Modyfi

modyfi

174 AI Tools To Try

Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix 6 Results 6.1 Malware Evasion To evaluate MEME, we tested its evasion capabilities compared to four other algorithms. Figure 3 shows the evasion rates of all algorithms against four different targets. Generally, we can see that some targets are more challenging than others. For example, the Sorel-FFNN target seems the hardest to evade, while Ember seems the easiest. MEME outperforms the PPO algorithm in all four targets, with a difference in mean evasion rate between 5-10% depending on the target. This result indicates that creating a surrogate model and running additional training steps is beneficial and produces a better policy. MEME also outperforms MAB in three out of four targets and GAMMA in all four, showing stable overall performance. GAMMA is the only method affected by the time constraint and, in most experiments, manages to process less than 100 binaries. However, it performs almost as well as MEME on the Sorel-FFNN. This shows that the RL methods, including MAB, did not learn the simple strategy of section injection that GAMMA uses, which is part of their action set. Average number of binary modifications to evade the target. The numbers for MAB correspond to the minimal samples created. Random, PPO, and MEME numbers correspond to the mean episode length of the final evaluation on the test set. Table 2. The AV evasion rate was over 50% for both PPO and MEME, with MEME achieving an average evasion rate of 59%. This demonstrates that even though AV is more complex than a single classifier, it can still be bypassed. In terms of required modifications for a malicious binary to appear benign (Table 2), MEME has the lowest average changes against Sorel-FFNN and Microsoft Defender. MAB performs better with lower average changes against Ember and Sorel-LGB, as it aims to minimize the required actions. However, if MAB struggles with evasion, the average modifications increase. Unfortunately, it was not possible to get the number of modifications for each binary from the GAMMA attack since the framework that implements the attack does not provide this metric. Surrogate agreement metrics with the test set for each target model. Table 3. 6.2 Surrogate Evaluation Table 3 shows the agreement scores of the surrogate models with their respective targets. The label agreement scores were higher than 97% for all targets. In the feature agreement metrics, 9 out of 10 top features, according to SHAP feature importances were the same for the Ember target and the respective surrogates. The percentage is slightly lower for Sorel-LGB, with 7.2 out of the top 10 feature agreement and 16.2 out of the top 20. However, the feature agreement metrics get significantly lower regarding the Sorel-FFNN target, even though the label agreement is higher than 98%. This target is also the harder one to evade for the RL-based methods. This high level of label agreement was reached with only 2,048 queries, significantly lower than reported in prior work (25k) [32]. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

MEME vs. PPO: Analyzing Evasion Rates and Binary Modifications in Malware Detection

Vim Commands for Text Modification In the realm of Vim, mastering essential commands like append, change word, and replace can significantly enhance your text editing prowess. Append ( ) a Appending text using `a` places you in insert mode, allowing for seamless additions after the cursor position. For instance, adding a new field in a file becomes intuitive: # Go to the end of the line $ (to reach the end of a line) # Use 'a' to append text a Change Word ( ) and Replace ( ) cw r/R - : Alter a word or overwrite text until the next punctuation character or space. Example: Correcting a word, say 'Hackernoon' to 'World' involves using `cw` and editing the text. cw - : Replace one character (`r`) or overwrite text (`R`). Typing errors or changing your mind about the edit becomes effortless: r/R # Change 'Hackernoon' to 'World' (with a typo) cw World # Correct the typo using 'r' (replaces one character) re # Overwrite the text with 'R' R Discover Anything Refining Text and Streamlining Edits Vim offers shortcuts for effective text refinement: - : Use arrow keys for navigation and `$` to reach the end of a line. Navigate - : Trim trailing spaces by maneuvering with arrow keys and using to delete from the cursor position to the end of the line. Deleting Spaces D Wrapping Up Modifications and Writing Files Once edits are complete, ensure to save changes: # Save changes and exit :wq (write and quit) Conclusion Mastering these foundational Vim commands empowers efficient text editing and streamlines the process of modifying files. With practice, these commands become second nature, boosting productivity and precision in your editing endeavors.

Vim -  2 Commands for Text Modification

Smartphones have become indispensable tools for a wide variety of work and personal tasks. However, many people admit that mobile devices have also grown into our ultimate distractions, with an endless stream of notifications, an overwhelming number of icons on the screen, and plenty of apps vying for our attention. The issue of maintaining the ‘life-phone’ balance is getting evergreen, but potential controversy can be eliminated – with a responsible and mindful approach. It applies to both quantity and quality aspects, meaning you cut back on screen time when it’s too much, prioritize functional phone usage depending on your professional and leisure needs, and optimize your mobile experience for convenience and efficiency by introducing some screen modifications. As the first two are pretty straightforward, let’s touch upon the third facet more closely and discuss four concentration-promoting and productivity-boosting screen customization strategies that can help you alleviate distractions, stay more focused on your daily to-dos, and reap some extra motivation when using your iPhone. Declutter and Organize Your Screen Space Like your living space, your digital space should be kept in order. Messy environments are shown to trigger stress and induce disorientation and procrastination, thus degrading focus and productivity. Flip through your screen pages indicating the apps you haven’t been using for a couple of months now, and delete them. The accumulation of irrelevant icons provokes digital noise and annoyance, occupying and cluttering your screen space and distracting your eyes from the ones you really need. The next step is organizing your space to address your functional necessities. You can use a ready-to-go home screen pack template for the structure, and fill it out with widgets and icons in your preferred sequence. These visual arrangements will help you eliminate the risk of randomness while browsing your phone, saving time and promoting mental focus. Overall, there are many auxiliary apps available to pick some drafts and ideas. For instance, the app provides the whole package of the latest and most creative screen customization features, including themes, wallpapers, widgets, icon packs, keyboards, and more. MyScreen Determine Essential Work and Lifestyle Tools and Set Priorities Highlighting the things you regularly need for work or leisure activities makes them quickly accessible, promoting concentration on your intentions and speeding up the outcomes. If your main goal is enhancing your screen time for work, prioritize the tools that you actually use for work, think documents, messengers, scanning apps, or other utilities. If your smartphone usage is more lifestyle, ensure easy reach for the stuff you engage with most frequently. You can also add widgets for the things you want to keep at your fingertips or at the top of your mind. For example, you can track the UV index, weather forecast, stock exchange, calendar events, steps count, shopping lists, and more besides standard date and time. Social media icon placement requires special attention as these are the most time-consuming and interrupting apps of all. Consider placing them on the second or third pages of your screen so they don’t spot your eye each time you unlock your phone. You can go further and switch off notifications to avoid random alerts popping up on the screen. These ‘out of sight, out of mind’ tweaks will help you alleviate the presence of social media in your daily life and stay focused on what you are up to. Display Personally Beneficial Content Since your phone screen is something you observe repeatedly during the day, it’s a no-brainer to fill it with content that resonates with your objectives and facilitates your goals. Once the screen space is cleaned and organized, it’s time to review what else can be added to promote your mindfulness and motivation. For example, you can introduce a widget listing your intentions for the day or displaying your progress toward your daily health goals, such as 10K or water intake. Another advantageous tweak is setting the wallpaper, which addresses you personally and appeals to your state of mind. Even with a brief and unintentional eye contact, what we see launches a chain of cognitive and psycho-emotional reactions in the brain. Naturally, as long as you bring a sense of purpose to the table, you can turn your phone screen into a stress reliever, mood booster, source of motivation, or whatever else you’re looking for. It may sound too good to be true, but it’s science-backed that seeing calming nature scenery – even on your phone screen – promotes stress relief and relaxation, funny pictures make you smile, and positive affirmations keep you encouraged and uplifted. offers a vast, constantly updating catalog of live and still wallpapers if you need to pick some fresh ideas that capture your eye. MyScreen To enhance the effect, consider dividing the screen area between functional and inspirational constituents. Place the icons above, below, or around the central theme of your wallpaper so they don’t overlap, and you can still see the purposeful part of the picture without barriers. Optimize Color Schemes and Design Elements The colors and patterns we see can subconsciously impact our mental and emotional states. While your favorite ones are the clear winners, it’s also worth taking into account some common associations. Darker colors can induce gloominess and sadness, while brighter and lighter colors foster happiness and optimism. Calm pastel colors promote serenity and focus, and warm neutrals are comforting and uplifting. Besides, the color choice should not be generalized, as each color has several shades that can induce either positive, neutral, or negative responses in you. Overstimulating bold patterns and combinations of colors that clash with each other can provoke mental discomfort, overload your eyes and brain, cause disturbance and anxiety, disrupt concentration, and drain your productivity without you even realizing it. And vice versa, when all design elements are tuned with one another, your screen will feel harmonious and pleasing, complementing your productive mode. Takeaway As the digital environment we inhabit via our phones is where we spend quite a bit of time daily, its design should naturally be approached as a matter of personal requirements and preferences. Unpersonalized and messy screens can provoke a negatively persuaded, disorienting experience and blur focus and productivity. In contrast, an organized, visually appealing screen with clear accents aligned with your needs will support your mental sharpness and promote your performance.

4 iPhone Screen Modification Strategies to Improve Focus and Productivity

ফোকাস এবং উত্পাদনশীলতা উন্নত করার জন্য 4 আইফোন স্ক্রীন পরিবর্তনের কৌশল

4 iPhone-Bildschirmmodifikationsstrategien zur Verbesserung von Fokus und Produktivität

4 estrategias de modificación de la pantalla del iPhone para mejorar el enfoque y la productividad

4 stratégies de modification de l'écran de l'iPhone pour améliorer la concentration et la productivité

फोकस और उत्पादकता में सुधार के लिए 4 iPhone स्क्रीन संशोधन रणनीतियाँ

集中力と生産性を向上させるための 4 つの iPhone 画面変更戦略

집중력과 생산성을 향상시키는 4가지 iPhone 화면 수정 전략

4 estratégias de modificação da tela do iPhone para melhorar o foco e a produtividade

4 стратегии модификации экрана iPhone для улучшения концентрации внимания и производительности

Odaklanmayı ve Üretkenliği Artırmak için 4 iPhone Ekran Değiştirme Stratejisi

4 chiến lược sửa đổi màn hình iPhone để cải thiện sự tập trung và năng suất

提高注意力和工作效率的 4 个 iPhone 屏幕修改策略

DOE v. Github (original complaint) Court Filing, retrieved on November 3, 2022 is part of . You can jump to any part in this filing . This is part 14 of 37. HackerNoon’s Legal PDF Series here VII. FACTUAL ALLEGATIONS A. Introduction 43. This class action against Defendants concerns an OpenAI product called Codex and a GitHub product called Copilot. 44. OpenAI began development of Codex sometime after OpenAI was founded in December 2015 and released Codex on a limited basis in August 2021. 45. GitHub began development of Copilot sometime in 2019, released it on a limited basis in June 2021, and released it as a paid subscription service in June 2022. 46. Codex and Copilot are assistive AI-based systems offered to software programmers. These AI systems are each trained on a large corpus of publicly accessible software code and other materials, including all the Licensed Materials. Defendants describe these products as making coding easier by accepting a code “prompt” from a programmer and emitting a possible completion of that code, which is referred to herein as Output, as set forth above. Defendants claim Codex and Copilot do not retain copies of the materials they are trained on. In practice, however, the Output is often a near-identical reproduction of code from the training data. 47. Codex and Copilot are related. Copilot is a joint venture between GitHub and OpenAI. Codex is a standalone product released by OpenAI that also “powers GitHub Copilot, which [OpenAI] built and launched in partnership with GitHub.”[5]“GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor.”[6] The version of Codex used to power Copilot is modified from OpenAI’s standalone version. Defendants have kept secret the details of Codex’s modifications and its integration into or interaction with Copilot. [5] See . https://openai.com/blog/openai-codex/ [6] See . https://github.com/features/copilot/ Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on September 5, 2023, from Storage.Courtlistener is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. 3:22-cv-06823-KAW

OpenAI Has Kept Secret the Details of Codex's Modifications and Integrations With Copilot

Code Book for Annotation of Diverse Cross-Document Coreference: Annotation Tool

A Comprehensive Guide to Building DolphinScheduler 3.2.0 Production-Grade Cluster Deployment

When we first came up with our “big idea” back in 2017, my partner Mike Wagner and I didn’t know how much impact it would have. The idea was simple enough: We wanted to develop a virtual appliance that could discover, provision, and maintain servers from any manufacturer. By 2020, that vision became a reality with the launch of our flagship platform, which we built entirely on open-source technologies. That strategy was working well from the start. Well enough, I should say. We knew we could improve our metal-as-a-service software appliance if we could just find a way to boost performance, ensure higher system uptime, and streamline the architecture. The only way to do that, we realized, was to replace the Linux operating system and KVM virtual machine solution with something that would give us those benefits. What we did know was that we wanted to continue with our strategy of utilizing open source—and ideally tied to a very permissive license and open governance model. That would give us more flexibility as our roadmap unfolded and enable extensive use and modification without stringent (or murky) conditions. Taking the plunge After our due diligence, we transitioned to the Unix-like open source FreeBSD operating system, along with the BSD hypervisor Bhyve. We liked that the FreeBSD project had off-the-shelf capabilities within a single distribution, and had plenty of mission-critical enterprise use cases (Netflix and Sony being two of the most well-known). Together with existing technologies like the Redfish standard, the new and improved platform we’d built (called Mojo) was ready to really take off. With FreeBSD and bhyve fully in place, we were now able to add edge data center provisioning to the platform, which helps discover, configure, and provision bare metal servers and storage within a data center and at the edge. It works really well with another new technology (PhotoISP, which I’ll discuss in a moment) to manage geographically distributed bare metal assets. These open-source changes to our infrastructure proved to be the silver bullet we were looking for. More organizations began to contact us, and we started landing some really interesting use cases, including Major League Baseball. Before the 2021 season, MLB was looking for a way to move from VMware to a containerized environment across 30 data centers. In a nutshell, they needed a way to quickly process 15 different input sources to track all the player and team statistics generated during a game. By more quickly provisioning servers, that data could be quickly gathered at the edge and uploaded into Google Cloud. The Mojo platform, backed by an evolved open-source strategy, helped streamline MLB’s deployment pipelines (cutting deployment time in half). It also simplified the provisioning and management of MLB’s bare metal edge sites and made it really easy to provision Anthos, BIOS, firmware, OS patches, and upgrades. They also saw big performance gains for their GPU-enabled apps and more optimized CI/CD pipelines. Expanding a vision backed by open source Albert Einstein once said that necessity is the mother of all invention. My family lives in the Blue Ridge Mountains, and when COVID hit, word spread quickly that several teachers and students were unable to stream their classes because they had insufficient rural broadband access. Mike and I brainstormed about how we could help, and we immediately recognized how Mojo, paired with a reliable and secure CPE, could solve the problem. That led development of the Photon ISP platform, which delivers routing, firewall, and QoS through the open-source FreeBSD operating system. It also monitors IDS/IPS for wireless and fixed fiber networks and ensures uninterrupted 4G/5G and Starlink failover as a hedge to wireless ISP services. Today, the Photon ISP platform has 50 rural customers, and that number continues to grow. And its uptime is unparalleled; we have FreeBSD CPEs that have been running for more than a year without issue. Speeds have been very consistent, streaming content has been reliable, and there has been almost zero latency. We’re still pushing the limits of what we can do with our optimized open-source infrastructure. Next, we plan to finish moving the remainder of the stack to FreeBSD once a viable OCI-compliant stack becomes available. In the meantime, we still use Linux within Bhyve virtual machines for a few of our services. Simply put, the strategic shift to open-source FreeBSD and Bhyve for our platforms has catalyzed innovation and customer growth. The success with MLB—cutting deployment times and improving performance—is a great example of how the right open-source pieces can have an outsized impact while being far more flexible (not to mention cost-efficient!) than proprietary alternatives. When we first came up with our “big idea” back in 2017, my partner Mike Wagner and I didn’t know how much impact it would have. The idea was simple enough: We wanted to develop a virtual appliance that could discover, provision, and maintain servers from any manufacturer. By 2020, that vision became a reality with the launch of our flagship platform, which we built entirely on open-source technologies. That strategy was working well from the start. Well enough , I should say. enough We knew we could improve our metal-as-a-service software appliance if we could just find a way to boost performance, ensure higher system uptime, and streamline the architecture. The only way to do that, we realized, was to replace the Linux operating system and KVM virtual machine solution with something that would give us those benefits. What we did know was that we wanted to continue with our strategy of utilizing open source—and ideally tied to a very permissive license and open governance model. That would give us more flexibility as our roadmap unfolded and enable extensive use and modification without stringent (or murky) conditions. Taking the plunge After our due diligence, we transitioned to the Unix-like open source FreeBSD operating system, along with the BSD hypervisor Bhyve . We liked that the FreeBSD project had off-the-shelf capabilities within a single distribution, and had plenty of mission-critical enterprise use cases (Netflix and Sony being two of the most well-known). Together with existing technologies like the Redfish standard, the new and improved platform we’d built (called Mojo) was ready to really take off. open source FreeBSD Bhyve Redfish With FreeBSD and bhyve fully in place, we were now able to add edge data center provisioning to the platform, which helps discover, configure, and provision bare metal servers and storage within a data center and at the edge. It works really well with another new technology (PhotoISP, which I’ll discuss in a moment) to manage geographically distributed bare metal assets. These open-source changes to our infrastructure proved to be the silver bullet we were looking for. More organizations began to contact us, and we started landing some really interesting use cases, including Major League Baseball. Before the 2021 season, MLB was looking for a way to move from VMware to a containerized environment across 30 data centers. In a nutshell, they needed a way to quickly process 15 different input sources to track all the player and team statistics generated during a game. By more quickly provisioning servers, that data could be quickly gathered at the edge and uploaded into Google Cloud. The Mojo platform, backed by an evolved open-source strategy, helped streamline MLB’s deployment pipelines (cutting deployment time in half). It also simplified the provisioning and management of MLB’s bare metal edge sites and made it really easy to provision Anthos, BIOS, firmware, OS patches, and upgrades. They also saw big performance gains for their GPU-enabled apps and more optimized CI/CD pipelines. Expanding a vision backed by open source Albert Einstein once said that necessity is the mother of all invention. My family lives in the Blue Ridge Mountains, and when COVID hit, word spread quickly that several teachers and students were unable to stream their classes because they had insufficient rural broadband access. Mike and I brainstormed about how we could help, and we immediately recognized how Mojo, paired with a reliable and secure CPE, could solve the problem. That led development of the Photon ISP platform, which delivers routing, firewall, and QoS through the open-source FreeBSD operating system. It also monitors IDS/IPS for wireless and fixed fiber networks and ensures uninterrupted 4G/5G and Starlink failover as a hedge to wireless ISP services. Today, the Photon ISP platform has 50 rural customers, and that number continues to grow. And its uptime is unparalleled; we have FreeBSD CPEs that have been running for more than a year without issue. Speeds have been very consistent, streaming content has been reliable, and there has been almost zero latency. We’re still pushing the limits of what we can do with our optimized open-source infrastructure. Next, we plan to finish moving the remainder of the stack to FreeBSD once a viable OCI-compliant stack becomes available. In the meantime, we still use Linux within Bhyve virtual machines for a few of our services. Simply put, the strategic shift to open-source FreeBSD and Bhyve for our platforms has catalyzed innovation and customer growth. The success with MLB—cutting deployment times and improving performance—is a great example of how the right open-source pieces can have an outsized impact while being far more flexible (not to mention cost-efficient!) than proprietary alternatives.

How Our Open Source OS Pivot Accelerated Product Innovation

Writing Effective Integration Tests in Spring: Organized Testing Strategies for HTTP Request Mocking

ReactJS and the Future of AI-Powered Web Components: What Will the Future Look Like?

About 80% of the team at my early-stage startup have stock options. These talented individuals could have chosen to work for big-name companies if I hadn’t made them co-owners. For startups, offering options to employees is the most effective approach to attract talent. When you're small, it's nearly impossible to match the salaries paid by tech giants like Google, Microsoft, or Netflix. What you can provide is ownership in a potentially multi-million dollar company. If you're a fellow founder, you've likely already googled how to issue stock options. Here, I offer tips from my own journey. Show $$$ but Explain Risks When I hire people, I offer them three choices: no stock options and a higher salary, a middle-level option, and the highest possible option at a lower salary. For me, it's a positive sign if a potential employee accepts an option for compensation. It means they aren’t just interested in making money but also in my startup’s product, and they will do their best to make the company successful. To get employees interested in the stock option, you have to explain your startup idea to them (just like when you talked to investors). Prepare your pitch; describe the opportunities and risks. Stock options allow employees to buy company shares at a fixed price (the strike price) and potentially sell them at a much higher market price later. This can lead to big financial gains if the company's value goes up. Provide concrete figures when discussing this with employees. If the startup becomes very successful and is bought for $200M, the employee who had 1% of the option receives $2M. Saying “two million dollars” can serve as a strong selling point. But startups often fail, and yours may too. And the stock options might end up being worthless. It is crucial to prepare the team for this scenario, making sure everyone understands the terms they are agreeing to. Managing expectations is one of the most critical aspects of effective management. Sell, but don’t oversell. Don't Get Greedy I have allocated 15% of my startup’s total equity for the employee stock option pool, a rate considered optimal. Each employee receives between 0.2% to 2% of the options. Go through the checklist below to try to see the fairest way to issue stock options: Consider the employee's market salary and/or their requested salary. Determine the amount you are willing to offer as compensation. Compensate the difference with stock options based on the estimated company valuation during the next round. Don't skimp on employee stock options — you are aiming to attract the best talent in the market, so come up with a fair compensation. Evaluate the employee's role, responsibilities, and contributions to the company. Roles with significant impact or employees who notably contribute to the company's growth and success typically receive a larger share of the stock options than others. Senior software engineers, along with elite marketing professionals like CMOs or heads of sales, are instrumental in driving product development and attracting customers. Their expertise is not only crucial for engaging customers but also for appealing to investors. Given their significance, specialists like these can effortlessly be entitled to up to 3% of the total option pool. Regularly review and adjust stock options as the company expands and develops. What may have seemed like a fair allocation in the early stages might need modification as the company scales and hires more people. Establish Vesting Rules To align your interests with those of your employees, promote long-term commitment, and ensure that equity is earned gradually. First, determine the total duration for which equity will vest. While the standard vesting period is typically 4 years, this timeframe can vary depending on the specific circumstances. Consider implementing a "cliff" period where no equity vests until a specific milestone is reached. In our startup, we incorporated a 1-year cliff. This approach allows sufficient time to assess an employee's performance and track progress. After that year, an employee receives the initial portion of stock options, around 25%, and then the shares vest monthly. Clearly communicate the vesting rules to employees in their offer letters, employment agreements, or equity grant agreements. Documenting the vesting rules is crucial to prevent misunderstandings later on. Besides, having a structure for the employee option pool is important during the funding process: Investors will certainly examine whether you have established clear rules. About 80% of the team at my early-stage startup have stock options. These talented individuals could have chosen to work for big-name companies if I hadn’t made them co-owners. About 80% of the team at my early-stage startup have stock options. These talented individuals could have chosen to work for big-name companies if I hadn’t made them co-owners. For startups, offering options to employees is the most effective approach to attract talent. When you're small, it's nearly impossible to match the salaries paid by tech giants like Google, Microsoft, or Netflix. What you can provide is ownership in a potentially multi-million dollar company. If you're a fellow founder, you've likely already googled how to issue stock options. Here, I offer tips from my own journey. Show $$$ but Explain Risks When I hire people, I offer them three choices: no stock options and a higher salary, a middle-level option, and the highest possible option at a lower salary. For me, it's a positive sign if a potential employee accepts an option for compensation. It means they aren’t just interested in making money but also in my startup’s product, and they will do their best to make the company successful. To get employees interested in the stock option, you have to explain your startup idea to them (just like when you talked to investors) . Prepare your pitch; describe the opportunities and risks. (just like when you talked to investors) Stock options allow employees to buy company shares at a fixed price (the strike price) and potentially sell them at a much higher market price later. This can lead to big financial gains if the company's value goes up. Provide concrete figures when discussing this with employees. (the strike price) If the startup becomes very successful and is bought for $200M, the employee who had 1% of the option receives $2M. Saying “two million dollars” can serve as a strong selling point. But startups often fail, and yours may too. And the stock options might end up being worthless. It is crucial to prepare the team for this scenario, making sure everyone understands the terms they are agreeing to. Managing expectations is one of the most critical aspects of effective management. Sell, but don’t oversell. Don't Get Greedy I have allocated 15% of my startup’s total equity for the employee stock option pool, a rate considered optimal. Each employee receives between 0.2% to 2% of the options. Go through the checklist below to try to see the fairest way to issue stock options: Consider the employee's market salary and/or their requested salary. Consider the employee's market salary and/or their requested salary. Determine the amount you are willing to offer as compensation. Determine the amount you are willing to offer as compensation. Compensate the difference with stock options based on the estimated company valuation during the next round. Compensate the difference with stock options based on the estimated company valuation during the next round. Compensate the difference with stock options based on the estimated company valuation during the next round. Don't skimp on employee stock options — you are aiming to attract the best talent in the market, so come up with a fair compensation. Don't skimp on employee stock options — you are aiming to attract the best talent in the market, so come up with a fair compensation. Evaluate the employee's role, responsibilities, and contributions to the company. Roles with significant impact or employees who notably contribute to the company's growth and success typically receive a larger share of the stock options than others. Senior software engineers, along with elite marketing professionals like CMOs or heads of sales, are instrumental in driving product development and attracting customers. Their expertise is not only crucial for engaging customers but also for appealing to investors. Given their significance, specialists like these can effortlessly be entitled to up to 3% of the total option pool. Regularly review and adjust stock options as the company expands and develops. What may have seemed like a fair allocation in the early stages might need modification as the company scales and hires more people. Establish Vesting Rules To align your interests with those of your employees, promote long-term commitment, and ensure that equity is earned gradually. First, determine the total duration for which equity will vest. While the standard vesting period is typically 4 years, this timeframe can vary depending on the specific circumstances. Consider implementing a "cliff" period where no equity vests until a specific milestone is reached. In our startup, we incorporated a 1-year cliff. This approach allows sufficient time to assess an employee's performance and track progress. After that year, an employee receives the initial portion of stock options, around 25%, and then the shares vest monthly. Clearly communicate the vesting rules to employees in their offer letters, employment agreements, or equity grant agreements. Documenting the vesting rules is crucial to prevent misunderstandings later on. Besides, having a structure for the employee option pool is important during the funding process: Investors will certainly examine whether you have established clear rules.

How to Give Employees Stock Options: Tips From Founder to Founders

3 Quick Ways to Optimize RecyclerView

RecyclerView অপ্টিমাইজ করার 3টি দ্রুত উপায়

3 schnelle Möglichkeiten zur Optimierung von RecyclerView

Tres formas rápidas de optimizar RecyclerView

3 façons rapides d'optimiser RecyclerView

रीसाइक्लरव्यू को अनुकूलित करने के 3 त्वरित तरीके

RecyclerView を最適化する 3 つの簡単な方法

RecyclerView를 최적화하는 3가지 빠른 방법

3 maneiras rápidas de otimizar o RecyclerView

3 быстрых способа оптимизировать RecyclerView

RecyclerView'ı Optimize Etmenin 3 Hızlı Yolu

3 cách nhanh chóng để tối ưu hóa RecyclerView

优化 RecyclerView 的 3 种快速方法

The Linux command line is a powerful tool with a wide range of functions. Users mostly use it to interact with their computers, and it gives them access to a variety of tools that can expedite and streamline their work. To fully utilize this capacity, you must be familiar with a few fundamental commands and shortcuts. Furthermore, grasping the fundamental commands of Linux is essential to making the most of all of its features. I recently posed the question, “What is your favourite Linux command?” on X (previously Twitter), and I received a lot of responses. https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true After viewing most of the comments, I’ve selected a couple that might be significant and also important to know. rm Command: rm removes all the files from the current directory with a prompt. rm -RF removes all the files and directories recursively without a prompt. Example: rm test.txt touch Command: An empty file is created in the current directory by the touch command. Example: touch test.txt rmdir Command: The rmdir command deletes an empty directory from the current working directory. Example: rmdir example_directory cd Command: It’s used to navigate between directories or folders within a file system. To go directly to the root directory, we use the cd command alone. Example: cd /home/user ls Command: This command is used to list files and directories in a directory. When used with the -lrh options, it provides a detailed list with specific formatting. -l: Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time.
-r: Lists files and directories in reverse order.
-h: Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). Example: ls -lrh cat Command: The cat command displays the contents of a text file. Example: cat example.txt pwd Command: The pwd command displays the path of the current working directory. Example: pwd man Command: To access the manual pages for a specific command, use the man command. It offers comprehensive details on the syntax, options, and operation of a command. Example: man ls sudo Command: In Unix-like operating systems, the sudo command represents “superuser do.” As determined by the security policy set up on the system, it permits a permitted user to run a command as the superuser (root) or as any other user. Example: sudo nano /etc/hosts Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Connect ⬇️ Twitter / Instagram / Github / Youtube / Newsletter / Discord The Linux command line is a powerful tool with a wide range of functions. Users mostly use it to interact with their computers, and it gives them access to a variety of tools that can expedite and streamline their work. To fully utilize this capacity, you must be familiar with a few fundamental commands and shortcuts. Furthermore, grasping the fundamental commands of Linux is essential to making the most of all of its features. I recently posed the question, “What is your favourite Linux command?” on X (previously Twitter), and I received a lot of responses. https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true After viewing most of the comments, I’ve selected a couple that might be significant and also important to know. rm Command: rm removes all the files from the current directory with a prompt. rm -RF removes all the files and directories recursively without a prompt. Example: rm test.txt touch Command: An empty file is created in the current directory by the touch command. Example: touch test.txt rmdir Command: The rmdir command deletes an empty directory from the current working directory. Example: rmdir example_directory cd Command: It’s used to navigate between directories or folders within a file system. To go directly to the root directory, we use the cd command alone. Example: cd /home/user ls Command: This command is used to list files and directories in a directory. When used with the -lrh options, it provides a detailed list with specific formatting. -lrh -l: Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time. -r: Lists files and directories in reverse order. -h: Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). -l : Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time. -l -r : Lists files and directories in reverse order. -r -h : Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). -h Example: ls -lrh cat Command: The cat command displays the contents of a text file. Example: cat example.txt pwd Command: The pwd command displays the path of the current working directory. Example: pwd man Command: To access the manual pages for a specific command, use the man command. It offers comprehensive details on the syntax, options, and operation of a command. Example: man ls sudo Command: In Unix-like operating systems, the sudo command represents “superuser do.” As determined by the security policy set up on the system, it permits a permitted user to run a command as the superuser (root) or as any other user. Example: sudo nano /etc/hosts Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Connect ⬇️ Connect Twitter / Instagram / Github / Youtube / Newsletter / Discord Twitter Twitter / Instagram Instagram / Github Github / Youtube Youtube / Newsletter Newsletter / Discord Discord

The Most Important Linux Commands You Need To Know!!!

Neural Network for Valuing Bitcoin: Numerical Results, Implementation and Discussion

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com];
(2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za];
(3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za];
(4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction
Methodology
Neural Network Methodology
Numerical results, implementation and discussion
Conclusion, Acknowledgments, and Funding
Availability of data, code and materials, Contributions and Declarations
References 2. Methodology Jump-diffusion models are continuous-time stochastic processes introduced in quantitative finance by Merton [34], extending the work on option pricing by Black and Scholes [3]. These models reproduce stylized facts observed in asset price dynamics, such as mean-reversion and jumps. There are two ways to expound the description of jump processes. 1. One is to describe the jump in absolute terms (this is useful if we are focusing on prices or interest rates), 2. The other is to describe the jump in proportional terms (this is more useful if our focus is on returns, as in this study). In addition to jump-diffusion models, Levy processes appear as an alternative for capturing large deviations in asset prices. Levy processes allow sample paths with frequent discontinuities, enabling them to generate heavy-tailed distributions. Some key examples include variance gamma processes, normal inverse Gaussian processes, and generalized hyperbolic processes. [51] demonstrated that Bitcoin returns exhibit heavy tails and proposed using a variance gamma process to model the price dynamics. [25] found evidence that variance gamma processes fit cryptocurrency log returns compared to classical diffusion. Levy processes have also been employed to model stochastic volatility in Bitcoin prices [5]. Overall, Levy processes provide an alternative class of models with more flexibility to account for extreme deviations. They have shown promise in fitting the empirical distributional properties of cryptocurrency returns. As this work focuses initially on extending jump-diffusion models, exploring Levy processes represents a worthwhile direction for future research. However, their ability to capture heavy tails and discontinuities suggests that Levy processes could serve as a valuable modelling technique in this domain. Theorem 2.1. In the market model described previously, the following holds: 2.1. Strengths and limitations of the proposed model The bivariate jump-diffusion model provides an essential first step in applying financial engineering techniques to this new domain. By capturing stochastic volatility, discrete jumps, and a sentiment indicator, it incorporates several salient features of cryptocurrencies. The preliminary pricing and derivatives valuation framework can be built upon as the market evolves. While the assumptions require validation as more data emerges, the model offers valuable inputs for investment analysis at present. We acknowledge the need to refine techniques as cryptocurrency finance matures into its own field. This paper aims to provide the initial modelling foundation, which can be improved incrementally as research progresses. Future enhancements may include more flexible return distributions, multifactor models to capture additional risks, regime-switching models to reflect market phases, and calibration of the sentiment factor from various data sources. In addition, the current proposed model offers a useful starting point, we recognize this initial framework may require modifications over time to align with market realities. As more cryptocurrency data becomes available, the distributional properties and other intricacies of these markets will become clearer. If the actual return distributions exhibit higher kurtosis than the normal distribution assumed, the model may need to be adjusted accordingly. Furthermore, if inefficiencies and arbitrage opportunities remain prevalent, the dynamics may diverge from a pure jump-diffusion process. As researchers expand their knowledge of the microstructure, behavioural components, and risks in cryptocurrency markets, our modelling approach can be enhanced. In summary, we emphasize this work represents a starting point for modelling cryptocurrencies and enabling financial applications. The proposed bivariate jump-diffusion model and sentiment indicator capture essential dynamics but may require adjustments as knowledge develops. We welcome future research to build on these initial techniques for pricing and valuation of cryptocurrency-denominated assets. 2.2. Applications This extended Black-Scholes PDE poses challenges for analytical solutions due to the unbounded upside of the European call payoff. Therefore, numerical methods are often used to approximate the solution. Two standard techniques are finite difference methods and Monte Carlo simulation. Finite difference methods involve discretizing the price domain and time dimensions and replacing the derivatives with finite difference approximations. This transforms the PDE into a system of algebraic equations that can be solved recursively. Various finite difference schemes can be employed, such as explicit, implicit, and Crank-Nicolson. Monte Carlo simulation generates sample paths of the underlying price using the assumed stochastic process. The discounted payoffs from each sample path are averaged to estimate the option price. Variance reduction techniques like antithetic sampling and control variates can improve efficiency. Both methods have tradeoffs regarding accuracy, speed, and implementation complexity. Our proposed neural network approach serves as an alternative way to numerically solve the pricing PDE without discretizing the domain. This offers benefits in terms of generalization and scaling. This paper is available on arxiv under CC 4.0 license. Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction Methodology Neural Network Methodology Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations References Abstract and Introduction Abstract and Introduction Methodology Methodology Neural Network Methodology Neural Network Methodology Numerical results, implementation and discussion Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations Availability of data, code and materials, Contributions and Declarations References References 2. Methodology Jump-diffusion models are continuous-time stochastic processes introduced in quantitative finance by Merton [34], extending the work on option pricing by Black and Scholes [3]. These models reproduce stylized facts observed in asset price dynamics, such as mean-reversion and jumps. There are two ways to expound the description of jump processes. 1. One is to describe the jump in absolute terms (this is useful if we are focusing on prices or interest rates), 2. The other is to describe the jump in proportional terms (this is more useful if our focus is on returns, as in this study). In addition to jump-diffusion models, Levy processes appear as an alternative for capturing large deviations in asset prices. Levy processes allow sample paths with frequent discontinuities, enabling them to generate heavy-tailed distributions. Some key examples include variance gamma processes, normal inverse Gaussian processes, and generalized hyperbolic processes. [51] demonstrated that Bitcoin returns exhibit heavy tails and proposed using a variance gamma process to model the price dynamics. [25] found evidence that variance gamma processes fit cryptocurrency log returns compared to classical diffusion. Levy processes have also been employed to model stochastic volatility in Bitcoin prices [5]. Overall, Levy processes provide an alternative class of models with more flexibility to account for extreme deviations. They have shown promise in fitting the empirical distributional properties of cryptocurrency returns. As this work focuses initially on extending jump-diffusion models, exploring Levy processes represents a worthwhile direction for future research. However, their ability to capture heavy tails and discontinuities suggests that Levy processes could serve as a valuable modelling technique in this domain. Theorem 2.1. In the market model described previously, the following holds: Theorem 2.1. 2.1. Strengths and limitations of the proposed model 2.1. Strengths and limitations of the proposed model The bivariate jump-diffusion model provides an essential first step in applying financial engineering techniques to this new domain. By capturing stochastic volatility, discrete jumps, and a sentiment indicator, it incorporates several salient features of cryptocurrencies. The preliminary pricing and derivatives valuation framework can be built upon as the market evolves. While the assumptions require validation as more data emerges, the model offers valuable inputs for investment analysis at present. We acknowledge the need to refine techniques as cryptocurrency finance matures into its own field. This paper aims to provide the initial modelling foundation, which can be improved incrementally as research progresses. Future enhancements may include more flexible return distributions, multifactor models to capture additional risks, regime-switching models to reflect market phases, and calibration of the sentiment factor from various data sources. In addition, the current proposed model offers a useful starting point, we recognize this initial framework may require modifications over time to align with market realities. As more cryptocurrency data becomes available, the distributional properties and other intricacies of these markets will become clearer. If the actual return distributions exhibit higher kurtosis than the normal distribution assumed, the model may need to be adjusted accordingly. Furthermore, if inefficiencies and arbitrage opportunities remain prevalent, the dynamics may diverge from a pure jump-diffusion process. As researchers expand their knowledge of the microstructure, behavioural components, and risks in cryptocurrency markets, our modelling approach can be enhanced. In summary, we emphasize this work represents a starting point for modelling cryptocurrencies and enabling financial applications. The proposed bivariate jump-diffusion model and sentiment indicator capture essential dynamics but may require adjustments as knowledge develops. We welcome future research to build on these initial techniques for pricing and valuation of cryptocurrency-denominated assets. 2.2. Applications 2.2. Applications This extended Black-Scholes PDE poses challenges for analytical solutions due to the unbounded upside of the European call payoff. Therefore, numerical methods are often used to approximate the solution. Two standard techniques are finite difference methods and Monte Carlo simulation. Finite difference methods involve discretizing the price domain and time dimensions and replacing the derivatives with finite difference approximations. This transforms the PDE into a system of algebraic equations that can be solved recursively. Various finite difference schemes can be employed, such as explicit, implicit, and Crank-Nicolson. Monte Carlo simulation generates sample paths of the underlying price using the assumed stochastic process. The discounted payoffs from each sample path are averaged to estimate the option price. Variance reduction techniques like antithetic sampling and control variates can improve efficiency. Both methods have tradeoffs regarding accuracy, speed, and implementation complexity. Our proposed neural network approach serves as an alternative way to numerically solve the pricing PDE without discretizing the domain. This offers benefits in terms of generalization and scaling.

Neural Network for Valuing Bitcoin: Methodology

Neural Network for valuing Bitcoin: References 

Neural Network for Valuing Bitcoin: Abstract and Introduction

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References ABSTRACT Keywords: Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References ABSTRACT Keywords : Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) Keywords 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Bridging Scales in Black Hole Accretion and Feedback: Studying Supermassive Black Holes

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