BeeKeeperAI

beekeeperai

Authors:
(1) Gonzalo J. Aniano Porcile, LinkedIn;
(2) Jack Gindi, LinkedIn;
(3) Shivansh Mundra, LinkedIn;
(4) James R. Verbus, LinkedIn;
(5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro
Data sets
Model
Results
Discussion, Acknowledgements, and References Abstract AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. 1. Introduction The past three decades have seen remarkable advances in the statistical modeling of natural images. The simplest power-spectral model [20] captures the 1/ω frequency magnitude fall-off typical of natural images, Figure 1(a). Because this model does not incorporate any phase information, it is unable to capture detailed structural information. By early 2000, new statistical models were able to capture the natural statistics of both magnitude and (some) phase [25], leading to breakthroughs in modeling basic texture patterns, Figure 1(b). While able to capture repeating patterns, these models are not able to capture the geometric properties of objects, faces, or complex scenes. Starting in 2017, and powered by large data sets of natural images, advances in deep learning, and powerful GPU clusters, generative models began to capture detailed properties of human faces and objects [16, 18]. Trained on a large number of images from a single category (faces, cars, cats, etc.), these generative adversarial networks (GANs) capture highly detailed properties of, for example, faces, Figure 1(c), but are constrained to only a single category. Most recently, diffusion-based models [2,26] have combined generative image models with linguistic prompts allowing for the synthesis of images from descriptive text prompts like “a beekeeper painting a self portrait”, Figure 1(d). Traditionally, the development of generative image models were driven by two primary goals: (1) understand the fundamental statistical properties of natural images; and (2) use the resulting synthesized images for everything from computer graphics rendering to human psychophysics and data augmentation in classic computer vision tasks. Today, however, generative AI has found more nefarious use cases ranging from spam to fraud and additional fuel for disinformation campaigns. Detecting manipulated or synthesized images is particularly challenging when working on large-scale networks with hundreds of millions of users. This challenge is made even more significant when the average user struggles to distinguish a real from a fake face [24]. Because we are concerned with the use of generative AI in creating fake online user accounts, we seek to develop fast and reliable techniques that can distinguish real from AI-generated faces. We next place our work in context of related techniques. 1.1. Related Work Because we will focus specifically on AI-generated faces, we will review related work also focused on, or applicable to, distinguishing real from fake faces. There are two broad categories of approaches to detecting AI-generated content [10]. In the first, hypothesis-driven approaches, specific artifacts in AI-generated faces are exploited such as inconsistencies in bilateral facial symmetry in the form of corneal reflections [13] and pupil shape [15], or inconsistencies in head pose and the spatial layout of facial features (eyes, tip of nose, corners of mouth, chin, etc.) [23, 33, 34]. The benefit of these approaches is that they learn explicit, semanticlevel anomalies. The drawback is that over time synthesis engines appear to be – either implicitly or explicitly – correcting for these artifacts. Other non-face specific artifacts include spatial frequency or noise anomalies [5,8,12,21,35], but these artifacts tend to be vulnerable to simple laundering attacks (e.g., transcoding, additive noise, image resizing). In the second, data-driven approaches, machine learning is used to learn how to distinguish between real and AIgenerated images [11, 29, 32]. These models often perform well when analyzing images consistent with their training, but then struggle with out-of-domain images and/or are vulnerable to laundering attacks because the model latches onto low-level artifacts [9]. We attempt to leverage the best of both of these approaches. By training our model on a range of synthesis engines (GAN and diffusion), we seek to avoid latching onto a specific low-level artifact that do not generalize or may be vulnerable to simple laundering attacks. By focusing on only detecting AI-generated faces (and not arbitrary synthetic images), we show that our model seems to have captured a semantic-level artifact distinct to AI-generated faces which is highly desirable for our specific application of finding potentially fraudulent user accounts. We also show that our model is resilient to detecting AI-generated faces not previously seen in training, and is resilient across a large range of image resolutions and qualities. This paper is available on arxiv under CC 4.0 license. Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Authors: Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro Data sets Model Results Discussion, Acknowledgements, and References Abstract and Intro Abstract and Intro Data sets Data sets Model Model Results Results Discussion, Acknowledgements, and References Discussion, Acknowledgements, and References Abstract AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. 1. Introduction The past three decades have seen remarkable advances in the statistical modeling of natural images. The simplest power-spectral model [20] captures the 1/ω frequency magnitude fall-off typical of natural images, Figure 1(a). Because this model does not incorporate any phase information, it is unable to capture detailed structural information. By early 2000, new statistical models were able to capture the natural statistics of both magnitude and (some) phase [25], leading to breakthroughs in modeling basic texture patterns, Figure 1(b). While able to capture repeating patterns, these models are not able to capture the geometric properties of objects, faces, or complex scenes. Starting in 2017, and powered by large data sets of natural images, advances in deep learning, and powerful GPU clusters, generative models began to capture detailed properties of human faces and objects [16, 18]. Trained on a large number of images from a single category (faces, cars, cats, etc.), these generative adversarial networks (GANs) capture highly detailed properties of, for example, faces, Figure 1(c), but are constrained to only a single category. Most recently, diffusion-based models [2,26] have combined generative image models with linguistic prompts allowing for the synthesis of images from descriptive text prompts like “a beekeeper painting a self portrait”, Figure 1(d). Traditionally, the development of generative image models were driven by two primary goals: (1) understand the fundamental statistical properties of natural images; and (2) use the resulting synthesized images for everything from computer graphics rendering to human psychophysics and data augmentation in classic computer vision tasks. Today, however, generative AI has found more nefarious use cases ranging from spam to fraud and additional fuel for disinformation campaigns. Detecting manipulated or synthesized images is particularly challenging when working on large-scale networks with hundreds of millions of users. This challenge is made even more significant when the average user struggles to distinguish a real from a fake face [24]. Because we are concerned with the use of generative AI in creating fake online user accounts, we seek to develop fast and reliable techniques that can distinguish real from AI-generated faces. We next place our work in context of related techniques. 1.1. Related Work Because we will focus specifically on AI-generated faces, we will review related work also focused on, or applicable to, distinguishing real from fake faces. There are two broad categories of approaches to detecting AI-generated content [10]. In the first, hypothesis-driven approaches, specific artifacts in AI-generated faces are exploited such as inconsistencies in bilateral facial symmetry in the form of corneal reflections [13] and pupil shape [15], or inconsistencies in head pose and the spatial layout of facial features (eyes, tip of nose, corners of mouth, chin, etc.) [23, 33, 34]. The benefit of these approaches is that they learn explicit, semanticlevel anomalies. The drawback is that over time synthesis engines appear to be – either implicitly or explicitly – correcting for these artifacts. Other non-face specific artifacts include spatial frequency or noise anomalies [5,8,12,21,35], but these artifacts tend to be vulnerable to simple laundering attacks (e.g., transcoding, additive noise, image resizing). In the second, data-driven approaches, machine learning is used to learn how to distinguish between real and AIgenerated images [11, 29, 32]. These models often perform well when analyzing images consistent with their training, but then struggle with out-of-domain images and/or are vulnerable to laundering attacks because the model latches onto low-level artifacts [9]. We attempt to leverage the best of both of these approaches. By training our model on a range of synthesis engines (GAN and diffusion), we seek to avoid latching onto a specific low-level artifact that do not generalize or may be vulnerable to simple laundering attacks. By focusing on only detecting AI-generated faces (and not arbitrary synthetic images), we show that our model seems to have captured a semantic-level artifact distinct to AI-generated faces which is highly desirable for our specific application of finding potentially fraudulent user accounts. We also show that our model is resilient to detecting AI-generated faces not previously seen in training, and is resilient across a large range of image resolutions and qualities. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Finding AI-Generated Faces in the Wild: Abstract and Intro

AI-জেনারেটেড ফেস ইন দ্য ওয়াইল্ড খোঁজা: অ্যাবস্ট্রাক্ট এবং ইন্ট্রো

KI-generierte Gesichter in freier Wildbahn finden: Zusammenfassung und Einführung

Encontrar rostros generados por IA en la naturaleza: resumen e introducción

Trouver des visages générés par l'IA dans la nature : résumé et introduction

जंगल में एआई-जनरेटेड चेहरे खोजना: सार और परिचय

AI が生成した顔の発見: 概要と導入

야생에서 AI가 생성한 얼굴 찾기: 초록 및 소개

Encontrando rostos gerados por IA na natureza: resumo e introdução

Поиск лиц, созданных искусственным интеллектом, в дикой природе: краткое содержание и введение

Vahşi Doğada Yapay Zeka Tarafından Üretilen Yüzleri Bulma: Soyut ve Giriş

Tìm kiếm khuôn mặt do AI tạo ra trong thế giới hoang dã: Tóm tắt và giới thiệu

在野外寻找人工智能生成的面孔：摘要和简介

Traditional RAG systems work by indexing raw data. This data is simply chunked and stored in vector DBs. Whenever a query comes from the user, it queries the stored chunks and retrieves relevant chunks. If you wish to learn the fundamentals of RAG I have written a comprehensive intro about it here. As the retrieval step happens for every single query from the user, it is the most crucial bottleneck to speed up naive RAG systems. Would it not be logical to make the retrieval process super efficient? This is the promise of LightRAG. Why Not GraphRAG? Before we look at them, you may ask, “Wait. Do we not have GraphRAG from Microsoft?”. Yes, but GraphRAG seems to have a couple of drawbacks. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. So, without further adieu, let's dive into LightRAG. Visual Learning If you are a visual learner like me, why not check this YouTube video explaining the idea of LightRAG: https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true LightRAG The two main selling points of LightRAG are Graph-based indexing and a dual-level retrieval framework. So, let's look into each of them. Graph-based Indexing Below are the steps LightRAG follows to incorporate graph-based indexing. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Dual-level Retrieval A query to a RAG system can be one of two types — specific or abstract. In the same bee example, a specific query could be “How many queen bees can be there in the hive?” An abstract query could be, “What are the implications of climate change on honey bees?” To address this diversity, LightRAG employs two retrieval types: Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. Why Bother Doing All This? Doing all this exercise and switching to LightRAG improves execution time indeed. During indexing, the LLM needs to be called just once per chunk to extract entities and their relationships. Likewise, during user query, we only retrieve entities and relationships from chunks using the same LLM we used for indexing. This is a huge saving on the retrieval overhead and hence computation. So, we have a “light” RAG at last! Integrating new knowledge into existing graphs seems to be a seamless exercise. Instead of re-indexing the whole data whenever we have new information, we can simply append new knowledge to the existing graph. Evaluation In their evaluations, they have compared against Naive RAG, RQ-RAG, HyDE, and GraphRAG. To keep the comparison fair, they have used GPT-4o-mini as the LLM across the board with a fixed chunk size of 1200 for all datasets. The answers were evaluated for comprehensiveness, diversity, and effectiveness in answering the user(a.k.a. empowerment in the paper). As we can see from the underlined results, LightRAG beats all of the state-of-the-art methods currently available. In general, they draw the following conclusions: Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG can deal with complex queries better Conclusion Though RAG is a fairly recent technique, we are seeing rapid progress in the area. Techniques like LightRAG which can take RAG pipelines to cheap commodity hardware are the most welcome. While the hardware landscape is ever-growing, there is always an increasing need to run LLMs and RAG pipelines in compute-constrained hardware in real time. Would you like to see some hands-on study of LightRAG? Please stay tuned… Traditional RAG systems work by indexing raw data. This data is simply chunked and stored in vector DBs. Whenever a query comes from the user, it queries the stored chunks and retrieves relevant chunks. If you wish to learn the fundamentals of RAG I have written a comprehensive intro about it here . retrieves here here As the retrieval step happens for every single query from the user, it is the most crucial bottleneck to speed up naive RAG systems. Would it not be logical to make the retrieval process super efficient? This is the promise of LightRAG . LightRAG Why Not GraphRAG? Before we look at them, you may ask, “Wait. Do we not have GraphRAG from Microsoft?”. Yes, but GraphRAG seems to have a couple of drawbacks. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Incremental knowledge update. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. Computational intensity. So, without further adieu, let's dive into LightRAG. Visual Learning If you are a visual learner like me, why not check this YouTube video explaining the idea of LightRAG: https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true LightRAG The two main selling points of LightRAG are Graph-based indexing and a dual-level retrieval framework. So, let's look into each of them. Graph-based Indexing Below are the steps LightRAG follows to incorporate graph-based indexing. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Entity and Relationship (ER) extraction. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Key-value (KV) pair generation using LLM. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Deduplication. Dual-level Retrieval A query to a RAG system can be one of two types — specific or abstract. In the same bee example, a specific query could be “How many queen bees can be there in the hive?” An abstract query could be, “What are the implications of climate change on honey bees?” To address this diversity, LightRAG employs two retrieval types: Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. Low-level retrieval. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. High-level retrieval. Why Bother Doing All This? Doing all this exercise and switching to LightRAG improves execution time indeed. During indexing, the LLM needs to be called just once per chunk to extract entities and their relationships. Likewise, during user query, we only retrieve entities and relationships from chunks using the same LLM we used for indexing. This is a huge saving on the retrieval overhead and hence computation. So, we have a “light” RAG at last! Integrating new knowledge into existing graphs seems to be a seamless exercise. Instead of re-indexing the whole data whenever we have new information, we can simply append new knowledge to the existing graph. Evaluation In their evaluations, they have compared against Naive RAG, RQ-RAG, HyDE, and GraphRAG. To keep the comparison fair, they have used GPT-4o-mini as the LLM across the board with a fixed chunk size of 1200 for all datasets. The answers were evaluated for comprehensiveness, diversity, and effectiveness in answering the user(a.k.a. empowerment in the paper). empowerment As we can see from the underlined results, LightRAG beats all of the state-of-the-art methods currently available. In general, they draw the following conclusions: Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG can deal with complex queries better LightRAG can deal with complex queries better Conclusion Though RAG is a fairly recent technique, we are seeing rapid progress in the area. Techniques like LightRAG which can take RAG pipelines to cheap commodity hardware are the most welcome. While the hardware landscape is ever-growing, there is always an increasing need to run LLMs and RAG pipelines in compute-constrained hardware in real time. Would you like to see some hands-on study of LightRAG? Please stay tuned…

LightRAG - Is It a Simple and Efficient Rival to GraphRAG?

In the modern age of development, having an efficient developer workflow plays a huge role in shipping quality software as fast as possible. Open source is a valuable resource with tons of projects that are supported by the community and allows us to better tailor the solutions for our particular needs. In this article, I decided to manually handpick 16 of my favorite open-source projects that can boost your development workflow and allow you to save time. From testing environments, and auth solutions to robust analytics, database managers, and deployment solutions, I hope that these tools will improve your craft. I also added direct links, descriptions, and previews of each project so that you can get an idea about them right away while reading the article. 1. Firecamp Firecamp is a cross-platform API development tool. It puts REST, GraphQL, and WebSocket API testing into one interface. This allows you to test and debug in a single application, reducing wasted time on context switches between different tools. 2. Budibase Budibase is an open-source, low-code platform for building powerful and effective internal tools and business apps. It enables rapid application development, reduces manual coding, and can be integrated with many popular databases/services. 3. Hanko Hanko is an open-source auth solution for creating passwordless logins with passkeys and passcodes. It produces a secure yet easy-to-implement system for authentication, reducing complexity and time spent managing user authentications. 4. Cerbos Cerbos is a policy-based access control system that makes it easy for developers to manage user permissions. It decouples the access control logic from the application code, which provides flexibility in managing authorization rules. 5. Primo Primo is a visual CMS for static sites that allows developers to edit the content of the sites you are building on the go. It is also integrated with an intuitive visual editor to give the best developer experience with an instant preview. 6. Typebot Typebot is a platform to create chatbots in a no-code interface. You can further embed them anywhere on your web/mobile apps, and collect results in real-time to help in user support. 7. Mermaid Mermaid is a diagramming tool using JavaScript that takes the textual definition of diagrams and charts and draws them. It allows users to visually create flowcharts, sequence diagrams, and classes, among others, in a dynamic way in either a browser or markdown files. 8. Flipt Flipt is an enterprise-ready, GitOps-enabled, CloudNative system for managing the feature roadmap of your applications. It allows gradual rollout, A/B testing, and experimentation to follow DevOps best practices and separate releases from deployments. 9. SigNoz SigNoz is a free-to-use observability platform with out-of-the-box metrics, logs, and traces for troubleshooting. This provides an easy-to-use interface and detailed overview for real-time diagnosis of performance issues and application optimization. 10. Plausible Plausible Analytics is an open-source web analytics tool that extensively focuses on privacy and is considered easier to use than Google Analytics. It provides clear and actionable insights without unnecessary bloat and clutter, making it easier for the developers to monitor performance on the website. 11. Supabase Supabase is an open-source alternative to Firebase. It can also serve as a backend-as-a-service, providing real-time databases, authentication, and storage. It allows developers to quickly develop and scale their applications with minimal setup while utilizing tools they already know. 12. Prisma Prisma is an open-source object-relational mapping tool that provides easier database access for Node.js and TypeScript applications. It provides type-safe database queries, automated migrations, and a rich data model, leading to more efficient and error-free database management. 13. Beekeeper Beekeeper Studio is a modern, open-source SQL editor and DB manager. It provides an interface to write queries, manage the databases, and visualize your data for MySQL, Postgres, SQLite, SQL Server, and more. 14. Coolify Coolify is an open-source, self-hosted Heroku / Netlify / Vercel alternative for the deployment and management of web apps and databases. It helps you manage your servers, applications, and databases on your own hardware; you only need an SSH connection. 15. Jest Jest is a fast and safe JavaScript testing framework with a focus on simplicity. It comes with an easy-to-use test environment with lots of features to enhance performance, like snapshot testing or parallelization. 16. Oh My Zsh Oh My Zsh is a community-driven framework for managing your Zsh configuration. It is equipped with a number of helpful plugins and themes, which extend the terminal with customizability with little effort. Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter! Also, connect with me on Twitter, LinkedIn, and GitHub! Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch! In the modern age of development, having an efficient developer workflow plays a huge role in shipping quality software as fast as possible. Open source is a valuable resource with tons of projects that are supported by the community and allows us to better tailor the solutions for our particular needs. In this article, I decided to manually handpick 16 of my favorite open-source projects that can boost your development workflow and allow you to save time. From testing environments, and auth solutions to robust analytics, database managers, and deployment solutions, I hope that these tools will improve your craft. I also added direct links, descriptions, and previews of each project so that you can get an idea about them right away while reading the article. 1. Firecamp Firecamp Firecamp is a cross-platform API development tool. It puts REST, GraphQL, and WebSocket API testing into one interface. This allows you to test and debug in a single application, reducing wasted time on context switches between different tools. 2. Budibase Budibase Budibase is an open-source, low-code platform for building powerful and effective internal tools and business apps. It enables rapid application development, reduces manual coding, and can be integrated with many popular databases/services. 3. Hanko Hanko Hanko is an open-source auth solution for creating passwordless logins with passkeys and passcodes. It produces a secure yet easy-to-implement system for authentication, reducing complexity and time spent managing user authentications. 4. Cerbos Cerbos Cerbos is a policy-based access control system that makes it easy for developers to manage user permissions. It decouples the access control logic from the application code, which provides flexibility in managing authorization rules. 5. Primo Primo Primo is a visual CMS for static sites that allows developers to edit the content of the sites you are building on the go. It is also integrated with an intuitive visual editor to give the best developer experience with an instant preview. 6. Typebot Typebot Typebot is a platform to create chatbots in a no-code interface. You can further embed them anywhere on your web/mobile apps, and collect results in real-time to help in user support. 7. Mermaid Mermaid Mermaid is a diagramming tool using JavaScript that takes the textual definition of diagrams and charts and draws them. It allows users to visually create flowcharts, sequence diagrams, and classes, among others, in a dynamic way in either a browser or markdown files. 8. Flipt Flipt Flipt is an enterprise-ready, GitOps-enabled, CloudNative system for managing the feature roadmap of your applications. It allows gradual rollout, A/B testing, and experimentation to follow DevOps best practices and separate releases from deployments. 9. SigNoz SigNoz SigNoz is a free-to-use observability platform with out-of-the-box metrics, logs, and traces for troubleshooting. This provides an easy-to-use interface and detailed overview for real-time diagnosis of performance issues and application optimization. 10. Plausible Plausible Plausible Analytics is an open-source web analytics tool that extensively focuses on privacy and is considered easier to use than Google Analytics. It provides clear and actionable insights without unnecessary bloat and clutter, making it easier for the developers to monitor performance on the website. 11. Supabase Supabase Supabase is an open-source alternative to Firebase. It can also serve as a backend-as-a-service, providing real-time databases, authentication, and storage. It allows developers to quickly develop and scale their applications with minimal setup while utilizing tools they already know. 12. Prisma Prisma Prisma is an open-source object-relational mapping tool that provides easier database access for Node.js and TypeScript applications. It provides type-safe database queries, automated migrations, and a rich data model, leading to more efficient and error-free database management. 13. Beekeeper Beekeeper Beekeeper Studio is a modern, open-source SQL editor and DB manager. It provides an interface to write queries, manage the databases, and visualize your data for MySQL, Postgres, SQLite, SQL Server, and more. 14. Coolify Coolify Coolify is an open-source, self-hosted Heroku / Netlify / Vercel alternative for the deployment and management of web apps and databases. It helps you manage your servers, applications, and databases on your own hardware; you only need an SSH connection. 15. Jest Jest Jest is a fast and safe JavaScript testing framework with a focus on simplicity. It comes with an easy-to-use test environment with lots of features to enhance performance, like snapshot testing or parallelization. 16. Oh My Zsh Oh My Zsh Oh My Zsh is a community-driven framework for managing your Zsh configuration. It is equipped with a number of helpful plugins and themes, which extend the terminal with customizability with little effort. Did you like the resources? Here is more 👇 Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter ! my newsletter my newsletter Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter Twitter LinkedIn LinkedIn GitHub GitHub Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch ! get in touch get in touch

16 Handpicked Open-Source Projects to Improve Your Developer Workflow

In today's fast-paced world, there's a vast array of productivity tools at our disposal, designed to simplify our lives and enhance our efficiency. However, with so many tools available, it's easy to get lost in the sea of options, leading to decision fatigue and inefficiency. In this article, I've handpicked a list of 17 tools to address specific productivity challenges you may face from screen recording, keyboard shortcuts, and clipboard management to working with Regex, terminals, DB entries, APIs, and more. I've included direct links, descriptions, and visuals so that you can get an initial impression of each tool right away. 1. FocuSee (Sponsored) is a screen recording tool that automatically zooms in on the video, tracks cursor movement, and adds a nice background, without the need for manual editing. FocuSee 5 most useful features of FocuSee: FocuSee automatically zooms in on the most important part of the screen, making it easy for viewers to follow along. Automatic zoom: FocuSee tracks the movement of your cursor and keeps it in focus, even if you're moving it quickly or erratically. Cursor movement tracking: FocuSee adds a nice, blurred background to your videos, making you look more professional and polished. Nice background: FocuSee automatically polishes your videos for you, so you can spend less time editing and more time creating. No manual editing required: FocuSee is very easy to use, even for beginners. Simply click the record button and start recording your screen. FocuSee will do the rest. Easy to use: 2. Keypirinha A powerful keyboard launcher that allows you to quickly access applications and files by typing. It's highly customizable and can boost your productivity. 3. Ditto A clipboard manager that extends your clipboard's functionality, making it easier to manage and reuse copied items. It saves time and enhances your workflow. 4. Fancy Zones A Windows Management tool by Microsoft that helps you organize and manage open windows on your desktop for improved multitasking. 5. Typora A markdown editor that offers a distraction-free writing experience and real-time preview. It simplifies the process of creating markdown documents. 6. Ray.so A tool for creating beautiful code snippets in no time with instant sharing capabilities. It makes sharing code snippets a breeze. 7. GradientHunt A curated collection that allows you to easily access and use beautiful gradients, enhancing your design and development projects. 8. Hyper A cross-platform terminal built on Electron. It is highly customizable and extensible, with a wide range of themes and plugins available. .png) 9. Sourcetrail A code visualization tool that aids in codebase exploration, making it easier to understand complex projects. 10. RegExr An online tool for regular expressions, helping developers create, test, and debug regular expressions. 11. Beekeeper A SQL editor with a clean interface, making database management and SQL query writing more efficient. 12. Lucidchart A wireframing tool for UI/UX design. It simplifies the creation of diagrams and prototypes. 13. MindMup An online mind-mapping tool that aids in brainstorming and planning, helping you organize your thoughts and ideas. 14. Swagger A tool for API documentation, making it easy to create and maintain documentation for your APIs. 15. Insomnia An API testing tool that streamlines the testing of REST and GraphQL APIs. It simplifies the debugging process. 16. f.lux A screen light adjuster that reduces blue light from your screen, helping reduce eye strain and improve sleep quality. 17. Bitwarden A password manager that securely stores your passwords and makes it easy to manage login credentials. It enhances security and convenience. Writing has always been my passion and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Connect me on , , and ! Twitter LinkedIn GitHub Visit my for more articles like this. Blog Also published . here

The 17 Essential Tools You Need to Boost Your Productivity 🚀🔥

Father Ferapont

Food and Flavor: A Gastronomic Guide to Health and Good Living: Chapter XII 

Obscure Python Libraries I picked 4 Python libraries that you probably haven't heard of that I find useful. I'll show you how to use them and then we'll make a fun little program with them. Great Scott Marty! Introducing Delorean You mean Delorean is more than a time-traveling car? Yes, random Internet Stranger, it is. Datetime objects in Python can be a little difficult to work with. Delorean is a library that makes dealing with Datetime objects easier. According to the , Delorean is a library for clearing up the inconvenient truths that arise dealing with datetimes in Python. Understanding that timing is delicate enough of a problem Delorean hopes to provide a cleaner less troublesome solution to shifting, manipulating, generating datetimes. documentation Fire Up The Flux Capacitor Since the docs for Delorean are pretty good, so I won't go into a lot of detail here. Below are a few examples of using it. (You can run these yourself in your Python interpreter or IDE.) before you start. Buy your Flux Capacitor delorean Delorean d = Delorean()
d d = d.shift( )
d d.datetime d.date from import # create delorean obj # set obj to central timezone "US/Central" # print datetime # print date In the example above, we create a Delorean object, set it to , and then print its datetime and date values. Central timezone d = Delorean()
d d.next_tuesday() d.last_tuesday( ).midnight # create obj # print next tuesday's date # print datetime two tuesdays ago at midnight 2 In our second example, we print the datetime of next Tuesday based on the current date set by Delorean. Finally, we count back to 2 previous Tuesdays and get the datetime at midnight. That would've been painful to do manually. Bears and Fuzzy Pattern Matching. Introducing FuzzyWuzzy Our next library, FuzzyWuzzy, gives us a way to easily fuzzy match strings. Fuzzy matching involves comparing the input to a variety of values and computing their amount of "sameness". The gives more information. FuzzyWuzzy documentation fuzzywuzzy fuzz fuzzywuzzy process

fuzz.ratio( , )
fuzz.partial_ratio( , )
fuzz.token_sort_ratio( , )

choices = [ , , , ]
process.extract( , choices, limit= )
process.extractOne( , choices) from import from import "this is a test" "this is a test!" "this is a test" "this is a test!" "fuzzy was a bear" "fuzzy fuzzy was a bear" "Atlanta Falcons" "New York Jets" "New York Giants" "Dallas Cowboys" "new york jets" 2 "cowboys" With the , we compare "this is a test" to "this is a test!" which gives us a score of 97 (97% the same). , scoring on the first string existing in the second, gives us 100 because 100% of the first string exists in the second. , using "tokens" to compare the first set of words compared to the second, gives us a score of 84. fuzz.ratio fuzz.partial_ratio fuzz.token_sort_ratio The second section of the code compares the first string to matches in a list of strings. returns a list of tuples containing the matched string from the list and the score. (limit sets the number of matches returned.) does the same thing but only returns one match which is always the (highest score). process.extract process.extractOne best match Wink, Smile, Thumbs Up. Introducing Emoji Emoji is a Python library that unsurprisingly allows us to display emojis. is easy to follow but it doesn't give much detail. Emoji's documentation emoji

print(emoji.emojize( ))
print(emoji.emojize( , use_aliases= ))

print(emoji.emojize( ))

print(emoji.emojize( ,variant= )) import 'Python is :thumbs_up:' 'Python is :thumbsup:' True "Python is fun :red_heart:" "Python is fun :red_heart:" "emoji_type" There's not much to Emoji but it does its job well. replaces any emoji code with the actual emoji symbol. allows you to use alternate names for the emoji (:thumbs_up: vs :thumbsup:). seems to force your computer to load an actual emoji in the terminal instead of an ascii code character. (That's just a guess based on my experimenting with it but it doesn't say anything in the docs.) emoji.emojize use_aliases=True variant The Last Stop On Our Magical Mystery Tour. Introducing Inflect Inflect is a bit harder to explain because it does a lot. It's a toolbox that helps us build messages/sentences to display to the user that is "smart". If you give Inflect a verb, nouns or adjectives, it's smart enough to return the correct version of the word. For example, the plural form of "dog" is "dogs". Inflect also allows you to switch numbers (1, 100, 1000) to words (one, one hundred, one thousand). See the for more information. Inflect documentation inflect
inflector = inflect.engine()

num_words = inflector.number_to_words( )
num_words

num_turkeys = print( , num_turkeys, inflector.plural(word, num_turkeys))

num_turkeys = print( , num_turkeys, inflector.plural(word, num_turkeys))

n1 = n2 = print(
	inflector.plural_noun( , n1),
	inflector.plural_verb( , n1),
	inflector.plural_adj( , n2),
	inflector.plural_noun( , n2)
) import 1632976 1 "I saw" 17 "I saw" 1 2 "I" "saw" "my" "saw" Let's Build A Project! Now that we've seen how to use these libraries, let's put them all together to build a mini-project. Our project is going to be a very small version of MadLibs. I'll show you the code and then give a tiny bit of explanation. random datetime timedelta delorean emoji inflect delorean Delorean fuzzywuzzy process verbs = [ , , , , , , , , , , , ]
    nouns = [ , , , , , , , , , , , ]
    descriptors = [ , , , , , , , , , , , ]
    places = [ , , , , , , , , , , , ]

    subjects = { : [ , , , , , , , , , , , ], : [ , , , , , , , , , , , ]
    }

    adjectives = [ , , , , , , , ]
    prepositions = [ , , , , , , ]
    intervals = [ , , , ]
    feelings = { : , : , : , : , : , : , : , : , : }
    lunch_stuff = [ , , , ]
    num_stories = self.inflector = inflect.engine()
        self.num_stories = num_stories process.extractOne(noun, self.lunch_stuff)[ ] >= : emotion = random.choice(
            list(self.feelings)
        ) (
            emotion,
            self.emoji_by_emotion(emotion)
        ) selected_emoji = self.feelings.get(emotion) emoji.emojize(selected_emoji, use_aliases= ) num = random.randint( , ) num % == : num = random.randint( , ) num % == : num self.inflector.number_to_words(num) random.choice(self.prepositions) random.choice(self.descriptors) random.choice(self.places) self.inflector.plural_noun(
            random.choice(self.subjects.get(idx)),
            num
        ) adjective = random.choice(self.adjectives) num == : adjective self.inflector.plural_adj(adjective) noun = random.choice(self.nouns) num == : noun self.inflector.plural(noun) self.inflector.plural_verb(
            random.choice(self.verbs), num
        ) num = self.single_or_plural() [
            self.random_adjective(num),
            self.random_subject(subject_idx, num),
            self.random_verb(num)
        ] num = self.single_or_plural()
        subject = self.random_subject(subject_idx, num) num == :
            subject += :
            subject += [
            self.random_adjective(num),
            subject,
            self.random_place()
        ] num = self.random_number() [
            self.random_adjective(num),
            self.number_to_words(num),
            self.random_descriptor(),
            self.random_noun(num)
        ] self.delorean = Delorean()
        self.delorean = self.delorean.shift( )
        
        interval = random.choice(self.intervals)
        num = random.randint( , )

        self.delorean += timedelta(
            **{interval: num}
        )

        out = out sentences = orig.split( )
        sentences2 = [sentence[ ].capitalize() + sentence[ :] sentence sentences] .join(sentences2) part1 = self.adjective_subject_verb( )
        part2 = self.adjective_num_descriptor_noun()
        part3 = self.adjective_subject_place( )
        preposition = self.random_preposition()
        time = self.timeframe()
        (emotion, feeling) = self.random_emotion()
        lunch = out = self.capitalize_sentences( )

        print(out) __name__ == :
    story_maker = Story(
        int(input( ))
    ) i range(story_maker.num_stories):
        story_maker.create_story() import from import import import import from import from import : class Story 'runs' 'swims' 'walks' 'flies' 'paints' 'sleeps' 'eats' 'drives' 'spells' 'bakes' 'slices' 'waters' 'taco' 'elephant' 'cheeseburger' 'shoe' 'ogre' 'broccoli' 'lightbulb' 'cloud' 'coffee' 'mailbox' 'slime' 'hat' 'green' 'tired' 'huge' 'sleepy' 'delicious' 'stinky' 'funky' 'hairy' 'moldy' 'expensive' 'wet' 'droopy' 'house' 'stadium' 'theater' 'store' 'bakery' 'amusement park' 'canyon' 'moon' 'valley' 'circus tent' 'forest' 'mountain' 0 'boy' 'girl' 'rabbit' 'platypus' 'astronaut' 'horse' 'alligator' 'clown' 'dentist' 'beekeeper' 'mushroom' 'monster' 1 'candle' 'toe' 'headache' 'spoon' 'cat' 'toaster' 'racecar' 'baseball' 'toothbrush' 'towel' 'sandbox' 'grampa' 'this' 'that' 'a' 'my' 'your' 'his' 'her' 'the' 'at' 'in' 'under' 'beside' 'behind' 'above' 'from' 'minutes' 'hours' 'days' 'weeks' 'sad' ':frowning:' 'happy' ':smile:' 'tired' ':tired_face:' 'sleepy' ':sleepy:' 'angry' ':angry:' 'worried' ':worried:' 'confused' ':confused:' 'disappointed' ':disappointed:' 'amazed' ':open_mouth:' 'Chicken Tacos' 'Cheeseburgers' 'Steamed Broccoli' 'Black Coffee' 1 : def __init__ (self, num_stories = ) 1 : def about_lunch (self, noun) if 1 75 return 'is' return 'isn\'t' : def random_emotion (self) return : def emoji_by_emotion (self, emotion) return True : def single_or_plural (self) 1 10000 if 2 0 return 1 return 2 : def random_number (self) 1 10000 if 3 0 return 1 return : def number_to_words (self,num) return : def random_preposition (self) return : def random_descriptor (self) return : def random_place (self) return : def random_subject (self, idx, num) return : def random_adjective (self, num) if 1 return return : def random_noun (self, num) if 1 return return : def random_verb (self, num) return : def adjective_subject_verb (self, subject_idx) return : def adjective_subject_place (self, subject_idx) if 2 '\'' else '\'s' return : def adjective_num_descriptor_noun (self) return : def timeframe (self) "US/Central" 1 10 f'In , it will be ' {num} {interval} {self.delorean.datetime.strftime( )} "%a %b %d, %Y %I:%M %p" return : def capitalize_sentences (self, orig) ". " 0 1 for in return '. ' : def create_story (self) 0 1 f'The first sentence about lunch.' {self.about_lunch(part2[ ])} 3 f" .\n .\nI\'m .\n \n\n" { .join(part1)} ' ' { .join(part2)} ' ' {preposition} { .join(part3)} ' ' {time} {feeling} {emotion} {lunch} if '__main__' 'How many stories: ' for in Basic Overview Ask the user how many "stories" he/she wants to create. Input: Select whether a noun is singular or plural and randomly get an adjective, subject, and verb with the correct choice of singular or plural. Random adjective, subject, verb: Randomly select an adjective, number, descriptor, and noun. Get more random words: You know the drill by now. Pick another random adjective, subject, and place: Randomly choose a preposition from our list. Preposition: Set the timezone and randomly set a time in the future. Use our Delorean object: Pick a random feeling and an associated emoji. Express yourself: Fuzzy matches the first sentence against our list of food words. Does the first sentence contain a food word? Concatenate all of the sentences together and capitalize the first word of each one. Capitalization: Print out the result. Display: Continue the process for each other story we need to create. Repeat: Support Me By Buying Me A Coffee! If you find this tutorial helpful, please consider . Thanks! buying me a coffee Sample Output Previously published at https://www.iceorfire.com/post/four-useful-python-libraries-you-dont-know-about

4 Great Python Libraries You Wish You Came Across Earlier

If you decide to take your online personas seriously, here are seven fun ways to help answer the question: "How do I come up with a memorable pseudonym?" Open it again and add the adjective to the noun. Get ready for a few fun surprises. Take any dictionary from the shelf, open it on any page and select the first noun that comes across. Automatic algorithms allow you to select the first letter of a fictitious name, length, presence of capital letters and numbers, alphabet, take into account the gender of the user. Full names, surnames and patronymics also create such algorithms. Use the japanese name generator . It will not be unique, but definitely beautiful. Use the nick rating and choose the one you like. Don't forget to add a few numbers or special characters. Add a few letters of your last name, hobby, profession or parents' names to your real name. Write down your real name and surname backwards. Don't forget about hyphens and underscores. Choose a few things you like and combine them into one word. You could, for example, put a cat on the keyboard and see what happens. Type a completely random set of characters. Remember that nickname is a way of identification. It will be good if at least you remember it. Other Things to Note When Creating Your Handle It is advisable to invent it according to the rules. Nothing insulting or crude. The rules of some sites stipulate that if a nickname is offensive or promotes something bad, then the account can be deleted. Your nickname, username or pseudonym is the name that everyone will see. Nickname hdjysnyxnypyfte5mcjsfhfuuxdoes not offend anyone, but you (or someone else) are unlikely to be able to read it. Readability. ram67584098WAit is already read better, but the combination of numbers is unlikely to be remembered the first time. Ease of remembering. It is unlikely that anyone will believe that someone Donald Trump is sitting at the Kherson forum of beekeepers, but if you take the name and surname of your colleague and start using them, it wouldn't be appreciated by your colleague. Match with real names. If you are thinking about how to choose an name on the Internet, then remember a simple recipe: a little prudence + more imagination will make your account memorable and unique.

How To Choose Your Next Username: 7 Creative Ways to Hack a Great Handle

24 Content Strategy Tips From Pros For Technologists Who Want to Start Writing

Three months into the pandemic, we finally have a cause for cautious optimism. The latest stats suggest that the coronavirus has started to loosen its grip. Now is the best time to recalibrate your brand’s story and (re)inspire your community. As devastating as this crisis has been, it has also presented opportunities for reevaluating your efforts and strategies. Here are a few things I’ve learned from my clients about business and marketing self-reinvention. Find New Angles for Your Story Most successful brands have a that explains its origins and articulates its mission. It sends a clear and powerful message about a company’s ethos, connecting customers to its vision and values. signature story If your brand does not have a signature story, think about the one you casually tell others. If you’ve been in business for a while, you’ve probably already told this story a hundred times. How would you describe your business to your friends? What inspired the idea for it? How about the steps you’ve thought of taking to scale it? Surely you’ve talked about the challenges you faced. Now try to recall your friends’ reactions. Which version of your story resonated with them the most? These intimate moments with friends or family help you discover your true values, and by extension, the core of your brand. For some reason, many entrepreneurs or their PR teams leave the soul out of their story once they begin drafting it, as though it’s no longer welcome. Your story should present a challenge your customers might face and show how your product provides a solution. For example, the founder of the organic , often talks about his experience of growing up in Belarus (former USSR) with his dad (a beekeeper) and having a spoon of healthy raw honey every day. He was shocked when he came to New York and realized all the honey here is processed and lacking in nutrients. He started importing and then producing honey in the US, which now sells in the world’s largest natural food supermarket chain, Whole Foods. This brief account of the company’s origins gives you an idea of its values, its background, and its product. honey production company, Apiterra Even if you drafted your story many years ago, now is a good time to take another look and ask: Is it as good as it was? Or could it use a twist? Maybe you’ve overcome another interesting challenge since then, like adjusting to confinement during the pandemic. Your clients and partners want to know how your company has weathered a crisis. While working on your story, ask yourself these questions: What’s the biggest struggle for you as a CEO and for your company right now? Have you joined the fight against the coronavirus by donating time, resources, or money? Have you reached out to local communities? What health and safety measures have you introduced? What lessons has your company learned going forward? Your answers to these and other questions can be woven into your brand story and used to better connect with your audience. Read ’s for inspiration. David Aaker Creating Signature Stories: Strategic Messaging that Energizes” and Donald Miller’s “Storybook” Unsplash/Nick Morrison Strengthen Your Community Now more than ever, it is vital to show both your team and your customers how much they mean to the company and how important it is to work . Your efforts to build stronger and more resilient communities during this crisis will pay off in the future, when consumers resume their normal spending habits. together Right now, traditional communications tactics like conferences, exclusive events, and pop-up stores are out of reach. A great way to get around this problem is by organizing free webinars for your community members. For example, we recently helped Travelpayouts in collaboration with our media partner Affiliate Valley to host a for travel bloggers around the world. The participants discussed how COVID-19 has affected their way of living and their business. webinar These free initiatives, which you can now host on Facebook, Instagram, Zoom, and professional webinar platforms, make people feel seen, heard, and supported — especially in such trying times. A human approach to your customers and partners will go a long way and help your brand in the long term. Unsplash/Tim MarshallEmpower Your Team Being there for your team is just as important as supporting your clients and external partners. Whether your employees work from home or have to show up at the office every day, you need to empower and motivate them. Perhaps the best way to do that in these uncertain times is by being honest. As a leader, you have had to make difficult decisions in the past few weeks. More such decisions will likely follow soon. However, beating yourself up about not knowing what to do will get you nowhere. Instead, focus on building trust in your team members. Be open about the challenges you are facing in this extraordinary situation. Make it clear that you, too, are having a hard time. The heartbreaking he had to lay off is a good example of how to communicate your values to your team. During such times, it’s crucial to let people know you care about them — those who are leaving and those who are staying — to help maintain a healthy and trusting environment. letter from Airbnb’s founder to the employees You might want to encourage your employees to suggest ideas on how to move forward as a team. In a time when no one has all the answers, every contribution is valuable. Spend Time on Search Engine Optimization and Email Lists As more people and companies are going online, you want to reach as many potential clients as possible. I recently interviewed about how his company is handling the crisis. Sweetwater opened a new location in the Oculus World Trade Center right before the lockdown. Like most public places, it too was shut down, so the company had to change its strategy quickly. Being forced to go online, he realized it was the first time in five years that he could reach a higher percentage of clients more quickly. His team enhanced their website with SEO and email lists, yielding amazing results right away. the founder of Sweetwater Labs With most normal business activity on hold, now is the perfect time to reevaluate your approach to SEO. This will have a tremendous impact on your online presence. And make sure to include earned media and reviews in your updated strategy. A growing number of consumers rely on word-of-mouth recommendations, so update your Google company page, ask partners to review your services on sites like Trustpilot. What’s more, your marketing budget might experience serious cuts in the coming months. You want to make use of free advertising as much as you can. Unsplash/Austin DistelEmbrace Personal Branding through Altruism In a recent for , of NYU’s Stern School of Business says that how each of us reacts to the current crisis will define how we are remembered for generations to come. He claims that, instead of giving in to our primal instincts of egoism and panic, we should aim to remain calm, collected, disciplined, and reasonable. piece Business Insider Professor Scott Galloway That is precisely the type of leader and company you want to be right now. Rather than be seen panicking over how to achieve last year’s profit margins, reach out to the broader community. Think of ways in which you could support your employees, your customers, and their families. Extend a helping hand to first responders or small local businesses that might be struggling — the opportunities are virtually endless. However, tread carefully. Here again, there is a fine line between doing what is right and good, and overdoing it just for the sake of your personal branding. While the COVID-19 pandemic is unprecedented in modern history, it is not the first and certainly won’t be the last crisis the world is facing. The tools, strategies, and communities we build now will determine how we respond to the next global challenge. I believe now is the best time to shift focus on creating resilient businesses and robust support networks for employees, clients, and partners.

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