CERN has made a significant breakthrough in particle physics with the confirmation of an ultra-rare particle transformation, which may hint at new physics beyond the established Standard Model. This discovery comes from the NA62 experiment, led by particle physicist Cristina Lazzeroni and her team at the University of Birmingham. They have successfully observed and measured the decay of a charged kaon particle into a charged pion and a neutrino-antineutrino pair, a process that has been the focus of their research for over a decade.The decay channel they studied is referred to as a "golden" channel due to its rarity and the precision with which it can be predicted by the Standard Model. The rarity of this decay makes it a sensitive indicator for potential new physics. The team collected an extensive amount of data from numerous particle collisions to confirm their findings, achieving the rigorous 'five sigma' standard of statistical certainty, which is a benchmark for significant discoveries in particle physics.Kaons, which are composed of a quark and an antiquark, exhibit unique decay patterns that have made them valuable for understanding particle behavior. The researchers produced kaons by directing a high-energy proton beam at a beryllium target, generating a secondary beam of particles, including charged kaons. These kaons decay rapidly, typically into a muon and a neutrino, but in a rare instance, they decay into a pion and a neutrino-antineutrino pair.The decay process involves a change in quark flavor mediated by a Z boson, which contributes to its rarity. The challenge for the researchers was to isolate the specific decay they were interested in while filtering out the background noise from other kaon decays. Initially, their results did not meet the five-sigma threshold, but with further analysis, they have now confirmed their findings.With the decay channel established, the researchers are now looking for any deviations from the predictions of the Standard Model that could indicate new physics. They have observed a higher number of kaon to pion and neutrino/antineutrino decays than the Standard Model predicts, although it remains within the uncertainty range. The team anticipates that new physics may manifest as additional particles or forces, especially given the known limitations of the Standard Model, such as its inability to account for dark matter and the matter-antimatter imbalance in the universe.The NA62 experiment is set to continue for three more years, during which the team will gather more data to further investigate the consistency of their findings with the Standard Model and potentially uncover new physics. This exciting development in particle physics opens the door to deeper understanding and exploration of the fundamental forces and particles that govern the universe.

Cloudflare recently reported successfully mitigating the largest recorded distributed denial-of-service (DDoS) attack, which peaked at an astonishing 3.8 terabits per second (Tbps). This attack targeted various organizations within the financial services, internet, and telecommunications sectors, marking a significant escalation in the scale of DDoS threats. The assault unfolded over a month, characterized by over 100 hyper-volumetric attacks that inundated the network infrastructure with excessive data, effectively overwhelming it.In a volumetric DDoS attack, the objective is to flood the target with massive amounts of data, consuming their bandwidth and exhausting the resources of applications and devices. This leaves legitimate users unable to access the services. The recent attacks were particularly intense, with many exceeding two billion packets per second and three Tbps. The compromised devices involved in these attacks were globally distributed, with a notable concentration in countries such as Russia, Vietnam, the United States, Brazil, and Spain.The threat actor behind this campaign utilized a diverse array of compromised devices, including Asus home routers, MikroTik systems, DVRs, and web servers. Cloudflare managed to autonomously mitigate all the DDoS attacks, with the peak attack lasting a mere 65 seconds. The attacks primarily employed the User Datagram Protocol (UDP), which allows for rapid data transfers without the need for a formal connection, making it a favored method for such assaults.Prior to this incident, Microsoft held the record for defending against the largest volumetric DDoS attack, which peaked at 3.47 Tbps and targeted an Azure customer in Asia. Typically, DDoS attackers rely on extensive networks of infected devices, known as botnets, or seek methods to amplify the data sent to the target, which can be achieved with fewer systems.In a related report, Akamai, a cloud computing company, highlighted vulnerabilities in the Common Unix Printing System (CUPS) that could be exploited for DDoS attacks. Their research indicated that over 58,000 systems were exposed to potential DDoS attacks due to these vulnerabilities. Testing revealed that numerous vulnerable CUPS servers could repeatedly send thousands of requests, demonstrating a significant risk for amplification attacks.This incident underscores the evolving landscape of cybersecurity threats, particularly the increasing scale and sophistication of DDoS attacks, and the importance of robust defenses against such challenges.

OpenAI has recently secured a $4 billion revolving credit line, significantly enhancing its financial flexibility and bringing its total liquidity to over $10 billion. This development follows the closure of a substantial funding round that valued the company at $157 billion, during which it raised $6.6 billion from a diverse group of investors, including major players like Microsoft, Nvidia, and SoftBank. The credit line, which is unsecured and can be accessed over three years, has a base amount of $4 billion with the potential for an additional $2 billion. The interest rate is tied to the Secured Overnight Financing Rate (SOFR), currently resulting in an approximate borrowing cost of 6%.The infusion of capital is expected to empower OpenAI to invest in new initiatives, expand its infrastructure, and attract top talent. The company expressed its commitment to leveraging this liquidity to enhance its operations and maintain agility as it scales. OpenAI's recent funding round was led by Thrive Capital, with participation from various investment firms and tech giants, reflecting the growing interest and investment in artificial intelligence technologies.OpenAI's rapid growth trajectory has been remarkable, particularly since the launch of ChatGPT in late 2022, which has propelled the company into the spotlight and attracted significant investments in AI infrastructure. The company reported a staggering 1,700% increase in revenue, generating $300 million last month and projecting sales of $11.6 billion for the upcoming year. However, this growth comes with substantial costs, particularly in acquiring Nvidia's graphics processing units necessary for training its large language models, leading to an expected loss of around $5 billion this year.In light of its expansion, OpenAI is also navigating internal changes, including the departure of key executives and discussions about restructuring the company to operate more like a traditional business. The CFO, Sarah Friar, indicated that the company is exploring various avenues for capital, including public and debt markets, to ensure sustainable growth. OpenAI's leadership is focused on maintaining a balance between innovation and operational efficiency, aiming to solidify its position in the competitive AI landscape while addressing the complexities of its evolving business model.

Data leakage is a critical issue in data science that occurs when information is used during the training or evaluation of a model that would not be available during deployment. This can lead to overly optimistic performance metrics and ultimately result in poor model performance in real-world applications. The article presents three subtle examples of data leakage encountered in various projects, illustrating the complexities and potential pitfalls of data handling in predictive modeling.In the first example, the author worked with a company aiming to win sealed-bid auctions by predicting the price-to-beat. Initially, the company suggested filtering out lots priced above $1000 before building the model. The author quickly recognized that this approach was flawed, as it would lead to data leakage by excluding relevant information that could affect predictions. Instead, they proposed training on all available data but only reporting performance metrics for lots predicted to be below the $1000 threshold. This adjustment allowed for a more accurate assessment of the model's performance without falling into the trap of data leakage.The second example involved a different company that wanted to model potential earnings from auctioned lots. The author initially planned to use random sampling for training and testing datasets. However, they realized that this method would inadvertently mix data from different time periods, effectively creating a "time travel" scenario that could lead to leakage. After investigating, the author found that while the conventional random split approach worked adequately in some contexts, a strict chronological split yielded better performance for this specific dataset. This was due to the nature of the auction process, where similar lots were often sold in quick succession, making chronological splits more effective in preventing leakage.In the third example, the author encountered a situation where they identified leakage in a model designed to improve auction outcomes. They initially proposed a solution that they believed would not introduce leakage but later discovered that it did. This experience underscored the importance of vigilance in detecting and addressing leakage, as well as the necessity of thoroughly understanding the data-generating process.The key takeaways from these experiences emphasize that leakage always comes with a cost, which may vary in significance depending on the context. While some leakage may be tolerable, it is crucial to assess its potential impact. The article also highlights that just because a practice is common in the industry does not mean it is free from leakage. Moreover, detecting leakage is often easier than quantifying its effects, and sometimes the damage can be identified through the performance issues it causes.Overall, the discussion serves as a reminder of the complexities involved in data science and the importance of maintaining rigorous standards to avoid data leakage, ensuring that models perform reliably in real-world scenarios.

Elon Musk recently hosted a recruiting event for his new AI startup, xAI, at the original headquarters of OpenAI in San Francisco. This gathering, which featured free food, drinks, and live music created by AI, was marked by heightened security measures, including metal detectors and ID checks. The event coincided with OpenAI's annual Dev Day, where CEO Sam Altman was discussing the company's significant funding achievements, creating a competitive atmosphere.During the event, Musk articulated his vision for xAI, emphasizing the goal of developing digital superintelligence that is as benign as possible. He invited attendees to join his mission to create useful applications from this intelligence. Musk expressed his belief that artificial general intelligence (AGI) could be achieved within a couple of years and compared the rapid growth of xAI to the SR-71 Blackbird, a high-speed reconnaissance aircraft known for its strategic advantage during the Cold War. He identified xAI, along with OpenAI, Anthropic, and Google, as the key players in the AI landscape for the next five years, aiming for xAI to achieve a level of dominance in AI similar to SpaceX's in the aerospace industry.xAI was founded in March 2023 and has quickly expanded from a small office to a larger space in Palo Alto. Musk has recruited a team from his other companies and brought in experienced researchers from leading tech firms. The startup secured $6 billion in funding, significantly boosting its valuation and resources. However, xAI's initial product, Grok, has faced challenges, relying on external technologies for core features due to the need for rapid development.Musk's competitive stance against OpenAI is fueled by a history of conflict, including his departure from the organization and subsequent legal disputes. He has expressed distrust in OpenAI's profit-driven model and aims to create a more open and accessible AI. The recruiting event attracted engineers from rival companies, highlighting Musk's ability to sell his vision and attract talent despite the fierce competition in the AI sector.Musk's approach to AI emphasizes speed and innovation, appealing to those who prefer a less conventional work environment. He believes that a "maximum, truth-seeking AI" is essential for achieving safety in AI development. The event was organized quickly, reflecting Musk's commitment to advancing xAI and his broader ambitions in the tech industry.

The content revolves around a GitHub repository named "zero," which is described as an experimental approach to modern frontend development without relying on traditional frameworks. The repository is maintained by a user named "nhh" and has garnered attention with 72 stars and 1 fork.The core concept of Zero is to provide a set of types and functions that allow JSX to be transpiled directly into DOM nodes. This approach aims to eliminate the need for developers to update frameworks, as Zero operates directly with the DOM, making it a more stable and straightforward solution for building web applications.Zero is not intended to be a full-fledged framework; instead, it focuses on simplicity and direct interaction with the DOM. The creator emphasizes that modern frameworks often serve the needs of developers more than those of users, leading to unnecessary updates and complexity. By using Zero, developers can avoid these pitfalls and work with a more streamlined process.The repository includes example code demonstrating how to use Zero. It showcases the creation of DOM elements using JSX syntax, dependency injection, and the use of modern DOM APIs. The code snippets illustrate how to define components, manage state without reactivity, and perform asynchronous operations like fetching data from an API.Under the hood, Zero consists of a few snippets and configurations that facilitate the transpilation of JSX to JavaScript. The runtime JavaScript code provided in the repository outlines how elements are created and how event listeners are attached. It also includes a Vite configuration file that specifies how to handle TypeScript and JSX files, ensuring that the necessary functions are injected into the main JavaScript file.Additionally, Zero offers a set of types that enhance the developer experience by connecting JSX types with DOM types. This includes custom interfaces and type definitions that allow for better type checking and autocompletion in development environments.Overall, the Zero repository presents an innovative approach to frontend development, prioritizing simplicity and direct interaction with the DOM while providing a developer-friendly experience through TypeScript and JSX integration.

Dries Buytaert addresses the ongoing Maker-Taker problem within the open source community, particularly in light of a recent dispute between WordPress co-founder Matt Mullenweg and hosting company WP Engine. Mullenweg has accused WP Engine of profiting from WordPress without contributing adequately back to the project. Buytaert, as the founder of Drupal, shares his insights on this issue, emphasizing the importance of balancing contributions within open source projects.He highlights two significant challenges faced by open source communities: the disparity between major contributors (Makers) and those who contribute minimally (Takers), and the need for an environment that supports fair competition among open source businesses. These challenges, if left unaddressed, could deter entrepreneurs from engaging in open source initiatives, ultimately threatening the sustainability of the ecosystem.The Maker-Taker problem is defined as the situation where Makers, who invest in both their businesses and the open source project, see their work exploited by Takers, who focus solely on their profit without giving back. This imbalance can lead to a decline in contributions from Makers, as they may feel disadvantaged and less motivated to support the project.To combat this issue, Buytaert discusses Drupal's contribution credit system, which incentivizes organizations to contribute to the project by recognizing their efforts. This system tracks contributions transparently, rewarding organizations with visibility and benefits based on their level of engagement. By promoting the importance of choosing Makers over Takers, the system aims to direct commercial work towards those who actively support the open source project.The Drupal Association plays a crucial role in overseeing this credit system, ensuring fairness and impartiality. While the system has its challenges, such as accurately valuing diverse contributions, it has evolved over time to better serve the community.Buytaert suggests that WordPress could enhance its approach to the Maker-Taker problem by adopting a similar contribution credit system. He recommends expanding the governance model, clearly defining the roles of Makers and Takers, and implementing a structured rewards system for significant contributors. This would not only incentivize greater involvement but also create a more equitable environment for open source businesses.In conclusion, addressing the Maker-Taker challenge is vital for the sustainability of open source projects. By fostering collaboration and transparently rewarding contributions, communities can build healthier ecosystems that encourage growth and competitiveness. Buytaert expresses a willingness to learn from other open source projects and collaborate on solutions that benefit the entire community.

NASA is currently in the process of developing a plan to replace the International Space Station (ISS), as the agency faces a critical timeline with the ISS expected to reach the end of its operational life around 2030. This transition is essential for maintaining a human presence in low-Earth orbit, which is increasingly important as NASA shifts its focus toward lunar exploration through the Artemis Program. The agency is set to finalize its strategy for low-Earth orbit operations in the coming months and will soon award contracts to private companies to create commercial space stations.Pam Melroy, NASA's deputy administrator, emphasized the importance of continuing research in microgravity, which is vital for future missions to Mars and beyond. The agency has made significant strides in maximizing the scientific potential of the ISS, particularly in understanding the long-term health impacts of space travel and improving life support systems. NASA's draft "Microgravity Strategy" aims to outline its research and technology development goals for the 2030s and beyond, which will be crucial for the next phase of its commercial space station program.However, the path forward is fraught with challenges. NASA previously awarded contracts to several companies, including Blue Origin, Nanoracks, Northrop Grumman, and Axiom Space, to develop commercial space stations. Yet, many of these companies have encountered financial difficulties and delays, raising concerns about their ability to deliver viable solutions. The upcoming request for proposals from NASA will be pivotal in determining the future of these commercial ventures, as the agency seeks to foster competition while ensuring that its requirements are met.Funding for the Commercial LEO Destinations (CLD) program has been inconsistent, with initial years seeing minimal allocations. However, as the reality of the ISS's impending retirement has set in, Congress has become more supportive of funding the program. Despite this, there are lingering doubts about NASA's commitment to maintaining a presence in low-Earth orbit, especially in light of geopolitical considerations and competition from other nations, particularly China.The potential for a gap in human presence in low-Earth orbit is a concern, with some experts suggesting that it may not be catastrophic if it occurs. However, the uncertainty surrounding the future of commercial space stations complicates fundraising efforts for private operators, who need assurance of demand from NASA. The viability of the CLD program hinges on whether there is sufficient market demand beyond government astronauts, as the lack of a clear commercial application for human activity in space remains a significant hurdle.Ultimately, for NASA to successfully transition to a new era of commercial space stations, it must provide robust support to private companies, recognizing the complexity and cost associated with developing safe and functional habitats in space. The urgency of this situation cannot be overstated, as the clock is ticking toward the end of the ISS's operational life, and the future of human activity in low-Earth orbit hangs in the balance.

Training a model on a massive scale, such as utilizing 10,000 H100 GPUs, involves a complex interplay of strategies and techniques that are essential for efficient performance. The process can be broken down into three main components: fitting a large network with substantial batch sizes, ensuring rapid communication between GPUs, and implementing robust recovery mechanisms for failures.The first component focuses on maximizing the utilization of the GPUs by fitting as large a network and batch size as possible. This involves various parallelization strategies. Data parallelism allows for the distribution of batches across multiple GPUs, while layer parallelism can split individual layers across different GPUs. Additionally, layers can be distributed such that certain layers are processed on specific GPUs, optimizing resource use. The goal is to achieve maximum GPU utilization through continuous parallelization.Another critical aspect of fitting large networks is the management of memory. Techniques such as checkpointing are employed to save necessary data for backpropagation while balancing memory usage. In scenarios where the network is particularly large, it may be more efficient to recompute certain values during backpropagation rather than storing them, thus allowing for larger batch sizes. Advanced methods like Fully Sharded Data Parallel (FSDP) help manage memory by distributing weight shards across GPUs, retrieving them only when needed.The second component emphasizes the importance of rapid communication between GPUs. Effective communication strategies can significantly enhance performance. For instance, overlapping communication with computation allows for more efficient use of time; while one layer is processing, another can begin its communication tasks. Understanding the underlying networking topology is crucial, as it influences how data is transmitted across nodes. Techniques such as tree-reduction can optimize collective communication operations like all-reduce, which is essential for synchronizing gradients across GPUs. Libraries like NVIDIA Collective Communications Library (NCCL) facilitate this process by intelligently managing the communication pathways and ensuring efficient data transfer.The third component addresses the inevitability of failures at such a large scale. With thousands of GPUs, hardware and software failures are common, necessitating robust monitoring and recovery systems. Tools are developed to quickly detect and isolate failed nodes, ensuring minimal disruption to the training process. Additionally, silent data corruption can occur, leading to unexpected loss of data integrity. To mitigate these risks, frequent model state saving is crucial. This involves saving model states to CPU memory quickly, with subsequent transfers to disk or remote storage. Utilizing distributed checkpointing allows each GPU to save only a portion of the model weights, facilitating faster recovery from failures.In conclusion, training a model on 10,000 H100 GPUs requires a sophisticated approach that encompasses efficient resource utilization, rapid communication, and effective failure recovery. By leveraging advanced techniques and tools, engineers can navigate the complexities of large-scale training and optimize performance. For those interested in delving deeper into this topic, resources such as the Llama3 paper, AI Infrastructure talks, and the Torchtitan codebase provide valuable insights and practical examples of these concepts in action.

OpenAI has recently unveiled a new interface for ChatGPT called "Canvas," designed specifically for writing and coding projects. This innovative feature introduces a separate workspace that operates alongside the traditional chat window, allowing users to generate text or code directly within this canvas. Users can highlight portions of their work to request edits from the AI, enhancing the collaborative experience. The Canvas feature is currently in beta, available to ChatGPT Plus and Teams users, with plans to extend access to Enterprise and Edu users shortly thereafter.The introduction of editable workspaces like Canvas reflects a broader trend among AI providers, who are increasingly focusing on creating practical tools for generative AI applications. This new interface is similar to offerings from competitors such as Anthropic’s Artifacts and the coding assistant Cursor. OpenAI aims to keep pace with these competitors while also expanding the capabilities of ChatGPT to attract more paid users.While current AI chatbots struggle to complete extensive projects from a single prompt, they can still provide valuable starting points. The Canvas interface allows users to refine the AI's output without needing to rework their initial prompts, making the process more efficient. Daniel Levine, a product manager at OpenAI, emphasized that this interface facilitates a more natural collaboration with ChatGPT.In a demonstration, Levine showcased how users can generate an email using ChatGPT, which then appears in the canvas window. Users can adjust the length of the email and request specific changes, such as making the tone friendlier or translating it into another language. The coding aspect of Canvas offers unique features as well. For instance, when generating code, users can request in-line documentation to clarify the code's functionality. Additionally, a new "review code" button allows users to receive suggestions for code improvements, which they can approve or modify.OpenAI plans to make the Canvas feature available to free users once it exits the beta phase, further broadening access to this enhanced collaborative tool.

Recent data from ADP indicates a significant slowdown in pay increases for workers who change jobs, marking the slowest growth rate in over three years. In September, the median year-over-year pay increase for job switchers dropped to 6.6%, down from 7.3% in August. This decline represents the lowest growth rate since April 2021. The disparity between pay increases for those who switch jobs and those who remain in their positions has also narrowed, with job stayers experiencing a 4.7% pay increase in August. This trend contrasts sharply with the higher pay gains seen during the "Great Resignation" period of 2022-2023.ADP's chief economist, Nela Richardson, noted that the shrinking gap in pay gains suggests a less dynamic labor market. She described the current situation as one of equilibrium, although it remains uncertain how long this stability will last. While the rapid pay increases of previous years have cooled, the overall job market remains resilient. ADP's latest report revealed that the private sector added 143,000 jobs in September, surpassing economists' expectations of 125,000 and significantly higher than the 99,000 jobs added in August. This marks a rebound after five months of declining job additions.Despite these positive job growth figures, other indicators suggest a potential slowdown in the labor market. The Bureau of Labor Statistics reported a decrease in the quits rate, which fell to 1.9% in August from 2% in July, the lowest level since June 2020. Richardson pointed out that the lack of substantial pay increases for job changers may discourage workers from leaving their current positions, indicating a shift towards stability in the labor market.Looking ahead, Richardson anticipates that this trend of stable growth may characterize labor market data for the remainder of 2024, as both quits and layoffs remain low, leading to muted worker turnover while hiring continues. The upcoming release of the September jobs report is expected to provide further insights, with economists predicting the addition of 150,000 nonfarm payroll jobs and an unemployment rate steady at 4.2%. Overall, while the labor market shows signs of stability, the dynamics of job switching and pay increases are evolving, reflecting broader economic trends and worker sentiments.

Venator is a flexible detection platform designed to streamline rule management and deployment, utilizing Kubernetes CronJob and Helm for its operations. It is adaptable enough to function independently or in conjunction with other job schedulers like Nomad. The platform is particularly optimized for Kubernetes environments, providing a robust detection engine that emphasizes simplicity, extensibility, and ease of maintenance.One of the primary advantages of Venator is its ability to address common challenges faced by existing threat detection solutions. Many of these solutions struggle with monitoring and managing scheduled detection rules effectively. Users often encounter difficulties in verifying the success of detection jobs, troubleshooting failures, and executing backfills or ad-hoc queries. Additionally, the integration of new detection rules or log sources can introduce unnecessary complexity. Venator aims to mitigate these issues by allowing each detection rule to operate as an independent job, which facilitates flexible query execution and result handling.The operational framework of Venator involves running detection rules as separate jobs, each utilizing a designated query engine, such as OpenSearch or BigQuery. This modular approach ensures that the failure of one rule does not affect the execution of others. Each rule is defined in YAML files, specifying its query engine and the destinations for publishing results. For instance, one rule might query logs from OpenSearch and send alerts to a PubSub system, while another could pull data from BigQuery and deliver results to Slack.To enhance the accuracy of its detections, Venator incorporates exclusion lists that filter out known benign events, thereby reducing false positives. These lists are also defined in YAML format and support various logical conditions. Furthermore, Venator integrates with Large Language Models (LLMs) to improve signal analysis, particularly for lower-confidence signals that may not warrant immediate alerts.The deployment of Venator is automated through Helm, which manages configuration files, including detection rules and exclusions, as Kubernetes ConfigMaps. This automation is integrated into a CI/CD pipeline, ensuring that any updates to detection rules or code trigger new deployments automatically, keeping the system current without manual intervention.For those interested in implementing Venator, a detailed deployment guide is available, outlining the steps necessary to set up the platform using Helm and Kubernetes. Overall, Venator represents a significant advancement in threat detection technology, offering a flexible and efficient solution for managing detection rules and enhancing security operations.

Avra Capital, a new venture firm founded by Anu Hariharan and her colleagues from Y Combinator, is emerging as a unique educational institution for growth-stage entrepreneurs. Unlike traditional venture capital firms, Avra operates as a "trade school" focused on teaching the art of company building to a select group of elite founders. The program is designed to help these entrepreneurs navigate the complexities of scaling their businesses, with a curriculum that includes tactical masterclasses led by successful founders and industry experts.The origins of Avra can be traced back to the closure of Y Combinator's Growth team, which was responsible for supporting companies in their post-Series A stages. After the abrupt shutdown in March 2023, Hariharan and her team leveraged their extensive experience to create Avra, which has quickly gained a reputation for its rigorous and selective program. The curriculum emphasizes learning from the mistakes and successes of established entrepreneurs, fostering an environment of vulnerability and openness.Avra's approach is characterized by its confidentiality, allowing instructors to share candid insights without the pressure of public scrutiny. This secrecy, combined with the high caliber of its educators, has made the program highly sought after among founders looking to scale their businesses effectively. The curriculum covers essential topics such as executive hiring, financial planning, and maintaining operational speed, all tailored to the specific challenges faced by growth-stage companies.The program is structured around a series of steps that include data-driven sourcing of startups, one-on-one evaluations, and a kick-off retreat to build camaraderie among participants. Founders engage in classes that encourage discussion and sharing of experiences, rather than traditional lectures. Weekly office hours provide additional support, allowing entrepreneurs to delve deeper into their unique challenges.One of the standout features of Avra is its "mini S-1" exercise, where founders create a mock filing to understand how their business fits into the broader market landscape. This exercise not only prepares them for potential public offerings but also serves as a valuable investment research tool for Avra itself.As Avra continues to refine its curriculum and expand its network, it aims to establish itself as a leading institution for growth-stage entrepreneurs. The firm is also exploring opportunities to support companies approaching the public markets, further solidifying its role in the startup ecosystem.Overall, Avra Capital represents a novel approach to venture capital, blending education and investment in a way that prioritizes the needs of founders. By focusing on the unique challenges of scaling businesses, Avra is carving out a niche that could redefine how venture firms operate in the future.

Meta has confirmed that it may utilize any images users request Ray-Ban Meta AI to analyze for training its artificial intelligence systems. Initially, the company was reticent to provide details on this matter, but further clarification has emerged. According to Emil Vazquez, a policy communications manager at Meta, images and videos shared with Meta AI in regions where multimodal AI is available, such as the U.S. and Canada, can be used to enhance the AI's capabilities in accordance with their Privacy Policy.The distinction is made that while photos and videos taken with Ray-Ban Meta smart glasses are not used for training unless submitted to the AI, once a user requests an analysis, those images are subject to different policies. This means that users inadvertently contribute to a growing database that Meta can leverage to refine its AI models. The only way to avoid this is to refrain from using the AI features altogether.This situation raises significant privacy concerns, as users may not fully grasp that they are providing Meta with potentially sensitive images, which could include personal spaces or identifiable individuals. Although Meta asserts that this process is made clear in the user interface, there seems to have been a lack of initial transparency from the company regarding these practices. Previously, it was known that Meta trains its Llama AI models on publicly available data from platforms like Instagram and Facebook, but this new approach extends that definition to include any images analyzed through the smart glasses.The timing of this revelation is particularly pertinent, as Meta has recently introduced new AI features that encourage users to interact with the AI in a more intuitive manner, increasing the likelihood of sharing new data for training purposes. A notable addition is a live video analysis feature that streams images to Meta’s AI models, allowing users to receive outfit suggestions based on their wardrobe. However, the company does not prominently disclose that these images are also being sent to Meta for training purposes.Meta's privacy policy explicitly states that interactions with AI features can be utilized for training AI models, which encompasses images shared through the Ray-Ban smart glasses. Furthermore, the terms of service indicate that by sharing images, users consent to Meta analyzing those images, including facial features.This situation is compounded by Meta's recent legal history, having settled a $1.4 billion lawsuit in Texas concerning its facial recognition practices. The case revolved around a feature called "Tag Suggestions" on Facebook, which was made opt-in after significant backlash. Notably, some of Meta AI's image features are not available in Texas due to these legal issues.Additionally, Meta retains transcriptions of voice interactions with Ray-Ban Meta by default to train future AI models, although users can opt out of having their voice recordings used for this purpose when they first log into the app.The broader context involves a trend among tech companies, including Meta and Snap, to promote smart glasses as a new computing platform. These devices, equipped with cameras, raise privacy concerns reminiscent of the issues surrounding Google Glass. Reports have surfaced of individuals hacking Ray-Ban Meta glasses to access personal information about those they encounter, further highlighting the potential risks associated with this technology.

Tesla has made a significant change to its vehicle lineup by discontinuing the Model 3 Standard Range Rear-Wheel-Drive, which was the company's most affordable electric car priced at $39,000. This update was reflected in Tesla's online configurator, and it marks a shift in the options available to consumers looking for a budget-friendly electric vehicle.With the removal of the Standard Range model, the least expensive Tesla now is the Model 3 Long Range Rear-Wheel-Drive, which starts at $42,500. This model offers an additional 90 miles of range compared to the discontinued version, making it a more appealing option for buyers despite the higher price. For those eligible for the federal tax credit, the effective cost of the Long Range model could drop to $35,000 after accounting for the $7,500 tax credit, along with potential state incentives and savings on fuel.The decision to discontinue the Standard Range model is likely influenced by recent changes in tariffs on Chinese battery cells, which were used in this particular trim. These tariffs have made it less competitive in the market, especially since the LFP (lithium iron phosphate) batteries used in the Standard Range model complicate access to the tax credit. As a result, Tesla appears to have opted to streamline its offerings to maintain competitiveness in the electric vehicle market.This move reflects Tesla's ongoing strategy of adjusting its vehicle trims and pricing in response to market conditions and regulatory changes, often without prior announcements. The company continues to focus on providing a range of electric vehicles that cater to different consumer needs while navigating the complexities of battery sourcing and pricing.

TinyJS is a lightweight JavaScript library designed to facilitate the dynamic creation of HTML elements. It streamlines the process of manipulating the Document Object Model (DOM) by allowing developers to generate standard HTML tags programmatically, apply properties, append content, and select DOM elements with ease.One of the key features of TinyJS is its ability to dynamically create HTML elements. Users can generate any standard HTML tag effortlessly, which is particularly useful for building user interfaces. The library also supports deep property assignment, enabling developers to work with nested property structures for more complex elements. Additionally, it simplifies content appending by accepting both strings and elements as child content, making it versatile for various use cases.TinyJS introduces two helper functions for DOM selection: the `$` function, which acts as a wrapper around `document.querySelector`, and the `$$()` function, which wraps `document.querySelectorAll` and returns an array of DOM elements. This allows for straightforward element selection and iteration, enhancing the overall usability of the library.To illustrate its functionality, an example is provided where a `div` element is created with specific attributes and child elements, such as an `h1` and a `p`. This demonstrates how TinyJS can be used to generate and manipulate HTML elements dynamically.For installation, users simply need to include the `tiny.js` script in their project. Once included, they can utilize any valid HTML tag as a function to create elements, assign properties, and append children to the DOM. An advanced example showcases how properties can be deeply assigned to elements, such as styling a button directly through its properties.TinyJS supports a wide range of HTML tags, including basic text elements, interactive elements, media elements, and container elements, making it a comprehensive tool for web development.The library encourages contributions from the community, asking users to open an issue before submitting a pull request. Overall, TinyJS provides a simple yet powerful utility for developers looking to enhance their web applications with dynamic HTML element creation.

The document outlines the offline user experience (UX) patterns for the Expensify app, emphasizing the importance of enabling users to interact with the app even when they are not connected to the internet. The primary goal is to ensure that users can perform as many actions as possible offline, which is crucial for maintaining productivity in environments with unstable internet connections.The document begins by discussing the motivation behind these patterns, highlighting the need for an optimistic approach to offline interactions. This means that the app can assume certain actions will succeed when the user is back online, allowing for a smoother user experience. For instance, when a user pins a chat, the app can immediately reflect this change in the user interface without waiting for the API request to complete, as the outcome is predictable.Several UX patterns are defined to guide developers in implementing offline functionality:1. **None - No Offline Behavior**: This pattern applies when there is no interaction with the server. The feature operates normally or displays stale data until a connection is reestablished.2. **A - Optimistic Without Feedback**: In this scenario, the app queues the request to be sent later and allows the user to continue as if the request succeeded. This is suitable for actions that do not require immediate server feedback.3. **B - Optimistic With Feedback**: Similar to the previous pattern, but it provides visual feedback to the user that the request is pending. This is particularly useful for actions that the user should be aware are not yet completed.4. **C - Blocking Form UI Pattern**: This pattern prevents form submission when offline, greying out the submit button while allowing users to fill out the form. The data is saved locally for later submission.5. **D - Full Page Blocking UI Pattern**: This extreme measure blocks user interaction with an entire page when critical data cannot be fetched due to being offline or when an error occurs. It ensures that users do not see outdated or incorrect information.The document also includes a flowchart to help developers determine which UX pattern to apply based on specific questions about the feature's interaction with the server, the type of request being made, and whether the user needs to know the success of their action.Overall, the guidelines aim to create a seamless offline experience, allowing users to continue their tasks without interruption while ensuring that they are informed about the status of their actions. This approach not only enhances user satisfaction but also aligns with the app's mission to support users in various environments.

Gmail has introduced an update to its "summary cards" feature, aimed at enhancing the user experience by making it easier to access important information buried within emails. This update focuses on surfacing key details related to shopping, travel, events, and bills, allowing users to find relevant information without the need to sift through numerous emails.Previously, summary cards were primarily associated with order confirmation emails, displaying details such as purchased items and tracking links. The new iteration of summary cards is designed to be more dynamic and contextually relevant. For instance, when waiting for a package, users will see information about its expected arrival, and once it arrives, a link to the return policy will be provided. This approach ensures that the information presented is pertinent to the user's current situation.The summary cards will not only appear at the top of individual emails but also in search results. For example, searching for "Delta" will bring up a card for upcoming flights, streamlining the process of locating important travel information. Additionally, these cards will be visible at the top of the inbox for time-sensitive matters, such as upcoming trips or packages due to arrive soon. While the introduction of these cards may be met with some resistance from users who prefer a clutter-free inbox, the Gmail team believes that their utility will outweigh any initial concerns.This update does not represent a major shift towards artificial intelligence but rather an improvement in Gmail's ability to extract useful information from emails. The new summary cards are currently rolling out for purchases, with plans to expand to other categories in the future. Google is focusing on the understanding that much of email communication consists of information rather than direct messaging, and it aims to help users manage their inboxes more effectively.

The blog post by Arne Bahlo expresses a growing appreciation for tools that function effectively without requiring extensive configuration. This sentiment is particularly relevant in the context of software development, where many tools demand significant setup time and effort. Bahlo highlights the contrast between customizable tools, like Emacs, and those that are ready to use right away, emphasizing the appeal of the latter.Bahlo begins by referencing Julia Evans' praise for the fish shell, which is designed to work without the need for configuration. The fish shell includes features such as autosuggestions by default, which are often reliant on plugins in other shells like ZSH. This ease of use is reflected in Bahlo's own minimal configuration for fish, which consists mainly of abbreviations and two plugins that require no additional setup.The discussion then shifts to Helix, a code editor that Bahlo has adopted after struggling with a complex Neovim configuration that involved multiple external plugins. Helix stands out for its built-in support for features like Language Server Protocol (LSP) and tree-sitter, which enhance coding efficiency without the need for extensive configuration. Bahlo shares his simple configuration for Helix, which consists of just a few lines of code, demonstrating how streamlined the setup process can be.Lazygit is another tool Bahlo praises for its user-friendly design, allowing for effective Git management without the need for configuration. He appreciates its intuitive interface and the ease with which users can navigate its features.Bahlo also mentions Zellij, a terminal multiplexer that offers a similar no-configuration experience. It allows users to create layouts and manage panes without additional plugins, with a standout feature being the ability to toggle floating panes, which enhances workflow.The post concludes with a call for readers to share their own zero or minimal configuration tools, fostering a community of developers who value simplicity and efficiency in their tools. Bahlo encourages developers to prioritize a seamless default experience in their creations, reflecting a broader trend towards user-friendly software solutions. Overall, the blog post serves as a celebration of tools that prioritize ease of use, highlighting how they can enhance productivity and reduce the friction often associated with software setup.

Automattic, the parent company of WordPress.com, recently made headlines by demanding that WP Engine, a competing hosting service, pay 8 percent of its monthly revenue. This demand came before a public dispute regarding the use of the WordPress trademark and the open-source nature of the WordPress project. Automattic's proposal, which was shared on September 20, outlined a seven-year agreement that would allow WP Engine to use the WordPress trademark in exchange for this revenue share, which could be paid either as a royalty or as salaries for WP Engine employees contributing to the WordPress.org project.If WP Engine opted for the royalty payment, Automattic indicated it would publicly acknowledge WP Engine's contributions to the Five for the Future initiative, which encourages companies to allocate resources to the WordPress.org project. Conversely, if WP Engine chose to pay the 8 percent through employee contributions, Automattic would gain extensive audit rights over WP Engine's operations, including access to employee records and time-tracking.However, WP Engine rejected Automattic's proposal, asserting that its use of the WordPress trademark and related abbreviations fell under fair use. Following this rejection, Automattic abandoned the terms, citing WP Engine's alleged deceptive practices. The conflict escalated during the WordCamp conference, where Automattic's CEO, Matt Mullenweg, publicly criticized WP Engine for not contributing adequately to the WordPress community and threatened legal action over trademark usage.In response, WP Engine issued a cease and desist order against Automattic, claiming harassment from Mullenweg. Automattic dismissed these claims as false and reiterated its request for a revenue-sharing agreement. As tensions mounted, WordPress.org, under Mullenweg's leadership, banned WP Engine from its servers, effectively cutting off access to updates and plugins. In response, WP Engine developed its own solution to maintain service continuity for its customers.This ongoing dispute highlights the complexities of trademark rights and contributions within the open-source community, as both companies navigate their competitive landscape while addressing the expectations of the WordPress ecosystem.

Samsung is reportedly developing a competitor to the Ray-Ban Meta glasses, collaborating with Google on the project that will utilize Google’s Gemini AI technology. This initiative was approved earlier in the year following extensive discussions within Google about whether to pursue full augmented reality (AR) glasses or simpler smart glasses akin to the Ray-Ban Meta model. Ultimately, executives from both companies decided to focus on the latter option. This development comes nearly a year after Samsung filed a trademark for "Samsung Glasses" in the UK, hinting at a potential product name.In the competitive landscape of smart eyewear, Google is also aiming to secure the partnership with EssilorLuxottica, the company that owns Ray-Ban, which would be a significant advantage in the market. EssilorLuxottica holds a dominant position in the global eyewear sector, with its brands being some of the most recognized worldwide. Despite Google's efforts to take this partnership from Meta, they were unsuccessful, as Meta and EssilorLuxottica recently announced an extension of their collaboration for the next decade to create multi-generational smart eyewear products.In addition to the smart glasses project, Samsung and Google are working together on a high-end mixed reality headset designed to compete with Apple's Vision Pro. Google is responsible for the software aspect, while Samsung is handling the hardware, utilizing Qualcomm's XR2+ Gen 2 chipset. However, there are indications that the release of this mixed reality headset may face further delays, potentially pushing it into 2025.Overall, the collaboration between Samsung and Google signifies a strategic move in the evolving market of augmented and mixed reality technologies, as both companies seek to carve out their share in a space that is becoming increasingly competitive.

North Korea has successfully infiltrated the cryptocurrency industry by employing IT workers who operate under false identities, leading to significant cybersecurity and legal risks for various blockchain firms. A CoinDesk investigation revealed that over a dozen crypto companies, including notable projects like Injective, ZeroLend, and Sushi, unknowingly hired these North Korean workers, who managed to pass interviews and reference checks while presenting authentic-looking work histories.The hiring of North Korean workers is illegal in the U.S. and other countries due to sanctions against the Democratic People's Republic of Korea (DPRK). These workers are believed to generate substantial revenue for the North Korean regime, with estimates suggesting they contribute up to $600 million annually to fund the country's nuclear weapons program. The investigation highlighted that many companies faced security breaches after hiring these workers, as North Korean hackers often target firms through their employees.Zaki Manian, a blockchain developer, shared his experience of inadvertently hiring two North Korean IT workers while developing the Cosmos Hub blockchain. Similarly, Stefan Rust, founder of Truflation, recounted how he hired a developer named "Ryuhei," who claimed to be based in Japan. Rust later discovered that Ryuhei and several other team members were actually from North Korea, part of a broader scheme to secure remote jobs and funnel earnings back to Pyongyang.The investigation found that North Korean IT workers are more prevalent in the crypto sector than previously understood, with many hiring managers acknowledging they had encountered suspected North Korean applicants. The ease of remote hiring in the crypto industry, combined with a lack of rigorous background checks, has made it a prime target for North Korean infiltration.CoinDesk's findings also revealed that many of these workers were able to conduct their tasks effectively, leading to a false sense of security among employers. However, evidence indicated that some of these employees funneled their wages to blockchain addresses linked to the North Korean government. In several instances, companies that employed DPRK IT workers later experienced hacking incidents, with some attacks directly traced back to these employees.Despite the legal implications of hiring North Korean workers, U.S. authorities have not prosecuted any crypto companies for such actions, often viewing them as victims of sophisticated identity fraud. The investigation underscored the ethical concerns surrounding the employment of North Korean workers, who are often exploited by their regime, retaining only a fraction of their earnings.CoinDesk identified numerous companies that had employed suspected DPRK IT workers, with many coming forward to share their experiences in hopes of raising awareness. The investigation also highlighted the challenges of identifying these workers, as they often used convincing fake documents and maintained a professional demeanor during their employment.The infiltration of North Korean IT workers into the crypto industry poses a dual threat: it not only violates international sanctions but also endangers the security of the companies involved. As the investigation concluded, it became evident that the connection between North Korean IT workers and hacking activities is more pronounced than many in the industry had previously believed, with social engineering tactics being a common method of attack.In a striking coincidence, as the article was being finalized, Truflation's Rust experienced a hack that resulted in the loss of millions of dollars, further illustrating the ongoing risks associated with North Korean infiltration in the crypto space. The investigation serves as a cautionary tale for the industry, emphasizing the need for more stringent hiring practices and awareness of the potential threats posed by remote workers from sanctioned nations.

Apple's recent update to its iOS 18 operating system has sparked significant concern among developers of social and messaging applications. While the update introduced various artificial intelligence features, a less publicized change regarding contact-sharing permissions has raised alarms about the future viability of new social apps. This change, referred to as "contact sync," has historically been crucial for the growth of platforms like Instagram, WhatsApp, and Snapchat, enabling them to connect users with their existing contacts and facilitate rapid user acquisition.The modification in iOS 18 allows users to selectively share their contacts with apps, rather than granting blanket access to their entire address book. This shift has led some developers, such as Nikita Bier, to express dire predictions about the impact on new social applications, suggesting that they may struggle to gain traction in a landscape where established players like Facebook and Instagram already benefit from extensive user networks.While there is sympathy for the challenges faced by new app developers, there is also recognition of Apple's rationale for enhancing user privacy. The company argues that users should have more control over their personal information, allowing them to choose which contacts to share rather than being forced into an all-or-nothing decision. Apple believes that this could potentially lead to increased contact sharing, as users who previously opted out might be more willing to share selected contacts.However, many developers contest this view, citing data indicating a significant decline in contact sharing since the implementation of the new permissions. The ability to connect with friends quickly is critical for the success of social apps, and even a modest decrease in contact sharing can hinder user engagement and retention. Developers have also pointed out that Apple's own services, such as iMessage, do not face the same restrictions, raising concerns about competitive fairness and self-preferencing.The implications of these changes could lead to a shift in the social app landscape, with a potential decline in friend-based applications in favor of content-driven platforms like TikTok or AI companionship apps that do not rely on human connections. This evolution highlights the powerful influence that major tech companies like Apple wield over the industry and the delicate balance between promoting competition and ensuring user privacy.

It seems that the content you provided is a fragment that appears to be a message from a website, specifically from the Wall Street Journal (wsj.com). The message suggests that users need to enable JavaScript and disable any ad blockers to access the full features of the site. Websites often require JavaScript to function properly, as it allows for interactive elements and dynamic content. Ad blockers can interfere with the revenue model of many online platforms, which rely on advertising to support their operations. By asking users to disable ad blockers, the site is encouraging them to support the platform while ensuring that they can access all the content available.This message highlights the ongoing tension between user experience, privacy concerns, and the financial sustainability of online media. As users become more aware of their online privacy and the use of ad blockers, websites must find a balance between providing valuable content and maintaining their business models. In summary, the message serves as a reminder of the technical requirements for accessing certain online content and reflects broader themes in the digital landscape regarding user engagement and monetization strategies.

Researchers have achieved a significant milestone in neuroscience by mapping the brain of a fruit fly, Drosophila melanogaster, in unprecedented detail. This new connectome, which is the most comprehensive brain map created for any organism to date, includes nearly 140,000 neurons and over 54.5 million synapses, the connections between these nerve cells. The project, known as FlyWire, was co-led by neuroscientists Mala Murthy and Sebastian Seung at Princeton University and has been in development for more than four years.The mapping process utilized advanced electron microscopy to capture images of the fly's brain slices, which were then stitched together using artificial intelligence tools. Despite the efficiency of AI, the researchers undertook a rigorous manual proofreading process, making over three million edits to ensure accuracy. This effort was bolstered by the involvement of volunteers, particularly during the COVID-19 pandemic when many researchers were working remotely.In addition to mapping the neurons, the team identified 8,453 distinct types of neurons, with 4,581 of these being newly discovered. This revelation opens up new avenues for research, as each identified cell type presents a unique question for scientists to explore. The interconnectivity of the neurons was also surprising; many neurons previously thought to be dedicated to specific sensory pathways were found to receive input from multiple senses, highlighting the complexity of the fruit fly's brain.The FlyWire map has been made available for researchers to explore, leading to various studies that leverage this data. For instance, one study created a computer model of the fruit fly's brain, simulating how it processes taste signals. The model demonstrated a high degree of accuracy in predicting the behavior of real fruit flies when specific neurons were activated.Another study focused on the wiring circuits that signal a fruit fly to stop walking, revealing two distinct pathways that control this behavior. One pathway halts walking signals from the brain, while the other processes these signals in the nerve cord, allowing the fly to stop and groom itself.While the connectome represents a significant advancement, it is based on a single female fruit fly, which may limit its applicability. Previous research had produced a less comprehensive map of a portion of the fly's brain, known as the hemibrain, which contained around 25,000 neurons. Comparisons between the two maps revealed notable differences, particularly in the number of neurons in specific brain structures, suggesting that environmental factors may influence brain development.The researchers acknowledge that much work remains to fully understand the fruit fly's brain. The current connectome primarily details chemical synapses and does not account for electrical connectivity or other forms of neuronal communication. Future efforts may include mapping the brains of male fruit flies to investigate sex-specific behaviors, such as singing.Overall, this groundbreaking work not only enhances our understanding of the fruit fly's neural architecture but also sets the stage for future research into the complexities of brain function across different species.

Pipet is a versatile command-line tool designed for scraping and extracting data from online sources, particularly aimed at developers and hackers. It operates in three primary modes: HTML parsing, JSON parsing, and client-side JavaScript evaluation. By leveraging existing tools like curl and utilizing Unix pipes, Pipet enhances its functionality, allowing users to automate data retrieval tasks efficiently.The tool can be employed for various practical applications, such as tracking shipments, monitoring ticket availability, and observing stock price fluctuations. Users can create Pipet files that define how to scrape specific data from websites. For instance, a simple Pipet file can be created to fetch the latest news from Hacker News, demonstrating the tool's straightforward syntax and ease of use.Pipet supports customization through various command-line flags. Users can specify custom separators for text output, output results in JSON format, or render data using templates. The tool also allows for monitoring changes on a webpage, enabling notifications when specific conditions are met.Installation of Pipet can be done through pre-built binaries, Go installation, or package managers like Arch Linux and Homebrew. The usage of Pipet requires only the path to a .pipet file, with additional flags available for enhanced functionality. The structure of a Pipet file consists of resource lines that define the URL and scraping method, query lines that specify the data to extract, and optional next page lines for pagination.Pipet's querying capabilities are robust, supporting HTML queries using CSS selectors, JSON queries with GJSON syntax, and Playwright queries that execute JavaScript in a headless browser environment. This flexibility allows users to extract data from a variety of sources, whether they are simple HTML pages or complex web applications.Overall, Pipet stands out as a powerful tool for data extraction, combining ease of use with advanced features that cater to the needs of developers looking to automate their data scraping tasks.

Google is intensifying its competition with OpenAI by developing advanced artificial intelligence models that possess reasoning capabilities. Recent reports indicate that teams at Google have made significant strides in creating software that mimics human-like reasoning, particularly in solving multistep problems. This development is part of Google's broader focus on enhancing the reasoning abilities of large language models (LLMs), which includes techniques like chain-of-thought prompting.Chain-of-thought prompting allows LLMs to tackle complex inquiries by breaking them down into a series of intermediate reasoning steps, akin to human thought processes. This method results in longer response times, as the models analyze similar prompts before formulating a comprehensive answer. The ability to engage in such reasoning enables these models to handle intricate tasks related to mathematics and computer programming more effectively.OpenAI is also employing chain-of-thought prompting in its latest model, known internally as Strawberry, which was released in September. Initially, there were concerns within Google's DeepMind unit about falling behind OpenAI, but these worries have diminished as Google has introduced more competitive products. OpenAI's new model, however, lacks some features present in the current version of ChatGPT, such as web browsing and file uploads, which are considered useful.In addition to its work on reasoning capabilities, Google is enhancing its Gemini chatbot. The company recently launched its 1.5 Flash model, which is designed to provide faster and more efficient responses. This update aims to improve Gemini's reasoning and image processing skills, promising users a more effective interaction experience.Overall, Google's advancements in AI reasoning reflect its commitment to staying competitive in the rapidly evolving landscape of artificial intelligence, particularly against the backdrop of OpenAI's innovations.

Job advertisements play a crucial role in attracting the right talent, and the language and structure used can significantly impact the perception of a company. Stevie Buckley shares insights from years of experience in writing and reviewing job ads, highlighting common pitfalls and offering guidance on how to create more effective listings.One of the most frequent issues is the vague term "competitive salary." This phrase often fails to convey meaningful information and can be perceived as a lack of transparency. Buckley argues that providing a clear salary range is essential, as it respects the time and effort of job applicants. Common excuses for not disclosing salary include concerns about existing team members' reactions or fears of attracting candidates who will only seek the highest end of the range. Buckley counters these excuses by emphasizing the importance of fair pay and the need for honesty in job postings.Another common mistake is the requirement for a specific number of years of experience. Buckley points out that this can deter potentially qualified candidates who may have relevant skills but lack the exact years of experience specified. Instead of focusing on arbitrary experience metrics, he suggests describing what success looks like in the role within the first year, which can attract a broader range of applicants.The length and content of job ads are also critical. Research indicates that candidates take less than 50 seconds to assess job fit, and ads that are concise yet informative tend to attract more applications. Buckley advises against lengthy descriptions that delve into company history, recommending a brief overview with links to more detailed information.Furthermore, Buckley stresses the importance of transparency regarding the interview process. Candidates appreciate knowing what to expect, including the typical duration of the hiring process. Providing context through external links, photos of the workplace, and insights from current employees can enhance the appeal of the job ad.Finally, Buckley encourages companies to seek feedback from their existing team members and applicants about the job ad. This collaborative approach can lead to improvements in how roles are presented and help ensure that the advertisement accurately reflects the work environment and expectations.By addressing these common issues and focusing on clarity, transparency, and relevance, companies can create job advertisements that not only attract the right candidates but also reflect positively on their brand.

Jake Lazaroff shares his experience of creating Waypoint, a local-first web application designed for trip planning, after finding existing tools inadequate for his needs during a six-month travel sabbatical. The planning process was challenging, leading him to develop a solution that allows for quick data entry, easy comparisons, and the integration of unstructured data alongside structured data.Waypoint features a dual-panel interface with a text editor on one side and a map on the other, enabling users to jot down notes about potential destinations and visualize routes simultaneously. This design addresses the shortcomings of other tools, such as Apple Notes and Google Maps, which either lack flexibility in data entry or complicate the visualization of locations. The app allows users to create route lists easily, with the ability to toggle between driving directions and straight-line routes, enhancing the planning experience.The underlying technology of Waypoint is built using SvelteKit, with custom components from the Shoelace library and a rich text editor powered by ProseMirror. The app utilizes Stadia Maps for location services and employs Yjs, a CRDT library, for local data storage. This local-first approach means that data is stored on the client rather than a centralized server, allowing for instantaneous editing and offline functionality. The app also supports real-time collaboration through Y-Sweet, a WebSocket sync backend that facilitates document sharing and synchronization between users.Lazaroff discusses the principles of local-first software, emphasizing that the client should maintain the canonical copy of the data. He evaluates Waypoint against several criteria proposed by Ink & Switch, concluding that it meets most of the ideals of local-first software, with a few exceptions regarding security and privacy.Through this project, Lazaroff learned that building a local-first app is feasible with existing tools, and the integration of various libraries can create a seamless user experience. He highlights the ease of adding offline support and the overall developer experience as significant advantages of this architecture. The article concludes with an invitation to explore the code behind Waypoint on GitHub, encouraging others to engage with the local-first ecosystem.

The discussion surrounding the availability of sand has gained significant attention in recent years, particularly with the rise of documentaries and literature highlighting its importance and potential scarcity. However, the assertion that the world is running out of sand is misleading. The reality is more nuanced, and understanding the complexities of sand as a resource reveals that while it is a finite material, it is not necessarily on the brink of depletion.Sand is a fundamental component in various aspects of modern life, particularly in construction, where it serves as a crucial ingredient in concrete. Concrete itself is a vital material in civil engineering, known for its durability and versatility. The demand for concrete is immense, driven by its low cost and the ability to mold it into various shapes. However, the extraction of sand, especially from riverbeds, can lead to significant environmental impacts, raising concerns about sustainability and ecological balance.Interestingly, while natural sand is a non-renewable resource, it is possible to manufacture sand from larger rocks. This process involves crushing rocks and sieving them to achieve the desired particle size. Manufactured sand can often be produced as a byproduct of other mining operations, making it a viable alternative to natural sand. This method not only reduces the environmental impact associated with mining but can also enhance the strength of concrete due to the angularity of the manufactured sand particles.The properties of sand significantly influence the characteristics of concrete. For instance, the shape and texture of sand grains affect the workability and strength of concrete mixes. Rounded grains, often found in natural sand, can improve the flow and ease of placement, allowing for a lower water-to-cement ratio, which ultimately enhances the strength of the cured concrete. Conversely, angular grains from manufactured sand can provide higher strength when used in equal water conditions, but they may require more water to achieve the same workability.The economic factors surrounding sand extraction and production are also critical. The costs associated with transporting sand from distant locations can be substantial, and as environmental regulations tighten, the price of sand is likely to increase. This economic shift may prompt the construction industry to adapt by exploring alternative materials or methods of production.Moreover, the recycling of concrete into aggregates presents another avenue for reducing the demand for virgin sand. As the construction industry evolves, the potential for using recycled materials could mitigate some of the pressures on natural sand resources.In conclusion, while the world is not running out of sand in a literal sense, the complexities of its extraction, production, and use highlight the need for a more sustainable approach to managing this essential resource. Awareness of the environmental costs and the potential for innovation in material sourcing can lead to more responsible practices in the construction industry, ensuring that sand remains available for future generations.