Before

before

The CSS properties are what also known as . They are used to add something before or after the content of an element. There are a lot of great uses for these pseudo elements, and we are here to explore some of them. :before and :after pseudo elements The Syntax If we have an element like this one: welcome to our website < > h2 </ > h2 We can add a pseudo element before it using CSS, like this: { : “Hello“; : blue;
} h2 :before content color The result will be: This is a quite simple principle. You are adding content before or after a certain element. It can be extremely helpful when , and in many other cases. adding icons, clearing floats The property of the pseudo element can be left with empty quotes like this .  This way you can treat the pseudo element like a . If the content property is removed altogether, the pseudo element content empty `content: “”` box with no content will not work. Adding Icons Probably the most popular usage of the before and after pseudo elements is using them to . We can add a simple .png icon. add icons Let’s look at the markup. HTML Weather report < > h2 </ > h2 CSS { : “ ”; : block; : ; : ; : (‘images/icon.png’’); : ; h2 :before content display height 15px width 25px url margin-right 5px The result: Now we have successfully added an icon before the text. Easy, right? Clearing Floats After elements are , another one needs to be added in order to clear that float. You can and simply using a pseudo one. floated avoid adding a new element Let's say we have this situation: We can use the next code to achieve clearing of the floats. HTML Lorem ipsum dolor amet truffaut kombucha roof party iPhone forage farm-to-table. < = > div class "box-container" < = > div class ”box” </ > div < = > div class ”box” </ > div </ > div < > p </ > p CSS , { : ; : block;
} { : both;
} { : ; : ; : ; : ; : left;
} .box-container :before .box-container :after content "" display .box-container :after clear .box height 100px width 100px background-color #C98EED margin 5px float Now, let's take a look at the result. By using this method we are and the paragraph is moved below the two elements. clearing the float Using a Background Image We can also to a pseudo element. This is commonly used when styling a header. add a background image HTML Hello World < > h2 </ > h2 CSS { : “”; : ; : ; : (‘underline.png’) center center no-repeat; : block;
} h2 :after content width 100% height 30px background url display The result achieved: Browser Support As with everything else in CSS, browser support needs to be checked. By consulting the , we see that these pseudo classes have a high browser support ( ) and we won’t have a headache when using them. CanIUse over 98% Summary Here, we have explained of the CSS pseudo elements. The examples illustrate just some of the many possible usages. Don't worry if it's not completely clear in the beginning. A little practice goes a long way. basic principles Hopefully, this article will help with any future projects. Thank you for reading! Previously published at https://kolosek.com/css-before-and-after/

How to use CSS pseudo-classes :before and :after

In RSpec, there are two different ways to write DRY tests, by using or . Their purpose is to create variables that are common across tests. In this post, we will explore differences between before and let and explain . before let why let is preferred by the Ruby community let Let creates lazily-evaluated local variables. This means that let() is not evaluated until the method that it defines is run for the first time. It DRYs up the spec and makes it more readable. $count = describe let( ) { $count += }
  it expect(count).to eq( )
    expect(count).to eq( ) it expect(count).to eq( ) 0 "let" do :count 1 "stores the value" do 1 1 end "is not cached across examples" do 2 end end Let should not be used for local variables, which have to be saved to the database, as they will not be saved to the database unless they have already been referenced. In this case, you should use or . let! before blocks The the tests we are creating are done using . RSpec and Capybara Also, have a let block inside of a before the block, this is what let! is made for! never let! Unlike let, you can . It means that, even if you didn't invoke the helper method inside the example, it will be invoked before your example runs. use let! to force the method's invocation before each example $count = describe invocation_order = []
  let!( ) invocation_order << $count += it invocation_order << expect(invocation_order).to eq([ , ])
    expect(count).to eq( ) 0 "let!" do :count do :let! 1 end "calls the helper method in a before hook" do :example :let! :example 1 end end As with let blocks, if multiple let! blocks are defined with the same name, the most recent one will be executed. The core difference is that will be executed multiple times if used like this, whereas the will only execute the last time. let! blocks let block before(:each) Before(:each) block will run before each example, even if the example doesn't use any of the instance variables defined in the block. This can noticeably slow down the setup of the instance variables. @tests = [] describe User before( ) @user = User.new describe it @user.should have( ).tests it @user.tests << Object.new it @user.should have( ).tests class User def tests || end end do :each do end "initialized in before(:each)" do "has 0 tests" do 0 end "can accept new tests" do end "does not share state across examples" do 0 end end end Depending on your personal preference you could use before blocks when: In nearly every situation, it is better to use let over before blocks. There is a reasonable amount of variables. There are variables that don't need to be referenced directly but are required. There are many commands to be executed because its syntax is more clear when many commands are involved. Creating mocks/stubs. Give it a try and create your own model or ! controller tests before(:all) This block is executed only once, before all of the examples in a group. There are certain situations this can cut down on execution and effort. @tests = [] describe User before( ) @user = User.new describe it @user.should have( ).tests it @user.tests << Object.new it @user.should have( ).tests class User def tests || end end do :all do end "initialized in before(:all)" do "has 0 tests" do 0 end "can get accept new tests" do end "shares state across examples" do 1 end end end It runs outside of transactions, so the data created here will bleed into other specs. Using before(:all) in RSpec will cause you lots of trouble unless you know what you are doing! Conclusion It all depends on what and how you need to make the work or consider using for creating data. Let blocks bring more to the table than before blocks. RSpec tests FactoryGirl Besides being slower, one of the major problems with before blocks is that spelling errors can lead to bugs and false positives, allowing certain types of tests to pass when they shouldn't. before( ) @user = User.find( )
    @user.logout it expect(@usr).to be_nil :each do username: "kolosek" end "should log the user out" do end Since @usr wasn't previously defined, the test will pass, @usr is nil by default. The same test using let would raise NameError because @usr isn't defined. Hope this will help you to better understand the differences between let and before blocks. Thank you for reading! Previously published at https://kolosek.com/rspec-let-vs-before/

RSpec Testing: Let Vs. Before

PALO ALTO, Calif., USA, December 30th, 2024/CyberNewsWire/--SquareX, an industry-first Browser Detection and Response (BDR) solution, leads the way in browser security. About a week ago, SquareX reported large-scale attacks targeting Chrome Extension developers aimed at taking over the Chrome Extension from the Chrome Store. On December 25th, 2024, a malicious version of Cyberhaven’s browser extension was published on the Chrome Store that allowed the attacker to hijack authenticated sessions and exfiltrate confidential information. The malicious extension was available for download for more than 30 hours before being removed by Cyberhaven. The data loss prevention company declined to comment on the extent of the impact when approached by the press, but the extension had over 400,000 users on the Chrome Store at the time of the attack. Unfortunately, the attack took place as SquareX's researchers had identified a similar attack with a video demonstrating the entire attack pathway just a week before the Cyberhaven breach. The attack begins with a phishing email impersonating Chrome Store containing a supposed violation of the platform’s “Developer Agreement”, urging the receiver to accept the policies to prevent their extension from being removed from Chrome Store. Upon clicking on the policy button, the user gets prompted to connect their Google account to a “Privacy Policy Extension”, which grants the attacker access to edit, update and publish extensions on the developer’s account. Fig 1. Phishing email targeting extension developers Fig 2. Fake Privacy Policy Extension requesting access to “edit, update or publish” the developer’s extension Extensions have become an increasingly popular way for attackers to gain initial access. This is because most organizations have limited purview on what browser extensions their employees are using. Even the most rigorous security teams typically do not monitor subsequent updates once an extension is whitelisted. SquareX has conducted extensive research and demonstrated at DEFCON 32, how MV3-compliant extensions can be used to steal video stream feeds, add a silent GitHub collaborator, and steal session cookies, among others. Attackers can create a seemingly harmless extension and later convert it into a malicious one post-installation or, as demonstrated in the attack above, deceive the developers behind a trusted extension to gain access to one that already has hundreds of thousands of users. In Cyberhaven’s case, attackers were able to steal company credentials across multiple websites and web apps through the malicious version of the extension. Given that developer emails are publicly listed on Chrome Store, it is easy for attackers to target thousands of extension developers at once. These emails are typically used for bug reporting. Thus, even support emails listed for extensions from larger companies are usually routed to developers who may not have the level of security awareness required to find suspicion in such an attack. As per SquareX’s attack disclosure and the Cyberhaven breach that occurred within the span of less than two weeks, the company has strong reason to believe that many other browser extension providers are being attacked in the same way. SquareX urges companies and individuals alike to conduct a careful inspection before installing or updating any browser extensions. Fig 3. Contact details of extension developers are publicly available on Chrome Store SquareX team understands that it can be non-trivial to evaluate and monitor every single browser extension in the workforce amidst all the competing security priorities, especially when it comes to zero-day attacks. As demonstrated in the video, the fake privacy policy app involved in Cyberhaven’s breach was not even detected by any popular threat feeds. SquareX’s Browser Detection and Response (BDR) solution takes this complexity off security teams by: Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account
Blocking and/or flagging any suspicious extension updates containing new, risky permissions
Blocking and/or flagging any suspicious extensions with a surge of negative reviews
Blocking and/or flagging installations of sideloaded extensions
Streamline all requests for extension installations outside the authorized list for quick approval based on company policy
Full visibility on all extensions installed and used by employees across the organization SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” About SquareX: SquareX helps organizations detect, mitigate, and threat-hunt client-side web attacks happening against their users in real-time. SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can provide contractors and remote workers with secure access to internal applications, and enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here PALO ALTO, Calif., USA, December 30th, 2024/CyberNewsWire/--SquareX, an industry-first Browser Detection and Response (BDR) solution, leads the way in browser security. About a week ago, SquareX reported large-scale attacks targeting Chrome Extension developers aimed at taking over the Chrome Extension from the Chrome Store. reported reported On December 25th, 2024, a malicious version of Cyberhaven’s browser extension was published on the Chrome Store that allowed the attacker to hijack authenticated sessions and exfiltrate confidential information. The malicious extension was available for download for more than 30 hours before being removed by Cyberhaven. The data loss prevention company declined to comment on the extent of the impact when approached by the press, but the extension had over 400,000 users on the Chrome Store at the time of the attack. malicious extension malicious extension Chrome Store Chrome Store Unfortunately, the attack took place as SquareX's researchers had identified a similar attack with a video demonstrating the entire attack pathway just a week before the Cyberhaven breach. identified identified video video The attack begins with a phishing email impersonating Chrome Store containing a supposed violation of the platform’s “Developer Agreement”, urging the receiver to accept the policies to prevent their extension from being removed from Chrome Store. Upon clicking on the policy button, the user gets prompted to connect their Google account to a “Privacy Policy Extension”, which grants the attacker access to edit, update and publish extensions on the developer’s account. Fig 1. Phishing email targeting extension developers Fig 2. Fake Privacy Policy Extension requesting access to “edit, update or publish” the developer’s extension Extensions have become an increasingly popular way for attackers to gain initial access. This is because most organizations have limited purview on what browser extensions their employees are using. Even the most rigorous security teams typically do not monitor subsequent updates once an extension is whitelisted. SquareX has conducted extensive research and demonstrated at DEFCON 32, how MV3-compliant extensions can be used to steal video stream feeds, add a silent GitHub collaborator, and steal session cookies, among others. DEFCON 32, DEFCON 32, Attackers can create a seemingly harmless extension and later convert it into a malicious one post-installation or, as demonstrated in the attack above, deceive the developers behind a trusted extension to gain access to one that already has hundreds of thousands of users. In Cyberhaven’s case, attackers were able to steal company credentials across multiple websites and web apps through the malicious version of the extension. Given that developer emails are publicly listed on Chrome Store, it is easy for attackers to target thousands of extension developers at once. These emails are typically used for bug reporting. Thus, even support emails listed for extensions from larger companies are usually routed to developers who may not have the level of security awareness required to find suspicion in such an attack. As per SquareX’s attack disclosure and the Cyberhaven breach that occurred within the span of less than two weeks, the company has strong reason to believe that many other browser extension providers are being attacked in the same way. SquareX urges companies and individuals alike to conduct a careful inspection before installing or updating any browser extensions. Fig 3. Contact details of extension developers are publicly available on Chrome Store SquareX team understands that it can be non-trivial to evaluate and monitor every single browser extension in the workforce amidst all the competing security priorities, especially when it comes to zero-day attacks. As demonstrated in the video , the fake privacy policy app involved in Cyberhaven’s breach was not even detected by any popular threat feeds. video video SquareX’s Browser Detection and Response (BDR) solution takes this complexity off security teams by: SquareX’s Browser Detection and Response (BDR) solution SquareX’s Browser Detection and Response (BDR) solution Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account Blocking and/or flagging any suspicious extension updates containing new, risky permissions Blocking and/or flagging any suspicious extensions with a surge of negative reviews Blocking and/or flagging installations of sideloaded extensions Streamline all requests for extension installations outside the authorized list for quick approval based on company policy Full visibility on all extensions installed and used by employees across the organization Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account Blocking and/or flagging any suspicious extension updates containing new, risky permissions Blocking and/or flagging any suspicious extensions with a surge of negative reviews Blocking and/or flagging installations of sideloaded extensions Streamline all requests for extension installations outside the authorized list for quick approval based on company policy Full visibility on all extensions installed and used by employees across the organization SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” Vivek Ramachandran Vivek Ramachandran About SquareX: SquareX helps organizations detect, mitigate, and threat-hunt client-side web attacks happening against their users in real-time. SquareX SquareX SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can provide contractors and remote workers with secure access to internal applications, and enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

SquareX Researchers Expose OAuth Attack On Chrome Extensions Days Before Major Breach

Даследчыкі SquareX выкрылі атаку OAuth на пашырэнні Chrome за некалькі дзён да сур'ёзнага ўзлому

Investigadores de SquareX descubren un ataque OAuth en extensiones de Chrome días antes de una importante vulneración

Istraživači SquareX-a razotkrivaju OAuth napad na Chromeova proširenja danima prije većeg proboja

חוקרי SquareX חושפים את התקפת OAuth על הרחבות Chrome ימים לפני הפרה גדולה

SquareX の研究者が、大規模な侵害の数日前に Chrome 拡張機能への OAuth 攻撃を暴露

SquareX зерттеушілері Chrome кеңейтімдеріндегі OAuth шабуылын үлкен бұзылулардан бірнеше күн бұрын ашты

SquareX 연구원, 주요 침해 발생 며칠 전 크롬 확장 프로그램에 대한 OAuth 공격 폭로

SquareX изилдөөчүлөрү Chrome кеңейтүүлөрүнө OAuth чабуулун ири бузуулардан бир нече күн мурун ашкерелешти

ນັກຄົ້ນຄວ້າ SquareX ເປີດເຜີຍການໂຈມຕີ OAuth ກ່ຽວກັບ Chrome Extensions ມື້ກ່ອນການລະເມີດທີ່ສໍາຄັນ

Badacze SquareX ujawniają atak OAuth na rozszerzenia Chrome na kilka dni przed poważnym naruszeniem

SquareX-forskare avslöjar OAuth-attack på Chrome-tillägg dagar innan större intrång

Abaphandi be-SquareX Baveza uhlaselo lwe-OAuth kwiiNtsuku zoLwandiso lwe-Chrome ngaphambi kokwaphulwa okukhulu

Analog, a blockchain protocol focusing on cross-chain communication, launched its network today, enabling connections between different blockchain platforms. The initial release operates on a Proof-of-Authority mechanism, with plans to transition to Nominated Proof of Stake by the time of its token release. The network launch follows testing phases that processed 1.9 million cross-chain data transfers among 345,000 participating accounts. This testing period allowed developers to examine the protocol's capabilities and provide technical feedback before the main release. "Cross-chain communication remains a central challenge in blockchain development," says Dr. Elena Martinez, blockchain researcher at MIT's Digital Currency Institute. "Solutions that enable different chains to interact could reduce fragmentation in the blockchain space." The protocol's architecture includes components for processing data between chains, executing smart contracts across networks, and maintaining consistent information across systems. These features aim to address current limitations in blockchain interoperability, where applications often operate in isolation on single networks. Several blockchain projects have begun implementing the protocol. Frax Finance, which operates a decentralized stablecoin system, plans to use the network for transmitting price data across different blockchain platforms. This implementation could help maintain consistent asset pricing across multiple networks. XYO Network, which operates location-tracking systems on blockchain, intends to use the protocol to make geographical data accessible across different chains. This application demonstrates potential uses beyond financial transactions, extending to real-world data integration. The protocol also enables governance activities across chains. Token holders of various projects can participate in voting and decision-making regardless of which blockchain holds their assets, potentially increasing participation in project governance. Technical metrics from the testing period indicate sustained usage, with users listing 290,000 smart contracts across multiple chains. The protocol processed these contracts while maintaining data consistency, suggesting capability for sustained operation at scale. Security measures include identity verification systems, with 42,000 users completing verification processes before participating. These measures aim to maintain network integrity while enabling broad participation. Looking forward, the development team plans additional features, including the consensus mechanism transition and expanded cross-chain messaging capabilities. These updates aim to increase the network's capability for handling complex multi-chain operations. The launch represents progress in blockchain interoperability research, potentially contributing to increased communication between previously isolated networks. As blockchain systems continue operating on separate networks, protocols enabling cross-chain interaction may become increasingly relevant for developers building distributed applications. Developers interested in examining the protocol's capabilities can access technical documentation through official channels, with the network now processing live transactions and cross-chain communications. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Analog , a blockchain protocol focusing on cross-chain communication, launched its network today, enabling connections between different blockchain platforms. The initial release operates on a Proof-of-Authority mechanism, with plans to transition to Nominated Proof of Stake by the time of its token release. Analog The network launch follows testing phases that processed 1.9 million cross-chain data transfers among 345,000 participating accounts. This testing period allowed developers to examine the protocol's capabilities and provide technical feedback before the main release. "Cross-chain communication remains a central challenge in blockchain development," says Dr. Elena Martinez, blockchain researcher at MIT's Digital Currency Institute. "Solutions that enable different chains to interact could reduce fragmentation in the blockchain space." The protocol's architecture includes components for processing data between chains, executing smart contracts across networks, and maintaining consistent information across systems. These features aim to address current limitations in blockchain interoperability, where applications often operate in isolation on single networks. Several blockchain projects have begun implementing the protocol. Frax Finance , which operates a decentralized stablecoin system, plans to use the network for transmitting price data across different blockchain platforms. This implementation could help maintain consistent asset pricing across multiple networks. Frax Finance XYO Network, which operates location-tracking systems on blockchain, intends to use the protocol to make geographical data accessible across different chains. This application demonstrates potential uses beyond financial transactions, extending to real-world data integration. The protocol also enables governance activities across chains. Token holders of various projects can participate in voting and decision-making regardless of which blockchain holds their assets, potentially increasing participation in project governance. Technical metrics from the testing period indicate sustained usage, with users listing 290,000 smart contracts across multiple chains. The protocol processed these contracts while maintaining data consistency, suggesting capability for sustained operation at scale. Security measures include identity verification systems, with 42,000 users completing verification processes before participating. These measures aim to maintain network integrity while enabling broad participation. Looking forward, the development team plans additional features, including the consensus mechanism transition and expanded cross-chain messaging capabilities. These updates aim to increase the network's capability for handling complex multi-chain operations. The launch represents progress in blockchain interoperability research, potentially contributing to increased communication between previously isolated networks. As blockchain systems continue operating on separate networks, protocols enabling cross-chain interaction may become increasingly relevant for developers building distributed applications. Developers interested in examining the protocol's capabilities can access technical documentation through official channels, with the network now processing live transactions and cross-chain communications. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Analog's Cross-Chain System Processes 1.9M Data Points Before Launch

Analoog se kruiskettingstelsel verwerk 1,9 miljoen datapunte voor bekendstelling

يقوم نظام Cross-Chain من Analog بمعالجة 1.9 مليون نقطة بيانات قبل الإطلاق

Analog ukax Sistema de Cadena Cruzada ukax 1.9M Puntos de Datos Janïr Lanzamiento ukaruw puri

Analoqun Çarpaz Zəncirli Sistemi işə salınmazdan əvvəl 1,9 milyon məlumat nöqtəsini emal edir

Analogov cross-chain sistem obrađuje 1,9 miliona tačaka podataka prije lansiranja

El sistema de cadena cruzada de Analog procesa 1,9 millones de puntos de datos antes del lanzamiento

מערכת חוצת השרשרת של Analog מעבדת 1.9 מיליון נקודות נתונים לפני ההשקה

アナログのクロスチェーンシステムは発売前に190万のデータポイントを処理

Analogā starpķēžu sistēma pirms palaišanas apstrādā 1,9 miljonus datu punktu

Analógový cross-chain systém pred spustením spracuje 1,9 milióna dátových bodov

Sistemi ndër-zinxhiror i Analog përpunon 1,9 milion pikë të dhënash përpara nisjes

Analogs tvärkedjesystem behandlar 1,9 miljoner datapunkter före lansering

Authors:
(1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors;
(2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors;
(3) Sahal Shaji, Mohamed bin Zayed University of AI;
(4) Abdelrahman Shaker, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Hisham Cholakkal, Mohamed bin Zayed University of AI;
(7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University;
(8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University;
(9) Ming-Hsuan Yang, University of California - Merced and Google Research;
(10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 4 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Work
3. Method
4. Data Annotation Pipeline
5. Experiments
6. Conclusion and References Supplementary Material (Part 1) A. Additional Implementation Details
B. Additional Downstream Tasks
C. Additional Qualitative Results Supplementary Material (Part 2) D. Dataset Visualization
E. Limitations and Future Work
F. Ethics and Societal Impact 4. Data Annotation Pipeline We introduce our automated annotation pipeline used to create the Grounding-anything Dataset (GranD). GranD is a comprehensive, multi-purpose image-text dataset offering a range of contextual information, from fine-grained to high-level details. It aims to overcome challenges in image understanding and dense pixel-level grounding, thereby expanding the capabilities of visual instruction tuning in LMMs. The pipeline contains four distinct levels (see Fig. 4). i) Level-1 focuses on object localization and provides semantic labels, segmentation masks, attributes, and depth information. ii) Level-2 defines relationships between detected objects. iii) Level-3 organizes information from the first two levels into a hierarchical scene graph, used to generate dense captions using LLM with in-context examples. iv) Level-4 offers enriched contextual information for a deeper understanding of the scene, going beyond what’s observed (e.g., historical information of a landmark). Please refer to Appendix A.4 for pipeline implementation details. 4.1. Object Localization and Attributes (Level-1) In level-1, the focus is on detailed object identification within images. First, object-bounding boxes are identified using multiple SoTA object detection models. Classagnostic NMS is applied to each model to filter out false positives. After this step, bounding boxes from different models are compared using IoU, with a bounding box retained as an object only if detected by at least two other detection models. We also generate attributes for each filtered object using region-based vision-language models and incorporate depth information to contextualize each object’s relative position within the scene. 4.2. Relationships and Landmarks (Level-2) In level-2, multiple short textual descriptions of the overall scene are generated. Phrases extracted from these descriptions are grounded to specific objects in level-1 to form relationships. These relationships articulate connections between multiple objects or define an object’s role within the scene. Further, each scene is assigned a landmark category that includes a primary and a more specific sub-category (see Tab. 7 in Appendix 7). 4.3. Scene Graph and Dense Captioning (Level-3) In level-3, object attributes and labels from level-1 are combined with the relationships and phrases obtained from level-2 to form a hierarchical scene graph. This structured data serves as a query for LLM to generate dense image captions. To provide additional context, depth values and bounding box coordinates are used to assign each object to specific spatial layers within the scene, such as immediate foreground, foreground, midground, or background. Additionally, short scene-level captions are incorporated into the scene graph to enhance LLMs’ contextual understanding. Dense Captioning Verification: To enhance the fidelity of the LLM-generated dense captions, we implement an automatic verification pipeline using chain-of-thoughts prompting. This pipeline produces a checklist of objects derived from the generated dense caption expected to be present in the image. The associated caption is flagged as inaccurate if any object specified in the checklist is absent from the scene graph. Such captions are then regenerated, incorporating feedback from the initial assessment. 4.4. Extra Contextual Insights (Level-4) Level-4 builds on the scene graph from level-3 to obtain a more detailed visual understanding. we query LLM to extract extended contextual insights beyond basic object identification and relationships, including details about the landmarks, historical context, guidelines for interacting with the scene, and even predictive elements about future events. To facilitate this, we prompt LLM with in-context examples. Utilizing our automated annotation pipeline, we annotate a corpus of 11M SAM images [18], which are inherently diverse, high-resolution, and privacy-compliant. The resulting dataset comprises 810M regions, each associated with a segmentation mask, and includes 7.5M unique concepts. Further, the dataset features 84M referring expressions, 22M grounded short captions, and 11M densely grounded captions. To our knowledge, this is the first dataset of this scale generated entirely through an automated annotation pipeline (see Tab. 2 for details and Fig. 15 for dataset sample visualizations). We extend open-source datasets—namely Flickr30K [37], RefCOCOg [16], and PSG [49] by generating compatible GCG annotations. For RefCOCOg, we use the dataset’s referring expressions and their connected masks. These expressions offer concise descriptions of distinct objects in the image. With the aid of GPT-4, we seamlessly blend these referring expressions with contextual information from COCO captions, crafting detailed yet accurate grounded captions while preserving the original referring expressions. This ensures zero error in matching phrases with their corresponding segmentation masks. This technique yields approximately 24K GCG samples. For PSG, we leverage the dataset’s triplet structures, which describe relations between two objects in a scene. These triplets are integrated with COCO captions using GPT-4, resulting in densely annotated captions that can be mapped to segmentation masks. This gives us around 31K additional GCG samples. For Flickr-30K, we use the 158K Flickr captions and their referring expressions alongside associated bounding boxes. These boxes are then accurately segmented using HQ-SAM [17]. In addition, we contribute a minor, high-quality manual annotation set to benchmark the GCG task. Using GranD’s automatic annotations as a base, annotators refine referring expressions to match SAM GT masks, yielding around 1000 focused samples for training and 1000 for evaluation (refer to Appendix D and Fig. 14 for designed prompts and dataset visualizations). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 4 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 4 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 4 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 4 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Work 3. Method 4. Data Annotation Pipeline 5. Experiments 6. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method 3. Method 4. Data Annotation Pipeline 4. Data Annotation Pipeline 5. Experiments 5. Experiments 6. Conclusion and References 6. Conclusion and References Supplementary Material (Part 1) Supplementary Material (Part 1) A. Additional Implementation Details B. Additional Downstream Tasks C. Additional Qualitative Results A. Additional Implementation Details A. Additional Implementation Details B. Additional Downstream Tasks B. Additional Downstream Tasks C. Additional Qualitative Results C. Additional Qualitative Results Supplementary Material (Part 2) Supplementary Material (Part 2) D. Dataset Visualization E. Limitations and Future Work F. Ethics and Societal Impact D. Dataset Visualization D. Dataset Visualization E. Limitations and Future Work E. Limitations and Future Work F. Ethics and Societal Impact F. Ethics and Societal Impact 4. Data Annotation Pipeline We introduce our automated annotation pipeline used to create the Grounding-anything Dataset (GranD). GranD is a comprehensive, multi-purpose image-text dataset offering a range of contextual information, from fine-grained to high-level details. It aims to overcome challenges in image understanding and dense pixel-level grounding, thereby expanding the capabilities of visual instruction tuning in LMMs. The pipeline contains four distinct levels (see Fig. 4). i) Level-1 focuses on object localization and provides semantic labels, segmentation masks, attributes, and depth information. ii) Level-2 defines relationships between detected objects. iii) Level-3 organizes information from the first two levels into a hierarchical scene graph, used to generate dense captions using LLM with in-context examples. iv) Level-4 offers enriched contextual information for a deeper understanding of the scene, going beyond what’s observed (e.g., historical information of a landmark). Please refer to Appendix A.4 for pipeline implementation details. 4.1. Object Localization and Attributes (Level-1) In level-1, the focus is on detailed object identification within images. First, object-bounding boxes are identified using multiple SoTA object detection models. Classagnostic NMS is applied to each model to filter out false positives. After this step, bounding boxes from different models are compared using IoU, with a bounding box retained as an object only if detected by at least two other detection models. We also generate attributes for each filtered object using region-based vision-language models and incorporate depth information to contextualize each object’s relative position within the scene. 4.2. Relationships and Landmarks (Level-2) In level-2, multiple short textual descriptions of the overall scene are generated. Phrases extracted from these descriptions are grounded to specific objects in level-1 to form relationships. These relationships articulate connections between multiple objects or define an object’s role within the scene. Further, each scene is assigned a landmark category that includes a primary and a more specific sub-category (see Tab. 7 in Appendix 7). 4.3. Scene Graph and Dense Captioning (Level-3) In level-3, object attributes and labels from level-1 are combined with the relationships and phrases obtained from level-2 to form a hierarchical scene graph. This structured data serves as a query for LLM to generate dense image captions. To provide additional context, depth values and bounding box coordinates are used to assign each object to specific spatial layers within the scene, such as immediate foreground, foreground, midground, or background. Additionally, short scene-level captions are incorporated into the scene graph to enhance LLMs’ contextual understanding. Dense Captioning Verification: To enhance the fidelity of the LLM-generated dense captions, we implement an automatic verification pipeline using chain-of-thoughts prompting. This pipeline produces a checklist of objects derived from the generated dense caption expected to be present in the image. The associated caption is flagged as inaccurate if any object specified in the checklist is absent from the scene graph. Such captions are then regenerated, incorporating feedback from the initial assessment. Dense Captioning Verification: 4.4. Extra Contextual Insights (Level-4) Level-4 builds on the scene graph from level-3 to obtain a more detailed visual understanding. we query LLM to extract extended contextual insights beyond basic object identification and relationships, including details about the landmarks, historical context, guidelines for interacting with the scene, and even predictive elements about future events. To facilitate this, we prompt LLM with in-context examples. Utilizing our automated annotation pipeline, we annotate a corpus of 11M SAM images [18], which are inherently diverse, high-resolution, and privacy-compliant. The resulting dataset comprises 810M regions, each associated with a segmentation mask, and includes 7.5M unique concepts. Further, the dataset features 84M referring expressions, 22M grounded short captions, and 11M densely grounded captions. To our knowledge, this is the first dataset of this scale generated entirely through an automated annotation pipeline (see Tab. 2 for details and Fig. 15 for dataset sample visualizations). We extend open-source datasets—namely Flickr30K [37], RefCOCOg [16], and PSG [49] by generating compatible GCG annotations. For RefCOCOg, we use the dataset’s referring expressions and their connected masks. These expressions offer concise descriptions of distinct objects in the image. With the aid of GPT-4, we seamlessly blend these referring expressions with contextual information from COCO captions, crafting detailed yet accurate grounded captions while preserving the original referring expressions. This ensures zero error in matching phrases with their corresponding segmentation masks. This technique yields approximately 24K GCG samples. For PSG, we leverage the dataset’s triplet structures, which describe relations between two objects in a scene. These triplets are integrated with COCO captions using GPT-4, resulting in densely annotated captions that can be mapped to segmentation masks. This gives us around 31K additional GCG samples. For Flickr-30K, we use the 158K Flickr captions and their referring expressions alongside associated bounding boxes. These boxes are then accurately segmented using HQ-SAM [17]. In addition, we contribute a minor, high-quality manual annotation set to benchmark the GCG task. Using GranD’s automatic annotations as a base, annotators refine referring expressions to match SAM GT masks, yielding around 1000 focused samples for training and 1000 for evaluation (refer to Appendix D and Fig. 14 for designed prompts and dataset visualizations). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Researchers Develop Groundbreaking Method to Teach AI to 'Understand' Images Like Never Before

Learn how to programmatically fetch and embed your liked Bluesky posts using authentication, API calls, and oEmbed endpoints. I've recently made the move over to Bluesky. I can already confirm that there is a vibrant tech community there with tons of interesting, useful, and inspiring content. I'm a happy new user! As a result, I've been wanting to embed my top liked Bluesky posts in my "Dev roundup" monthly newsletter posts. My aim is to provide a curated list of Bluesky posts that is specifically tailored to Software Developers. Luckily, Bluesky's API is completely free to use, allowing programmatic access to all of the content within. This tutorial will walk you through the process of retrieving and embedding liked Bluesky posts using their API, perfect for personal blogs, portfolios, or content aggregation projects. Understanding my Bluesky API Workflow I have built a script that allows me to automatically embed my Bluesky posts in a markdown blog post. I think that any or all of the steps used in this script are valuable for many use-cases. To summarize my workflow for embedding liked posts, we follow these key steps: Create an authenticated session
Retrieve liked post URIs for an "actor"
Use these URIs to fetch oEmbed embed HTML
Clean and format the embed code The Complete Implementation Let's break down each function and its purpose: 1. Creating a Bluesky Session export const createSession = async (): Promise<string | null> => {
  try {
    const response = await fetch(
      "https://bsky.social/xrpc/com.atproto.server.createSession",
      {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          identifier: "your-handle",
          password: "your-password",
        }),
      }
    );
    const responseJson = await response.json();
    return responseJson.accessJwt;
  } catch (e) {
    console.error("Failed to create session: ", e);
    return null;
  }
}; Key Insights: This function authenticates your Bluesky account.

Note: this example hardcodes strings for credentials, but this should be avoided in production use cases.


It returns an accessJwt JWT (JSON Web Token) for subsequent API calls
Uses the createSession endpoint from Bluesky's ATP (Authenticated Transfer Protocol)
Error handling ensures graceful failure if authentication fails 2. Retrieving Liked Post URIs export const getBlueskyLikeUris = async (actor: string, limit: number = 40) => {
  const token = await createSession();
  if (!token) {
    console.error("Failed to get token");
    return;
  }
  
  const response = await fetch(
    "https://bsky.social/xrpc/app.bsky.feed.getActorLikes?actor=${actor}&limit=${limit}",
    {
      method: "GET",
      headers: {
        Authorization: `Bearer ${token}`,
      },
    }
  );
  
  const responseJson = await response.json();
  const uris = responseJson.feed.map((entry: any) => entry.post.uri);

  return uris;
}; Key Insights: Requires an authenticated session token
Uses the getActorLikes endpoint to fetch liked posts
Important -- the endpoint domain should be https://bsky.social, as this is an authenticated request.
Extracts unique URIs for each liked post
Limits to 40 posts (configurable) 3. Converting URIs to Embeddable HTML export const getBlueskyPostEmbedMarkup = async (uri: string) => {
  try {
    const response = await fetch(`https://embed.bsky.app/oembed?url=${uri}`);
    const responseJson = await response.json();
    
    const formattedHTML = prettier.format(responseJson.html, {
      parser: "html",
      plugins: [require("prettier/parser-html")],
      htmlWhitespaceSensitivity: "ignore",
      printWidth: 1000,
    });
    
    return formattedHTML.replace(/<script[\s\S]*?<\/script>/g, "");
  } catch (e) {
    console.error("Failed to get Bluesky post embed markup");
    return null;
  }
}; Key Insights: Uses Bluesky's oEmbed endpoint with post URIs to access a post's embeddable HTML
Optional: Utilizes prettier to format the HTML consistently
Optional: Removes <script> tags for security and clean embedding

The reason for this is that I embed a single Bluesky script for each post containing Bluesky content.


Flexible error handling Putting It All Together: A Complete Example async function embedLikedPosts() {
  try {
    // Get liked post URIs
    const likedPostUris = await getBlueskyLikeUris();
    
    if (!likedPostUris) {
      console.error("No liked posts found");
      return;
    }
    
    // Convert URIs to embed HTML
    const embedPromises = likedPostUris.map(getBlueskyPostEmbedMarkup);
    const embedHtmlArray = await Promise.all(embedPromises);
    
    // Filter out any failed embeds
    const validEmbeds = embedHtmlArray.filter(embed => embed !== null);
    
    // Return the markup for all liked posts
    return `
      ## Some Fave Posts 🦋
      ${validEmbeds.join(`\n\n`)}
    `
  } catch (error) {
    console.error("Error embedding Bluesky posts:", error);
  }
} Potential Enhancements This solution works for me because all that I require is a statically generated monthly blog post. Some improvements could include: Add pagination support for fetching more than 40 liked posts
Implement caching to reduce unnecessary API calls
Create a more robust error handling mechanism
Creating mechanism for refreshing accessJwt token if used in long running processes
Sorting liked posts by popularity (likes) Troubleshooting Tips Verify your Bluesky credentials are correct
Check that the Bearer token is being set correctly on your authenticated requests.
Verify that the endpoint domains you are using are all valid. Conclusion Embedding Bluesky posts provides a dynamic way to showcase your social media interactions. By understanding the API workflow and implementing robust error handling, you can create engaging, personalized, and curated content integrations. Next Steps Experiment with the code
Customize the embed styling
Explore additional Bluesky API endpoints Enjoy and happy tinkering! 🚀 Learn how to programmatically fetch and embed your liked Bluesky posts using authentication, API calls, and oEmbed endpoints. Learn how to programmatically fetch and embed your liked Bluesky posts using authentication, API calls, and oEmbed endpoints. I've recently made the move over to Bluesky . I can already confirm that there is a vibrant tech community there with tons of interesting, useful, and inspiring content. I'm a happy new user! As a result, I've been wanting to embed my top liked Bluesky posts in my "Dev roundup" monthly newsletter posts. My aim is to provide a curated list of Bluesky posts that is specifically tailored to Software Developers. Bluesky Luckily, Bluesky's API is completely free to use, allowing programmatic access to all of the content within. This tutorial will walk you through the process of retrieving and embedding liked Bluesky posts using their API, perfect for personal blogs, portfolios, or content aggregation projects. Bluesky's API Understanding my Bluesky API Workflow I have built a script that allows me to automatically embed my Bluesky posts in a markdown blog post. I think that any or all of the steps used in this script are valuable for many use-cases. To summarize my workflow for embedding liked posts, we follow these key steps: Create an authenticated session Retrieve liked post URIs for an "actor" Use these URIs to fetch oEmbed embed HTML Clean and format the embed code Create an authenticated session Retrieve liked post URIs for an "actor" Use these URIs to fetch oEmbed embed HTML oEmbed embed HTML Clean and format the embed code The Complete Implementation Let's break down each function and its purpose: 1. Creating a Bluesky Session export const createSession = async (): Promise<string | null> => {
  try {
    const response = await fetch(
      "https://bsky.social/xrpc/com.atproto.server.createSession",
      {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          identifier: "your-handle",
          password: "your-password",
        }),
      }
    );
    const responseJson = await response.json();
    return responseJson.accessJwt;
  } catch (e) {
    console.error("Failed to create session: ", e);
    return null;
  }
}; export const createSession = async (): Promise<string | null> => {
  try {
    const response = await fetch(
      "https://bsky.social/xrpc/com.atproto.server.createSession",
      {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          identifier: "your-handle",
          password: "your-password",
        }),
      }
    );
    const responseJson = await response.json();
    return responseJson.accessJwt;
  } catch (e) {
    console.error("Failed to create session: ", e);
    return null;
  }
}; Key Insights: Key Insights: This function authenticates your Bluesky account.

Note: this example hardcodes strings for credentials, but this should be avoided in production use cases. It returns an accessJwt JWT (JSON Web Token) for subsequent API calls Uses the createSession endpoint from Bluesky's ATP (Authenticated Transfer Protocol) Error handling ensures graceful failure if authentication fails This function authenticates your Bluesky account. Note: this example hardcodes strings for credentials, but this should be avoided in production use cases. Note: this example hardcodes strings for credentials, but this should be avoided in production use cases. Note: this example hardcodes strings for credentials, but this should be avoided in production use cases. It returns an accessJwt JWT (JSON Web Token) for subsequent API calls accessJwt Uses the createSession endpoint from Bluesky's ATP (Authenticated Transfer Protocol) createSession Error handling ensures graceful failure if authentication fails 2. Retrieving Liked Post URIs export const getBlueskyLikeUris = async (actor: string, limit: number = 40) => {
  const token = await createSession();
  if (!token) {
    console.error("Failed to get token");
    return;
  }
  
  const response = await fetch(
    "https://bsky.social/xrpc/app.bsky.feed.getActorLikes?actor=${actor}&limit=${limit}",
    {
      method: "GET",
      headers: {
        Authorization: `Bearer ${token}`,
      },
    }
  );
  
  const responseJson = await response.json();
  const uris = responseJson.feed.map((entry: any) => entry.post.uri);

  return uris;
}; export const getBlueskyLikeUris = async (actor: string, limit: number = 40) => {
  const token = await createSession();
  if (!token) {
    console.error("Failed to get token");
    return;
  }
  
  const response = await fetch(
    "https://bsky.social/xrpc/app.bsky.feed.getActorLikes?actor=${actor}&limit=${limit}",
    {
      method: "GET",
      headers: {
        Authorization: `Bearer ${token}`,
      },
    }
  );
  
  const responseJson = await response.json();
  const uris = responseJson.feed.map((entry: any) => entry.post.uri);

  return uris;
}; Key Insights: Key Insights: Requires an authenticated session token Uses the getActorLikes endpoint to fetch liked posts Important -- the endpoint domain should be https://bsky.social, as this is an authenticated request. Extracts unique URIs for each liked post Limits to 40 posts (configurable) Requires an authenticated session token Uses the getActorLikes endpoint to fetch liked posts the getActorLikes Important -- the endpoint domain should be https://bsky.social, as this is an authenticated request. an authenticated request. Extracts unique URIs for each liked post Limits to 40 posts (configurable) 3. Converting URIs to Embeddable HTML export const getBlueskyPostEmbedMarkup = async (uri: string) => {
  try {
    const response = await fetch(`https://embed.bsky.app/oembed?url=${uri}`);
    const responseJson = await response.json();
    
    const formattedHTML = prettier.format(responseJson.html, {
      parser: "html",
      plugins: [require("prettier/parser-html")],
      htmlWhitespaceSensitivity: "ignore",
      printWidth: 1000,
    });
    
    return formattedHTML.replace(/<script[\s\S]*?<\/script>/g, "");
  } catch (e) {
    console.error("Failed to get Bluesky post embed markup");
    return null;
  }
}; export const getBlueskyPostEmbedMarkup = async (uri: string) => {
  try {
    const response = await fetch(`https://embed.bsky.app/oembed?url=${uri}`);
    const responseJson = await response.json();
    
    const formattedHTML = prettier.format(responseJson.html, {
      parser: "html",
      plugins: [require("prettier/parser-html")],
      htmlWhitespaceSensitivity: "ignore",
      printWidth: 1000,
    });
    
    return formattedHTML.replace(/<script[\s\S]*?<\/script>/g, "");
  } catch (e) {
    console.error("Failed to get Bluesky post embed markup");
    return null;
  }
}; Key Insights: Key Insights: Uses Bluesky's oEmbed endpoint with post URIs to access a post's embeddable HTML Optional: Utilizes prettier to format the HTML consistently Optional: Removes <script> tags for security and clean embedding

The reason for this is that I embed a single Bluesky script for each post containing Bluesky content. Flexible error handling Uses Bluesky's oEmbed endpoint with post URIs to access a post's embeddable HTML Bluesky's oEmbed endpoint Optional: Utilizes prettier to format the HTML consistently prettier Optional: Removes <script> tags for security and clean embedding The reason for this is that I embed a single Bluesky script for each post containing Bluesky content. <script> The reason for this is that I embed a single Bluesky script for each post containing Bluesky content. The reason for this is that I embed a single Bluesky script for each post containing Bluesky content. Flexible error handling Putting It All Together: A Complete Example async function embedLikedPosts() {
  try {
    // Get liked post URIs
    const likedPostUris = await getBlueskyLikeUris();
    
    if (!likedPostUris) {
      console.error("No liked posts found");
      return;
    }
    
    // Convert URIs to embed HTML
    const embedPromises = likedPostUris.map(getBlueskyPostEmbedMarkup);
    const embedHtmlArray = await Promise.all(embedPromises);
    
    // Filter out any failed embeds
    const validEmbeds = embedHtmlArray.filter(embed => embed !== null);
    
    // Return the markup for all liked posts
    return `
      ## Some Fave Posts 🦋
      ${validEmbeds.join(`\n\n`)}
    `
  } catch (error) {
    console.error("Error embedding Bluesky posts:", error);
  }
} async function embedLikedPosts() {
  try {
    // Get liked post URIs
    const likedPostUris = await getBlueskyLikeUris();
    
    if (!likedPostUris) {
      console.error("No liked posts found");
      return;
    }
    
    // Convert URIs to embed HTML
    const embedPromises = likedPostUris.map(getBlueskyPostEmbedMarkup);
    const embedHtmlArray = await Promise.all(embedPromises);
    
    // Filter out any failed embeds
    const validEmbeds = embedHtmlArray.filter(embed => embed !== null);
    
    // Return the markup for all liked posts
    return `
      ## Some Fave Posts 🦋
      ${validEmbeds.join(`\n\n`)}
    `
  } catch (error) {
    console.error("Error embedding Bluesky posts:", error);
  }
} Potential Enhancements This solution works for me because all that I require is a statically generated monthly blog post. Some improvements could include: Add pagination support for fetching more than 40 liked posts Implement caching to reduce unnecessary API calls Create a more robust error handling mechanism Creating mechanism for refreshing accessJwt token if used in long running processes Sorting liked posts by popularity (likes) Add pagination support for fetching more than 40 liked posts Implement caching to reduce unnecessary API calls Create a more robust error handling mechanism Creating mechanism for refreshing accessJwt token if used in long running processes accessJwt Sorting liked posts by popularity (likes) Troubleshooting Tips Verify your Bluesky credentials are correct Check that the Bearer token is being set correctly on your authenticated requests. Verify that the endpoint domains you are using are all valid. Verify your Bluesky credentials are correct Check that the Bearer token is being set correctly on your authenticated requests. Verify that the endpoint domains you are using are all valid. Conclusion Embedding Bluesky posts provides a dynamic way to showcase your social media interactions. By understanding the API workflow and implementing robust error handling, you can create engaging, personalized, and curated content integrations. Next Steps Experiment with the code Customize the embed styling Explore additional Bluesky API endpoints Experiment with the code Customize the embed styling Explore additional Bluesky API endpoints Enjoy and happy tinkering! 🚀

Bluesky's Free API Helps Me Curate My Top Posts Like Never Before

Bluesky ukan API Gratis ukax Nayan Jach’a Posts ukanakar Curar ukham yanapt’itu, kunjamatix Janipuniw Nayrjax ukhama

La API gratuita de Bluesky me ayuda a organizar mis publicaciones más destacadas como nunca antes

API رایگان Bluesky به من کمک می‌کند تا پست‌های برتر خود را مانند قبل تنظیم کنم

A Bluesky ingyenes API-ja úgy segít összeválogatni a legnépszerűbb bejegyzéseimet, mint még soha

API Gratis Bluesky Membantu Saya Menyusun Postingan Terbaik Saya Seperti Sebelumnya

Bluesky の無料 API は、これまでにないほど私の人気投稿をキュレートするのに役立ちます

API ya ofele ya Bluesky Esalisaka Ngai Na Curer Ba Posts Na Ngai Ya Likolo Neti Jamais Avant

Bluesky को नि: शुल्क API ले मलाई मेरो शीर्ष पोष्टहरू पहिले कहिल्यै क्युरेट गर्न मद्दत गर्दछ

Bezpłatne API Bluesky pomaga mi w jak nigdy dotąd selekcjonowaniu moich najlepszych postów

د بلوسکي وړیا API زما سره زما د غوره پوسټونو په ترتیب کولو کې مرسته کوي لکه مخکې هیڅکله

Bluesky's API Bilaashka ah waxay iga caawisaa inaan hagaajiyo qoraaladayda ugu sarreeya si aan waligeed horay loo arag

Бесплатни АПИ Блуески-а ми помаже да бирам моје најпопуларније постове као никада раније

Table of Links Abstract and Introduction
Related Work
Problem Definition
Method
Experiments
Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference c Authors:
(1) Chao Yu, Tsinghua University;
(2) Hong Lu, Tsinghua University;
(3) Jiaxuan Gao, Tsinghua University;
(4) Qixin Tan, Tsinghua University;
(5) Xinting Yang, Tsinghua University;
(6) Yu Wang, with equal advising from Tsinghua University;
(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;
(8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). This paper is available on arxiv under CC 4.0 license. Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work Problem Definition Problem Definition Method Method Experiments Experiments Conclusion and References Conclusion and References A. Appendix A. Appendix A.1. Full Prompts and A.2 ICPL Details A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A. 3 Baseline Details A.4 Environment Details A.4 Environment Details A.5 Proxy Human Preference A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.6 Human-in-the-Loop Preference c Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). Authors: Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University ( zoeyuchao@gmail.com ). zoeyuchao@gmail.com 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

Human Preferences Help Scientists Train AI 30x Faster Than Before

Seven Things Angel Investors Want To See In A Startup Before Investing

Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy The AI Guy

The World Before AI

Everything You Need to Know Before Building on the Stellar Blockchain

The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 15 of 18. Count V – Violation of 17 U.S.C. § 1202(b)(3) by OpenAI Defendants 146. The above paragraphs are incorporated by reference into this Count. 147. Upon information and belief, the OpenAI Defendants shared copies of Plaintiff’s works without author, title, copyright notice, and terms of use information with Defendant Microsoft in connection with the development of ChatGPT and 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 June 27, 2024, motherjones.com 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. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 15 of 18. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 15 of 18. HackerNoon’s Legal PDF Series here Count V – Violation of 17 U.S.C. § 1202(b)(3) by OpenAI Defendants 146. The above paragraphs are incorporated by reference into this Count. 147. Upon information and belief, the OpenAI Defendants shared copies of Plaintiff’s works without author, title, copyright notice, and terms of use information with Defendant Microsoft in connection with the development of ChatGPT and Copilot. Continue Reading Here. Continue Reading Here . 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 June 27, 2024, motherjones.com 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. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com 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. motherjones.com

Non-profit Newsroom Claims OpenAI Removed Copyright Before Sharing Works with Microsoft

The Noonification: Five Questions to Ask Yourself Before Creating a Web Project (8/1/2024)

Five Questions to Ask Yourself Before Creating a Web Project

একটি ওয়েব প্রকল্প তৈরি করার আগে নিজেকে জিজ্ঞাসা করার জন্য পাঁচটি প্রশ্ন

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Cinco preguntas que debe hacerse antes de crear un proyecto web

Cinq questions à se poser avant de créer un projet Web

वेब प्रोजेक्ट बनाने से पहले खुद से पूछने वाले पांच सवाल

Web プロジェクトを作成する前に自問すべき 5 つの質問

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Web Projesi Oluşturmadan Önce Kendinize Sormanız Gereken Beş Soru

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创建 Web 项目前要问自己的五个问题

The most important obstacle standing in the way of developing blockchain technology is scalability. Layer 2 solutions represent the leading edge in the effort to address this, allowing for higher transaction throughput on blockchain networks, cost reduction, and better user interface. This article focuses on top trends that determine how Layer 2 technology will evolve, as well as how they affect other aspects within the overall blockchain landscape. Understanding Layer 2 Solutions Layer 2 solutions are protocols that are created on top of an existing blockchain, which acts as the first layer, thereby enhancing what it can do. However, these solutions work by completing transactions through different means, not on the main chain, although it will still possess all the securities inherent from being decentralized while still ensuring that no single transaction is lost throughout its network. This they do by making it highly possible for many blocks to be pushed through the network since each block does not have to be associated with one specific transaction anymore…rather they can all fall under specific types or categories depending upon some criteria determined by those who came up with them…such as Sharding for instance.…so long as there are still some available resources at any given time (whether money or energy). Let us see what is happening now in these quick-developing fields. Scalability Improvements The primary aim of layer 2 solutions is to solve scalability limits in layer 1 blockchains. Different methods have been developed based on different ideas to achieve this. One of the most distinguished traditional solutions for Scalable Systems covering the Layer 2, optimistic rollups operate out of the main chain transactions and simply present the exchange data. When the need arises, optimistic rollups conduct their verification of correctness, taking truth for granted. This method effectively cuts down on the computational work done on the main chain, hence making it easier to handle more transactions per unit of time. ZK-Rollups (Zero-Knowledge Rollups): Zero-knowledge rollups leverage zero-knowledge proofs in verifying transactions off-chain for several transactions to be checked using a single proof translated to the main chain. It increases scalability, meaning developers like it for security reasons. Interoperability It is more important, as the blockchain ecosystem expands that diverse blockchains interoperate smoothly. The crucial role being played in enhancing the interoperability are solutions in Layer 2. Interconnected Bridges can make it easy for people to send money or information across a variety of types of blockchains. By allowing each chain of blocks to be able to interact with one another via them, interconnected bridges in use now allow users access into whole new realms where decentralized applications (dApps) grow best if one’s desires go beyond just making transactions but rather seek systems made from more than a single system. Reduced Transaction Costs Layer 1 networks, especially Ethereum, are more expensive because of expensive gas prices. Therefore, various Layer 2 solutions with substantially decreased transactional costs have been developed. Without this, the use of blockchain technology on a larger scale would not be possible. Payment Channels: Technological advancements in Bitcoin, such as Lightning Network and Ethereum, such as Raiden Network, make the creation of off-chain payment mechanisms between users possible. Whenever these channels are open, there are instant low-fee and off-main chain transaction settlements, only in case of their closure do they become floating-upon the primary blockchain reducing the cost by eliminating some transfers on it. Enhanced Privacy The concern of a lot of blockchain users is about privacy. But technologies for this matter increasingly adopt layer 2 solutions that have privacy-preserving features as an answer. Layer 2 solutions are incorporating protocols like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) and zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) to provide for various privacy levels. This ensures confidential transactions can occur without any identifiable information of the parties involved being shared. Decentralized Finance (DeFi) Expansion The sector of DeFi has had a massive expansion, with the sustainable momentum requiring Layer 2 solutions to support it. Thus, they offer an opportunity to improve the number of transactions per unit of time that DeFi communities can handle at a given point in the future. Scalable Decentralized Financial Protocols have started migrating onto Layer 2 solutions as a way of getting around some of the limitations associated with Layer 1 blockchains that emerged due to the ever-increasing popularity of decentralized systems.. User Experience Enhancements Layer 2 solutions are mainly focused on enhancing client experience. Two main facets of this trend are quicker transaction finality and less waiting time. Developers are working on enhanced protocols to get near-instant confirmation times with respect to transactions. Such improvement is not only improving client satisfaction but also making blockchain technology more user-friendly. Enterprise Adoption Businesses are turning to Layer 2 solutions, which provide scalable, safe, and affordable blockchain applications that can meet their company requirements. Enterprise Solutions: We are developing customized Layer 2 applications designed for supply chain management, finance, and healthcare in different business sectors to promote the operational efficiency and scalability of large enterprise applications. Security Innovations It is imperative that security for Layer 2 solutions is ensured, resulting in discoveries into security mechanisms meant to safeguard against cyber crimes or fraud. Better mechanisms for fraud proofs and validity proofing that come with optimistic rollups have improved the integrity and security of off-chain transactions significantly. These proofs ensure that transactions are verified without compromising on the security element. Community and Ecosystem Growth The Layer 2 ecosystem is growing quickly, with more community involvement, development activity, and institutional interest.\ Developer Tools and SDKs Developer tools and software kits (SDKs) are being developed to improve the way Layer 2 solutions are created and integrated. They make it simpler for developers to construct and launch scalable blockchain applications. Conclusion Layer 2 solutions are affecting scalability, cost, privacy, and user knowledge hurdles as far as revolutionizing the blockchain landscape goes. Layer 2 solutions are these advancements taken further on to the extent that for blockchain technology adoption and growth, they are projected to serve as highly significant. The stage is being set for increased scalability through optimistic and ZK-rollups, interoperability is being driven by cross-chain bridges, and transaction costs are being decreased; these are some of the features you can find more efficient and accessible blockchain ecosystems or platforms. These are additional proofs that show how transformative these technologies can be; for example, enhanced privacy features, expansion into DeFi, adoption in NFTs, and gaming of Layer 2 solutions. In addition, the corporate adoption, user experience, and security improvements are significant, showing how the focus is on making blockchain technology more useful and safe across a variety of applications. This trend emphasizes the collaborative spirit of the expanding network and environment around Layer 2 solutions. To move on, we need more work done on Layer 2 solutions so that problems faced by existing blockchain networks can be dealt with conclusively; hence, new prospects opened up for decentralized applications. If the important issues of scalability, cost, privacy, and security are tackled, Layer 2 solutions will drive the development of next-level blockchain technologies. If you are to navigate the ever-changing world of blockchain technology, you need to be well-apprised of what is happening in Layer 2 solutions, whether you are in an enterprise or developing apps. There are more to come, and so far, what we have only represents a tip of the iceberg. The most important obstacle standing in the way of developing blockchain technology is scalability. Layer 2 solutions represent the leading edge in the effort to address this, allowing for higher transaction throughput on blockchain networks, cost reduction, and better user interface. This article focuses on top trends that determine how Layer 2 technology will evolve, as well as how they affect other aspects within the overall blockchain landscape. Understanding Layer 2 Solutions Layer 2 solutions are protocols that are created on top of an existing blockchain, which acts as the first layer, thereby enhancing what it can do. However, these solutions work by completing transactions through different means, not on the main chain, although it will still possess all the securities inherent from being decentralized while still ensuring that no single transaction is lost throughout its network. This they do by making it highly possible for many blocks to be pushed through the network since each block does not have to be associated with one specific transaction anymore…rather they can all fall under specific types or categories depending upon some criteria determined by those who came up with them…such as Sharding for instance.…so long as there are still some available resources at any given time (whether money or energy). Let us see what is happening now in these quick-developing fields. Scalability Improvements The primary aim of layer 2 solutions is to solve scalability limits in layer 1 blockchains. Different methods have been developed based on different ideas to achieve this. One of the most distinguished traditional solutions for Scalable Systems covering the Layer 2, optimistic rollups operate out of the main chain transactions and simply present the exchange data. When the need arises, optimistic rollups conduct their verification of correctness, taking truth for granted. This method effectively cuts down on the computational work done on the main chain, hence making it easier to handle more transactions per unit of time. ZK-Rollups (Zero-Knowledge Rollups): Zero-knowledge rollups leverage zero-knowledge proofs in verifying transactions off-chain for several transactions to be checked using a single proof translated to the main chain. It increases scalability, meaning developers like it for security reasons. Interoperability It is more important, as the blockchain ecosystem expands that diverse blockchains interoperate smoothly. The crucial role being played in enhancing the interoperability are solutions in Layer 2. Interconnected Bridges can make it easy for people to send money or information across a variety of types of blockchains. By allowing each chain of blocks to be able to interact with one another via them, interconnected bridges in use now allow users access into whole new realms where decentralized applications (dApps) grow best if one’s desires go beyond just making transactions but rather seek systems made from more than a single system. Reduced Transaction Costs Layer 1 networks, especially Ethereum, are more expensive because of expensive gas prices. Therefore, various Layer 2 solutions with substantially decreased transactional costs have been developed. Without this, the use of blockchain technology on a larger scale would not be possible. Layer 2 solutions Layer 2 solutions Payment Channels: Technological advancements in Bitcoin, such as Lightning Network and Ethereum, such as Raiden Network, make the creation of off-chain payment mechanisms between users possible. Whenever these channels are open, there are instant low-fee and off-main chain transaction settlements, only in case of their closure do they become floating-upon the primary blockchain reducing the cost by eliminating some transfers on it. Enhanced Privacy The concern of a lot of blockchain users is about privacy. But technologies for this matter increasingly adopt layer 2 solutions that have privacy-preserving features as an answer. Layer 2 solutions are incorporating protocols like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) and zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) to provide for various privacy levels. This ensures confidential transactions can occur without any identifiable information of the parties involved being shared. Decentralized Finance (DeFi) Expansion The sector of DeFi has had a massive expansion, with the sustainable momentum requiring Layer 2 solutions to support it. Thus, they offer an opportunity to improve the number of transactions per unit of time that DeFi communities can handle at a given point in the future. Scalable Decentralized Financial Protocols have started migrating onto Layer 2 solutions as a way of getting around some of the limitations associated with Layer 1 blockchains that emerged due to the ever-increasing popularity of decentralized systems.. User Experience Enhancements Layer 2 solutions are mainly focused on enhancing client experience. Two main facets of this trend are quicker transaction finality and less waiting time. Developers are working on enhanced protocols to get near-instant confirmation times with respect to transactions. Such improvement is not only improving client satisfaction but also making blockchain technology more user-friendly. Enterprise Adoption Businesses are turning to Layer 2 solutions, which provide scalable, safe, and affordable blockchain applications that can meet their company requirements. Enterprise Solutions: We are developing customized Layer 2 applications designed for supply chain management, finance, and healthcare in different business sectors to promote the operational efficiency and scalability of large enterprise applications. Security Innovations It is imperative that security for Layer 2 solutions is ensured, resulting in discoveries into security mechanisms meant to safeguard against cyber crimes or fraud. Better mechanisms for fraud proofs and validity proofing that come with optimistic rollups have improved the integrity and security of off-chain transactions significantly. These proofs ensure that transactions are verified without compromising on the security element. Community and Ecosystem Growth The Layer 2 ecosystem is growing quickly, with more community involvement, development activity, and institutional interest.\ Developer Tools and SDKs Developer tools and software kits (SDKs) are being developed to improve the way Layer 2 solutions are created and integrated. They make it simpler for developers to construct and launch scalable blockchain applications. Conclusion Layer 2 solutions are affecting scalability, cost, privacy, and user knowledge hurdles as far as revolutionizing the blockchain landscape goes. Layer 2 solutions are these advancements taken further on to the extent that for blockchain technology adoption and growth, they are projected to serve as highly significant. The stage is being set for increased scalability through optimistic and ZK-rollups, interoperability is being driven by cross-chain bridges, and transaction costs are being decreased; these are some of the features you can find more efficient and accessible blockchain ecosystems or platforms. These are additional proofs that show how transformative these technologies can be; for example, enhanced privacy features, expansion into DeFi, adoption in NFTs, and gaming of Layer 2 solutions. In addition, the corporate adoption, user experience, and security improvements are significant, showing how the focus is on making blockchain technology more useful and safe across a variety of applications. This trend emphasizes the collaborative spirit of the expanding network and environment around Layer 2 solutions. To move on, we need more work done on Layer 2 solutions so that problems faced by existing blockchain networks can be dealt with conclusively; hence, new prospects opened up for decentralized applications. If the important issues of scalability, cost, privacy, and security are tackled, Layer 2 solutions will drive the development of next-level blockchain technologies. If you are to navigate the ever-changing world of blockchain technology, you need to be well-apprised of what is happening in Layer 2 solutions, whether you are in an enterprise or developing apps. There are more to come, and so far, what we have only represents a tip of the iceberg.

A Founder’s Guide to Layer 2 Blockchains before starting your next project

Many crypto games aim to "onboard the next 1 billion users," but is this feasible if users have to pay gas fees while playing and even pay to play? This alone deters new users from entering the crypto space. With over 100 million people playing tap-to-earn games currently, it appears that Telegram may have found a hack to onboard the next billion users into the crypto space effortlessly. More non-crypto natives have joined Telegram, and even those who had abandoned their accounts have become interested in the app again after discovering they can earn money by playing a game. How fancy! Some of the popular tap-to-earn games on Telegram have amassed millions of users by just conveying simplicity. Notcoin - 40 million players
Tapswap - 48 million players
Hamster Kombat - 100 million players
Avacoin- 5 million players
W-Coin- 10 million players Ordinarily, it's quite rare for a new project to amass such traffic in barely 4-8 weeks. This is a jinx that Telegram mining games have broken. Could Telegram Surpass Solana in Massive User Adoption With Toncoin? The team behind Solana is making incredible efforts to drive massive adoption for the project. One notable example is the recent partnership with PayPal, which has made the PayPal stablecoin, PYUSD, available on the Solana blockchain. This enables PayPal users (only in the US for now) to leverage Solana's speed and low transaction fees. According to PayPal: “Solana’s low cost, close to instant settlement, high throughput and advanced token extensions functionality makes it an ideal network for issuing PYUSD”. While this may be clearly understood by crypto natives, those unfamiliar with the technology might not see any reason to adopt Solana for transactions or own a Solana-based wallet. How do you advise a non-crypto native, who has many fears about crypto, to download a Phantom wallet? Yes, it's simple for those accustomed to storing keys in a non-custodial wallet. But how does that appeal to someone new to crypto? Telegram may have unlocked a new stream of onboarding by using a familiar environment to attract the attention of crypto newbies. About 3 billion people globally use WhatsApp to send and receive messages instantly, and Telegram is similar, with almost 1 billion users. This familiarity makes it easy for new crypto users to join Telegram and start playing crypto games without needing to learn how the platform works from scratch. So the Funnel May Look Like This 👇🏽 Create a Telegram bot crypto game. Make rewards enticing. Constantly update players to keep them interested. Educate players about the future of your game, so they are willing to stay long-term. Introduce them to a Telegram in-app wallet or Ton Keeper to secure their rewards. Introduce them to staking - investing their rewards for a period of time with an interest top-up. Along the way, they may want to swap their tokens, earn through crypto, or purchase an NFT. Then they can find wallets that suit their needs. This is a sketch of how simple onboarding for non-crypto natives should be: presenting what's familiar and introducing touchpoints into the decentralized space without overwhelming them with technical details. Many of them don't want technical jargon—it can be boring. Just like a person using an electric car doesn't care about the wiring that makes it work, as long as it works. While using the car, the person may now get to learn a few things about its framework that will make them appreciate the car more. Many crypto games aim to "onboard the next 1 billion users," but is this feasible if users have to pay gas fees while playing and even pay to play? This alone deters new users from entering the crypto space. With over 100 million people playing tap-to-earn games currently, it appears that Telegram may have found a hack to onboard the next billion users into the crypto space effortlessly. More non-crypto natives have joined Telegram, and even those who had abandoned their accounts have become interested in the app again after discovering they can earn money by playing a game. How fancy! Some of the popular tap-to-earn games on Telegram have amassed millions of users by just conveying simplicity. Notcoin - 40 million players Tapswap - 48 million players Hamster Kombat - 100 million players Avacoin- 5 million players W-Coin- 10 million players Notcoin - 40 million players Tapswap - 48 million players Hamster Kombat - 100 million players Avacoin- 5 million players W-Coin- 10 million players Ordinarily, it's quite rare for a new project to amass such traffic in barely 4-8 weeks. This is a jinx that Telegram mining games have broken. Could Telegram Surpass Solana in Massive User Adoption With Toncoin? The team behind Solana is making incredible efforts to drive massive adoption for the project. One notable example is the recent partnership with PayPal, which has made the PayPal stablecoin, PYUSD , available on the Solana blockchain. This enables PayPal users (only in the US for now) to leverage Solana's speed and low transaction fees. According to PayPal: PYUSD “Solana’s low cost, close to instant settlement, high throughput and advanced token extensions functionality makes it an ideal network for issuing PYUSD”. “Solana’s low cost, close to instant settlement, high throughput and advanced token extensions functionality makes it an ideal network for issuing PYUSD”. PYUSD”. While this may be clearly understood by crypto natives, those unfamiliar with the technology might not see any reason to adopt Solana for transactions or own a Solana-based wallet. How do you advise a non-crypto native, who has many fears about crypto, to download a Phantom wallet? Yes, it's simple for those accustomed to storing keys in a non-custodial wallet. But how does that appeal to someone new to crypto? Telegram may have unlocked a new stream of onboarding by using a familiar environment to attract the attention of crypto newbies. About 3 billion people globally use WhatsApp to send and receive messages instantly, and Telegram is similar, with almost 1 billion users. This familiarity makes it easy for new crypto users to join Telegram and start playing crypto games without needing to learn how the platform works from scratch. So the Funnel May Look Like This 👇🏽 So the Funnel May Look Like This Create a Telegram bot crypto game. Create a Telegram bot crypto game. Make rewards enticing. Make rewards enticing. Constantly update players to keep them interested. Constantly update players to keep them interested. Educate players about the future of your game, so they are willing to stay long-term. Educate players about the future of your game, so they are willing to stay long-term. Introduce them to a Telegram in-app wallet or Ton Keeper to secure their rewards. Introduce them to a Telegram in-app wallet or Ton Keeper to secure their rewards. Introduce them to staking - investing their rewards for a period of time with an interest top-up. Introduce them to staking - investing their rewards for a period of time with an interest top-up. Along the way, they may want to swap their tokens, earn through crypto, or purchase an NFT. Then they can find wallets that suit their needs. This is a sketch of how simple onboarding for non-crypto natives should be: presenting what's familiar and introducing touchpoints into the decentralized space without overwhelming them with technical details. Many of them don't want technical jargon—it can be boring. Just like a person using an electric car doesn't care about the wiring that makes it work, as long as it works. While using the car, the person may now get to learn a few things about its framework that will make them appreciate the car more.

Tap to Earn : Telegram May Onboard The Next 10 Billion Crypto Users Before Solana

আয় করতে ট্যাপ করুন: টেলিগ্রাম সোলানার আগে পরবর্তী 10 বিলিয়ন ক্রিপ্টো ব্যবহারকারীদের অনবোর্ড করতে পারে

Tippen, um zu verdienen: Telegram könnte die nächsten 10 Milliarden Krypto-Benutzer vor Solana an Bord holen

Toque para ganar: Telegram puede incorporar a los próximos 10 mil millones de usuarios de criptomonedas antes de Solana

Appuyez pour gagner : Telegram pourrait intégrer les 10 prochains milliards d'utilisateurs de crypto avant Solana

टैप टू अर्न: टेलीग्राम सोलाना से पहले अगले 10 बिलियन क्रिप्टो उपयोगकर्ताओं को शामिल कर सकता है

タップして稼ぐ：テレグラムはソラナより先に次の100億人の暗号通貨ユーザーを獲得する可能性がある

탭하여 수익 창출: 텔레그램에서 솔라나 이전에 100억 명의 암호화폐 사용자를 확보할 수 있음

Toque para ganhar: Telegram pode integrar os próximos 10 bilhões de usuários criptográficos antes de Solana

Нажмите, чтобы заработать: Telegram может привлечь следующие 10 миллиардов пользователей криптовалюты до Соланы

Kazanmak için Dokun: Telegram, Solana'dan Önce Sonraki 10 Milyar Kripto Kullanıcısına Katılabilir

Nhấn để kiếm tiền: Telegram có thể tiếp cận 10 tỷ người dùng tiền điện tử tiếp theo trước Solana

点击赚钱：Telegram 可能会在 Solana 之前吸引下一个 100 亿加密用户

While data breaches and security threats loom, innovative and robust authentication security mechanisms become the need of the hour. The best business strategy in today’s modern landscape is to please your customers. And when you’re delivering services online, it all depends on how your customers are treated at your platform, how their data is managed, and how much they trust your platform. However, the first interaction with your platform is the biggest factor deciding the overall customer satisfaction rate. And it all begins with a reliable authentication system! Yes, your authentication system is all that can create a great, lasting impression on your users and potential customers. But what’s worrisome is that most businesses are reluctant to switch to cutting-edge authentication mechanisms like a customer identity and access management (CIAM) solution. They rely on legacy authentication systems that aren’t powerful enough to handle the security and user experience challenges of the modern digital landscape. And here’s where businesses lag behind competitors in the ever-evolving modern business environment. Let’s learn why enterprises must take a giant leap toward adopting a reliable, innovative CIAM solution to stay competitive. Why CIAM is the Missing Piece in Your Business Strategy? Businesses must emphasize seamless customer experience in the modern digital world. However, ignoring the true potential of a secure and seamless user experience makes all the difference. Brands creating a perfect symphony between user experience and security in their authentication process can convert and retain more users than those that aren’t. And here’s where the critical role of a robust CIAM comes into play! A CIAM allows businesses to incorporate cutting-edge security mechanisms that reinforce user account security and offer a streamlined user experience. CIAM helps deliver great authentication experiences backed with adequate security, which is why users trust businesses that have incorporated CIAM into their platforms. In a nutshell, if you wish to build lasting relationships with your customers and potential customers, you have to lay the foundation for trust through secure data management best practices. By integrating CIAM into your business strategy, you can flawlessly integrate security, user experience, and compliance with data protection regulations to drive customer loyalty. 5 Reasons You Must Incorporate CIAM into Your Business Strategy 1. Enhanced Security The conventional and legacy authentication systems mostly rely on single-factor authentication. And most of them leverage password-based security, which is highly prone to data and privacy breaches. On the other hand, using a CIAM can simplify the authentication process and add multiple layers of authentication into the user authentication process. This means a user has to go through a number of verification processes, including passwords, security questions, or one-time passwords, to access their account. This kind of authentication security ensures that even one aspect of authentication, i.e., passwords, is compromised; another stringent layer      of security safeguards an individual’s account and hence significantly reduces the risk of unauthorized access. 2. Great User Experience All these authentication methods ensure there’s zero or no friction at all while users access resources and their accounts. Furthermore, the added layers of security in most advanced CIAMs are automatically added in a situation where the system detects any unusual behavior. This means a user has to go through a number of authentication layers only if it is required. Otherwise, they can enjoy a seamless login experience every time they access their account. So, if an enterprise is offering a better user experience, it always contributes to higher customer satisfaction and overall retention rates. To be precise, a CIAM not only just secures your system but eventually increases the overall user experience. 3. Regulatory Compliance Requirements While data privacy and security regulations like the GDPR and CCPA are becoming more stringent than ever, choosing a reliable CIAM has become an absolute necessity. With a CIAM solution, enterprises can adhere to a number of global data privacy and security compliance standards while catering to their citizens. Features like adaptive authentication, multi-factor authentication, and consent management are some of the basic requirements of various regulations in different nations, which must be adhered to to offer services and products to their citizens. 4. Scalability and Flexibility As your business grows, so does the complexity of managing many customer identities and infinite login attempts. Advanced CIAM solutions can offer auto-scalable infrastructures that can scale up to millions of identities per minute. This helps maintain a great user experience during peak hours without a glitch. Modern CIAMs are designed to handle large volumes of users and data seamlessly, allowing you to adapt to your diverse business needs. 5. Data Insights and Personalization With a comprehensive CIAM, businesses can harness the power of data analytics that allows them to gain valuable insights regarding user behavior and preferences. This data can be further leveraged to create personalized user journeys and experiences for your customers, enhancing engagement and loyalty. Final Thoughts In the modern digital business landscape, where cyber-attacks and breaches are becoming increasingly sophisticated, relying on legacy authentication systems could be quite risky. Upgrading to a CIAM system is undeniably a proactive step toward securing your business and sensitive customer data. Advanced CIAM offers a great user experience and ease of access so businesses can quickly achieve overall success. While data breaches and security threats loom, innovative and robust authentication security mechanisms become the need of the hour. The best business strategy in today’s modern landscape is to please your customers. And when you’re delivering services online, it all depends on how your customers are treated at your platform, how their data is managed, and how much they trust your platform. However, the first interaction with your platform is the biggest factor deciding the overall customer satisfaction rate. And it all begins with a reliable authentication system! Yes, your authentication system is all that can create a great, lasting impression on your users and potential customers. But what’s worrisome is that most businesses are reluctant to switch to cutting-edge authentication mechanisms like a customer identity and access management (CIAM) solution. They rely on legacy authentication systems that aren’t powerful enough to handle the security and user experience challenges of the modern digital landscape. And here’s where businesses lag behind competitors in the ever-evolving modern business environment. Let’s learn why enterprises must take a giant leap toward adopting a reliable, innovative CIAM solution to stay competitive. Why CIAM is the Missing Piece in Your Business Strategy? Businesses must emphasize seamless customer experience in the modern digital world. However, ignoring the true potential of a secure and seamless user experience makes all the difference. Brands creating a perfect symphony between user experience and security in their authentication process can convert and retain more users than those that aren’t. And here’s where the critical role of a robust CIAM comes into play! A CIAM allows businesses to incorporate cutting-edge security mechanisms that reinforce user account security and offer a streamlined user experience. CIAM helps deliver great authentication experiences backed with adequate security, which is why users trust businesses that have incorporated CIAM into their platforms. In a nutshell, if you wish to build lasting relationships with your customers and potential customers, you have to lay the foundation for trust through secure data management best practices. By integrating CIAM into your business strategy, you can flawlessly integrate security, user experience, and compliance with data protection regulations to drive customer loyalty. 5 Reasons You Must Incorporate CIAM into Your Business Strategy 1. Enhanced Security The conventional and legacy authentication systems mostly rely on single-factor authentication. And most of them leverage password-based security, which is highly prone to data and privacy breaches. On the other hand, using a CIAM can simplify the authentication process and add multiple layers of authentication into the user authentication process. This means a user has to go through a number of verification processes, including passwords, security questions, or one-time passwords, to access their account. This kind of authentication security ensures that even one aspect of authentication, i.e., passwords, is compromised; another stringent layer      of security safeguards an individual’s account and hence significantly reduces the risk of unauthorized access. This kind of authentication security ensures that even one aspect of authentication, i.e., passwords, is compromised; another stringent layer      of security safeguards an individual’s account and hence significantly reduces the risk of unauthorized access. 2. Great User Experience All these authentication methods ensure there’s zero or no friction at all while users access resources and their accounts. Furthermore, the added layers of security in most advanced CIAMs are automatically added in a situation where the system detects any unusual behavior. This means a user has to go through a number of authentication layers only if it is required. Otherwise, they can enjoy a seamless login experience every time they access their account. So, if an enterprise is offering a better user experience, it always contributes to higher customer satisfaction and overall retention rates. To be precise, a CIAM not only just secures your system but eventually increases the overall user experience. 3. Regulatory Compliance Requirements While data privacy and security regulations like the GDPR and CCPA are becoming more stringent than ever, choosing a reliable CIAM has become an absolute necessity. With a CIAM solution, enterprises can adhere to a number of global data privacy and security compliance standards while catering to their citizens. Features like adaptive authentication, multi-factor authentication, and consent management are some of the basic requirements of various regulations in different nations, which must be adhered to to offer services and products to their citizens. 4. Scalability and Flexibility As your business grows, so does the complexity of managing many customer identities and infinite login attempts. Advanced CIAM solutions can offer auto-scalable infrastructures that can scale up to millions of identities per minute. This helps maintain a great user experience during peak hours without a glitch. Modern CIAMs are designed to handle large volumes of users and data seamlessly, allowing you to adapt to your diverse business needs. 5. Data Insights and Personalization With a comprehensive CIAM, businesses can harness the power of data analytics that allows them to gain valuable insights regarding user behavior and preferences. This data can be further leveraged to create personalized user journeys and experiences for your customers, enhancing engagement and loyalty. Final Thoughts In the modern digital business landscape, where cyber-attacks and breaches are becoming increasingly sophisticated, relying on legacy authentication systems could be quite risky. Upgrading to a CIAM system is undeniably a proactive step toward securing your business and sensitive customer data. Advanced CIAM offers a great user experience and ease of access so businesses can quickly achieve overall success.

You Should Upgrade Your Authentication System to CIAM Now Before It's Too Late

If you watch a market hibernate long enough, eventually, you will witness its awakening, followed by the emergence of new projects. Something similar has been happening to the blockchain market for the past few months. This situation is typical for any market entering its bullish phase: Market capitalization goes up, and updated multipliers attract the interest of investors and developers alike. Startup owners flood the market with new ideas and products. A new era begins. Poetic statements aside, creating a fresh startup might leave its owner with nothing but a shirt on their back. There are five mistakes that can ruin your product before it launches. For a detailed study of this topic, I recommend the book INSPIRED: How to Create Tech Products Customers Love by Marty Cagan. Five Fatal Mistakes In Bullish Development 1. Failure Of Leadership A team is only as good as its leader. Still, you can always benefit from someone else’s expertise, and you will absolutely have tasks to delegate. Your right-hand person in this quest is your blockchain product manager, so choose wisely. Your PM needs to have a solid understanding of blockchain technology, markets, and the regulatory landscape. But the most valuable asset is the ability to identify opportunities that offer real value to a customer. A value that transcends mere hype: Every market still has hundreds, if not more, unsolved issues. Knowing how to locate them and offer a solution is what we all look for in a PM. 2. I Can Sell Anything Just because you want to innovate something doesn’t mean you should. We have two words for you: New Coke. This is, hands down, the most disastrous example of a product failure, when something nobody asked for made its way to the market. Believe that you don’t want your product to become the New Coke of the blockchain market. Choosing the right product to build is arguably the most important call you will ever have to make. Product discovery is a whole discipline that combines elements of marketing, development, and sociology. Before rolling out a product or even an idea, you must spend time researching. Any book would suggest figuring out the pain points and needs of users. When it comes to the blockchain space, it’s the knowledge and user experience that can play a crucial part. How well do you and your PM know what a regular person from your target audience experiences when they interact with it? Use surveys, interviews, and prototype testing with potential users active in trading and investing—or use blockchain for other tasks. The issues may include security, wallet usability, transaction fees, trading strategies, and so on. Make sure your product truly answers the requests of the community. 3. What Works For Me Works For Everyone User-centric design is more of an issue than more of us realize. It’s crucial to remember who you are building the product for. It’s not just your team. Back in the day, Myspace was a huge platform that catered to the needs of the tech-savvy community. It offered personalization and complex UX design with many features. Yet, it couldn’t survive the competition when far less complicated Facebook and Twitter came around. Indeed, when it comes to products, the comfort of many will prevail over the individuality of a few. A user-friendly interface will go a long way, and it’s even more valid when we talk blockchain. This space is intimidating enough for newcomers. If you don’t believe it, just consider how many advantages it offers, yet it’s still not commonly adopted. Yes, we might revel in our technical knowledge, but if we create the product for ourselves, we would be the only ones using it. That is a UI/UX/design failure. Make sure to test the product with your target audience. If you want an average person to be comfortable with it, let them try it, let them explore it, and listen to the feedback. 4. Let’s Spend It All in One Place While the leadership team behind ScaleFactor attributes its problems to Covid-19, many former employees have said that the company focused on costly marketing strategies instead of responding to the customers’ expectations. To avoid the need to blame a global pandemic, concentrate on lean product development. Your minimum viable product has to address the core functionality. It can be a secure wallet or a user-friendly trading platform. When that’s done, continue improving the product gradually, according to the feedback. Prototype and test rapidly, mostly paying attention to the features and seeing what works best. All the improvements have to align with your key value proposition. When you have a core group of users, you will be able to provide agile and continuous delivery. Because the tech market and blockchain, in particular, develop rapidly, you will have to regularly update your product and add new features. But always start small. 5. Develop Now, Promote Later Here’s the pièce de résistance: marketing. It’s last but not least. Quite the contrary, marketing channels and potential should be studied prior to developing the product. You have to go into development knowing how, when, and where you are planning to market your product. Only then you will be free to focus on MVPs and roadmaps. It’s a simple case of “Who am I doing this for?” This is a question that every startup in blockchain or any other market has to face eventually. And it’s best to answer it right away. If you don’t find your audience and marketing channels now, you certainly won’t find them later. Applying this framework to the development of a blockchain product allows you to create a solution that not only takes advantage of the unique capabilities of the technology but also addresses user needs, fosters innovation, and adapts to the ever-changing landscape of the market. At the same time, it’s these five simple facts that help manage your initial funds and even save some for a pivot in case the original product falls short of your expectations. Follow me on LinkedIn. Check out my website. If you watch a market hibernate long enough, eventually, you will witness its awakening, followed by the emergence of new projects. Something similar has been happening to the blockchain market for the past few months. This situation is typical for any market entering its bullish phase: Market capitalization goes up, and updated multipliers attract the interest of investors and developers alike. Startup owners flood the market with new ideas and products. A new era begins. Poetic statements aside, creating a fresh startup might leave its owner with nothing but a shirt on their back. There are five mistakes that can ruin your product before it launches. There are five mistakes that can ruin your product before it launches. For a detailed study of this topic, I recommend the book INSPIRED: How to Create Tech Products Customers Love by Marty Cagan. INSPIRED: How to Create Tech Products Customers Love Five Fatal Mistakes In Bullish Development 1. Failure Of Leadership A team is only as good as its leader. Still, you can always benefit from someone else’s expertise, and you will absolutely have tasks to delegate. Your right-hand person in this quest is your blockchain product manager, so choose wisely. Your PM needs to have a solid understanding of blockchain technology, markets, and the regulatory landscape. But the most valuable asset is the ability to identify opportunities that offer real value to a customer. A value that transcends mere hype: Every market still has hundreds, if not more, unsolved issues. Knowing how to locate them and offer a solution is what we all look for in a PM. 2. I Can Sell Anything Just because you want to innovate something doesn’t mean you should. We have two words for you: New Coke . This is, hands down, the most disastrous example of a product failure, when something nobody asked for made its way to the market. Believe that you don’t want your product to become the New Coke of the blockchain market. New Coke Choosing the right product to build is arguably the most important call you will ever have to make. Product discovery is a whole discipline that combines elements of marketing, development, and sociology. Before rolling out a product or even an idea, you must spend time researching. Any book would suggest figuring out the pain points and needs of users. When it comes to the blockchain space, it’s the knowledge and user experience that can play a crucial part. How well do you and your PM know what a regular person from your target audience experiences when they interact with it? Use surveys, interviews, and prototype testing with potential users active in trading and investing—or use blockchain for other tasks. The issues may include security, wallet usability, transaction fees, trading strategies, and so on. Make sure your product truly answers the requests of the community. 3. What Works For Me Works For Everyone User-centric design is more of an issue than more of us realize. It’s crucial to remember who you are building the product for. It’s not just your team. Back in the day, Myspace was a huge platform that catered to the needs of the tech-savvy community. It offered personalization and complex UX design with many features. Yet, it couldn’t survive the competition when far less complicated Facebook and Twitter came around. Indeed, when it comes to products, the comfort of many will prevail over the individuality of a few. Facebook Twitter A user-friendly interface will go a long way, and it’s even more valid when we talk blockchain. This space is intimidating enough for newcomers. If you don’t believe it, just consider how many advantages it offers, yet it’s still not commonly adopted. Yes, we might revel in our technical knowledge, but if we create the product for ourselves, we would be the only ones using it. That is a UI/UX/design failure. Make sure to test the product with your target audience. If you want an average person to be comfortable with it, let them try it, let them explore it, and listen to the feedback. 4. Let’s Spend It All in One Place While the leadership team behind ScaleFactor attributes its problems to Covid-19, many former employees have said that the company focused on costly marketing strategies instead of responding to the customers’ expectations. ScaleFactor To avoid the need to blame a global pandemic, concentrate on lean product development . Your minimum viable product has to address the core functionality. It can be a secure wallet or a user-friendly trading platform. When that’s done, continue improving the product gradually, according to the feedback. product development Prototype and test rapidly, mostly paying attention to the features and seeing what works best. All the improvements have to align with your key value proposition. When you have a core group of users, you will be able to provide agile and continuous delivery. Because the tech market and blockchain, in particular, develop rapidly, you will have to regularly update your product and add new features. But always start small. 5. Develop Now, Promote Later Here’s the pièce de résistance: marketing. It’s last but not least. Quite the contrary, marketing channels and potential should be studied prior to developing the product. You have to go into development knowing how, when, and where you are planning to market your product. Only then you will be free to focus on MVPs and roadmaps. It’s a simple case of “Who am I doing this for?” This is a question that every startup in blockchain or any other market has to face eventually. And it’s best to answer it right away. If you don’t find your audience and marketing channels now, you certainly won’t find them later. Applying this framework to the development of a blockchain product allows you to create a solution that not only takes advantage of the unique capabilities of the technology but also addresses user needs, fosters innovation, and adapts to the ever-changing landscape of the market. At the same time, it’s these five simple facts that help manage your initial funds and even save some for a pivot in case the original product falls short of your expectations. Follow me on LinkedIn . Check out my website . LinkedIn website

Five Mistakes That Can Ruin Your Blockchain Startup Before It Launches

Gartner predicts generative AI will become a general-purpose technology akin to the steam engine, electricity, and the internet. While the initial hype will fade, its impact will increase as innovative applications are discovered in daily work and life. Look at some of their predictions for AI, particularly Generative AI’s adoption: As entire industries, enterprises, and digital transformation specialists race to adopt this new tech, there are critical factors that must be taken into account. Improper adoption of technology can lead to disastrous results for the business and poor satisfaction for customers and employees who have to use them. I want to share what those factors are in this article. Why Integrate GenAI?
How to Implement GenAI?
What to Use GenAI For? Why Integrate GenAI? Value Proposition The primary reason to consider integrating GenAI is the substantial value it can bring to your business. GenAI has the potential to revolutionize various aspects of your operations, ultimately driving growth and efficiency. Top-Line Benefits One of the most significant advantages of GenAI is its ability to increase user engagement with your target segment, which can lead to higher revenue or new revenue streams altogether. By leveraging GenAI, you, as a business, can create more personalized and interactive content and enhance your customer experience and satisfaction with your products and services. For example, GenAI can help summarize and synthesize large amounts of information quickly, making it easier for users to find what they need and stay engaged with your platform. Bottom-Line Savings On the other hand, GenAI can also lead to significant cost reductions. Automating repetitive tasks such as customer support queries and data entry, allows your team to focus on more strategic activities. AI-powered tools can support your help center, providing instant responses to common questions and reducing the need for extensive human intervention. How to Implement GenAI? Building the GenAI Tech Stack Successful integration of GenAI requires a systematic approach. This involves Applications and use cases that align with your business goals


Choosing the right AI models to power these applications


Using the right infrastructure to deploy these applications and models


Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Implementation and Application To start, you need to identify the areas within your business that will benefit most from AI integration. This could include content generation for marketing and sales, content conversion for streamlined communication, and productivity tools for various teams. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content.


Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate.


Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks.


General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. Ensuring Trust and Security Integrating AI also necessitates maintaining high standards of trust and security. Implement robust reporting mechanisms to allow users to flag AI-generated content that may be inaccurate or controversial. This helps build user trust and maintain content quality. Moreover, ethical training and fine-tuning of AI models are crucial. Ensure that user data is collected and used transparently and with explicit consent. Handle all user interactions responsibly to protect privacy and build confidence in your AI systems. What to Use GenAI For? Enhancing Employee Productivity Deploying AI applications can significantly boost employee productivity across various departments. As previously mentioned above, in the implementation and application layer of the GenAI tech stack, these are some use cases for employee productivity. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts.
Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible.
Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D.
General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills.
Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction.
Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills.
Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Integrating GenAI into your business can unlock new levels of efficiency, engagement, and cost savings. So consider the value it brings, the implementation process, and the specific applications for your business, and you can ensure a successful integration of GenAI that drives your business forward. Embrace the future of AI to stay competitive and innovative in today’s rapidly evolving landscape. Hope that helps! Gartner predicts generative AI will become a general-purpose technology akin to the steam engine, electricity, and the internet. While the initial hype will fade, its impact will increase as innovative applications are discovered in daily work and life. Look at some of their predictions for AI, particularly Generative AI’s adoption: As entire industries, enterprises, and digital transformation specialists race to adopt this new tech, there are critical factors that must be taken into account. Improper adoption of technology can lead to disastrous results for the business and poor satisfaction for customers and employees who have to use them. I want to share what those factors are in this article. Why Integrate GenAI? How to Implement GenAI? What to Use GenAI For? Why Integrate GenAI? How to Implement GenAI? What to Use GenAI For? Why Integrate GenAI? Value Proposition Value Proposition Value Proposition The primary reason to consider integrating GenAI is the substantial value it can bring to your business. GenAI has the potential to revolutionize various aspects of your operations, ultimately driving growth and efficiency. Top-Line Benefits Top-Line Benefits Top-Line Benefits One of the most significant advantages of GenAI is its ability to increase user engagement with your target segment, which can lead to higher revenue or new revenue streams altogether. By leveraging GenAI, you, as a business, can create more personalized and interactive content and enhance your customer experience and satisfaction with your products and services. For example, GenAI can help summarize and synthesize large amounts of information quickly, making it easier for users to find what they need and stay engaged with your platform. Bottom-Line Savings Bottom-Line Savings Bottom-Line Savings On the other hand, GenAI can also lead to significant cost reductions. Automating repetitive tasks such as customer support queries and data entry, allows your team to focus on more strategic activities. AI-powered tools can support your help center, providing instant responses to common questions and reducing the need for extensive human intervention. How to Implement GenAI? Building the GenAI Tech Stack Building the GenAI Tech Stack Building the GenAI Tech Stack Successful integration of GenAI requires a systematic approach. This involves Applications and use cases that align with your business goals Choosing the right AI models to power these applications Using the right infrastructure to deploy these applications and models Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Applications and use cases that align with your business goals Applications and use cases that align with your business goals Choosing the right AI models to power these applications Choosing the right AI models to power these applications Using the right infrastructure to deploy these applications and models Using the right infrastructure to deploy these applications and models Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Implementation and Application Implementation and Application Implementation and Application To start, you need to identify the areas within your business that will benefit most from AI integration. This could include content generation for marketing and sales, content conversion for streamlined communication, and productivity tools for various teams. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Generation: Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Content Conversion :  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Content Conversion Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. Spreadsheets and Slides : For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. Spreadsheets and Slides General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. General Productivity : Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. General Productivity Ensuring Trust and Security Ensuring Trust and Security Ensuring Trust and Security Integrating AI also necessitates maintaining high standards of trust and security. Implement robust reporting mechanisms to allow users to flag AI-generated content that may be inaccurate or controversial. This helps build user trust and maintain content quality. Moreover, ethical training and fine-tuning of AI models are crucial. Ensure that user data is collected and used transparently and with explicit consent. Handle all user interactions responsibly to protect privacy and build confidence in your AI systems. What to Use GenAI For? Enhancing Employee Productivity Enhancing Employee Productivity Enhancing Employee Productivity Deploying AI applications can significantly boost employee productivity across various departments. As previously mentioned above, in the implementation and application layer of the GenAI tech stack, these are some use cases for employee productivity. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts. Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible. Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D. General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills. Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction. Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills. Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts. Marketing and Sales: Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible. Content Conversion: Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D. Spreadsheets and Slides: General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills. General Productivity: Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction. Customer Support: Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills. Specific Applications for HR and Hiring Managers: Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Customer Support: Integrating GenAI into your business can unlock new levels of efficiency, engagement, and cost savings. So consider the value it brings, the implementation process, and the specific applications for your business, and you can ensure a successful integration of GenAI that drives your business forward. Embrace the future of AI to stay competitive and innovative in today’s rapidly evolving landscape. Hope that helps!

3 Things to Consider Before Adding GenAI to Your Business

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