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Gabriel Weinberg, the founder of DuckDuckGo, argues that AI surveillance should be banned to protect privacy. He explains that AI chatbots pose greater privacy risks than traditional online tracking because they encourage users to share more personal information. Unlike simple search queries, conversations with chatbots can reveal detailed insights into a person's thoughts and communication styles, making them more susceptible to manipulation for advertising or political purposes.

Weinberg highlights incidents where chatbot conversations were leaked, exposing users' private information, and warns that AI companies are increasingly tracking interactions without proper consent. He calls for Congress to enact laws that ensure privacy protections for AI chat services. While he acknowledges that significant privacy legislation is still lacking in the U.S., he emphasizes the need for urgent action to prevent the same mistakes made in online tracking from happening with AI.

DuckDuckGo is working to provide privacy-respecting AI services, aiming to offer users productivity without compromising their privacy.

The oldest transaction record dates back to 3100 BC, showing accounts of malt and barley. This ancient database has survived for 5,000 years without any downtime, which is impressive compared to modern databases. The author wonders about the oldest date that can be used in today’s databases.

They checked three popular databases:

• MySQL supports dates only up to 1000 AD.
• PostgreSQL and SQLite can handle dates back to January 1, 4713 BC.

The author notes that while you can insert this date into PostgreSQL and SQLite, trying to use a date earlier than that (like 4714 BC) results in an error. They ponder how institutions like museums manage dates older than these limits, questioning if they use different methods like text storage or custom systems.

The author also thanks several people for reviewing an early draft of the post and shares the source of the image, which is from the Sumer civilization.

Summary:

Restaurant Brands International (RBI), which owns Burger King, Tim Hortons, and Popeyes, operates over 30,000 locations globally. They have a digital platform for managing drive-thru systems, but it was found to have serious security flaws.

Key issues included:

1. Signup Vulnerability: Users could create accounts without proper verification, allowing unauthorized access.
2. Store Information Access: Once logged in, users could access sensitive data about all stores, including employee information.
3. Token Generator Flaw: A feature allowed creation of access tokens without authentication, enabling users to gain admin rights across the platform.
4. Drive-Thru Equipment Site: The equipment ordering site had minimal security, with hardcoded passwords visible in the HTML code.
5. Voice Recording Access: The system allowed access to actual recordings of customer orders, raising significant privacy concerns.
6. Bathroom Feedback System: Users could submit reviews without authentication, enabling potential spam.

The vulnerabilities were discovered quickly, and RBI responded promptly to fix them. However, they did not comment on the specific issues raised. The researchers ensured that no customer data was retained during their investigation.

The text discusses a project called "distributed-llama" by the user b4rtaz. It focuses on running a model called Qwen3 on four Raspberry Pi 5 devices. Here are the key points:

• Project Overview: The project involves using a distributed setup with four Raspberry Pi 5 computers.
• Model Details: The model used is Qwen3 version 0.16.0, which has specific characteristics such as 48 layers and a vocabulary size of 151,936.
• Performance Metrics: The setup achieved a performance of 14.33 tokens per second during evaluation and 13.04 tokens per second during prediction.
• Setup Information: The devices are connected through a TP-Link switch, with designated roles for each Raspberry Pi (one as the root and others as workers).
• Technical Issues: There are some errors in loading and configuration, specifically with tokenizer and model vocab sizes not matching.

Overall, the text outlines the technical aspects of setting up and running a model on a distributed system of Raspberry Pi devices.

Summary of Giles' Blog Post on Understanding LLMs

In this blog post, Giles discusses the mathematics needed to understand Large Language Models (LLMs), aimed at readers with basic tech knowledge. The key points include:

1. Understanding Vectors and Spaces: Vectors are used to represent points in high-dimensional spaces, which are crucial for LLMs. For example, a vector can indicate the likelihood of different words following a given input.

2. Logits and Vocabulary Space: The outputs from an LLM can be viewed as logits, which are converted into probabilities using a function called softmax. This helps normalize the output, making it easier to understand.

3. Embedding Spaces: These are high-dimensional spaces where similar meanings are clustered together. This allows LLMs to understand relationships between words and concepts.

4. Matrix Multiplication: Matrices are used in LLMs for transformations between different dimensions. This is important for how data is processed within neural networks.

5. Neural Networks: The functioning of a neural network can be simplified to matrix multiplication, which projects input data into an output space.

Giles concludes that the mathematics behind LLMs is not overly complex and is based on concepts familiar from high school. The next posts will build on these ideas to explain how LLMs operate in more detail.

Anthropic, a technology company, has agreed to pay $1.5 billion to settle a class-action lawsuit brought by authors. The lawsuit claimed that the company used their written works without permission. This settlement aims to address copyright concerns related to the use of authors' content in technology.

For more details, you can read the full articles from the Washington Post and Reuters.

Developers using GitHub, owned by Microsoft, are frustrated with the forced integration of Copilot, an AI tool that generates code suggestions. Many users want to disable Copilot but have found their requests ignored by GitHub. This ongoing issue has led to a growing movement among developers to consider alternative code hosting platforms, like Codeberg, as they feel increasingly pressured by Copilot's intrusive features.

Andi McClure, a developer who has been vocal about her objections to Copilot, has reported a surge in community support for opting out of the AI features. Concerns include the potential misuse of code and copyright issues, with some developers stating they will move away from GitHub if changes aren't made soon.

Despite Microsoft’s claims of Copilot’s success, many users are unhappy with the lack of control over the tool and feel that Microsoft is prioritizing AI metrics over customer satisfaction. As dissatisfaction grows, more developers are looking to migrate from GitHub to other platforms, reinforcing a long-standing sentiment in the open-source community against Microsoft and its AI initiatives.

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Summary of Disciplined AI Software Development Methodology

The Disciplined AI Software Development Methodology is a structured approach to developing AI projects, focusing on collaboration and systematic constraints to improve code quality and reduce common issues like code bloat and architectural inconsistencies.

Key Concepts:

1. Context Problem: AI often struggles with broad requests, leading to:

   • Lack of structured code
   • Repeated code
   • Inconsistent architecture
   • Increased debugging time

2. Four Stages of Methodology:

   • Stage 1: AI Configuration: Set custom instructions for the AI to manage its behavior.
   • Stage 2: Collaborative Planning: Work with the AI to define project scope, components, and tasks systematically.
   • Stage 3: Systematic Implementation: Implement one component at a time, keeping code files under 150 lines for easier management and debugging.
   • Stage 4: Data-Driven Iteration: Use performance data from testing to optimize and make informed decisions.

Benefits:

• Improved Decision Processing: Focused questions lead to better AI responses.
• Better Context Management: Smaller files help the AI handle tasks more effectively.
• Empirical Validation: Decisions are based on data rather than assumptions.
• Consistent Architecture: Regular checkpoints and constraints ensure stable development.

Example Projects:

• Discord Bot Template: A ready-to-use bot structure.
• PhiCode Runtime: A programming language runtime engine.
• PhiPipe: A CI/CD system for detecting regressions.

Implementation Steps:

1. Setup: Configure the AI, share planning documents, and establish project structure.
2. Execution: Start with benchmarking and implement components sequentially.
3. Quality Assurance: Regularly check for performance and architectural compliance.

Conclusion:

This methodology aims to enhance collaboration with AI, ensuring better project outcomes by following systematic processes and constraints. It is particularly effective for serious projects that require reliability and architectural discipline.

The Unreal Engine has become a feature-rich platform for various applications, but this complexity leads to slow startup times for the editor. Epic Games has implemented solutions like live coding and derived data caches to help, but these are often project-specific and require manual effort.

A recent investigation into the editor's startup process revealed that it generates around 38,000 tooltips, which significantly contributes to the lengthy startup time. While some optimizations reduced the disk space used for tooltip text, the sheer number of tooltips created is still a problem, as most users only interact with a handful during a session.

To improve performance, the suggestion is to delay the actual creation of tooltips until they are needed, rather than generating all of them at startup. This change would not negatively impact runtime performance since only one tooltip can be displayed at a time, and creating a tooltip is very fast.

Overall, reducing the number of unnecessary tooltips created during startup could lead to a much quicker launch for the Unreal Editor.

The text discusses the implementation of blur effects in video game graphics using WebGL, a technology that allows for real-time rendering in web browsers. Here are the main points:

1. Blur Effects in Graphics: Blurs are crucial for creating modern visual effects in games and interfaces, like Depth of Field and Bloom. These effects are created by averaging colors in a certain area around a pixel.

2. Technical Background: Achieving real-time blurring has involved decades of research in graphics programming. This article explores how to implement blur effects using fragment shaders, which are programs that run on the GPU for each pixel.

3. Post-Processing: The process involves rendering a 3D scene to an intermediary image (framebuffer) and then applying various effects like blurs in a post-processing step.

4. Interactive Visualizations: The article includes interactive examples where users can see different blur effects in action, such as a "Scene" mode for overall blurring, a "Lights" mode for focusing on glowing parts, and a "Bloom" mode for creating a soft glow effect.

5. Performance Considerations: The text emphasizes the importance of performance, detailing how the number of texture reads (texture taps) can impact frame rate.

6. Real-time Implementation: Users can experiment with different blur algorithms and their effects on performance and visuals through a user interface that includes sliders and buttons.

7. Learning and Exploration: The article invites readers to follow along and learn about graphics programming, even if they lack prior knowledge, by providing explanations of terms and concepts as they arise.

Overall, the text serves as a guide for understanding and implementing blur effects in real-time graphics using modern web technologies.

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Java 25 introduces several new language features that focus on data-oriented programming, helping new developers, and making Java easier to use for scripting and automation. José Paumard will explain these features. Don't forget to check out the Road to 25 playlist for more information.

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Summary of Developing a Space Flight Simulator in Clojure

In 2017, the author was inspired by the Orbiter 2016 space flight simulator to create their own simulator using Clojure. They initially experimented with physics in C and GNU Guile but switched to Clojure for its multi-methods and efficient data structures.

Over nearly five years, the author focused on complex features first, like 3D planet rendering and atmospheric effects, utilizing OpenGL for graphics. The software stack includes Clojure, LWJGL (for graphics and input), Jolt Physics (for vehicle dynamics), and various libraries for data handling.

Atmospheric rendering uses precomputed scattering tables, and detailed shader templates help manage OpenGL shader programs. Planet textures are derived from NASA data, with thousands of map tiles generated for different conditions.

The project also implements solar system dynamics based on the Skyfield library, and the author wrapped C functions for physics calculations using a library called Coffi.

Performance optimization includes using the ZGC garbage collector and profiling tools. The project structure supports building various components and creating executables for Linux and Windows.

Future work will involve adding features like graphics for control surfaces and a 3D cockpit, as well as modding support. The source code is available on GitHub, and interested players can wishlist the game for updates.

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The author is trying to make their personal website feel more unique and personal, rather than just like any corporate site. To do this, they decided to create a font that resembles their handwriting.

Initially, they attempted to make the font using open-source tools like Inkscape and FontForge, but found the process frustrating and complicated, especially with FontForge's difficult user interface. After struggling with these tools, they decided to use a paid service called Calligraphr, which allows users to create fonts by writing letters on printed templates and scanning them back in.

Calligraphr proved to be user-friendly, allowing the author to customize their font by adjusting letter spacing and making other tweaks. The result was a font that captured their handwriting style and was legible at smaller sizes. The author appreciated Calligraphr's transparent pricing model and user support, especially when they received a backup of their font data after their subscription lapsed.

In summary, the author successfully created a custom font for their website using Calligraphr after struggling with open-source tools, and they found the experience rewarding and the service user-friendly.

Book Review Summary:

Felice Frankel reviews "Flashes of Brilliance" by Anika Burgess, highlighting the significant role of photography in science and society. The book emphasizes that photography is not just for illustration; it serves as a powerful investigative tool that can reveal complex ideas and social issues.

Burgess discusses historical examples, like Jacob Riis' photographs of impoverished living conditions in New York, which convey more than words can express. However, she also addresses the potential for manipulation in photography, using Eadweard Muybridge's horse motion studies as an example of how images can be selectively arranged to present a certain narrative.

The review stresses the importance of understanding the balance between clarity and accuracy in scientific images. Beautiful images can mislead if their context and creation process aren't transparent. Overall, the book is a thoughtful exploration of how images shape our understanding of reality.

This text discusses a new method called "meschers" for representing impossible objects in computer graphics. Impossible objects are geometric shapes that look real but can't actually exist, similar to those created by artist M.C. Escher.

Key Points:

1. Problem with Current Methods: Existing techniques involve cutting or bending impossible objects, which can alter their shape and complicate further graphical operations like lighting and distance calculations.

2. Introduction of Meschers: Meschers are specialized mesh representations that can accurately depict impossible objects without distorting their geometry. They rely on a mathematical approach called discrete exterior calculus.

3. How Meschers Work: Instead of storing traditional 3D positions, meschers record 2D screen positions and depth differences between edges. This allows for a more accurate depiction of the visual impossibility.

4. Applications: Meschers enable various geometry processing tasks, such as smoothing and inverse rendering, which can transform a regular shape into an impossible one while preserving its impossibility.

5. Future Work: The paper promises to explore more applications and the underlying mathematics in a future publication.

Overall, meschers provide a new way to work with impossible objects in computer graphics, offering both theoretical insights and practical applications.

MentraOS Overview

MentraOS is an open-source operating system designed for smart glasses. It supports devices like Even Realities G1, Mentra Mach 1, and Mentra Live.

Key Features:

• App Store: The Mentra Store offers various apps for users, including Live Captions, Notes, Calendar, and Translation.
• Developer-Friendly: Developers can create apps that work on any compatible smart glasses without worrying about connection issues or compatibility, thanks to the system’s open-source nature.
• Easy Development: Using a TypeScript SDK, developers can quickly build apps, accessing smart glasses' features like displays and cameras.

Community Involvement: MentraOS encourages collaboration among developers and users to create a user-controlled and cross-compatible personal computing experience.

Get in Touch: For questions or to join the community, you can reach out via email or join the Discord server. The project welcomes contributions from anyone interested.

License: MentraOS is licensed under the MIT License.

Summary: My Own DNS Server At Home - Part 1: IPv4

In this blog post, the author discusses setting up a DNS server at home using BIND on a Raspberry Pi 4. The aim is to create a self-sufficient home network that can function without an internet connection.

Key Points:

1. Purpose of DNS: DNS translates hostnames (like jan.wildeboer.net) into IP addresses. A local DNS server is essential for managing network devices efficiently.

2. Home Network Setup: The setup includes three different IP networks and uses a Fritz Box router for forwarding DNS requests. The local domain is named "homelab.jhw."

3. Installation Steps:

   • Install BIND and necessary utilities.
   • Configure firewall settings to allow DNS traffic.

4. Configuration Files: The author describes four key configuration files needed for BIND:

   • named.conf: Main settings for the DNS server.
   • forward.homelab.jhw: Maps hostnames to IP addresses.
   • reverse.homelab.jhw and reverse2.homelab.jhw: Map IP addresses back to hostnames.

5. Important Configuration Details:

   • Ensure every DNS entry ends with a dot (.) to avoid resolution issues.
   • Increment the serial number in zone files whenever changes are made to prevent errors.

6. Testing: After configuration, the author tests the DNS server to confirm it resolves local hostnames correctly.

7. Final Steps: The DNS server is started and set to run at boot, ensuring that it remains available for network queries.

The post serves as both a report and a how-to guide for setting up a DNS server at home, encouraging readers to experiment and learn in a controlled environment.

In September 2025, the author bought a used 2023 Nissan Leaf, marking their first new car in 15 years. They previously drove various used cars, including a minivan and a Camry, but wanted something smaller and more efficient due to a short daily commute.

The author had test-driven a Tesla in 2012 and preferred the electric driving experience. They created a video and a GitHub project to share their electric vehicle (EV) journey, detailing improvements and monitoring tools for their Leaf, including battery health monitoring through an OBD-II device.

They chose the Leaf mainly for its affordability, noting that it meets their needs without being overly extravagant. The Leaf has some limitations, such as a lack of certain features and a somewhat outdated charging standard. Despite these drawbacks, the author appreciates the benefits of driving electric, like lower maintenance and the convenience of charging at home.

However, they acknowledge some challenges with EVs, such as range anxiety, varying charging standards, and the need for careful planning during trips. The author paid $15,000 for the Leaf after trading in their old car and expects to benefit from a tax rebate.

Overall, while the Leaf suits their lifestyle, the author does not advocate for EVs for everyone, given the current infrastructure and price issues.

The website is checking your browser. If you own the website, there’s a link for you to click to resolve the issue.

The author argues against the use of Protocol Buffers (protobuffers), describing them as poorly designed and overly complex. They highlight several key issues:

1. Bad Type System: The type system in protobuffers is criticized for being confusing and restrictive, making it difficult to use effectively. The author believes it was designed without proper understanding of modern type systems.

2. Lack of Compositionality: Protobuffers have many features that don’t work well together, leading to arbitrary restrictions that complicate data structure definitions. This is seen as a result of poor design choices.

3. Questionable Default Behaviors: The handling of scalar and message types is problematic. Scalar fields always have default values, making it hard to tell if a field was set or not. Message fields behave unexpectedly, introducing bugs.

4. Misleading Compatibility Claims: Although protobuffers claim to be backwards- and forwards-compatible, the author argues this is achieved by being overly permissive and not ensuring meaningful data structures.

5. Contamination of Codebases: The rigid nature of protobuffers leads to complications in code, as they often spread throughout a codebase, forcing developers to deal with their limitations repeatedly.

Overall, the author believes that adopting protobuffers leads to more problems than it solves, suggesting that developers should avoid them in favor of more robust solutions.

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Summary of the Fourier Transform

The Fourier Transform is a mathematical technique developed by Jean-Baptiste Joseph Fourier in the early 19th century. It breaks down complex functions into simpler wave components, known as frequencies. This method is essential in various fields of mathematics and physics, including harmonic analysis, which studies these components.

Fourier's work was inspired by the need to understand heat distribution in objects, leading him to propose that heat could be represented as a sum of simple waves. Despite initial skepticism from his peers, his ideas have since been validated and are now foundational in many areas, such as signal processing, data compression, and quantum mechanics.

The Fourier Transform works by identifying how much each frequency contributes to the original function. It can even be applied to images, allowing for efficient data compression, like in JPEG files. The fast Fourier transform, developed in the 1960s, made this process quicker and more accessible.

In summary, the Fourier Transform is crucial for analyzing and understanding complex functions and is used widely in technology and scientific research. Its influence is so significant that many areas of mathematics would be greatly diminished without it.

Tesla has updated its definition of "Full Self-Driving" and is no longer promising complete autonomy for its vehicles. This change indicates a shift in their approach to self-driving technology.

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Apertus LLM has a collection of 4 items that was updated 4 days ago.

Zach from Adam is introducing an AI tool designed to assist with mechanical CAD software. They have created a browser-based app that converts text and images into 3D models, which is now open source.

Key Features:

• Generates 3D models from natural language and image inputs.
• Outputs OpenSCAD code with adjustable parameters.
• Exports models in .STL or .SCAD formats.

Technical Details:

• Utilizes separate agents for conversation and code creation.
• Runs entirely in the browser using WebAssembly and Three.js for 3D rendering.
• Supports multiple CAD libraries and custom text fonts.

The goal of releasing this tool is to provide a foundation for developers interested in similar functionalities. Future upgrades aim to improve geometry support, enhance user interface for better spatial understanding, and integrate more libraries for advanced features. The community is encouraged to clone the repository and contribute to its development.

I created a phonetic Arabic keyboard that connects English letters to Arabic sounds. It includes special letters, the hamza, and diacritical marks, which helps learners and casual users type in Arabic more easily.

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This article discusses the power and energy usage of Visual Look Up (VLU) on an Apple silicon Mac, specifically the Mac mini M4 Pro. The author conducted measurements using a tool called powermetrics to assess the power consumption of the CPU, GPU, and neural engine (ANE) during a VLU operation on an image.

Key findings include:

1. Power Consumption: During the VLU process, the CPU accounted for 93% of the total power used, with the GPU and ANE using much less (4.6% and 2.2% respectively). The total power used during VLU was about 69 watts, with an energy cost of 6.9 joules over the duration of the task.

2. Process Duration: The VLU process took approximately 6.5 seconds, with most of the power being used in the first 2 seconds when the CPU and ANE were most active.

3. Overall Impact: Despite the impressive performance of VLU, it does not demand much power or energy from the hardware. The majority of the workload is handled by the CPU, and the neural engine's contribution is limited.

In conclusion, while VLU appears powerful, it is actually quite energy-efficient on Apple silicon Macs.

The author reflects on their 16-year journey with online comment culture, starting from platforms like Hacker News and expanding to Reddit, Substack, and Twitter. Initially, commenting felt rewarding and social, sharpening their debate skills and allowing them to express various personas. However, they've concluded that this engagement has not led to meaningful friendships, realizing that comment culture promotes interactions with strangers rather than building connections.

The author highlights the challenges of forming real friendships, noting that it requires significant time and effort, which is often lacking in online interactions. They argue that online platforms prioritize engagement over genuine connection, turning social interactions into performances for anonymous audiences rather than opportunities for building relationships.

Ultimately, the author decides to leave comment culture behind, recognizing the need for deeper connections with friends rather than fleeting online exchanges. They express a desire to seek out more fulfilling social experiences, suggesting they may turn to platforms like Discord for better community engagement.

The "Quantum Mechanics Concise Book" by user basketballguy999 is a brief introduction to quantum mechanics aimed at a general audience, including undergraduate students in computer science, engineering, mathematics, and physics, as well as anyone interested in learning about the topic. To understand the content, readers should have knowledge of linear algebra, calculus, and high school physics. The book currently has 101 stars on GitHub but has not been forked or had any releases.

The article discusses Apple's history with Mac clones, which were third-party computers designed to run MacOS. In the 1980s, during a challenging period for Apple, the idea of allowing clones could have been promising. However, it ultimately backfired.

Initially, unauthorized clones emerged, but in the early '90s, Apple began to license clones officially. Companies like Power Computing and UMAX made computers that competed directly with Apple's offerings. While Apple hoped this would expand its market, it instead led to price wars and diluted brand value.

Key figures like Chuck Colby created innovative Mac conversions, but Apple's strict licensing approach limited competition and innovation. By the mid-90s, as Windows gained market share, Apple reconsidered its stance on clones but struggled to find a successful strategy.

Ultimately, the clone program did not achieve the intended growth for Apple and led to challenges in maintaining control over its brand. Steve Jobs later ended the cloning licenses, emphasizing the need for control and quality in Apple's product lineup.

Summary: How JSTOR Became a Tech Powerhouse

JSTOR, a nonprofit focused on academic archiving, has grown significantly since its founding in 1994 with initial funding from the Andrew Mellon Foundation. Under the leadership of Kevin Guthrie, JSTOR has embraced technology, particularly the internet, to expand its offerings, which now include access to over 2,800 academic journals. It serves 14,000 libraries worldwide, managing over 120 million queries annually.

Key factors in JSTOR's success include:

1. Technological Innovation: JSTOR adapted to digital storage early on, rejecting outdated methods like microfilm.
2. Financial Stability: The organization built cash reserves to ensure reliability and support growth.
3. User Engagement: JSTOR designs its services based on feedback from users, publishers, and librarians.
4. Mission-Driven Focus: Unlike many for-profit services, JSTOR prioritizes public good over profits, maintaining reasonable pricing.

Despite its success, JSTOR faces challenges from AI and open-source publishing. To stay relevant, it is investing in new technologies and services, even if it means running deficits temporarily. JSTOR aims to continue evolving and adding value to its offerings while protecting scholarly materials.

The text discusses the evolution of embeddings, which are numerical representations used in machine learning for tasks like search and recommendations. A few years ago, embeddings with 200-300 dimensions were common, but this has changed as technology has advanced.

Key points include:

1. What Are Embeddings?

   • Embeddings represent features of data (like text or images) in numerical form, allowing for comparison in a shared space.

2. Historical Context:

   • Earlier models like Word2Vec used around 300 dimensions. With BERT's introduction in 2018, embedding sizes increased to 768 dimensions, driven by the need for efficient computation on GPUs.

3. Current Trends:

   • As of now, common embedding sizes have expanded significantly, with some models using up to 4096 dimensions. This growth is influenced by the rise of open-source platforms like HuggingFace, which standardize and share models.

4. Market Changes:

   • The availability of API-based models has made embeddings a commodity, with major providers like OpenAI offering larger embeddings (1536 dimensions) due to more extensive training data.

5. Benchmarking and Comparison:

   • Public benchmarks like MTEB allow for the comparison of different embedding models, revealing a wide range of sizes and performance levels.

6. Future Considerations:

   • There's a potential slowdown in embedding size growth as techniques like matryoshka representation learning optimize embeddings for efficiency. Some research suggests that smaller embeddings can still be effective for certain tasks.

In summary, the landscape of embeddings has evolved from smaller, custom models to larger, standardized sizes accessible via APIs, highlighting the ongoing trade-offs between model size, performance, and practical application in AI systems.

The text discusses the challenges of managing hierarchical data, such as movie information, in relational databases. Key points include:

1. Hierarchical Structure: Movie data is complex, including directors, genres, and actors with their respective characters. This cannot be easily represented in a flat database structure.

2. Bidirectional Relationships: The data can be viewed in different orders (e.g., movie to actors or actor to movies), necessitating the ability to navigate these relationships from both directions.

3. Data Retrieval Challenges: SQL queries often require multiple steps to gather related information, which can lead to inconsistent results and tedious processes. This is known as "the object-relational mismatch."

4. Use of ORMs: Object-Relational Mappers (ORMs) are tools created to simplify data handling. However, they may still result in multiple queries and can complicate data consistency.

5. Evolution of SQL: Recent developments allow SQL to produce structured data directly, enabling more efficient data retrieval in a single query, reducing the need for multiple roundtrips between the database and server.

6. Adaptation of Tools: As data use cases evolve, so too should the tools we use to manage data, reflecting the changing needs since the early days of computing.

In summary, while managing complex data relationships in SQL can be tedious, recent enhancements allow for more efficient querying, and adapting our tools is essential to meet modern demands.

The first half of my book, "Swimming in Tech Debt," is now available for a pre-launch price of $0.99 at this link. I've been working on it since January 2024, and it builds on ideas from my blog. In September 2024, excerpts were featured in Gergely Orosz’s Pragmatic Engineer newsletter, which provided valuable feedback that helped develop the book further. This first half covers my initial expectations, while the second half will focus on practices for teams and CTOs.

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The article discusses the challenges posed by Large Language Models (LLMs) that use online content without permission. Since LLMs often ignore rules meant to block them, like robots.txt, authors struggle to protect their work. Some developers suggest using robots.txt to prevent LLMs from accessing their content, but this is ineffective because LLMs don’t follow these rules.

As a response, some authors are creating “tainted” or nonsensical versions of their articles to trick LLMs into using incorrect information. This involves publishing distorted and gibberish content accessible through nofollow links, which search engines like Google respect. The idea is to confuse LLMs and diminish their output quality without harming the author’s search rankings.

The author shares their method for generating this nonsense content, which includes random word substitutions and specific coding techniques. They hope that if many writers adopt similar practices, it might lead to LLMs producing more gibberish, encouraging LLM companies to respect content ownership.

In summary, the article outlines a creative strategy for authors to protect their work from LLMs by misleading them with corrupted content, while also calling for collaboration and improvement of these methods among writers.

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Nepal's government plans to block major social media platforms like Facebook, X, and YouTube because they did not meet registration requirements set by authorities. This move aims to reduce online hate, rumors, and cybercrime. The government had given a deadline for these platforms to register and provide local contacts, but only a few, like TikTok and Viber, complied.

Digital Rights Nepal criticized the government's action, stating that it infringes on public rights and reflects a controlling approach. Previously, Nepal has restricted access to other platforms, citing issues like online fraud. This trend of tightening social media regulations is also seen in various countries worldwide due to concerns over misinformation and online safety.

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Summary of Debian 13.1 Release

Debian has released version 13.1 of its stable distribution, called "trixie," on September 6, 2025. This update includes security fixes and important corrections for several packages but does not represent a new version of Debian 13. Users can upgrade their existing installations using Debian's mirrors without needing to replace their original installation media.

Key Points:

• Security Fixes: The update addresses various security issues across numerous packages.
• Package Updates: Several packages received important bug fixes, including updates to the Linux kernel, LibreOffice, and security-related updates for software like Git and Imagemagick.
• New Installation Images: New installation images will be available soon.
• Removed Packages: The package "guix" was removed due to security issues.
• Debian Installer Update: The installer has been updated to reflect these changes.

For more details on package changes, users can check the Debian changelog and other resources through the provided links. For further inquiries, the Debian team can be contacted via their official website.

The text emphasizes that someone is completely correct, with the phrase "Absolutely right" repeated for emphasis. It also mentions that Claude Code did not say anything today.

Realtek has launched the RTL8127 10GbE PCIe network interface cards (NICs) and M.2 modules, which are now available for prices starting at around $35. These devices were introduced as affordable, low-power options for high-speed Ethernet connections.

Key points include:

• Auvidea M20E M.2 Module: This module features the RTL8127 chipset and connects via an M.2 Key-E interface. It can be powered through the edge connector or with Power over Ethernet (PoE). However, it's priced higher than expected, ranging from about €99.99 to €112.99.

• Performance: Users have reported good performance, with one testing the module at speeds of up to 7,400 Mbps without issues like packet loss.

• Affordable PCIe Card: A more affordable RTL8127 PCIe card is available on platforms like Alibaba for about $35. Initial reviews indicate that it is stable and efficient, using less power than competing products.

Overall, the Realtek RTL8127 products are gaining traction, and more options are expected to become available soon.

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The article discusses how to decode UTF-8 sequences by determining their length using a lookup table, which helps avoid complex branching in the code. Here's a simplified summary of the key points:

1. UTF-8 Decoding: The first part of the series covered what decoding UTF-8 means, and the second part focused on determining sequence lengths.

2. Lookup Table: A lookup table can streamline the process of determining the length of a UTF-8 sequence by mapping lead byte values (the first byte of the sequence) to their corresponding lengths. Since there are only 256 possible byte values (0-255), this table can be hard-coded.

3. Lead Byte Ranges:

   • 1-byte sequences: Values from 0x00 to 0x7F (0-127) correspond to a length of 1.
   • 2-byte sequences: Values from 0xC0 to 0xDF (192-223) correspond to a length of 2.
   • 3-byte sequences: Values from 0xE0 to 0xEF (224-239) correspond to a length of 3.
   • 4-byte sequences: Values from 0xF0 to 0xF7 (240-247) correspond to a length of 4.
   • Invalid lead bytes: Ranges from 0x81 to 0xBF and 0xF8 to 0xFF are marked as invalid (length = 0). Additionally, 0xC0-0xC1 and 0xF5-0xF7 are also invalid.

4. Function Implementation: The article provides a code snippet for a function that uses the lookup table to return the sequence length based on the lead byte.

5. Performance Consideration: While using a lookup table avoids branching in the code, it does introduce a 256-byte array, which could impact performance due to caching issues.

The next installment will explore further methods to reduce branching without a lookup table.

A sunscreen scandal has emerged in Australia, where many popular sunscreens have been found to offer far less sun protection than advertised. This has sparked anger among users, especially since Australia has the highest skin cancer rates in the world. A consumer advocacy group, Choice Australia, tested 20 sunscreens and found that 16 did not meet their SPF claims, including well-known brands like Ultra Violette, Neutrogena, and Banana Boat.

One user, Rach, was shocked to discover that the sunscreen she used had failed to protect her from skin cancer, leading to surgery for a basal cell carcinoma. The controversy has led to product recalls, investigations by health authorities, and calls for stricter regulations. Despite the uproar, some experts suggest that the panic may be overstated, as many sunscreens still provide effective protection against skin cancer when used correctly.

The scandal highlights issues in sunscreen testing and regulation, emphasizing the need for consumers to ensure they apply enough sunscreen and combine its use with other protective measures.

AdamW has been the leading optimizer for training language models, despite claims that other optimizers can be 1.4 to 2 times faster. However, fair comparisons have been hindered by two main issues: (1) inconsistent tuning of hyperparameters and (2) inadequate evaluation methods. To explore these issues, the authors studied ten deep learning optimizers across different model sizes (from 0.1B to 1.2B parameters) and data-to-model ratios.

Key findings include:

• Each optimizer needs its own optimal hyperparameters, so using the same settings for all can lead to unfair results.
• The actual speed advantage of many optimizers over well-tuned AdamW is often lower than claimed and decreases with larger models, dropping to just 1.1 times faster for the largest models.
• Evaluating optimizers based on early training checkpoints can be misleading since their performance can change as training progresses.

The research reveals that the fastest optimizers, like Muon and Soap, use matrix-based methods for adjusting gradients. However, their speed advantages diminish as model size increases, from 1.4 times faster for small models to only 1.1 times for large ones.

Rasterizer Summary

Rasterizer is a GPU-accelerated 2D vector graphics engine created for the iPhone and Mac, inspired by Adobe Flash. After ten years of development, it is now up to 60 times faster than CPU-rendering, making it perfect for vector animations in user interfaces.

The current version is designed for macOS using C++ 11 and Metal, but it will also work on any compatible GPU. An iOS version is planned. The demo app allows users to open SVG and PDF files, navigate pages, and manipulate the canvas easily.

Key Features:

• Follows the Postscript model for path objects, supporting various fill rules and stroking.
• Uses Scene and SceneList objects to manage drawing parameters.
• Rasterization occurs in two stages for filled paths and directly for stroked paths using GPU triangulation.
• Implements efficient techniques for pixel area coverage and geometry handling.

The library is open-source under a personal use zlib license, and credits are given to several supporting libraries.

Summary of SYSTEM•ILLUMINATION

Welcome to SYSTEM•ILLUMINATION, where I share my experiences debugging the Rustler library on the OmniOS (a type of illumos). This document serves as both a learning journal for myself and a resource for others exploring the illumos/Solaris system.

Transition to illumos

• I decided to explore illumos after previously using it for a small server. For my personal project, Katarineko, I chose illumos over my usual Linux setup.
• Katarineko is built with Elixir and includes Rust-written Native Implemented Functions (NIFs) for performance, using the Rustler library for integration.

Debugging NIFs

• Initially, everything seemed to work until an error appeared stating the NIFs were not loaded. This prompted me to use dtrace, a dynamic tracing tool in Solaris, to investigate.
• Dtrace allows us to observe system behavior and write scripts to trace specific system calls and functions. I wrote scripts to trace the loading of my NIF shared library.

Findings on NIF Loading

• The NIF loading process involves a shared library and a structure called ErlNifEntry, which contains details about the NIF functions. My investigation revealed that the functions were not being registered correctly under illumos.
• I discovered that the Rustler library was failing to populate the NIF functions due to issues with how Rust's macros interact with the illumos linker.

Issue with .init_array

• The root cause was attributed to multiple .init_array sections in the shared library, which led to incorrect initialization of NIFs. The illumos linker was not correctly handling these sections.
• I was able to fix the issue by adjusting the dynamic section entries of the ELF file to point to the correct .init_array.

Conclusion and Recommendations

• The problem stemmed from using certain Rust attributes that caused the functions to be placed in separate linkage sections. The solution involves using different attributes for illumos.
• I plan to propose changes to the Rustler library to mitigate this issue for other users.

This summary encapsulates the journey of troubleshooting NIFs in illumos and highlights the challenges faced when transitioning from Linux to illumos.

Summary of Fil's Unbelievable Garbage Collector (FUGC)

FUGC is an advanced garbage collector used in the Fil-C programming language. Here are its main features:

1. Parallel and Concurrent: FUGC operates using multiple threads, allowing it to mark and sweep memory faster, especially on systems with more cores. It works alongside your program (mutator threads) without needing to pause them.

2. On-the-Fly Operation: There’s no complete halt of the program (stop-the-world). Instead, it uses "soft handshakes" to request threads to do some background work without waiting, resulting in minimal pauses.

3. Grey-Stack: FUGC repeatedly scans thread stacks and marks objects, ensuring no pointers are missed. This helps avoid complex barriers, making the system efficient.

4. Dijkstra Barrier: This mechanism ensures that any new pointers created during the marking phase are marked correctly without requiring a load barrier.

5. Accurate and Non-Moving: FUGC precisely identifies all reachable objects without moving them, simplifying concurrency and reducing synchronization issues.

6. Incremental Updates: Objects that become unreachable during collection can be freed immediately.

7. Safepoints: FUGC uses safepoints to ensure safe memory access during garbage collection, preventing race conditions.

8. Fast Sweeping: The sweeping process is optimized, making it quicker compared to marking, and typically takes less than 5% of the total collection time.

Bonus Features:

• Freeing Objects: Objects can be freed safely, and accessing freed objects will result in a trap to prevent memory misuse.
• Finalizers: Allows the setup of custom finalizers similar to Java.
• Weak References and Weak Maps: Supports weak references and weak maps, functioning similarly to JavaScript’s weak references but with some differences.

In conclusion, FUGC offers strong safety guarantees regarding memory management. It minimizes the risks associated with accessing freed memory and ensures efficient garbage collection processes.

Hailey Somerville is a person who can be found on social media with the handle @haileys.

No summary available.

No summary available.

The text discusses the concept of "vibe coding," which refers to quickly developing products without extensive technical knowledge. The author, Pawel Brodzinski, argues that the speed of development is often misunderstood as the main bottleneck in product success. Instead, he emphasizes that understanding customer needs and validating ideas are more critical.

Key points include:

1. Prototyping vs. Building: Prototyping is useful for testing ideas but is disposable and often low-quality. In contrast, a successful product must consistently deliver value and quality to retain customers.

2. Successful Product Development: Successful products evolve through experimentation, not through a predefined path. Companies like Amazon and Google have explored numerous ideas, with many failing before finding successful ones.

3. Validation is Key: The real challenge in product development is validating whether ideas and changes will lead to growth and retention. This process can take time, regardless of development speed.

4. Communication Issues: Poor communication can lead to misunderstandings and rework, increasing costs and time. The complexity of collaboration often complicates project estimates.

5. Coding Speed is Not the Problem: The author asserts that faster coding does not equate to better products. Relying solely on speed can lead to more rework and complications.

6. Vibe Coding Limitations: While vibe coding offers quick results without needing technical expertise, it can lead to increased rework and misunderstandings, ultimately complicating the product development process.

In summary, building a successful product requires more than just quick coding; it demands thorough validation, clear communication, and a focus on quality.

No summary available.

Tempo is a new blockchain created specifically for payments. It was developed with input from major companies like Stripe and Paradigm, along with others in finance and technology. Tempo supports all major stablecoins, allowing businesses to make fast and inexpensive global transactions.

Key features of Tempo include:

1. Purpose-Built for Payments: Unlike other blockchains, Tempo is designed specifically for real-world payment needs.
2. High Speed and Reliability: It can process over 100,000 transactions per second, enabling real-time payments.
3. Low and Predictable Fees: Transaction fees are very low and can be paid in any stablecoin.
4. Privacy Measures: Transaction details are kept private while ensuring compliance with regulations.

Tempo can be used for various payment scenarios, such as cross-border remittances, global payouts, microtransactions, and more. It aims to provide a scalable and efficient infrastructure for businesses with significant financial operations.

Developers can build on Tempo, which is a permissionless blockchain, and it is currently in a testing phase with selected partners.

No summary available.

The text appears to be a collection of intricate ASCII art, which includes various patterns and shapes. These designs may represent different objects, characters, or abstract concepts, but there are no clear messages or themes presented in the text. The focus seems to be on visual creativity rather than conveying specific information or ideas.

No summary available.

Summary of "The Scam of Age Verification"

Introduction
Age verification (AV) laws are being implemented across multiple countries, including the UK, US, and EU, with claims they will protect minors from accessing adult content. However, these laws are criticized for being ineffective and burdensome.

What is Age Verification (AV)?
AV requires online platforms to confirm users' ages through methods like ID uploads or credit card checks. While it seems reasonable, there is no evidence that it works effectively, and it often leads users to other sites that bypass these checks.

Consequences of AV Laws
The implementation of AV is expected to drastically reduce user numbers on adult sites, resulting in significant financial losses. Many users will migrate to unregulated or dangerous platforms. The law disproportionately affects smaller adult businesses while larger mainstream sites remain exempt, raising questions about the true motivations behind these regulations.

The Real Agenda
The author argues that AV laws are not genuinely about protecting children but are instead a means to attack the adult industry. Critics of AV are often silenced with emotional arguments about child safety, despite the lack of evidence supporting AV's effectiveness.

Proposed Solutions
Instead of site-based AV, the author suggests implementing device-level parental controls that would be more effective in protecting minors without compromising user privacy.

Cultural Context
The text discusses the broader social panic surrounding pornography, likening it to past moral panics over media content. It questions the narrative that adult content is harmful to minors, pointing out that evidence on this issue is limited and inconclusive.

Country-Specific Insights

• UK: AV laws have faced criticism for being redundant and ineffective, with previous regulations already in place.
• US: Recent Supreme Court rulings have weakened protections against AV laws, allowing states to impose them without sufficient oversight.
• France: The AV implementation is criticized for being particularly flawed and burdensome for adult sites.
• EU: New regulations are seen as targeting adult content specifically, while mainstream platforms are largely exempt.

Conclusion
The current approach to AV laws reflects a failure of rational policymaking, driven by fear and misinformation. The author warns that these regulations will lead to increased censorship and harm to both users and content creators in the adult industry. Overall, the piece portrays AV as a misguided response to a complex issue, likely to result in more harm than good.

The text discusses a project called Carbonyl, a web browser that renders HTML directly in a terminal. Here are the key points:

1. Introduction to Carbonyl: The project aims to convert HTML to SVG, and now to render it in a terminal using a Chrome fork.

2. Terminal Drawing: It explains how to use escape sequences to draw in a terminal, including moving the cursor, changing text colors, and rendering pixels using Unicode characters.

3. Text Rendering: The text rendering process involves creating a device in C++ that captures text and sends it to the terminal, ensuring that text is displayed correctly without overlapping previous content.

4. Input Handling: The browser can track mouse movements and clicks in the terminal using control sequences, allowing user interaction.

5. Performance Issues: The initial implementation faced high CPU usage and inefficiencies in rendering. Modern browsers separate tasks into different processes to enhance security and performance.

6. Inter-Process Communication (IPC): The project uses a system called Mojo for communication between the renderer and browser processes to efficiently handle text rendering without costly round-trips.

7. Layout and Rendering: Challenges include rendering text in a monospaced font and managing the display scale to ensure proper layout in the terminal.

8. Color Handling: The text describes how to convert RGB colors for terminal use, addressing issues with true color support in different terminal environments.

9. Title Management: Carbonyl can set the terminal window title based on the page being viewed, enhancing user experience.

10. Final Thoughts: The author expresses enthusiasm for Rust, the programming language used in the project, and teases upcoming topics for future posts.

Overall, the article provides a detailed look at the technical challenges and solutions in developing a terminal-based web browser.

The text appears to be a simple outline of a website related to space topics. It includes sections on astronautics and astrophysics, a personal page for someone named Jonathan, a catalog of space objects, and lists related to human spaceflight. Additionally, there is a mention of "Jonathan's Legacy," which likely refers to his contributions or achievements in the field.

Wikipedia, the largest and most used encyclopedia online, is facing increasing scrutiny and criticism as it becomes a target for various political groups and influential figures. Known for its rapid updates and collaborative editing process, Wikipedia has built a reputation as a reliable source of information. However, recent events, such as Elon Musk's controversial gesture at a rally, sparked debates about how current events are documented on the site.

Editors on Wikipedia navigate complex disputes over content through established processes focused on neutrality and verifiability. Despite its success, Wikipedia's model is under threat from political pressures, particularly from conservative factions accusing it of bias. Notably, Musk has labeled it "wokepedia" and called for its defunding, echoing sentiments from various government officials questioning its objectivity.

Wikipedia's unique structure allows for contributions from a diverse range of editors globally, making it resilient to singular political influence. However, this openness also invites challenges, including harassment of editors and attempts to sway content to reflect specific ideologies, especially in politically charged topics like the Israel-Palestine conflict.

The ongoing battle for Wikipedia’s neutrality reflects broader societal struggles over facts and narratives in an era of misinformation. Despite these challenges, the consensus-driven model remains crucial for maintaining a shared understanding of reality. The site's founders and editors emphasize the importance of adhering to its core principles to defend against external pressures and maintain its role as a trusted information source.

Summary: Should We Revisit Extreme Programming in the Age of AI?

The rapid advancement of AI and software tools has made coding faster than ever, allowing for quick generation of products and features. However, despite this speed, many software projects still fail to meet expectations, with significant budget overruns and unsatisfied users. The real issue may not be the speed of coding but rather a lack of clear direction and understanding in the development process.

Extreme Programming (XP), developed in the late 1990s, emphasizes slowing down to improve learning and collaboration. Its practices, such as pair programming, may reduce immediate output but enhance team understanding, quality, and trust. XP was designed to prevent issues like unvalidated logic and complex code, which can arise from rapid coding without proper checks.

AI tools can generate code quickly, but they also risk producing unvalidated and brittle software. As a result, the human aspects of software development—like communication, feedback, and respect—remain crucial. XP’s core values encourage simplicity, collaboration, and adaptability, which are essential as software delivery continues to evolve.

The historical data shows that despite advances in technology and methodologies like agile and DevOps, the improvement in reliable software delivery has been minimal. Therefore, as we move forward with AI, it’s essential to prioritize human-centered practices and clear communication within teams.

In conclusion, revisiting XP could be beneficial in today’s fast-paced coding environment. It emphasizes the importance of building the right products through collaboration and understanding, rather than just focusing on speed.

The author shares a puzzle they've been working on called Inception, a Rust library that simplifies sharing behaviors in Rust using structural induction. Instead of needing separate macros for each behavior, Inception allows one macro to enable multiple behaviors through type-level programming, avoiding runtime reflection and maintaining efficiency similar to macro expansion. Although the current implementation has limitations and isn't ideal, it demonstrates the concept with examples for common behaviors like Clone and Eq. The author expresses concern about the code not being idiomatic, making it challenging to improve, but hopes others find the project interesting.

OpenAI's research addresses the issue of "hallucinations" in language models, which occur when these models confidently produce false information. Despite improvements, such as in GPT-5, hallucinations remain a significant challenge. The paper highlights that current evaluation methods encourage models to guess answers instead of admitting uncertainty, similar to how a student might guess on a test to avoid a zero score. This leads to models prioritizing accuracy over honesty, resulting in more hallucinations.

The research suggests that to reduce these errors, evaluation methods should penalize confident incorrect answers more than uncertain ones and reward expressions of uncertainty. Hallucinations arise because language models learn from vast amounts of text without clear labels for true or false statements, making it difficult to distinguish valid from invalid information.

Key points include:

• Hallucinations are misleading but confident statements from models.
• Current evaluations promote guessing rather than admitting uncertainty.
• A better evaluation approach would reward humility and accurate uncertainty.
• Improvements in model accuracy alone won't eliminate hallucinations, as some questions are inherently unanswerable.

Overall, while models like GPT-5 show progress, ongoing efforts are needed to further minimize hallucinations.

This text is a guide for Product Managers (PMs) on building AI agents, emphasizing that having advanced capabilities doesn't guarantee user adoption. A PM shares an experience where their AI agent performed well in simple tasks but failed when users encountered complex issues, leading to frustration and abandonment.

The article outlines four layers of AI agent architecture that PMs must consider:

1. Context & Memory: How much the agent remembers about the user, which influences the user experience. More memory can lead to better anticipation of needs.

2. Data & Integration: The systems the agent connects to, which impacts its usefulness and complexity. Starting with a few key integrations is often more effective than trying to connect everything at once.

3. Skills & Capabilities: The specific functions the agent should perform. It's more important to have the right capabilities than the most features.

4. Evaluation & Trust: How success is measured and communicated to users. Trust is built when agents admit uncertainty rather than claiming to be right all the time.

The article also discusses different architectural approaches for developing agents, from simple single-agent systems to more complex collaborative architectures. It concludes that users are more likely to trust agents that transparently communicate their limitations and that building trust is essential for adoption. Future discussions will explore autonomy and governance in AI agents.

Summary of Polars Cloud Launch

On September 3, 2025, Polars officially launched Polars Cloud, a managed data platform available on AWS, and introduced its Distributed Engine in Open Beta. Polars Cloud allows users to run Polars queries remotely, making it easier to scale data processing.

Key Features:

• Remote Execution: Users can execute queries in the cloud, eliminating the need to manage infrastructure.
• Distributed Engine: This engine supports multiple scaling strategies (horizontal, vertical, and diagonal), making it flexible and efficient for different data processing needs.
• Single API: Users can scale their operations from local to cloud seamlessly, simplifying costs and complexity.

Upcoming Features:

1. On-Premise Support: Plans are in place to offer Polars Cloud for on-premise use.
2. Live Cluster Dashboard: A new dashboard will provide detailed insights into cluster performance.
3. Task Orchestration: Users will be able to schedule queries within Polars Cloud, integrating with existing tools.
4. Autoscaling: Features for automatic scaling based on workload demands are in development.
5. Catalog Support: Improved organization of datasets, including support for the Iceberg table format.
6. Multi-Region Availability: Expansion to other regions for better performance is planned.

To get started, users can sign up for Polars Cloud on AWS or apply for on-premise solutions. More updates and features will be shared in upcoming communications.

No summary available.

The article discusses how various scoring systems that track our behavior, such as credit scores, social media engagement, and ride-sharing ratings, function like social credit systems. It highlights that while people associate social credit with oppressive government systems like China's, many similar practices already exist in the West, albeit less transparently.

Key points include:

1. Social Credit Defined: Social credit refers to metrics that assess individual behavior and can affect access to services and opportunities.

2. Current Systems: In the U.S., platforms like Uber, LinkedIn, and Amazon track user behavior and assign scores that influence aspects of daily life, like job opportunities and loan eligibility.

3. Chinese Context: The article clarifies that China's social credit system is not as extensive as commonly perceived, with most scoring focused on financial behavior rather than broad social tracking.

4. Fragmentation vs. Integration: Western behavioral scoring systems are currently fragmented and don’t directly affect one another, but there is potential for these systems to become interconnected in the future.

5. Transparency Issues: Unlike China’s explicit scoring criteria, Western systems often lack transparency, making it hard for users to understand how their data is used.

6. Future of Social Credit: As technology evolves, the question arises whether these systems will be transparent and accountable, or remain hidden. The article suggests that knowing the rules of these scoring systems could empower individuals rather than lead to panic.

In essence, we are already part of social credit systems, and recognizing this can help us navigate and understand our interactions with technology and services.

Summary: Building Supabase-like OAuth Authentication for MCP Servers

Jakob Steiner, an engineer at Hypr MCP, explains how to add OAuth authentication to MCP servers without modifying existing code. The Model Context Protocol (MCP), a standard for Large Language Models (LLMs), introduced an authorization framework based on OAuth2. However, implementing this framework is challenging due to compatibility issues with existing identity providers (IdPs).

Key points include:

1. MCP Server Gateway: A reverse proxy that adds OAuth2 support to MCP servers.

2. Challenges: Many IdPs primarily support OpenID Connect (OIDC), not OAuth2. This leads to difficulties in implementing the required extensions (Authorization Server Metadata and Dynamic Client Registration).

3. Keycloak: The only IdP found to support the necessary extensions, but it has limitations regarding CORS configuration.

4. Custom Solution: The author built a solution using Dex as the IdP, which offers flexibility through a gRPC API.

5. Implementation Steps:

   • Set up a basic reverse proxy using Go.
   • Add Cross-Origin Resource Sharing (CORS) for web clients.
   • Implement OAuth2 middleware to validate access tokens.
   • Create a protected resource server endpoint and proxy the IdP’s metadata.
   • Implement Dynamic Client Registration to allow on-demand client creation.
   • Ensure required authorization scopes are included in requests.

6. Testing: A simple open-source MCP named "MCP, Who am I?" was created for testing purposes.

7. Conclusion: The blog emphasizes the importance of a custom MCP server gateway for adding authentication easily. The author encourages the adoption of the solution and provides a link to their GitHub project for further exploration.

For more information, visit the Hypr MCP Gateway GitHub page.

Melvyn Bragg will step down from his role on BBC Radio 4 after 27 years. He has been a significant figure in broadcasting, known for his contributions to cultural discussions. His departure marks the end of an era for the program he has hosted.

Summary:

Goto 10 is a newsletter for Atari enthusiasts, focusing on Atari video game systems and 8-bit computers. It has over 3,000 subscribers and shares articles about retro computing and gaming.

One highlighted topic is the programming language Action!, created by Clinton Parker and released by Optimized Systems Software (OSS) in 1983 for Atari 8-bit computers. Action! was notable for being a compiled language optimized for the 6502 CPU, offering an integrated development environment (IDE) that included a text editor and debugger.

The Action! cartridge cost $99 at launch (about $320 today) and came with a manual. The editor was advanced for its time, featuring full-screen text and the ability to copy and paste. Action! supports structured programming with basic commands but lacks advanced features like floating-point data types.

Although it had limitations, such as requiring the cartridge to run programs, additional tools like Action! RunTime and Action! ToolKit expanded its functionality. Action! was mainly used by hobbyists and for public domain software. The author, Paul Lefebvre, plans to explore using Action! further. For more resources on Action! programming, the article links to various online references and learning materials.

The text provides links to articles discussing the acquisition of The Browser Company by Atlassian. This move highlights Atlassian's interest in expanding its digital tools and capabilities. The main focus is on how this acquisition could shape the future of web browsing and collaboration tools.

UC Santa Cruz is set to help create a new air mobility test program on the Central Coast. This program will be the first of its kind, focusing on developing and testing advanced air transportation technologies.

No summary available.

The author reflects on their journey in college, expressing a desire to innovate in electrical engineering but ultimately finding more fulfillment in computer science. Initially excited about hardware and interaction with computers, the author struggled with the rigid structure of engineering courses, leading to frustration and failure in some classes.

While working on hands-on projects, the author discovered a passion for software, enjoying the immediate feedback and real-world impact it offered. They felt a disconnect with engineering, perceiving it as less innovative compared to software development. The author eventually switched their major to computer science and physics, finding joy in projects that allowed for creativity and problem-solving.

In conclusion, they still believe in the need for improved computer interaction methods but are content with their chosen path in software and physics, recognizing they could have explored engineering further but are glad they didn't pursue it.

Summary of "Memory is slow, Disk is fast - Part 2"

Key Point: Sourcing data directly from disk can be faster than using cached memory due to changes in hardware performance over time.

Introduction:

• Traditional computer science suggests memory is faster than disk, but as disk bandwidth increases and memory access latency stagnates, this is no longer always true.

Experiment Setup:

• The author benchmarks data processing by counting occurrences of the number 10 in a dataset.
• The tests are conducted on a server with an AMD EPYC processor and Samsung SSDs.

Findings:

1. Initial disk reads are slower than expected, but as data is cached in memory, subsequent reads improve speed.
2. Despite improvements, memory speeds were not fully utilized due to CPU instruction limitations.
3. By optimizing code (loop unrolling and vectorization), performance improved, but it still didn't match memory bandwidth limits.

Direct Disk Access:

• The author implemented an io_uring based I/O engine, allowing direct disk access and significantly improved performance, surpassing memory read speeds.
• Mapped memory (using mmap()) introduces overhead due to page faults, slowing down access compared to direct reads.

Implications:

• The results suggest that traditional methods of accessing memory may be inefficient as systems scale.
• Using disks directly can leverage higher bandwidth and avoid the latency issues associated with memory.

Conclusion:

• Clever coding techniques can yield performance improvements, and engineers should adapt to new hardware capabilities.
• The trade-offs in implementing complex solutions must be evaluated, as sometimes simpler methods may suffice.

Future Considerations:

• The author raises questions about performance in computational complexity and the evolving role of AI in code optimization.

This summary highlights the main points and findings of the text while simplifying complex concepts for easier understanding.

No summary available.

Le Chat has introduced new features that enhance its integration capabilities and user experience. Key highlights include:

1. Enterprise Connectors: Le Chat now supports over 20 secure connectors for various business tools, making it easier to integrate workflows with popular platforms like Databricks, Snowflake, GitHub, and more.

2. Custom Extensibility: Users can add their own connectors to expand functionality and improve task management.

3. Memories Feature: This beta feature allows Le Chat to remember important information and provide personalized responses based on user preferences while ensuring privacy and control over stored data.

4. Flexible Deployment: Le Chat can be run on mobile devices, in browsers, or through on-premises and cloud solutions.

5. User Control: Admins can manage which connectors are available to users, ensuring secure access to data.

6. Upcoming Events: Mistral AI is hosting a webinar on September 9 to explain the new features and a hackathon on September 13-14 for developers to create innovative projects using Le Chat.

Both the new connectors and memories features are available for free to all users. For more information, users can visit the Mistral AI website or download the mobile app.

No summary available.

Hi HN! We’re Pranjali, Dhruv, and Harsha, and we’re creating Slashy, an AI agent designed to connect with various apps and perform tasks efficiently. You can check out our demo here.

While working on a different startup, we noticed we wasted a lot of time on repetitive tasks like updating spreadsheets and managing communications. This led us to focus on developing Slashy instead.

Slashy can interact directly with apps like Gmail, Calendar, Notion, and more, allowing it to search for information and take actions like sending emails or creating calendar events, all in one place. It currently integrates with 15 services, including G-Suite and Slack.

Here’s what makes Slashy stand out:

• Action-Oriented: Unlike other tools that just provide information, Slashy can perform tasks like creating Google Docs, scheduling meetings, and sending emails.
• Cross-Tool Understanding: Slashy connects data across different platforms, allowing it to pull relevant information from your past interactions and context.
• Memory and User Action Graphs: Slashy remembers past conversations and user actions to anticipate future needs.
• No Technical Setup: You can automate tasks just by describing them in natural language, making it user-friendly.
• Custom User Interface: We provide tailored interfaces for each tool to enhance the user experience.

Some example workflows include generating meeting briefs, reaching out to LinkedIn connections, creating investor pitch decks, and conducting financial analyses.

You can try Slashy for free with 100 daily credits and an additional 500 credits for new accounts. Use the code "HACKERNEWS" at checkout. We hope you enjoy using Slashy!

This text outlines a series of articles focused on building a WebAssembly (WASM) compiler using the Roc programming language. The series includes the following key topics:

1. Introduction to the project and the languages involved.
2. Handling arguments and input/output in Roc.
3. Creating a tokenizer and managing errors.
4. Testing the tokenizer.
5. Basics of parsing and refactoring code.
6. Transforming different types of nodes (memory, data, functions).
7. Introduction to code generation and creating various sections of the compiled output.
8. Concluding the project with generating exports.

The source code related to this project is available on GitHub.

This article discusses how to use ClickHouse, a real-time analytics database, to efficiently handle and process large amounts of data. Here's a simplified summary of the key points:

1. Real-Time Analytics with ClickHouse: ClickHouse allows for fast querying and processing of data, making it ideal for real-time analytics needs. It can handle streaming data from various sources and provide quick insights.

2. Data Flow Essentials: Data must flow quickly from sources (like databases or APIs) to visualization tools. Effective data transformation and aggregation are crucial for extracting useful insights.

3. Modeling Strategies: ClickHouse uses column-oriented storage, which enhances performance by only accessing relevant data. Strategies like denormalization (combining tables) and materialized views (pre-computing aggregates) help optimize data queries.

4. Real-Time Processing Considerations: There's a balance between how fresh the data is and its accuracy. Proper data modeling at the source ensures better analytics downstream, avoiding costly data cleaning later.

5. Example Use Case: The article provides a practical example of using ClickHouse to ingest NOAA weather data from S3, transform it in real-time, and visualize it. This showcases ClickHouse's capabilities in handling data without the need for traditional ETL processes.

6. Optimization Techniques: ClickHouse offers multiple levels of optimization such as partitioning data for faster access and using deduplication methods to ensure data quality.

7. Limitations: While ClickHouse excels in many areas, it has limitations in updates, joins, and transaction support, which users should be aware of.

8. Choosing the Right Strategy: The best approach depends on specific use cases, data volume, and team capabilities. ClickHouse's features can streamline analytics without heavy ETL overhead.

Overall, the article emphasizes that ClickHouse is a powerful tool for real-time analytics, capable of transforming how businesses work with large datasets efficiently.

The author, Paul Tarvydas, questions the programming industry's heavy reliance on type checking, suggesting that it may address the wrong problem. He argues that sophisticated type systems, like those in Haskell and Rust, are merely coping mechanisms for architectural flaws that create unnecessary complexity in software design.

While type checking is often regarded as essential for maintaining large codebases, the author believes this reliance reveals a deeper issue: our systems are too complex for human understanding. He contrasts software engineering with electronics engineering, where systems are designed with isolation and explicit interfaces, avoiding the need for elaborate type systems.

Tarvydas points out that the assumption that software must grow complex and unmanageable leads us to believe that type checking is unavoidable. He proposes that the real problem lies in the fundamental design of our systems, particularly in how we use function calls, which create tight coupling and complicate distributed systems.

He suggests that we should focus on simpler architectural principles that prioritize isolation and straightforward communication, similar to how UNIX systems operate. By doing so, we can manage complexity without needing sophisticated type checking. The goal should be to create systems that are easier to understand and maintain, rather than relying on complex tools to manage the intricacies of poorly designed systems.

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