And none of them gets explored in much depth. A character’s illness is revealed, and next thing you know, she dies. This happens right after Alice Paul pressures her into giving one more speech, but neither Taub’s writing nor her performance suggest Paul’s merciless drive.

A spirited musical about suffragists is not a triumph, but much improved

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The head of Iran&#x27;s Nuclear Protection and Security Corps Ahmad Haghtalab threatens to revise Iran&#x27;s nuclear doctrine, and to target Israel&#x27;s nuclear facilities if it strikes Iranian nuclear sites, during a televised interview, April 18, 2024. (IRIB/AFP)

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Parker Hall is a senior editor of product reviews at WIRED. He focuses on audiovisual and entertainment products. Hall is a graduate of the Oberlin Conservatory of Music, where he studied jazz percussion. After hours, he remains a professional musician in his hometown of Portland, Oregon.

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The Wiz Rolls Back Into Town

Revie

revie

What is Software Review? What is a review in software testing? There are different types of reviews that happen at different points in the software development lifecycle (SDLC), and we’ll dive into them all in this article. Reviewing throughout the entire software development process is key. You shouldn’t just test the final product at the end of development. That’s far too late. Reviewing in software ensures that you maintain quality, produce the right thing, and do so in a timely manner. I’ve been in all stages of the software review process many many times over the years. It’s one of the key skills that a software engineer has to be comfortable with outside of writing code. Objectives and Benefits of Software Review Whenever I’m thinking of reviews, I have a few key things I’m trying to achieve. The first purpose I think of for the review process, is to make sure you identify and issues as early as possible in the software development life cycle and fix them. In almost all cases, the earlier you find and fix a problem, the cheaper and easier it is. Another main purpose I’m striving for in the software review is the make sure that you’re actually creating the software that your users are expecting. In my experience, It’s pretty common for engineers to veer off from course a little and before too long you are creating quality software, but not the quality software that your user wanted. A successful review at any point in the review process aims to ensure both the quality and direction of the product. Types of Software Review There are many types of software reviews, including, but not limited to: Code Review
Peer Review
Technical Review
Pair Programming
Management review Peer Review / Code Review The peer review is one of the earliest forms of formal review for the actual code to be checked for quality and direction. Peer software developers (the reviewer, usually from the same development team) will look at the associated work item (Test Case, User Story, Documentation) and make sure that the code you’ve written matches the intent of that work item. Once they are happy that the written code matches the intent of the work item, they will do code inspections to look for any bugs or code that could be written in a more efficient or easier to understand manner. The peer review is usually a collaborative effort and can be picked up by one or more software engineers familiar with the software product and code base. When I review code, I make sure that I am not just looking for basic technical faults (divide by zero bugs, etc), but that I am also looking for code structure, well named variables, well architeted solutions. I am very picky with my code reviews as you I am a firm believer that code is written once and read a thousand times. Get it right before it gets merged to main. Technical Review A Technical Review is where a group of people from different roles get together to assess the overall state of may parts of the software project. Participants include: Software Architects
Senior Developers
Business Analysts
QA This is a fairly formal review meeting where the group will cover topics such as: Project Scope
Requirements documents and specifications
Test Plans
Prototypes or MVCs The idea behind the technical review process is to verify the correctness of the requirements, the direction of the project and the technical roadmap. All of this is to look forwards to try and ensure a smooth project process. Management Review This part of the review process is to give high-level stakeholders high-level details about the status of the project. Managers tend to want to know about whether deadlines are being met (if not, why not) and if the cost of the project is on target. There’s no software inspections, no looking at source code and no worrying about testing in this review. The key phrase here is “status report”. Software Audit Review Software audit review is an exterior review where a single or multiple reviewers outside the development team inspect the software development processes to check compliance with standards, requirements, and specifications. These tend to be infrequent and only really done at a high level. The Role of Review in the Software Development Process Each of the above review types is crucial in its own way to the success of a project. Reviews highlight issues and miscommunications early on. They keep the project on track and in the right direction. Whether formal or informal reviews, reviews get people talking. They gather stakeholders and knowledgeable people in the same place and get multiple minds solving and validating all of the work. Reviews help companies ensure that their software complies with regulatory and industry standards. Challenges and Solutions in Software Review Admin and beurocreacy. In my experience, these are the two most common complaints from engineers about the software review process. I’ve worked in many companies with many smart people. Unfortunately, smart engineers don’t often like many meetings. They don’t like doing Jira admin. They don’t like people scrutinizing their code. Adding time and effort between a coder and writing code is rarely something you want to do. However, going slower to get to the end goal faster is always a smart move. Showing engineers that putting the effort in upfront on generating the correct specifications, and test cases and how reviews play a critical role in delivering what the client actually wants will make your life a lot easier. Another issue is showing the people involved that the effort they are putting in to code reviews, technical reviews etc has a real world benefit is hard to do. You can’t run A-B tests on this type of thing. All you can do is highlight the successes that this process has had in the past and how running projects without these processes have failed in the past. Software development and the development process is about metrics. Prove how this type of project management has worked with stats from the past. Best Practices for Effective Software Review Let’s summarise some of the best practices I’ve mentioned throughout this post: Establish clear objectives and scope for the review.
Assign roles and responsibilities to team members.
Use a structured review process, such as entry evaluation, review process management, planning, preparation, and analysis and exit evaluation.
Use tool-assisted reviews for efficient and asynchronous reviews.
Track metrics to justify the time and brainpower required for code review. Software Testing and Review: A Collaborative Approach You should always have automated tests for your code. However, manual testing with QA is a vital step in creating a polished software product. QA think differently to Engineers. QA look to break things, engineers tend to think about the “happy path”. Once you as an engineering team have delivered and tested a vertical flow, hand it over to QA to do their thing. They will bend and twist your software in ways you can’t imagine. This is a vital step in the software review process. What is Software Review? What is a review in software testing? There are different types of reviews that happen at different points in the software development lifecycle ( SDLC ), and we’ll dive into them all in this article. SDLC Reviewing throughout the entire software development process is key. You shouldn’t just test the final product at the end of development. That’s far too late. Reviewing in software ensures that you maintain quality, produce the right thing, and do so in a timely manner. I’ve been in all stages of the software review process many many times over the years. It’s one of the key skills that a software engineer has to be comfortable with outside of writing code. Objectives and Benefits of Software Review Whenever I’m thinking of reviews, I have a few key things I’m trying to achieve. The first purpose I think of for the review process, is to make sure you identify and issues as early as possible in the software development life cycle and fix them. In almost all cases, the earlier you find and fix a problem, the cheaper and easier it is. Another main purpose I’m striving for in the software review is the make sure that you’re actually creating the software that your users are expecting. In my experience, It’s pretty common for engineers to veer off from course a little and before too long you are creating quality software, but not the quality software that your user wanted. A successful review at any point in the review process aims to ensure both the quality and direction of the product. Types of Software Review There are many types of software reviews , including, but not limited to: software reviews Code Review Peer Review Technical Review Pair Programming Management review Code Review Peer Review Technical Review Pair Programming Management review Peer Review / Code Review The peer review is one of the earliest forms of formal review for the actual code to be checked for quality and direction. quality Peer software developers (the reviewer, usually from the same development team) will look at the associated work item (Test Case, User Story, Documentation) and make sure that the code you’ve written matches the intent of that work item. Once they are happy that the written code matches the intent of the work item, they will do code inspections to look for any bugs or code that could be written in a more efficient or easier to understand manner. The peer review is usually a collaborative effort and can be picked up by one or more software engineers familiar with the software product and code base. When I review code, I make sure that I am not just looking for basic technical faults (divide by zero bugs, etc), but that I am also looking for code structure, well named variables, well architeted solutions. I am very picky with my code reviews as you I am a firm believer that code is written once and read a thousand times. Get it right before it gets merged to main. Technical Review A Technical Review is where a group of people from different roles get together to assess the overall state of may parts of the software project. Participants include: Software Architects Senior Developers Business Analysts QA Software Architects Senior Developers Business Analysts QA This is a fairly formal review meeting where the group will cover topics such as: Project Scope Requirements documents and specifications Test Plans Prototypes or MVCs Project Scope Requirements documents and specifications Test Plans Prototypes or MVCs The idea behind the technical review process is to verify the correctness of the requirements, the direction of the project and the technical roadmap. All of this is to look forwards to try and ensure a smooth project process. Management Review This part of the review process is to give high-level stakeholders high-level details about the status of the project. Managers tend to want to know about whether deadlines are being met (if not, why not) and if the cost of the project is on target. There’s no software inspections, no looking at source code and no worrying about testing in this review. The key phrase here is “status report”. Software Audit Review Software audit review is an exterior review where a single or multiple reviewers outside the development team inspect the software development processes to check compliance with standards, requirements, and specifications. These tend to be infrequent and only really done at a high level. The Role of Review in the Software Development Process Each of the above review types is crucial in its own way to the success of a project. Reviews highlight issues and miscommunications early on. They keep the project on track and in the right direction. Whether formal or informal reviews, reviews get people talking. They gather stakeholders and knowledgeable people in the same place and get multiple minds solving and validating all of the work. Reviews help companies ensure that their software complies with regulatory and industry standards. Challenges and Solutions in Software Review Admin and beurocreacy. In my experience, these are the two most common complaints from engineers about the software review process. I’ve worked in many companies with many smart people. Unfortunately, smart engineers don’t often like many meetings. They don’t like doing Jira admin. They don’t like people scrutinizing their code. Adding time and effort between a coder and writing code is rarely something you want to do. However, going slower to get to the end goal faster is always a smart move. Showing engineers that putting the effort in upfront on generating the correct specifications, and test cases and how reviews play a critical role in delivering what the client actually wants will make your life a lot easier. Another issue is showing the people involved that the effort they are putting in to code reviews, technical reviews etc has a real world benefit is hard to do. You can’t run A-B tests on this type of thing. All you can do is highlight the successes that this process has had in the past and how running projects without these processes have failed in the past. Software development and the development process is about metrics. Prove how this type of project management has worked with stats from the past. Best Practices for Effective Software Review Let’s summarise some of the best practices I’ve mentioned throughout this post: Establish clear objectives and scope for the review. Assign roles and responsibilities to team members. Use a structured review process, such as entry evaluation, review process management, planning, preparation, and analysis and exit evaluation. Use tool-assisted reviews for efficient and asynchronous reviews. Track metrics to justify the time and brainpower required for code review. Establish clear objectives and scope for the review. Assign roles and responsibilities to team members. Use a structured review process, such as entry evaluation, review process management, planning, preparation, and analysis and exit evaluation. Use tool-assisted reviews for efficient and asynchronous reviews. Track metrics to justify the time and brainpower required for code review. Software Testing and Review: A Collaborative Approach You should always have automated tests for your code. However, manual testing with QA is a vital step in creating a polished software product. QA think differently to Engineers. QA look to break things, engineers tend to think about the “happy path”. Once you as an engineering team have delivered and tested a vertical flow, hand it over to QA to do their thing. They will bend and twist your software in ways you can’t imagine. This is a vital step in the software review process.

Transform Your Development Process with Smarter Software Reviews

Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author:
(1) David Staines. This paper is available on arxiv under CC 4.0 license. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author: (1) David Staines. Author: Author: (1) David Staines. 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

A Critical Review of Lucas Jr's Famous Paper on Nominal Rigidity

As a PhD student, you’re likely feeling a mix of excitement and overwhelm as you embark on your research journey. Reflecting on my own experience, I remember the challenges and limitations of the tools available on the market to assist with conducting your literature review. During the COVID period, tools like Research Rabbit and Zotero were a bit more rudimentary and easier to use, and they proved invaluable in my first year. However, this realisation prompted me to share insights on how to effectively utilise such tools. This short blog was inspired after watching the potential of AI tools showcased in “Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies]” by Andy Stapleton, Please note that the advice and approaches discussed in this blog reflect my personal experience and are not professional academic advice. In this blog, I will introduce the Triple T Approach — Tactics, Techniques, and Tools — to streamline literature reviews. By integrating strategic planning, methodological execution, and AI tools like ChatGPT, Elicit, and Litmap, this approach ensures a comprehensive and efficient review process. Be Aware of the Texas sharpshooter! Before diving into the Triple T framework, it’s crucial to heed the advice of being wary of the Texas sharpshooter fallacy. that refers to a scenario where a person, lacking actual shooting skills, randomly fires bullets at a barn wall. Subsequently, they paint a bull’s-eye around a fortuitous cluster of bullet holes and proclaim themselves a sharpshooter. Falling into this trap can be tempting, particularly with tools that produce seemingly strong evidence, which may obscure their biases. This is why it’s essential to question everything produced by AI, by adopting a philosophical skepticism mindset. It’s crucial to question everything produced by AI to provoke awareness of any bias existing in the data set and challenge our assumptions while evaluating evidence generated by GenAI tools and exploring different perspectives and their implications on our research direction. This logical pitfall warns against being drawn to tools that appear to provide strong evidence, potentially concealing biases. Utilizing these tools effectively requires critical thinking skills; in this context, these skills act as your shooting skills for the Texas sharpshooter fallacy! This is why I always recommend reading more about this topic, as it introduces essential life skills to help not only grow your research but also grow as a researcher. This approach ensures a comprehensive and efficient review process while also keeping in mind the limitations of current AI tools and their biases. When choosing each tactic, try to link it with appropriate tools and techniques while considering how they align together and the implications of such an approach on the limitations of AI tools. I have only focused on the bias challenge, but of course, there are many limitations awaiting AI in academia. Always remember that you are leading the tools, not the other way around. What is the Triple T Approach? The Triple T framework breaks down into Tactics, Techniques, and Tools. Tactics involve strategic decisions in structuring the review, conducting thorough literature searches, and synthesizing information. Techniques encompass methods such as AI-assisted structuring and analytical reading, while Tools consist of resources like ChatGPT and Mendeley for efficient literature management. Applying the Triple T Approach to Your Literature Review Workflow: Here’s a quick walkthrough of some of the concepts and strategies applied in the approach to ensure a methodical and tool-driven process, enhancing the quality and depth of your review. 1. Tactics: • Structuring the Review: Develop a clear, organized structure for your literature review. • Comprehensive Literature Search: Systematically search for and select relevant literature. • Synthesizing Information: Integrating findings from various sources into a cohesive narrative. 2. Techniques: • AI-Assisted Structuring: Using AI to generate a preliminary structure for your review. • Targeted Literature Discovery: Employing specific queries based on your review structure to find relevant literature. • Analytical Reading and Organization: Critically reading and categorizing literature for easy reference and synthesis. 3. Tools: • ChatGPT and Perplexity: For generating the initial structure of your literature review. • PaperPal and Jenni AI: Tools to assist with proofreading your initial drafts and refining your literature structure. • Elicit or Scispace: To search for and summarize seed papers based on your structured queries. • Litmap and Connected Papers: For discovering related literature reviews. • Mendeley or Zetro: To manage and organize your references and PDFs. • Docanalyzer.AI: For analyzing documents and aligning them with your review structure. By aligning your research with this approach, you’ll streamline your workflow and lay a robust foundation for your literature review paper. Of course, this approach is flexible. For example, when you start structuring the plan for your literature review, you could use tools like Perplexity and then draft and proofread the initial paper structure using PaperPal. However, you could also gather information before you do any planning. Thus, a tactic like synthesizing information will need some techniques for targeted literature discovery through Elicit. So, please add or update any section of this approach that you feel fits your needs! As a PhD student, you’re likely feeling a mix of excitement and overwhelm as you embark on your research journey. Reflecting on my own experience, I remember the challenges and limitations of the tools available on the market to assist with conducting your literature review. During the COVID period, tools like Research Rabbit and Zotero were a bit more rudimentary and easier to use, and they proved invaluable in my first year. However, this realisation prompted me to share insights on how to effectively utilise such tools. This short blog was inspired after watching the potential of AI tools showcased in “ Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies ]” by Andy Stapleton, Please note that the advice and approaches discussed in this blog reflect my personal experience and are not professional academic advice. In this blog, I will introduce the Triple T Approach — Tactics, Techniques, and Tools — to streamline literature reviews. By integrating strategic planning, methodological execution, and AI tools like ChatGPT, Elicit, and Litmap, this approach ensures a comprehensive and efficient review process. Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies Be Aware of the Texas sharpshooter! Be Aware of the Texas sharpshooter! Before diving into the Triple T framework, it’s crucial to heed the advice of being wary of the Texas sharpshooter fallacy . that refers to a scenario where a person, lacking actual shooting skills, randomly fires bullets at a barn wall. Subsequently, they paint a bull’s-eye around a fortuitous cluster of bullet holes and proclaim themselves a sharpshooter. Falling into this trap can be tempting, particularly with tools that produce seemingly strong evidence, which may obscure their biases. Texas sharpshooter fallacy This is why it’s essential to question everything produced by AI, by adopting a philosophical skepticism mindset. It’s crucial to question everything produced by AI to provoke awareness of any bias existing in the data set and challenge our assumptions while evaluating evidence generated by GenAI tools and exploring different perspectives and their implications on our research direction. This logical pitfall warns against being drawn to tools that appear to provide strong evidence, potentially concealing biases. Utilizing these tools effectively requires critical thinking skills; in this context, these skills act as your shooting skills for the Texas sharpshooter fallacy! This is why I always recommend reading more about this topic, as it introduces essential life skills to help not only grow your research but also grow as a researcher. This approach ensures a comprehensive and efficient review process while also keeping in mind the limitations of current AI tools and their biases. When choosing each tactic, try to link it with appropriate tools and techniques while considering how they align together and the implications of such an approach on the limitations of AI tools. I have only focused on the bias challenge, but of course, there are many limitations awaiting AI in academia. Always remember that you are leading the tools, not the other way around. you are leading What is the Triple T Approach? What is the Triple T Approach? The Triple T framework breaks down into Tactics, Techniques, and Tools. Tactics involve strategic decisions in structuring the review, conducting thorough literature searches, and synthesizing information. Techniques encompass methods such as AI-assisted structuring and analytical reading, while Tools consist of resources like ChatGPT and Mendeley for efficient literature management. strategic decisions methods resources Applying the Triple T Approach to Your Literature Review Workflow: Applying the Triple T Approach to Your Literature Review Workflow: Here’s a quick walkthrough of some of the concepts and strategies applied in the approach to ensure a methodical and tool-driven process, enhancing the quality and depth of your review. 1. Tactics: 1. Tactics: • Structuring the Review : Develop a clear, organized structure for your literature review. Structuring the Review • Comprehensive Literature Search : Systematically search for and select relevant literature. Comprehensive Literature Search • Synthesizing Information : Integrating findings from various sources into a cohesive narrative. Synthesizing Information 2. Techniques: 2. Techniques: • AI-Assisted Structuring : Using AI to generate a preliminary structure for your review. AI-Assisted Structuring • Targeted Literature Discovery : Employing specific queries based on your review structure to find relevant literature. Targeted Literature Discovery • Analytical Reading and Organization : Critically reading and categorizing literature for easy reference and synthesis. Analytical Reading and Organization 3. Tools: 3. Tools: • ChatGPT and Perplexity : For generating the initial structure of your literature review. ChatGPT and Perplexity • PaperPal and Jenni AI : Tools to assist with proofreading your initial drafts and refining your literature structure. PaperPal and Jenni AI • Elicit or Scispace : To search for and summarize seed papers based on your structured queries. Elicit or Scispace • Litmap and Connected Papers : For discovering related literature reviews. Litmap and Connected Papers • Mendeley or Zetro : To manage and organize your references and PDFs. Mendeley or Zetro • Docanalyzer.AI : For analyzing documents and aligning them with your review structure. Docanalyzer.AI By aligning your research with this approach, you’ll streamline your workflow and lay a robust foundation for your literature review paper. Of course, this approach is flexible. For example, when you start structuring the plan for your literature review, you could use tools like Perplexity and then draft and proofread the initial paper structure using PaperPal. However, you could also gather information before you do any planning. Thus, a tactic like synthesizing information will need some techniques for targeted literature discovery through Elicit. So, please add or update any section of this approach that you feel fits your needs! structuring synthesizing information targeted literature discovery

AI Literature Reviews Are So Much Better With the Triple T Method

The tech world relies heavily on constructive criticism to evolve and make valuable changes. Innovation requires feedback from different perspectives that can assist in creating unique solutions. Hackathons and academic conferences are places where tech leaders can put their products to the test, showcasing their utility and desire to improve solutions. Experienced tech professionals like Avinab (Avi) Singh contribute to the space by guiding the next generation of designers and technologists. His participation as an academic journal reviewer and hackathon judge contributes to the continuous evolution of tech. As the use of AI rises in design and technology fields, creators require experienced professionals who can provide insight into both user needs and advanced technological capabilities. Early Career Challenges Avi began his journey in the tech industry in Chennai, India. He graduated with a Bachelor’s degree in Telecommunication Engineering but Avi wanted cutting edge education. He decided to move to the United States, where he pursued a master’s degree in Industrial Engineering, focusing on human factors at North Carolina State University in Raleigh, North Carolina. After graduate school, Avi moved to Silicon Valley with hopes of growing his professional career. However, he was left discouraged by the fact he didn’t have any mentors to guide him along the way. Undaunted, Avi persisted in his career to great success. It was after this long journey that Avi realized he wanted to provide the mentorship he never had to young professionals who were struggling. He doesn’t want anyone to feel left behind in the already competitive world of tech. Mentoring Up-and-Coming Designers Avi’s role as a mentor within his companies and through ADPList.org helps others aspire to make significant contributions in the industry. He has accumulated over 800 minutes of mentoring across 3 countries, demonstrating his desire to help professionals grow. His work in assisting global hiring initiatives through internship programs at EY GigNow and Dusty Robotics has jumpstarted the careers of novice designers and helped them navigate ongoing professional challenges & grow their careers. Providing mentorship to young professionals gives them the tools and insights they need to succeed in the tech industry. Having a competitive advantage against others in the industry is vital to a successful career. As a result of these internship programs, many junior designers have successfully secured full-time roles in these companies, contributing to the teams’ growth and innovation with the addition of their fresh perspectives. Serving as a Judge at Hackathons Avi’s role as a judge at notable hackathon events like HackMIT, SwiftHack, and GenLab2 has played a crucial role in bringing novel ideas to the tech industry. His experience allows him to provide insightful advice for novices and solidifies his longstanding knowledge in the field. At hackathons, Avi is responsible for evaluating new projects and guiding teams toward creating practical and impactful tech solutions. His judging experience helps him identify promising talent and encourages collaboration among participants. Reviewer for Reputed Journals and Conferences Avi is also an active reviewer for top-tier conferences such as ACM and IEEE in the field of Human-Computer Interaction and AI/ML. This growing industry is in need of the insight of experienced professionals to ground itself in tech. His ongoing involvement in reviewing scientific papers for major conferences has gained him recognition as a trusted reviewer by UIST and AutomotiveUI. He continues to review papers at other notable conferences like CHI, HAI, ICSI, NordiCHI, and UbiComp/ISWC, among others. Peer review is essential to maintaining the quality of research needed to develop impactful solutions and promote technological advancements. Avi’s contributions help ensure that emerging research aligns with high industry standards by ensuring paper reviews shape the current challenges in design and technology. How Avi Singh is Shaping Future Tech Leaders Through the mentorship of novice designers, reviewing academic papers, and judging at hackathons, Avi is actively contributing to the development of future leaders and their solutions in the tech industry. Avi is currently working as a Staff Product Designer at Moveworks, where he works on Generative AI tools to help small to large enterprises scale their employee experience organizations. His latest creation is a Generative AI tool called Brief Me that assists users with summarizing and question & answer with dense documents. Brief Me helps increase employee productivity by helping them read large documents, create reports and write emails using content from these documents. Avi creates software used daily by employees of Fortune 100 companies. Avi continues to provide valuable solutions to the field of Design of AI/ML products through his work and mentorship initiatives. Sharing knowledge and experience is vital for overcoming the challenges newcomers face in the field. Up-and-coming talents are more likely to succeed in the industry with the help of mentors like Avi. Avi’s efforts to create a collaborative, supportive environment for emerging professionals in both corporate and educational settings. His dedication to mentorship aims to alleviate the difficulties he faced early in his career by providing others with the guidance they need to thrive. The tech world relies heavily on constructive criticism to evolve and make valuable changes. Innovation requires feedback from different perspectives that can assist in creating unique solutions. Hackathons and academic conferences are places where tech leaders can put their products to the test, showcasing their utility and desire to improve solutions. Experienced tech professionals like Avinab (Avi) Singh contribute to the space by guiding the next generation of designers and technologists. His participation as an academic journal reviewer and hackathon judge contributes to the continuous evolution of tech. As the use of AI rises in design and technology fields, creators require experienced professionals who can provide insight into both user needs and advanced technological capabilities. Early Career Challenges Early Career Challenges Avi began his journey in the tech industry in Chennai, India. He graduated with a Bachelor’s degree in Telecommunication Engineering but Avi wanted cutting edge education. He decided to move to the United States, where he pursued a master’s degree in Industrial Engineering, focusing on human factors at North Carolina State University in Raleigh, North Carolina. After graduate school, Avi moved to Silicon Valley with hopes of growing his professional career. However, he was left discouraged by the fact he didn’t have any mentors to guide him along the way. Undaunted, Avi persisted in his career to great success. It was after this long journey that Avi realized he wanted to provide the mentorship he never had to young professionals who were struggling. He doesn’t want anyone to feel left behind in the already competitive world of tech. Mentoring Up-and-Coming Designers Mentoring Up-and-Coming Designers Avi’s role as a mentor within his companies and through ADPList.org helps others aspire to make significant contributions in the industry. He has accumulated over 800 minutes of mentoring across 3 countries, demonstrating his desire to help professionals grow. ADPList.org ADPList.org His work in assisting global hiring initiatives through internship programs at EY GigNow and Dusty Robotics has jumpstarted the careers of novice designers and helped them navigate ongoing professional challenges & grow their careers. EY GigNow EY GigNow Dusty Robotics Dusty Robotics Providing mentorship to young professionals gives them the tools and insights they need to succeed in the tech industry. Having a competitive advantage against others in the industry is vital to a successful career. As a result of these internship programs, many junior designers have successfully secured full-time roles in these companies, contributing to the teams’ growth and innovation with the addition of their fresh perspectives. Serving as a Judge at Hackathons Serving as a Judge at Hackathons Avi’s role as a judge at notable hackathon events like HackMIT, SwiftHack, and GenLab2 has played a crucial role in bringing novel ideas to the tech industry. His experience allows him to provide insightful advice for novices and solidifies his longstanding knowledge in the field. At hackathons, Avi is responsible for evaluating new projects and guiding teams toward creating practical and impactful tech solutions. His judging experience helps him identify promising talent and encourages collaboration among participants. Reviewer for Reputed Journals and Conferences Reviewer for Reputed Journals and Conferences Avi is also an active reviewer for top-tier conferences such as ACM and IEEE in the field of Human-Computer Interaction and AI/ML. This growing industry is in need of the insight of experienced professionals to ground itself in tech. His ongoing involvement in reviewing scientific papers for major conferences has gained him recognition as a trusted reviewer by UIST and AutomotiveUI. He continues to review papers at other notable conferences like CHI, HAI, ICSI, NordiCHI, and UbiComp/ISWC, among others. Peer review is essential to maintaining the quality of research needed to develop impactful solutions and promote technological advancements. Avi’s contributions help ensure that emerging research aligns with high industry standards by ensuring paper reviews shape the current challenges in design and technology. How Avi Singh is Shaping Future Tech Leaders How Avi Singh is Shaping Future Tech Leaders Through the mentorship of novice designers, reviewing academic papers, and judging at hackathons, Avi is actively contributing to the development of future leaders and their solutions in the tech industry. Avi is currently working as a Staff Product Designer at Moveworks , where he works on Generative AI tools to help small to large enterprises scale their employee experience organizations. His latest creation is a Generative AI tool called Brief Me that assists users with summarizing and question & answer with dense documents. Brief Me helps increase employee productivity by helping them read large documents, create reports and write emails using content from these documents. Avi creates software used daily by employees of Fortune 100 companies. Avi continues to provide valuable solutions to the field of Design of AI/ML products through his work and mentorship initiatives. Moveworks Moveworks Sharing knowledge and experience is vital for overcoming the challenges newcomers face in the field. Up-and-coming talents are more likely to succeed in the industry with the help of mentors like Avi. Avi’s efforts to create a collaborative, supportive environment for emerging professionals in both corporate and educational settings. His dedication to mentorship aims to alleviate the difficulties he faced early in his career by providing others with the guidance they need to thrive.

Shaping Future Tech Leaders: Avinab Singh’s Role as a Mentor and Reviewer

Jutïr Tech irpirinakar uñstayaña: Avinab Singh jupan lurawipax mä Mentor ukhamarak Revisor ukhamawa

Forjando a los futuros líderes tecnológicos: el papel de Avinab Singh como mentor y revisor

Ապագա տեխնոլոգիական առաջնորդների ձևավորում. Ավինաբ Սինգհի դերը որպես դաստիարակ և գրախոս

未来のテクノロジーリーダーを育てる: メンターおよびレビュアーとしてのアヴィナブ・シンの役割

მომავლის ტექნიკური ლიდერების ჩამოყალიბება: ავინაბ სინგჰის როლი, როგორც მენტორი და მიმომხილველი

미래 기술 리더 형성: 멘토 및 리뷰어로서의 Avinab Singh의 역할

Būsimų technologijų lyderių formavimas: Avinabo Singho, kaip mentoriaus ir apžvalgininko, vaidmuo

Обликување идни технолошки лидери: Улогата на Авинаб Синг како ментор и рецензент

Membentuk Pemimpin Teknologi Masa Depan: Peranan Avinab Singh sebagai Mentor dan Pengulas

Kształtowanie przyszłych liderów technologicznych: rola Avinaba Singha jako mentora i recenzenta

Ташаккули пешвоёни ояндаи техникӣ: Нақши Авинаб Сингҳ ҳамчун мураббӣ ва баррасӣ

การสร้างผู้นำด้านเทคโนโลยีในอนาคต: บทบาทของ Avinab Singh ในฐานะที่ปรึกษาและผู้ตรวจสอบ

The HackerNoon Newsletter: Better - An AI Powered Code Reviewer (11/21/2024)

Why Letting AI Handle Your Code Reviews Might Be the Best Decision You Make This Year

আরও ভাল - একটি AI চালিত কোড রিভিউয়ার

Besser – Ein KI-gestützter Code-Reviewer

Better: un revisor de código impulsado por IA

Better – Un outil de révision de code basé sur l'IA

बेहतर - एक AI संचालित कोड समीक्षक

Better - AI 搭載のコードレビューツール

Better - AI 기반 코드 검토기

Melhor - Um revisor de código com tecnologia de IA

Лучше — Рецензент кода на базе искусственного интеллекта

Daha İyi - Yapay Zeka Destekli Kod İncelemecisi

Tốt hơn - Một Trình đánh giá mã được hỗ trợ bởi AI

Better - 一款人工智能代码审查工具

I have been writing code professionally for more than five years now. For the first four years, I never cared about the size of my pull requests (PRs). However, in the last year, I transitioned from submitting massive PRs with thousands of lines of changes to breaking them down into smaller, more manageable ones. The benefits of this shift have been immense, and in this blog, I’ll share those advantages. According to GitHub, a pull request is: A pull request is a proposal to merge a set of changes from one branch into another. In a pull request, collaborators can review and discuss the proposed set of changes before they integrate the changes into the main codebase. Essentially, a pull request is a way of collaborating; we should do everything possible to enhance this collaboration. One effective method to improve this collaboration is to keep PRs small. There is no universal definition to differentiate between a small and a large PR. Relying solely on the number of lines changed is insufficient, as auto-generated code and tests can inflate line counts. When I refer to small PRs in this article, I mean dividing a larger PR into multiple smaller, logically coherent PRs. Each smaller PR should be standalone, mergeable, and deployable. I do not advocate artificial splitting like splitting a PR into two, one containing all the code and another with just the tests, as this approach fails to yield any benefits I share below. Efficient Reviews: Ask a programmer to review 10 lines of code, he’ll find 10 issues. Ask him to do 500 lines and he’ll say it looks good. While humorous, this quote carries truth. Everyone is busy with their own work, and when you ask someone to review a PR, you are essentially requesting their time. Reviewing a PR requires the reviewer to switch context from their own work, and if the review takes a considerable amount of time, it can be challenging for them to return back to their work, potentially affecting their motivation and commitment to the review. Smaller PRs, which only take about 20–30 minutes to review, are much easier to tackle compared to those that can take 2–3 hours. Plus, larger PRs often lead to oversights because our attention spans can only handle so much, and jumping between lots of changes in a single PR can be confusing. From my experience, smaller PRs tend to get better feedback and lead to more meaningful design conversations. Faster Reviews: At this point, I’m thinking of adding my PR to my will in case it’s still in review after I’m gone. Longer PRs require a significant time commitment from reviewers, making them less likely to get attention — especially if they’re not tied to high-impact features. Small PRs on the other hand are reviewed quickly, as they demand less of the reviewer’s time and are less intrusive. This speed of reviews can be crucial for meeting project deadlines; I’ve seen projects getting delayed because senior reviewers couldn’t allocate time for massive PRs (though this can happen with small PRs, the risk is inherently higher with large ones). Lesser Rework: Reworking a big PR after design changes is like rearranging deck chairs on a ship you just finished building… and then sinking it. We have all experienced situations where someone realizes during PR review that a different design would have been more maintainable and future-proof, and we need to spend additional time reworking the PR based on the new design (not to say that they were completely on board with the design that was initially implemented :p). This is very natural because sometimes things become clearer once you see them written in code, and you start noticing aspects you might have missed during the design phase. With large PRs, this can be a significant issue because you have to rework a lot of elements, but with smaller PRs, it is easier to make changes. More importantly, there is a lower chance of rework because reviewers are more likely to identify issues early on and address them in the initial PRs, allowing subsequent PRs to be based on the new designs. Better Testing: Smaller PRs also benefit you as the author of the PR. They help in testing smaller changes incrementally rather than testing the whole project in one go. Testing smaller changes results in more exhaustive testing of each component of the system, thus leading to fewer production bugs. This applies to both automated tests and manual testing performed by you or dedicated QA engineers. Moreover, smaller PRs reduce the likelihood of missed test cases, as you can focus on a limited scope rather than the entire system. Writing tests? That sounds like Future Me’s problem. I have seen developers (including myself in the past) hesitate to write automation tests due to the perceived time investment without immediate, “visible” value to the feature/product. Smaller PRs reduce this friction by limiting the number of tests required and the time spent writing them. Easier Debugging: No matter how thorough your testing is, production bugs will happen! Being able to debug a bug in production is crucial since production bugs directly impact users, the business, or both. With large PRs, the surface area of change is also large, making it time-consuming and difficult to find the root cause of issues. On the other hand, smaller PRs contain less code and thus make debugging much faster. Debugging small changes is like finding a typo; debugging big changes is like proofreading an encyclopedia. Phased Feature Deployment: Last but not least, smaller PRs are also helpful to your product manager and users. By using small PRs, you can continuously push parts of the system to production, which helps in obtaining early feedback from users and allows for early course corrections if needed. Skipping early feedback is like cooking a five-course meal without tasting anything — you’re just hoping it’s not a disaster. The benefits of small PRs are numerous, and the points outlined above are among the most impactful I’ve experienced personally. If you have encountered other advantages of small PRs or challenges with larger ones that I haven’t covered, do comment with your insights. I hope this article motivates you to embrace smaller PRs. If you’re already on board, I hope it reinforces the value of this practice. Thanks for reading, until next time, keep coding and stay curious! I have been writing code professionally for more than five years now. For the first four years, I never cared about the size of my pull requests (PRs). However, in the last year, I transitioned from submitting massive PRs with thousands of lines of changes to breaking them down into smaller, more manageable ones. The benefits of this shift have been immense, and in this blog, I’ll share those advantages. According to GitHub , a pull request is: GitHub A pull request is a proposal to merge a set of changes from one branch into another. In a pull request, collaborators can review and discuss the proposed set of changes before they integrate the changes into the main codebase. A pull request is a proposal to merge a set of changes from one branch into another. In a pull request, collaborators can review and discuss the proposed set of changes before they integrate the changes into the main codebase. A pull request is a proposal to merge a set of changes from one branch into another. In a pull request, collaborators can review and discuss the proposed set of changes before they integrate the changes into the main codebase. Essentially, a pull request is a way of collaborating; we should do everything possible to enhance this collaboration. One effective method to improve this collaboration is to keep PRs small. There is no universal definition to differentiate between a small and a large PR. Relying solely on the number of lines changed is insufficient, as auto-generated code and tests can inflate line counts. When I refer to small PRs in this article, I mean dividing a larger PR into multiple smaller, logically coherent PRs. Each smaller PR should be standalone, mergeable, and deployable. I do not advocate artificial splitting like splitting a PR into two, one containing all the code and another with just the tests, as this approach fails to yield any benefits I share below. Efficient Reviews: Efficient Reviews: Ask a programmer to review 10 lines of code, he’ll find 10 issues. Ask him to do 500 lines and he’ll say it looks good. Ask a programmer to review 10 lines of code, he’ll find 10 issues. Ask him to do 500 lines and he’ll say it looks good. Ask a programmer to review 10 lines of code, he’ll find 10 issues. Ask him to do 500 lines and he’ll say it looks good. While humorous, this quote carries truth. Everyone is busy with their own work, and when you ask someone to review a PR, you are essentially requesting their time. Reviewing a PR requires the reviewer to switch context from their own work, and if the review takes a considerable amount of time, it can be challenging for them to return back to their work, potentially affecting their motivation and commitment to the review. Smaller PRs, which only take about 20–30 minutes to review, are much easier to tackle compared to those that can take 2–3 hours. Plus, larger PRs often lead to oversights because our attention spans can only handle so much, and jumping between lots of changes in a single PR can be confusing. From my experience, smaller PRs tend to get better feedback and lead to more meaningful design conversations. Faster Reviews: Faster Reviews: At this point, I’m thinking of adding my PR to my will in case it’s still in review after I’m gone. At this point, I’m thinking of adding my PR to my will in case it’s still in review after I’m gone. At this point, I’m thinking of adding my PR to my will in case it’s still in review after I’m gone. Longer PRs require a significant time commitment from reviewers, making them less likely to get attention — especially if they’re not tied to high-impact features. Small PRs on the other hand are reviewed quickly, as they demand less of the reviewer’s time and are less intrusive. This speed of reviews can be crucial for meeting project deadlines; I’ve seen projects getting delayed because senior reviewers couldn’t allocate time for massive PRs (though this can happen with small PRs, the risk is inherently higher with large ones). Lesser Rework: Lesser Rework: Reworking a big PR after design changes is like rearranging deck chairs on a ship you just finished building… and then sinking it. Reworking a big PR after design changes is like rearranging deck chairs on a ship you just finished building… and then sinking it. Reworking a big PR after design changes is like rearranging deck chairs on a ship you just finished building… and then sinking it. We have all experienced situations where someone realizes during PR review that a different design would have been more maintainable and future-proof, and we need to spend additional time reworking the PR based on the new design (not to say that they were completely on board with the design that was initially implemented :p). This is very natural because sometimes things become clearer once you see them written in code, and you start noticing aspects you might have missed during the design phase. With large PRs, this can be a significant issue because you have to rework a lot of elements, but with smaller PRs, it is easier to make changes. More importantly, there is a lower chance of rework because reviewers are more likely to identify issues early on and address them in the initial PRs, allowing subsequent PRs to be based on the new designs. Better Testing: Better Testing: Smaller PRs also benefit you as the author of the PR. They help in testing smaller changes incrementally rather than testing the whole project in one go. Testing smaller changes results in more exhaustive testing of each component of the system, thus leading to fewer production bugs. This applies to both automated tests and manual testing performed by you or dedicated QA engineers. Moreover, smaller PRs reduce the likelihood of missed test cases, as you can focus on a limited scope rather than the entire system. Writing tests? That sounds like Future Me’s problem. Writing tests? That sounds like Future Me’s problem. Writing tests? That sounds like Future Me’s problem. I have seen developers (including myself in the past) hesitate to write automation tests due to the perceived time investment without immediate, “visible” value to the feature/product. Smaller PRs reduce this friction by limiting the number of tests required and the time spent writing them. Easier Debugging: Easier Debugging: No matter how thorough your testing is, production bugs will happen! Being able to debug a bug in production is crucial since production bugs directly impact users, the business, or both. With large PRs, the surface area of change is also large, making it time-consuming and difficult to find the root cause of issues. On the other hand, smaller PRs contain less code and thus make debugging much faster. Debugging small changes is like finding a typo; debugging big changes is like proofreading an encyclopedia. Debugging small changes is like finding a typo; debugging big changes is like proofreading an encyclopedia. Debugging small changes is like finding a typo; debugging big changes is like proofreading an encyclopedia. Phased Feature Deployment: Phased Feature Deployment: Last but not least, smaller PRs are also helpful to your product manager and users. By using small PRs, you can continuously push parts of the system to production, which helps in obtaining early feedback from users and allows for early course corrections if needed. Skipping early feedback is like cooking a five-course meal without tasting anything — you’re just hoping it’s not a disaster. Skipping early feedback is like cooking a five-course meal without tasting anything — you’re just hoping it’s not a disaster. Skipping early feedback is like cooking a five-course meal without tasting anything — you’re just hoping it’s not a disaster. The benefits of small PRs are numerous, and the points outlined above are among the most impactful I’ve experienced personally. If you have encountered other advantages of small PRs or challenges with larger ones that I haven’t covered, do comment with your insights. I hope this article motivates you to embrace smaller PRs. If you’re already on board, I hope it reinforces the value of this practice. Thanks for reading, until next time, keep coding and stay curious!

The Big Power of Small Pull Requests: How They Improve Reviews and Speed Up Development

El gran poder de las pequeñas solicitudes de incorporación de cambios: cómo mejoran las revisiones y aceleran el desarrollo

小さなプルリクエストの大きな力: レビューを改善し、開発をスピードアップする方法

Nguya monene ya masengi ya kobenda ya mike: Ndenge nini kobongisaka botali mpe kosala ete bokoli ya mbangu

ພະລັງອັນໃຫຍ່ຫຼວງຂອງການຮ້ອງຂໍດຶງຂະຫນາດນ້ອຍ: ເຮັດແນວໃດພວກເຂົາປັບປຸງການທົບທວນຄືນແລະເລັ່ງການພັດທະນາ

Nelielu pieprasījumu lielais spēks: kā tie uzlabo pārskatus un paātrina attīstību

Ny Hery lehibe amin'ny fangatahan'ny fisintonana kely: ny fomba hanatsarana ny hevitra sy ny fanafainganana ny fampandrosoana

सानो पुल अनुरोधहरूको ठूलो शक्ति: कसरी तिनीहरूले समीक्षाहरू सुधार गर्छन् र विकासलाई गति दिन्छ

Wielka moc małych żądań ściągnięcia: jak usprawniają przeglądy i przyspieszają rozwój

Fuqia e madhe e kërkesave për tërheqje të vogla: Si përmirësojnë rishikimet dhe përshpejtojnë zhvillimin

The Big Power of Small Pull Requests: Hur de förbättrar recensioner och påskyndar utvecklingen

พลังอันยิ่งใหญ่ของคำขอ Pull Request ขนาดเล็ก: วิธีปรับปรุงบทวิจารณ์และเร่งการพัฒนา

小型 Pull 请求的巨大威力：它们如何改善评审并加快开发速度

Disclaimer: This post includes affiliate links; I may receive compensation if you purchase the book from the different links provided in this post. This review is about DuckDB in Action by JoMark Needham, Michael Hunger, and Michael Simons from Manning. The book was sent to me by Michael Simons. He asked for my feedback: I changed my reading schedule, took a few months, and here it is. Facts 10 chapters
288 pages
$33.59 (eBook) Note that MotherDuck, the company behind DuckDB, offers a free PDF copy. Chapters An introduction to DuckDB
Getting started with DuckDB
Executing SQL queries
Advanced aggregation and analysis of data
Exploring data without persistence
Integrating with the Python ecosystem
DuckDB in the cloud with MotherDuck
Building data pipelines with DuckDB
Building and deploying data apps
Performance considerations for large datasets
Conclusion The book takes a step-by-step approach, starting from the basics of DuckDB and SQL. Pros and Cons After the mandatory section about introducing DuckDB and installing it, the book explains SQL. At first, I admit I thought that the book was for newbies, as it explained things as simple as JOIN. However, the chapter afterwards explained Common Table Expressions and built complex queries upon them. I like how the authors describe DuckDB in its "natural environment", i.e., how it integrates into different ecosystems, such as Python's Pandas and Apache Spark. In addition, a complete appendix section focuses on DuckDB in Java. Finally, the authors dedicate a section to MotherDuck, a DuckDB cloud provider. Conclusion My thoughts on the book are a bit divided. On one side, it tries to explain DuckDB itself and its integration in different environments. On the other hand, it teaches regular and advanced SQL, which users of an SQL database product should know IMHO. The book can't decide between a Learn SQL with DuckDB approach and a DuckDB in Action one. The role of a book editor is to help authors make hard decisions. It feels as if this one didn't want to stop the authors' enthusiasm and couldn't bring themselves to rein them in. Don't get me wrong: the book is great and can teach you a lot, but it could have been better with more focus. Originally published on A Java Geek on November 2nd, 2024 Disclaimer: This post includes affiliate links; I may receive compensation if you purchase the book from the different links provided in this post. This review is about DuckDB in Action by JoMark Needham, Michael Hunger, and Michael Simons from Manning. DuckDB in Action The book was sent to me by Michael Simons . He asked for my feedback: I changed my reading schedule, took a few months, and here it is. Michael Simons Facts 10 chapters 288 pages $33.59 (eBook) 10 chapters 288 pages $33.59 (eBook) Note that MotherDuck, the company behind DuckDB, offers a free PDF copy . free PDF copy Chapters An introduction to DuckDB Getting started with DuckDB Executing SQL queries Advanced aggregation and analysis of data Exploring data without persistence Integrating with the Python ecosystem DuckDB in the cloud with MotherDuck Building data pipelines with DuckDB Building and deploying data apps Performance considerations for large datasets Conclusion An introduction to DuckDB Getting started with DuckDB Executing SQL queries Advanced aggregation and analysis of data Exploring data without persistence Integrating with the Python ecosystem DuckDB in the cloud with MotherDuck Building data pipelines with DuckDB Building and deploying data apps Performance considerations for large datasets Conclusion The book takes a step-by-step approach, starting from the basics of DuckDB and SQL. and Pros and Cons After the mandatory section about introducing DuckDB and installing it, the book explains SQL. At first, I admit I thought that the book was for newbies, as it explained things as simple as JOIN . However, the chapter afterwards explained Common Table Expressions and built complex queries upon them. JOIN I like how the authors describe DuckDB in its "natural environment", i.e. , how it integrates into different ecosystems, such as Python's Pandas and Apache Spark. In addition, a complete appendix section focuses on DuckDB in Java. Finally, the authors dedicate a section to MotherDuck, a DuckDB cloud provider. i.e. Conclusion My thoughts on the book are a bit divided. On one side, it tries to explain DuckDB itself and its integration in different environments. On the other hand, it teaches regular and advanced SQL, which users of an SQL database product should know IMHO. The book can't decide between a Learn SQL with DuckDB approach and a DuckDB in Action one. The role of a book editor is to help authors make hard decisions. It feels as if this one didn't want to stop the authors' enthusiasm and couldn't bring themselves to rein them in. Don't get me wrong: the book is great and can teach you a lot, but it could have been better with more focus. Originally published on A Java Geek on November 2nd, 2024 Originally published on A Java Geek on November 2nd, 2024 A Java Geek

DuckDB in Action: A Review

In today's visually-driven world, users often need to expand images for purposes like enhancing social media posts, expanding promotional materials, and uncropping e-commerce product images. Conventional methods carry the risk of producing unnatural images that do not satisfy user requirements. Uncrop.org is a powerful AI tool designed to offer a more straightforward and efficient solution to uncrop photos. In this detailed review, we will explore the technology behind Uncrop.org, its unique features, and various practical applications. Join us as we uncover how Uncrop.org can elevate your image editing experience. What Is Uncrop.org? Uncrop.org is a powerful AI-driven tool designed for image extension, enabling users to outpaint images by adding high-quality, AI-generated content to the expanded areas. Leveraging advanced algorithms, the tool analyzes the provided image and intelligently predicts how to extend it, seamlessly enlarging the image's boundaries while maintaining the integrity of the original content. Uncrop.org meticulously processes your images, preserving their original quality and visual appeal. This enables you to effortlessly outpaint portraits, landscapes, product photos, and more to meet specific dimensions or aesthetic requirements. The platform ensures consistently high-quality and visually pleasing results, making it versatile for a wide range of image types. Uncrop.org is designed with ease of use in mind, catering to both novice and experienced image editors. Optimized for mobile devices, the platform processes images quickly based on their size and complexity. It also offers free uncropping and previewing features, ensuring a smooth and efficient experience for all users. How to Navigate Uncrop.org Uncrop.org provides a user-friendly platform designed to streamline the image uncropping process, catering to both beginners and experienced users alike. To start, just adhere to the steps described below: Step 1: Simply upload or drag the photo you want to process onto Uncrop.org. Step 2: Once the upload is complete, you can select your desired image size and aspect ratio. Step 3: Within minutes, the expander will produce an uncropped version of your image, ready for you to download and save. Why Should You Choose Uncrop.org? Several factors make Uncrop.org a leading AI extension tool in the market. We will explore these key aspects in detail below. Fast Image Extension Uncrop.org utilizes cutting-edge AI algorithms to provide quick and effective image extension. It can usually extend your images within minutes while maintaining high quality, making it perfect for handling extensive image editing tasks. Extensive Image Format Compatibility With Uncrop.org, you can upload various image formats without the need for prior file conversion. The platform supports JPG, PNG, and even WebP formats, handling images up to 50MB in size. This ensures smooth compatibility for outpainting your photos. Limitless Image Expansion Uncrop.org offers you unrestricted capabilities to extend your images. You can easily resize and adjust the aspect ratio to your liking with just a few clicks. This allows you to uncrop your image precisely as you envision. Effortless AI Photo Extension Uncrop.org works by analyzing the elements of an image to seamlessly extend it, ensuring the new sections blend perfectly with the original. This leads to natural and contextually accurate extensions that consistently prevent any image distortion. In What Ways Can You Make Use of Uncrop.org? Uncrop.org stands out as a highly flexible AI tool designed for outpainting images, making it valuable for numerous applications across diverse sectors, industries, and professional areas. Its utility shines in many typical situations, including but not limited to the following: Create for Social Media: To boost engagement on social media through visually captivating images, having flexibility with aspect ratios is essential. Each social media platform has its unique way of displaying images, so adjusting them accordingly is important. Uncrop.org enables you to tailor your photos to meet the specific dimensions needed for each platform, ensuring that your visuals are ideally suited for every social media site. Outpaint Advertising Materials: When creating promotional materials like posters and banners, it’s often essential to resize images to match various dimensions. Uncrop.org allows you to extend your visuals, ensuring they perfectly fit different advertising formats without losing their visual appeal or key message. This ensures consistent and visually appealing promotional content across multiple platforms. Uncrop Photographs & Artworks: Using Uncrop.org, you can seamlessly expand any photograph or artwork, enriching your visual storytelling and giving your audience a deeper experience of the narrative and composition. This tool is especially advantageous for crafting presentations or displays that thrive on larger, more engaging visuals. Extend Product Images: Making sure that product images have the appropriate dimensions and sizes is essential for attracting potential online customers. With Uncrop.org, you can easily extend your product photos and adjust their dimensions to boost their visual appeal. The tool is crafted to preserve picture quality with minimal distortion, ensuring that the images stay clear and attractive. To Sum Up Uncrop.org provides an outstanding solution for those looking to instantly enlarge images with seamless, naturally extended content. Leveraging superior AI technology, it guarantees smooth and visually appealing expansions, no matter the size requirement. Plus, it delivers results swiftly and is available at no cost. Try out Uncrop.org now! In today's visually-driven world, users often need to expand images for purposes like enhancing social media posts, expanding promotional materials, and uncropping e-commerce product images. Conventional methods carry the risk of producing unnatural images that do not satisfy user requirements. Uncrop.org is a powerful AI tool designed to offer a more straightforward and efficient solution to uncrop photos. In this detailed review, we will explore the technology behind Uncrop.org, its unique features, and various practical applications. Join us as we uncover how Uncrop.org can elevate your image editing experience. Uncrop.org Uncrop.org What Is Uncrop.org? Uncrop.org is a powerful AI-driven tool designed for image extension, enabling users to outpaint images by adding high-quality, AI-generated content to the expanded areas. Leveraging advanced algorithms, the tool analyzes the provided image and intelligently predicts how to extend it, seamlessly enlarging the image's boundaries while maintaining the integrity of the original content. Uncrop.org meticulously processes your images, preserving their original quality and visual appeal. This enables you to effortlessly outpaint portraits, landscapes, product photos, and more to meet specific dimensions or aesthetic requirements. The platform ensures consistently high-quality and visually pleasing results, making it versatile for a wide range of image types. Uncrop.org is designed with ease of use in mind, catering to both novice and experienced image editors. Optimized for mobile devices, the platform processes images quickly based on their size and complexity. It also offers free uncropping and previewing features, ensuring a smooth and efficient experience for all users. How to Navigate Uncrop.org Uncrop.org provides a user-friendly platform designed to streamline the image uncropping process, catering to both beginners and experienced users alike. To start, just adhere to the steps described below: Step 1: Simply upload or drag the photo you want to process onto Uncrop.org . Uncrop.org Uncrop.org Step 2: Once the upload is complete, you can select your desired image size and aspect ratio. Step 3: Within minutes, the expander will produce an uncropped version of your image, ready for you to download and save. Why Should You Choose Uncrop.org? Several factors make Uncrop.org a leading AI extension tool in the market. We will explore these key aspects in detail below. Fast Image Extension Uncrop.org utilizes cutting-edge AI algorithms to provide quick and effective image extension. It can usually extend your images within minutes while maintaining high quality, making it perfect for handling extensive image editing tasks. Extensive Image Format Compatibility With Uncrop.org, you can upload various image formats without the need for prior file conversion. The platform supports JPG, PNG, and even WebP formats, handling images up to 50MB in size. This ensures smooth compatibility for outpainting your photos. Limitless Image Expansion Uncrop.org offers you unrestricted capabilities to extend your images. You can easily resize and adjust the aspect ratio to your liking with just a few clicks. This allows you to uncrop your image precisely as you envision. Effortless AI Photo Extension Uncrop.org works by analyzing the elements of an image to seamlessly extend it, ensuring the new sections blend perfectly with the original. This leads to natural and contextually accurate extensions that consistently prevent any image distortion. In What Ways Can You Make Use of Uncrop.org? Uncrop.org stands out as a highly flexible AI tool designed for outpainting images, making it valuable for numerous applications across diverse sectors, industries, and professional areas. Its utility shines in many typical situations, including but not limited to the following: Create for Social Media: To boost engagement on social media through visually captivating images, having flexibility with aspect ratios is essential. Each social media platform has its unique way of displaying images, so adjusting them accordingly is important. Uncrop.org enables you to tailor your photos to meet the specific dimensions needed for each platform, ensuring that your visuals are ideally suited for every social media site. Create for Social Media: To boost engagement on social media through visually captivating images, having flexibility with aspect ratios is essential. Each social media platform has its unique way of displaying images, so adjusting them accordingly is important. Uncrop.org enables you to tailor your photos to meet the specific dimensions needed for each platform, ensuring that your visuals are ideally suited for every social media site. Outpaint Advertising Materials: When creating promotional materials like posters and banners, it’s often essential to resize images to match various dimensions. Uncrop.org allows you to extend your visuals, ensuring they perfectly fit different advertising formats without losing their visual appeal or key message. This ensures consistent and visually appealing promotional content across multiple platforms. Outpaint Advertising Materials: When creating promotional materials like posters and banners, it’s often essential to resize images to match various dimensions. Uncrop.org allows you to extend your visuals, ensuring they perfectly fit different advertising formats without losing their visual appeal or key message. This ensures consistent and visually appealing promotional content across multiple platforms. Uncrop Photographs & Artworks: Using Uncrop.org, you can seamlessly expand any photograph or artwork, enriching your visual storytelling and giving your audience a deeper experience of the narrative and composition. This tool is especially advantageous for crafting presentations or displays that thrive on larger, more engaging visuals. Uncrop Photographs & Artworks: Using Uncrop.org, you can seamlessly expand any photograph or artwork, enriching your visual storytelling and giving your audience a deeper experience of the narrative and composition. This tool is especially advantageous for crafting presentations or displays that thrive on larger, more engaging visuals. Extend Product Images: Making sure that product images have the appropriate dimensions and sizes is essential for attracting potential online customers. With Uncrop.org, you can easily extend your product photos and adjust their dimensions to boost their visual appeal. The tool is crafted to preserve picture quality with minimal distortion, ensuring that the images stay clear and attractive. Extend Product Images: Making sure that product images have the appropriate dimensions and sizes is essential for attracting potential online customers. With Uncrop.org, you can easily extend your product photos and adjust their dimensions to boost their visual appeal. The tool is crafted to preserve picture quality with minimal distortion, ensuring that the images stay clear and attractive. To Sum Up Uncrop.org provides an outstanding solution for those looking to instantly enlarge images with seamless, naturally extended content. Leveraging superior AI technology, it guarantees smooth and visually appealing expansions, no matter the size requirement. Plus, it delivers results swiftly and is available at no cost. Try out Uncrop.org now!

Uncrop.org Review: Premier AI Expander for Outpainting Images Effectively

Uncrop.org recenzija: Premier AI Expander za efikasno preslikavanje slika

Reseña de Uncrop.org: el mejor expansor de imágenes con inteligencia artificial para pintar imágenes de manera eficaz

Ulasan Uncrop.org: Pengembang AI Terkemuka untuk Menggambar Gambar Secara Efektif

Recensione di Uncrop.org: Premier AI Expander per dipingere efficacemente le immagini

Uncrop.org レビュー: 画像を効果的に塗りつぶすプレミア AI エクスパンダー

Uncrop.org Review: Premier AI Expander voor het effectief overschilderen van afbeeldingen

Uncrop.org Rikuy: Premier AI mast'ariq siq'ikunata allinta pintanapaq

Uncrop.org Ongororo: Premier AI Expander yeKubuda Kwemifananidzo Zvinobudirira

Uncrop.org விமர்சனம்: படங்களைத் திறம்பட அவுட் பெயிண்டிங் செய்வதற்கான பிரீமியர் AI எக்ஸ்பாண்டர்

รีวิว Uncrop.org: โปรแกรม Premier AI Expander สำหรับระบายสีภาพอย่างมีประสิทธิภาพ

Pagsusuri sa Uncrop.org: Premier AI Expander para sa Mabisang Pag-outpainting ng mga Larawan

Uncrop.org İncelemesi: Görüntüleri Etkili Şekilde Boyamak İçin Birinci Sınıf AI Genişletici

As powerful as AI detectors like GPTZero and Originality.ai can be, these tools are not unbeatable. With the right AI stealth writing tool, you can make any AI-generated content sound human-written and Humanizer.Pro is a popular choice among online users. In this review, we’ll explore what this platform is all about, how it works, its features, use cases, and more. Read on to learn what makes Humanizer.Pro so unique and such a valuable solution to all your AI content writing needs. What is Humanizer.Pro? Humanizer.Pro is an advanced AI humanization tool that rewrites AI content to sound more natural and authentic enough to bypass AI detectors. Using advanced language modification techniques, it can refine the overall vocabulary and structure of the content to imitate real human-like conversation. As a result, it produces less robotic-sounding output that can beat even the most stringent AI detection tools in the market. In addition, any rewritten output produced by Humanizer.Pro is guaranteed to be 100% unique and plagiarism-free. This means you can also pass any plagiarism scan, be it on Turnitin, Quetext, Copyscape, etc. Moreover, the tool does well to retain the original meaning of the text. This guarantees your key message will not be distorted in any way to prevent any misunderstandings among readers. Besides that, Humanizer.Pro does well to ensure the output is error-free, which spares you a lot of time and effort in making edits and revisions. What Makes Humanizer.Pro Worth It? Several key aspects of Humanizer.Pro makes it a worthwhile tool to use in your AI writing workflow. Some of these highlights include: Versatile AI Humanizer: With this tool, you can humanize AI text across a wide range of areas, subjects, and fields. This includes essays, reports, marketing copy, ad copy, emails, newsletters, and more. As a result, it is accessible to users in numerous fields, niches, and industries. Free User Access: You can test out Humanizer.Pro’s extensive features and capabilities at no charge using its free trial. The platform doesn’t even require you to submit any credit information to get started. Built-in AI Checker: Humanizer.Pro has a built-in AI checker linked to multiple AI detection tools like ZeroGPT, GPTZero, and Turnitin. This lets you scan your content for AI and get consolidated AI scores from all these tools in one place. ChatGPT Watermark Removal: Humanizer.Pro comes with a unique ChatGPT watermark removal feature that automatically erases and replaces any special phrasing that indicates the text is AI-generated. This does well to ensure 100% human-like results each time. Varied AI Bypass Options: This AI humanizer provides access to multiple AI bypass options that you can choose from. Each one humanizes AI text differently, which means you can benefit from multiple outputs of the same content. Testing Humanizer.Pro: Can It Beat AI Detection? Ultimately, the main question that needs to be answered is: Can Humanizer.Pro beat AI detection? To test this tool’s capabilities, we decided to put it up against three AI detectors such as GPTZero, ZeroGPT, and Originality. The first step was generating a short piece of AI content using ChatGPT. This was the output it produced below. Next, we scanned the AI content on all three AI detectors to verify its detectability. Here are the results of each tool below. From there, we pasted the AI text on Humanizer.Pro to rewrite the content. It produced a human-like version in a matter of seconds. Here’s the final output below. After this, we scanned the content on all three AI detectors to confirm that it truly was undetectable. You can see how the text performed below. As you can see, all three AI detectors confirmed that Humanizer.Pro’s output is 100% human. The tool did an effective job of reworking the vocabulary, syntax, and phrasing to ensure the new version sounds less artificial and more natural. Simply put, this is an AI humanizer that can be relied on to bypass AI detectors without fail. FAQs Is It Safe To Use Humanizer.Pro? Humanizer.Pro implements industry-standard data encryption measures to ensure any content you upload remains secure and confidential. It also has a strict privacy policy that guarantees your information will not be shared with any unauthorized third parties. Can I Access Humanizer.Pro On Mobile? Yes. Humanizer.Pro is mobile-optimized to ensure that you can humanize AI content at any time with no hassle, be it on mobile, tablet, or desktop devices. Simply head to the website on your browser, paste the AI content, and click ‘Humanize’. It’s that simple! Can Humanizer.Pro Rewrite Content In Multiple Languages? Absolutely. Humanizer.Pro offers multilingual support, which means you can humanize AI content in many languages to accommodate global audiences. You can use it to create undetectable AI content in English, French, Spanish, Russian, and much more. Is Humanizer.Pro The Best AI Humanizer Tool? If you want to make your AI content undetectable, then Humanizer.Pro is the most reliable solution to use. Its advanced AI bypass capabilities will comprehensively rework your content to sound so authentically human-like that it will consistently bypass any AI detection model without fail. Conclusion In this review, we’ve established that Humanizer.Pro is a powerful tool that can make AI content undetectable, no matter how stringent the AI detector may be. With access to advanced AI bypass technology and an easy-to-use interface, this is the perfect tool for anyone looking to quickly transform their AI writing for the better. Plus, it has a free trial, so you can try out Humanizer.Pro now without spending a dime! This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author As powerful as AI detectors like GPTZero and Originality.ai can be, these tools are not unbeatable. With the right AI stealth writing tool, you can make any AI-generated content sound human-written and Humanizer.Pro is a popular choice among online users. In this review, we’ll explore what this platform is all about, how it works, its features, use cases, and more. Read on to learn what makes Humanizer.Pro so unique and such a valuable solution to all your AI content writing needs. What is Humanizer.Pro? Humanizer.Pro is an advanced AI humanization tool that rewrites AI content to sound more natural and authentic enough to bypass AI detectors. Using advanced language modification techniques, it can refine the overall vocabulary and structure of the content to imitate real human-like conversation. Humanizer.Pro Humanizer.Pro As a result, it produces less robotic-sounding output that can beat even the most stringent AI detection tools in the market. In addition, any rewritten output produced by Humanizer.Pro is guaranteed to be 100% unique and plagiarism-free. This means you can also pass any plagiarism scan, be it on Turnitin, Quetext, Copyscape, etc. Moreover, the tool does well to retain the original meaning of the text. This guarantees your key message will not be distorted in any way to prevent any misunderstandings among readers. Besides that, Humanizer.Pro does well to ensure the output is error-free, which spares you a lot of time and effort in making edits and revisions. What Makes Humanizer.Pro Worth It? Several key aspects of Humanizer.Pro makes it a worthwhile tool to use in your AI writing workflow. Some of these highlights include: Versatile AI Humanizer: With this tool, you can humanize AI text across a wide range of areas, subjects, and fields. This includes essays, reports, marketing copy, ad copy, emails, newsletters, and more. As a result, it is accessible to users in numerous fields, niches, and industries. Versatile AI Humanizer: With this tool, you can humanize AI text across a wide range of areas, subjects, and fields. This includes essays, reports, marketing copy, ad copy, emails, newsletters, and more. As a result, it is accessible to users in numerous fields, niches, and industries. Free User Access: You can test out Humanizer.Pro’s extensive features and capabilities at no charge using its free trial. The platform doesn’t even require you to submit any credit information to get started. Free User Access: You can test out Humanizer.Pro’s extensive features and capabilities at no charge using its free trial. The platform doesn’t even require you to submit any credit information to get started. Built-in AI Checker: Humanizer.Pro has a built-in AI checker linked to multiple AI detection tools like ZeroGPT, GPTZero, and Turnitin. This lets you scan your content for AI and get consolidated AI scores from all these tools in one place. Built-in AI Checker: Humanizer.Pro has a built-in AI checker linked to multiple AI detection tools like ZeroGPT, GPTZero, and Turnitin. This lets you scan your content for AI and get consolidated AI scores from all these tools in one place. ChatGPT Watermark Removal: Humanizer.Pro comes with a unique ChatGPT watermark removal feature that automatically erases and replaces any special phrasing that indicates the text is AI-generated. This does well to ensure 100% human-like results each time. ChatGPT Watermark Removal: Humanizer.Pro comes with a unique ChatGPT watermark removal feature that automatically erases and replaces any special phrasing that indicates the text is AI-generated. This does well to ensure 100% human-like results each time. Varied AI Bypass Options: This AI humanizer provides access to multiple AI bypass options that you can choose from. Each one humanizes AI text differently, which means you can benefit from multiple outputs of the same content. Varied AI Bypass Options: This AI humanizer provides access to multiple AI bypass options that you can choose from. Each one humanizes AI text differently, which means you can benefit from multiple outputs of the same content. Testing Humanizer.Pro: Can It Beat AI Detection? Ultimately, the main question that needs to be answered is: Can Humanizer.Pro beat AI detection? To test this tool’s capabilities, we decided to put it up against three AI detectors such as GPTZero, ZeroGPT, and Originality. The first step was generating a short piece of AI content using ChatGPT. This was the output it produced below. Next, we scanned the AI content on all three AI detectors to verify its detectability. Here are the results of each tool below. From there, we pasted the AI text on Humanizer.Pro to rewrite the content. It produced a human-like version in a matter of seconds. Here’s the final output below. After this, we scanned the content on all three AI detectors to confirm that it truly was undetectable. You can see how the text performed below. As you can see, all three AI detectors confirmed that Humanizer.Pro’s output is 100% human. The tool did an effective job of reworking the vocabulary, syntax, and phrasing to ensure the new version sounds less artificial and more natural. Simply put, this is an AI humanizer that can be relied on to bypass AI detectors without fail. FAQs Is It Safe To Use Humanizer.Pro? Humanizer.Pro implements industry-standard data encryption measures to ensure any content you upload remains secure and confidential. It also has a strict privacy policy that guarantees your information will not be shared with any unauthorized third parties. Can I Access Humanizer.Pro On Mobile? Yes. Humanizer.Pro is mobile-optimized to ensure that you can humanize AI content at any time with no hassle, be it on mobile, tablet, or desktop devices. Simply head to the website on your browser, paste the AI content, and click ‘Humanize’. It’s that simple! Can Humanizer.Pro Rewrite Content In Multiple Languages? Absolutely. Humanizer.Pro offers multilingual support, which means you can humanize AI content in many languages to accommodate global audiences. You can use it to create undetectable AI content in English, French, Spanish, Russian, and much more. Is Humanizer.Pro The Best AI Humanizer Tool? If you want to make your AI content undetectable, then Humanizer.Pro is the most reliable solution to use. Its advanced AI bypass capabilities will comprehensively rework your content to sound so authentically human-like that it will consistently bypass any AI detection model without fail. Conclusion In this review, we’ve established that Humanizer.Pro is a powerful tool that can make AI content undetectable, no matter how stringent the AI detector may be. With access to advanced AI bypass technology and an easy-to-use interface, this is the perfect tool for anyone looking to quickly transform their AI writing for the better. Plus, it has a free trial, so you can try out Humanizer.Pro now without spending a dime! This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Humanizer.Pro Review: Tool To Make AI Content Human-Like and Undetectable

Humanizer.Pro İcmalı: AI məzmununu insana bənzəyən və aşkarlanmayan etmək üçün alət

Humanizer.Pro-Rezension: Tool, um KI-Inhalte menschenähnlich und unsichtbar zu gestalten

Reseña de Humanizer.Pro: una herramienta para que el contenido de IA sea similar al humano y no se pueda detectar

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Humanizer.Pro レビュー: AI コンテンツを人間らしく、検出不可能にするツール

Humanizer.Pro шолуы: AI мазмұнын адамға ұқсайтын және анықталмайтын етіп жасау құралы

Humanizer.Pro тойм: Хиймэл оюун ухааны агуулгыг хүнийхтэй адил, илрүүлэх боломжгүй болгох хэрэгсэл

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Humanizer.Pro Review: ትሕዝቶ AI ሰብ ዝመስልን ዘይርአን ንምግባር ዝሕግዝ መሳርሒ

Ukubuyekezwa kwe-Humanizer.Pro: Ithuluzi Lokwenza Okuqukethwe Kwe-AI Kufane Nobuntu Futhi Kungabonakali

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