physics demonstrations selected from the Dutch series "ShowdeFysica," created for physics teachers. It aims to provide valuable resources for educators, making physics both magical and educational. The demonstrations are categorized into four themes:

1. Nature of science
2. Scientific inquiry
3. Conceptual development
4. Special occasions

These demonstrations can help deepen students' understanding and engage them in thinking critically about physics.

The book is a collaborative effort by physics teachers and educators and includes well-tested experiments, teaching strategies, and questions to check students' understanding. It is available as an open-access resource under a Creative Commons license, allowing for non-commercial use with proper attribution.

Teachers can easily find demonstrations by searching for specific topics instead of reading the book cover to cover. The book also includes Python coding examples that can be run directly in the browser, enhancing the learning experience.

Readers are encouraged to contribute suggestions or report issues to improve the book. Additionally, there are 200 smaller "backpocket demos" available for quick demonstrations.

Safety is emphasized, and teachers are advised to test demonstrations before performing them in class.

In summary, "Show the Physics" is a practical guide for physics teachers, offering a variety of demonstrations to enrich classroom learning.

Summary:

Researchers from Science Tokyo have developed a new hydrogen battery that operates at a much lower temperature of 90 °C, making hydrogen storage more practical. This battery uses a solid electrolyte to efficiently transport hydride ions, allowing it to store and release hydrogen using magnesium hydride as the anode.

Previously, hydrogen storage required extremely low temperatures or high pressures, which were impractical. The new battery overcomes these issues, achieving high capacity and reversibility in hydrogen storage. It can store about 2,030 mAh/g of hydrogen, which is crucial for hydrogen-powered vehicles and clean energy systems.

This advancement could significantly contribute to a future with hydrogen energy, providing a more efficient and manageable solution for hydrogen storage.

Summary of Nostr: An Open Communication Protocol

Nostr is a decentralized communication protocol that allows free information sharing without corporate or government control. It provides a simple structure for multiple clients and servers to connect and interact, similar to the early internet's open and diverse environment.

• Key Features:

  • Open and Apolitical: Anyone can use or build on Nostr without political constraints.
  • Cryptographic Security: Each message is verified through digital signatures, ensuring authenticity and trust without needing a central authority.
  • Smart Clients: Applications can connect to multiple servers (relays) and manage data requests based on user preferences.
  • User Control: Users can easily switch between relays if needed, maintaining their audience and content flow.

• Content Diversity: Nostr supports various types of data and interactions, from microblogging to more complex applications like closed groups or decentralized marketplaces.

• Community and Ecosystem: Nostr is still developing, relying on open-source contributions to enhance its functionality and user experience.

• Addressing Challenges: Nostr aims to manage spam, harassment, and content discovery through user control and customizable client settings, though it acknowledges these issues are not fully solvable.

• Comparison to Other Platforms: Unlike centralized systems like Mastodon or Bluesky, Nostr offers a more flexible and resilient structure without relying on a single authority, making it more censorship-resistant.

Overall, Nostr represents a new approach to online communication, prioritizing user freedom and decentralized control.

No summary available.

This tool creates a fake-looking, malicious link that redirects to a real URL you provide. It works like a URL shortener, but instead of making links shorter, it makes them look suspicious.

You can choose a theme for the link, such as crypto, finance, online shopping, or dating. You can also select the length of the link, from small to very large.

To use it, just enter a link, pick your preferences, and generate a fake URL.

No summary available.

A campaign is underway to raise $200,000 to challenge Oracle's trademark on the term "JavaScript." More than 27,000 people have signed an open letter supporting this effort, leading to a formal petition filed with the US Patent and Trademark Office. The goal is to make "JavaScript" a public domain term, allowing developers to use it freely without fear of legal issues.

The funds will be used for critical legal processes, including:

• Conducting professional public surveys to show that "JavaScript" is widely recognized as a programming language, not a brand.
• Hiring expert witnesses to discuss JavaScript's history and usage.
• Gathering evidence from industry leaders that Oracle has not contributed to the language's development.
• Preparing legal documents to counter Oracle's claims.

If there are any leftover funds, they will be donated to OpenJS to support digital civil liberties. Oracle has responded by denying that "JavaScript" is a generic term, but many in the developer community disagree.

This case is important not only for JavaScript but also for the integrity of trademark laws, which should prevent companies from claiming commonly-used terms. The campaign encourages donations and sharing to ensure a strong legal challenge.

A friend discovered a Sangaku puzzle, which is a Japanese geometric problem from the Edo period. The puzzle asks for the radius of a small circle in relation to the side length of a square. Intrigued by the puzzle’s design, the writer and their friend decided to create a stained glass art piece based on it while learning about math-based art.

The writer spent over an hour trying to solve the problem using Geogebra and calculus, but found the algebra too complicated and was unable to arrive at a correct solution. They expressed their frustration and are seeking help to understand why the radius of the circle is ( \frac{4}{33}s ) (where ( s ) is the side length of the square).

Many responses from online communities suggested techniques like Descartes’ Circle Theorem and the method of inversion, which simplifies the problem. The writer is considering applying these suggestions but is feeling overwhelmed. They shared their struggles and the collaborative efforts of others trying to solve this geometric puzzle.

This essay reviews seven recent books by social scientists that explore the economic impacts of artificial intelligence (AI). The key insights from these works include AI's ability to make predictions cheaper, the barriers to its adoption, the economic value of data, and challenges in implementation. However, the books do not provide much guidance on the potential transformative effects of AI, such as major changes in the job market, rapid scientific advancements, or the risks associated with AI.

The author notes that while economists have made progress in understanding AI's effects on production and labor, there is still a need for clear policy advice to address issues like job displacement and economic shifts. The essay mentions bold predictions about AI's future capabilities and economic impact, citing various experts. It suggests that if these predictions hold true, they could lead to significant economic changes, likening this period to a new industrial revolution.

The review highlights the importance of books in summarizing complex research and making it accessible to a broader audience. Seven notable books are examined, each contributing different perspectives on AI's economic implications.

The essay also discusses the technological advancements that have fueled optimism about AI, particularly the rise of deep learning and generative models. It explains how breakthroughs in AI have changed expectations and raised questions about how AI might evolve and contribute to its own development.

Overall, while the reviewed books provide valuable insights, the author expresses concern that they do not fully address the critical economic questions surrounding AI's future, leaving policymakers and society with unanswered challenges.

Summary: Revamping an Old TV as a Gift

In 2017, I refurbished a vintage TV for my dad's 50th birthday to play shows from the 70s-80s. Here are the key steps I took:

1. Finding the TV: I bought an old TV from a flea market.

2. Connecting Video: I used a Raspberry Pi to send video to the TV. Since the Pi only outputs composite video, I needed a composite RF modulator to convert the signal for the TV.

3. Setting Up the Tuner: I fixed the TV tuner to match the output channel from the modulator, allowing the TV to display the video.

4. Creating Channel Functionality: To change channels, I used software-based channels controlled by a rotary switch connected to the Raspberry Pi.

5. Powering the Devices: I powered the Raspberry Pi and the modulator by using a 12v rail inside the TV and adjusted the voltage with LM7809 and LM7805 regulators.

6. Video Playback: I initially planned to have multiple short videos per channel but switched to a simpler method of using single 8-hour videos with advertisements included. This way, playback could resume from the last saved point when the TV was turned off.

7. Additional Feature: I also created a fake parcel-tracking website to show the package status.

This project was inspired by someone else's work, but I wanted my version to be stand-alone. You can find my code online, although it’s not very polished.

Summary:

Igor Daemen, a product designer from Eindhoven, has created a 3D-printed business card embosser. This device is designed to be modular, easy to print, and requires no additional hardware for assembly. Daemen notes that while the design has tight tolerances, the performance varies with materials, and he recommends using Basic PLA for the best results. The files for the embosser can be downloaded for free.

Some comments on the design express skepticism about using PLA due to its low strength, while others nostalgically recall business cards.

This text is about shader tutorials for game developers, focusing on improving color accuracy in lighting using techniques like tonemapping.

Key Points:

1. Understanding Color Values: Colors in shaders typically range from 0.0 to 1.0, known as 8-bit "unorm" color. While this is usually sufficient, there are instances where it can lead to poor color blending.

2. Tonemapping: This technique helps smooth out brightness and maintain color accuracy, especially in scenarios like sun shaders where colors can become washed out. Three common tonemapping functions are discussed: ACES, Hable, and Unreal.

3. High-Dynamic Range (HDR) Rendering: HDR allows for colors beyond the 0 to 1 range. It can require more advanced techniques and surfaces (like RGBA16float) to render accurately.

4. Practical Example: The author shares a personal example from their project "MandelBots," where they faced issues with washed out lighting and solved it using tonemapping.

5. Conclusion: To achieve better color results in shaders, developers can use tonemapping and HDR surfaces to avoid overexposure and maintain vibrant colors.

Overall, the tutorial aims to help developers enhance their shader work and avoid common pitfalls in color rendering.

Ellen Dash, a long-time member and maintainer of the Ruby community, shared her concerns about a recent situation involving RubyGems and Ruby Central. Between September 9 and September 19, 2025, a RubyGems maintainer unexpectedly renamed the RubyGems GitHub to Ruby Central, removed other maintainers, and added a new non-maintainer, Marty Haught, as an owner. Although some changes were later reverted, Haught still maintained control and revoked access for all previous admins, which Ellen views as a hostile takeover.

Ellen believes Ruby Central's actions threaten the Ruby community and show a lack of good faith. Due to this situation, she has decided to resign from Ruby Central, stating that they disregarded the wishes of her and the RubyGems team by revoking access without explanation.

A small library, about 2 kilobytes in size, helps developers create interactive user interfaces using plain JavaScript.

Summary of isingLenzMC: Monte Carlo for Classical Ising Model

The isingLenzMC package is a tool available on CRAN for simulating the Classical Ising Model, which is important in statistical physics. This model helps explain how spin glasses and magnetic materials behave, as well as phenomena like phase transitions and neural networks. The package allows users to simulate a one-dimensional Ising Model using two methods: Metropolis and Glauber Monte Carlo, with single spin flips and periodic boundaries. It also includes functions for exact solutions.

Related Research:

• A publication titled "Effective ergodicity in single-spin-flip dynamics" by Mehmet Suezen, published in Phys. Rev. E.
• A dataset on "Anomalous diffusion in convergence to effective ergodicity" by Mehmet Suezen, available on arXiv.

The XMonad development team has been gathering contributions for two years to fund a project that would port XMonad to Wayland, as none of the team members can do it themselves. They now believe they have enough monthly contributions to pay someone for this work.

However, they are unsure who can take on this task. There is an existing Wayland project, but it is outdated and has not been maintained well. Additionally, there is a challenge with Wayland programs not providing unique identifiers, making it difficult to adapt certain features from XMonad.

The team is seeking help and encourages anyone with insights or proposals to share their thoughts on the Discourse thread.

Summary of Safepoints and Fil-C

Safepointing Basics:

• Safepointing is crucial for multithreaded virtual machines (VMs) and garbage collectors (GCs), enabling accurate stack scanning, signal handling, and forking.
• It allows multiple threads to work on the heap without disrupting the garbage collector's operations.

What Safepointing Does:

• It serves as a synchronization mechanism, helping threads report their state and making assumptions about the VM's state.
• This allows threads to safely load pointers from memory while the garbage collector runs.

How Safepointing Works:

• Specific "safepoints" in functions are designated where the garbage collector can safely pause threads and scan for pointers.
• The Fil-C compiler inserts checks (pollchecks) at these safepoints to ensure threads report their pointers accurately.

Fil-C Implementation:

• Fil-C uses a simple approach that prioritizes concurrency by inserting pollchecks at backward control flow edges in the code.
• It tracks pointers using a structure called Pizderson frames, which helps manage pointers during pollchecks.

Garbage Collector Synchronization:

• Fil-C employs a soft handshake method where the GC instructs threads to reach the next safepoint without stopping all threads at once.
• Each thread has a state that can be read and modified under certain conditions, facilitating smooth communication with the GC.

Handling Native Code:

• Pollchecks are executed only in Fil-C code, so the system has mechanisms to manage state changes during native calls, ensuring the GC remains informed.

Addressing Other Issues:

• Safepoints help manage various synchronization issues, such as ensuring safe memory operations during garbage collection and handling thread-local caches.

Signal Handling and Debugging:

• Fil-C integrates safepointing with signal handling, ensuring signals are processed safely at defined points.
• It also supports stop-the-world operations for debugging and other critical tasks.

Further Exploration:

• For more technical details, refer to the Fil-C runtime source code and other resources about safepoint mechanisms in different VMs, particularly in JVMs.

No summary available.

In September 2025, the author received a set of 10 Compute Blades that they ordered in April 2023, just as Raspberry Pi upgraded its modules from CM4 to CM5. They also bought 10 CM5 Lite modules with 16GB each, totaling 160GB of RAM for their cluster, costing around $3,000.

The author built this Pi cluster but questioned its value compared to more powerful and expensive systems. After multiple rebuilds to improve reliability, they ran benchmarks showing the cluster achieved 325 Gflops, which is slower than a $8,000 Framework Cluster, though it was more energy-efficient.

For AI tasks, the Pi's performance was disappointing, with a small model running at just 0.28 tokens per second, significantly slower than the Framework system. Despite its compactness and efficiency, the author concluded that the cluster is not a great investment for most users, serving mainly niche applications like CI jobs or secure deployments.

The author also discussed the discontinued GBlade, a more industrial version of the Compute Blade, and mentioned their ongoing support from fans on various platforms. They provided a detailed parts list for those interested in replicating their setup. Overall, the author deemed the Pi cluster not ideal for heavy computing needs but suitable for specific use cases.

Summary of "The Ruliology of Lambdas"

What Are Lambdas? Lambdas are a concept in computation that originated in the 1930s, introduced by Alonzo Church. They represent "pure functions" that can be applied to arguments to produce values. In the Wolfram Language, lambdas are created using the Function construct.

Basic Computation with Lambdas Lambdas can represent data, like integers, purely through structure without predefined functions. For example, integers can be represented using nested lambdas, known as Church numerals. The core operation with lambdas is called beta reduction, which substitutes an argument into the body of a lambda.

Challenges with Lambdas When working with lambdas, there can be complications, especially with variable naming when one lambda is injected into another. This requires careful management to avoid conflicts, often leading to a need for renaming variables, known as alpha conversion.

Lambda Operations Common operations with lambdas include:

• Alpha reduction (renaming variables)
• Beta reduction (applying a function to an argument)
• Eta reduction (simplifying expressions)

Computation Examples Lambdas can be used for familiar operations such as addition and multiplication by defining these functions purely in terms of lambdas. For example, a successor function can be created to represent numbers and operations on them through beta reduction.

In summary, lambdas are a foundational concept in computation, allowing for the representation and manipulation of functions abstractly. They can express complex computations through structured applications, but they also introduce challenges that require careful handling of variables and substitutions.

No summary available.

The author is working on the second edition of the book "Observability Engineering" and has been focused on it for several months. They are adding a new section on "Observability Governance" to address the rise of observability engineering teams, which is a significant change since the first edition was published.

However, the author lacks experience in purchasing software for large tech companies and is seeking advice. They have specific questions about the software buying process, such as how to evaluate options, identify when to change vendors, and achieve consensus among stakeholders, especially when large amounts of money and resources are involved.

The author invites experienced software buyers to share their insights and advice via email, emphasizing that this request is for a writing project, not related to any specific company or product. They assure that any shared advice will be treated confidentially.

How to Play Minecraft Without Mojang's Code

You can experience Minecraft without using Mojang's official version by following these steps.

What You Need:

• Server: Cuberite (C++)
• Proxy: ViaProxy (Java)
• Client: Minosoft (Kotlin)
• Computer Requirements:
  • 64-bit processor with at least 4 cores (900 MHz or faster)
  • Minimum 4 GB RAM (more is better)
  • OpenGL-capable graphics card recommended

Installation Steps:

1. Install Cuberite Server:

   • Download the Cuberite server from their website.
   • For some systems, you may need to compile it from source, but it’s generally straightforward.
   • After downloading, place it in a folder and run it. Your server will be accessible at 127.0.0.1:25565.

2. Set Up ViaProxy:

   • Download ViaProxy from GitHub.
   • Make sure it matches your Java version (preferably Java 17).
   • Run the proxy software and note the IP address shown at the bottom.

3. Install Minosoft Client:

   • Download Minosoft from GitHub Actions builds, selecting the latest version.
   • Launch Minosoft using your Java 17 installation.

Launching the Game:

• In Minosoft, create an account (e.g., "singleplayer").
• Add the server using the IP from ViaProxy.
• Click "Connect" to start playing.

Troubleshooting:

If you can’t connect, check if Cuberite is set to "Online mode". If so, change Authenticate=1 to Authenticate=0 in the settings.ini file, then restart Cuberite and try again.

Congratulations, you’re ready to play!

The author received a message from someone developing a tool for improving software reliability, which also makes developers more productive by reducing the need for error detection and handling code. They noted a lack of research quantifying how much time developers spend on writing this error-handling code compared to the main code, even though estimates suggest it could be over two-thirds of code in production systems. The author consulted researchers and found no specific studies on this topic, which they find surprising given its significance.

They mention that developers spend about 11% of their time debugging, with some days dedicating up to 32% to fixing bugs, and up to 16% on testing. The author expresses concern that while there are many discussions around software metrics and improvements, basic figures about software development practices are still not well understood. They appreciate the feedback from researchers on this topic.

No summary available.

No summary available.

Summary of LLaMA Factory

LLaMA Factory is a tool used by major companies like Amazon and NVIDIA for fine-tuning large language models (LLMs). It is available on MacOS, Linux, and Windows, and allows users to customize over 100 LLMs through a user-friendly interface without needing to write code.

Key Features:

• Model Support: Works with various models like LLaMA, Qwen, and Mistral.
• Training Approaches: Supports multiple training methods, including full-tuning, freeze-tuning, and LoRA.
• Advanced Techniques: Incorporates cutting-edge algorithms and practical tricks for efficient model training and inference.
• User Community: Join their online user group on WeChat for support and collaboration.

Getting Started:

• Documentation: Comprehensive guides are available for setup and usage.
• Quick Start: Simple commands to begin fine-tuning and running models.
• Platforms: Options for local installations or cloud-based solutions like Colab.

Requirements:

• Minimum Python version: 3.9.
• Requires specific versions of libraries like PyTorch and transformers.

Installation Options:

• Install from source or using Docker for easier setup.
• Special instructions for different platforms, including Windows and Ascend NPU.

Conclusion: LLaMA Factory provides a robust platform for developers looking to fine-tune LLMs easily and effectively, with extensive documentation and community support.

No summary available.

A cyber-attack has caused significant delays at Heathrow and other European airports, disrupting electronic check-in and baggage systems. Flights were delayed as a result of this "technical issue," with Brussels and Berlin airports also reporting long waiting times. The software affected is provided by Collins Aerospace, and while British Airways is using a backup system, many other airlines are struggling.

Passengers at Heathrow reported lengthy queues, with some waiting over two hours to check in manually. The National Cyber Security Centre is working with authorities to address the situation. Hundreds of flights have been delayed, and passengers are advised to check their flight status before heading to the airport.

The cause of the cyber-attack is still unclear, but there are concerns it may be linked to criminal gangs or potentially state-sponsored hackers. Authorities are monitoring the situation closely, and there are calls for the government to investigate any links to Russia.

No summary available.

Henry and Roman are developing Cactus, an AI inference engine tailored for mobile phones. Cactus focuses on on-device AI, which offers benefits like reduced latency, enhanced privacy, offline functionality, and lower costs compared to cloud solutions.

Key points about Cactus:

• It addresses issues found in existing frameworks, such as optimizing for low to mid-budget phones, minimizing app sizes, and reducing battery drain.
• Cactus is built from the ground up to run efficiently on various mobile devices, considering energy efficiency and model support.
• It provides simple SDKs for developers to integrate AI with minimal code.
• Cactus is open-source for personal use but requires a paid license for commercial applications.
• A demo app is available on both the App Store and Google Play, with several apps already using Cactus in production, handling over 500,000 inference tasks weekly.

For computers, users are advised to use other platforms like HuggingFace or Llama.cpp, which are better suited for traditional hardware. The developers welcome feedback from the community.

Summary of pnpm 10.16 Update

1. New Delay for Dependency Updates:

   • A new setting called minimumReleaseAge has been introduced to delay the installation of newly released dependencies. This aims to reduce risks from compromised packages. For example, setting it to 1440 minutes (1 day) ensures only packages older than a day can be installed.
   • You can exclude specific dependencies from this delay using minimumReleaseAgeExclude, allowing immediate installation of certain packages like webpack.

2. Advanced Dependency Searching:

   • New "finder functions" allow users to search dependencies by properties beyond just their names. This can be set up in a .pnpmfile.cjs.
   • For example, you can create a finder to locate all packages with React 17 in their peer dependencies and see their locations in the dependency graph.

3. Additional Output Information:

   • Finder functions can also provide extra details, such as license information, when printing search results.

4. Patch Changes:

   • Fixes include removing deprecation warnings for Node.js 24, ensuring the node version is set correctly, enabling .tar.gz file publishing, and improving process cancellation feedback.

This update enhances security and usability for pnpm users.

Creating appealing user interfaces can be challenging, but there are key strategies to simplify the process. The author shares lessons learned from redesigning their product, Lighthouse, focusing on achieving good design with minimal effort.

Key Points:

1. Focus on Alignment and Consistency: Good design hinges on properly aligning elements and maintaining consistency across the interface. Misalignment and differences in element appearance can detract from the user experience.

2. Use a Component Library: Instead of creating custom components, leverage a component library (like HeroUI) to ensure design consistency. Adapt library components as little as possible to avoid inconsistencies.

3. Design Rules: Establish clear design rules, such as using only two font weights and two text colors, to maintain a cohesive look. This simplifies decision-making and enhances consistency.

4. Consider User Needs: Prioritize what users need to achieve their goals. Avoid clutter by limiting the information and functionality displayed.

5. Document Design Guidelines: Create a living document outlining design rules and standards for your project to streamline the design process and ensure consistency.

In summary, prioritize overall UI consistency over striving for perfection in individual elements. This approach leads to a smoother, more enjoyable user experience.

Wasm 3.0 Overview

Wasm 3.0 has been officially released as the new standard, introducing many significant updates since Wasm 2.0. Key features include:

1. 64-bit Address Space: Wasm can now support a much larger address space (up to 16 exabytes), allowing for bigger applications and data sets, especially in non-web environments.

2. Multiple Memories: Users can now declare and access multiple memory objects within a single module, improving functionality for tools and applications.

3. Garbage Collection: Wasm introduces a new form of automatically managed storage, allowing better memory management while keeping it low-level.

4. Typed References: The type system has been enhanced to provide more precise reference types, improving safety and performance in function calls.

5. Tail Calls: This feature allows functions to exit without using extra stack space, beneficial for various programming languages.

6. Exception Handling: Wasm now has built-in support for exceptions, enabling efficient local aborts in execution.

7. Relaxed Vector Instructions: New relaxed instruction variants allow for better performance by accommodating hardware differences.

8. Deterministic Profile: A default behavior is now specified for non-deterministic results, ensuring consistent execution across platforms.

9. Custom Annotation Syntax: Annotations can be added to Wasm source code for better readability and organization.

Additionally, Wasm 3.0 enhances its integration with JavaScript by allowing direct manipulation of JavaScript string values within Wasm.

With these updates, Wasm is now better equipped to support high-level programming languages, leading to the emergence of new languages targeting Wasm, such as Java, Kotlin, and Dart.

Overall, Wasm 3.0 aims to provide a more robust and versatile standard, with implementation already underway in major web browsers and engines.

No summary available.

Summary:

Elizabeth Goodspeed's newsletter, "Casual Archivist," shares a collection of mid-century North American restaurant placemats. She found these at the Brimfield Flea Market, purchasing 25 for $75. These placemats, dating from the late 1940s to the 1950s, served multiple purposes like menus and activity sheets, catering to the growing middle class during the postwar boom and the rise of road travel. They were produced cheaply using offset printing, resulting in bold colors and simple designs.

The newsletter highlights how this vintage design style influences modern nostalgic designs, which use similar visual tricks. Additionally, Goodspeed features an archive of historical Chinese diagrams and announces her upcoming talk at the AIGA Conference in LA, offering a discount code and a chance to win a free ticket for readers. Donations are welcomed to support the newsletter's production.

The author reflects on their website's design process, particularly their use of CSS classes. Initially, they realized they weren't fully applying their own advice about styling elements directly without classes. To improve this, they decided to completely remove classes from their website and instead focus on using semantic HTML elements.

They structured their CSS around three levels: base, components, and utilities, and aimed to use more default element styles. This involved careful review of their markup and increased use of contextual styling. However, they found that excessive contextual styling led to overly complex CSS selectors.

To manage components without classes, the author explored custom HTML tags and attributes, which provide valid HTML without needing JavaScript. This method allows for more semantic and clear code while reducing the amount of CSS needed. The author noted that this approach improved accessibility and streamlined the markup.

They acknowledged that while this method has benefits, it requires more careful planning and may not be suitable for larger projects with varying levels of frontend expertise. The author concluded that while they have not completely abandoned CSS classes, this experience has shifted their perspective on web design and will influence their future work. They still use some classes for specific plugins but are cautious about introducing JavaScript where unnecessary.

A new analysis from the Colorado School of Mines, published in the journal Science, reveals that the U.S. has enough critical minerals for its needs, but these minerals are often discarded during the mining process. Minerals like cobalt, lithium, and rare earth elements are found as byproducts in mines but are not being recovered, leading to waste. The study urges increased research, development, and policy changes to make recovery economically viable. By recovering even small amounts of these minerals, the U.S. could reduce its reliance on imports for many critical materials. The research highlights the environmental benefits of recovering these minerals, which could lessen mine waste and support reuse in various industries.

Google is introducing a new developer verification system for Android, which will require app developers to register their identities. This change is raising concerns among users, particularly about the ability to sideload apps without an internet connection.

Key points include:

• The new system will connect developers' names to apps, including those sideloaded from outside the Google Play Store.
• Users may face issues installing verified apps if they are offline, as the system may check the developer's status online before allowing installation.
• Some users are worried this change could lead to increased censorship and limit app availability, especially in areas with poor connectivity.
• While there are potential workarounds, such as using the Android Debug Bridge (ADB), these may not be feasible for all users.

The developer verification system is not expected to roll out until next year, giving time for further developments and discussions about its implications.

The Leatherman was a mysterious vagabond who roamed the northeastern United States from about 1857 to 1889, wearing a unique handmade leather outfit. Born around 1839 and believed to be of French-Canadian descent, he communicated mainly in French and rarely spoke English. He traveled a consistent 365-mile route between the Connecticut and Hudson Rivers, visiting towns every 34 days for supplies.

He lived in rock shelters, known as "leatherman caves," and was known for his quiet demeanor, often responding only in short phrases or gestures. Despite the harsh weather, he survived well and was a familiar figure, with townspeople often preparing food for him. The exact source of his income is unclear.

The Leatherman's health declined, and after being hospitalized in 1888, he was diagnosed as mentally sound but emotionally troubled. He passed away from mouth cancer in 1889, and a French prayer book was found among his belongings.

His identity is still debated; he was sometimes referred to as "Jules Bourglay," a name that later turned out to be unverified. His grave is located in Sparta Cemetery in Ossining, New York, where a new headstone was placed in 2011 after his remains were moved to a different location within the cemetery. The Leatherman has inspired songs and documentaries and remains a legendary figure in Connecticut folklore.

Summary of FLX1s Launch Announcement

On September 19, 2025, FuriLabs announced the launch of the FLX1s smartphone. Pre-sales have begun, and production is expected to finish by the end of October 2025, after which shipping will start. Existing orders will be automatically upgraded to the FLX1s or refunded if requested. The FuriLabs team expressed gratitude to their supportive community of FLX1 owners and customers awaiting their orders.

Summary: Apple Photos App Corrupts Images

The Apple Photos app sometimes corrupts images when importing from cameras. The author experienced this issue with their OM System OM-1 camera, losing about 30% of photos taken at a wedding. Initially, they thought the problem might be with their hardware, so they replaced cables, SD cards, laptops, and even the camera, but the corruption continued.

After troubleshooting, they discovered that the corruption likely occurs due to a software issue with the Photos app, especially when using the "delete after import" feature. To avoid losing more photos, they stopped using the Photos app for importing and switched to Darktable for managing their images. This new workflow has eliminated the corruption problem, allowing them to import, cull, and export photos without issues. Ultimately, while they spent time and money trying to solve the problem, they now have backup hardware and a more reliable photo management process.

In 2022, the focus on artificial intelligence (AI) was already significant, but now it's even bigger, with the market reaching trillions of dollars. Companies like Microsoft, with its AI-enhanced Azure cloud services, have seen their value approach $4 trillion. Alphabet, which is transforming Google with AI, has become a $3 trillion company. Amazon, also involved in cloud services, is close to this value as well. Meta, focusing more on AI alongside social media, is valued at around $2 trillion. Oracle is competing in the AI cloud market and is aiming for a $1 trillion valuation. Additionally, OpenAI, the creator of ChatGPT, along with its competitors Anthropic and xAI, might reach similar valuations by the end of the year.

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The text asks how someone is managing or dealing with their current situation.

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The article discusses a map created by Robert Simmon that presents the world with south at the top instead of the conventional north-up orientation. This unconventional view challenges our long-held perceptions about maps and encourages us to think critically about why we typically place north at the top. Historically, maps have been oriented in various ways, including south-up, reflecting the tools and knowledge of different times. The article highlights how map orientation can influence our perceptions, as things at the top are often viewed more favorably. Simmon's map serves as both a geographical reference and a philosophical prompt, inviting us to reconsider the traditions of cartography.

Summary of apple_ssh_and_filevault

When FileVault is turned on, the data on a Mac is locked until a user logs in with a password. The OpenSSH configuration files, which control how SSH works, are stored on this locked data volume. Normally, this means you can't use SSH during startup. However, if Remote Login is activated, you can use SSH to enter your password and unlock the data volume remotely. After unlocking it, there will be a brief disconnection as macOS finishes mounting the volume and starting necessary services. Once this is done, SSH and other services will be fully available.

History: This feature to unlock the data volume via SSH was introduced in macOS 26 Tahoe.

Related Resource: sshd(8)

Bess Stillman, an ER doctor and writer, reflects on her experience of grief following the death of her husband, Jake, who passed away from tongue cancer. A year after his death, she still struggles with disbelief and the persistent feeling that he is nearby. The American Psychiatric Association defines "disordered grief" as prolonged mourning that could indicate a problem, but Bess questions this notion, feeling that her grief is a natural response to loss.

She describes the confusion and searching feelings that come with reminders of Jake, recognizing that her brain still expects him in her life. Bess explores the complexities of grief, noting that it does not follow a linear path and can resurface unexpectedly. Despite functioning in daily life—caring for their daughter, Athena, and returning to work—she grapples with deep sadness and the feeling that part of her has died with Jake.

Bess criticizes the societal tendency to medicalize grief and rush its resolution, arguing that grief is a personal, evolving experience that resists being categorized. She shares her frustrations with others who seek to distance themselves from the randomness of death and questions the cultural framework around mourning, which often hides and sanitizes the reality of loss.

Ultimately, Bess emphasizes that grief is not an illness but a part of life, filled with love and memories. She acknowledges that while Jake is gone, her feelings for him remain strong, and she refuses to wait for a cure to her grief—this is her life now.

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The article discusses methods to efficiently find the index of the smallest floating-point number in a large array of positive numbers. Here are the key points:

1. Problem Overview: You need to find the index of the smallest value (argmin) in a large array of positive floating-point numbers.

2. Initial Solution: The simplest approach in Rust uses min_by with total_cmp, taking about 511 microseconds for an array of one million numbers.

3. Second Approach: A custom comparator that directly compares values can reduce the runtime to 489 microseconds, but it may not work correctly for all float comparisons.

4. Third Method: Using Rust's standard library for partial comparisons, which handles floats better, results in a runtime of 470 microseconds. This is slightly faster due to better compiler optimizations.

5. Fourth Method: Since the numbers are positive, converting them to their bit representation (using to_bits()) allows for fast comparisons. This method runs in just 370 microseconds, achieving a 30% speedup.

6. Benchmarking: The article includes benchmark tests comparing these methods using the Criterion library in Rust, illustrating their performance differences.

Overall, the best approach for this specific problem is sorting the positive floats by their bit representation for optimal speed.

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Summary of "The Many Broken Feeds"

RSS and Atom feeds are essential for keeping up with online content. The author subscribes to over 700 feeds and notes that while most work well, some frequently encounter issues. Here are the main problems and solutions for broken feeds:

1. Expired SSL Certificates: Websites sometimes forget to renew their SSL certificates, leading to broken feeds. Solution: Set reminders for renewal or use a CDN to manage renewals automatically.

2. Timeouts from Slow Servers: Feed readers stop fetching feeds if a server is too slow. Solution: Improve server performance by upgrading services or using caching.

3. Misconfigured Firewalls: New firewall settings may block feed readers. Solution: Exclude feed URLs from restrictions or provide a link-only feed.

4. Servers Going Down: Websites may occasionally go offline. Solution: Set up monitoring to be alerted when this happens.

5. Changed Feed URLs: Changing blogging platforms can alter feed URLs. Solution: Maintain the same URLs or redirect old ones, and inform users of any changes.

6. Feed Parsing Failures: Rarely, feeds fail to parse due to encoding issues. Solution: Regularly validate feeds to catch these problems.

7. Deleted Feeds: Some websites stop offering feeds entirely. The author encourages keeping feeds active.

8. Deleted Websites: If someone deletes their website, the feed disappears too, which is unfortunate.

The author hopes this information helps others and encourages sharing the post.