Cophi

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BAHIA BLANCA

Or Perelman, co-founder of Chromia, discusses the platform's innovative approach to blockchain technology, including its relational blockchain concept and elimination of direct gas fees. He shares insights on Chromia's development, partnerships, and vision for the future of blockchain adoption. Ishan Pandey: Hello Or Perelman, it's a pleasure to welcome you to our 'Behind the Startup' series. You have a notable history in the blockchain space, including being featured in TechCrunch's first article about Bitcoin and co-founding one of the earliest Bitcoin wallets. How did your early experiences in blockchain influence your journey at Chromia? Or Perelman: Thank you for having me, Ishan. My journey with Chromia really began from the same curiosity that drew me to Bitcoin early on. I like to keep an eye out for new technology, and then it always leads to the same question: “What can we build with it?”. Ishan Pandey: Chromia introduces innovative concepts like relational blockchain and eliminating direct gas fees for users. What inspired these unique features, and how do they address the limitations you observed in traditional blockchain technologies? Or Perelman: Chromia isn’t a fork of anything, but a new blockchain concept built from the ground up. The best thing about that is we could look at existing platforms and ask ourselves what we would want to do differently. The relational blockchain concept was inspired by traditional relational databases, which are excellent at handling complex queries and structured data. By integrating these concepts into a blockchain framework, we allow for more efficient data handling. Eliminating direct gas fees was driven by the need to improve user experience and adoption. Traditional gas fees can be a barrier, especially for new users. Ishan Pandey: Chromia’s model eliminates direct gas fees for users. How has this model, along with Chromia’s predictable weekly hosting fee structure, impacted both user adoption and developer engagement compared to traditional blockchain platforms? Or Perelman: When people hear about "no gas fees," it really grabs their attention and starts a conversation. When we designed the system, we began with a reordering of traditional blockchain economics. Chromia is still in its early stages, but I believe that in the long term, it gives us an advantage over other Layer-1s. The reason is that you can start using a Chromia based dapp and not even realize until later that you were using a blockchain! Ishan Pandey: Chromia has received investment from several notable VC groups. How have these partnerships influenced Chromia’s development and market strategy? Or Perelman: Having a well-developed network of partners, both in technology and investment, is crucial for success in Web3. The connections we’ve formed in the industry have been instrumental in integrating us into the larger crypto ecosystem. That said, we've also been selective, turning down some OTC investment offers. We have received help from our private investors and also new liquid funds who bought $CHR with regards to potential partnerships with external projects. Ishan Pandey: With Chromia’s mainnet recently launched, what are the immediate and long-term goals for the platform? How do you plan to expand the ecosystem? Or Perelman: For the next rest of the summer, we are refining some of our core components and completing the migration of our staking system from EVM to Chromia. From an ecosystem perspective, we have a very well developed gaming sector, and I think the most common perception of Chromia is that we are a ‘gaming chain’. While this is true, we are also a fully featured Layer-1 that can run all kinds of dapps. As the year progresses, we expect to see other areas like DeFi become more developed. Also, I can’t say too much but we are working on some new initiatives that will open up Chromia to a whole new range of use cases. Long-term, we are ambitious and plan to have a significant presence across multiple verticals. Ishan Pandey: As a pioneer in relational blockchain technology, how do you see Chromia influencing the broader blockchain industry in coming years? Or Perelman: Most blockchains, regardless of their scalability, struggle with handling complex data sets. Relational blockchain aims to address this and could establish a new standard for blockchains. Chromia is designed to be interoperable with Ethereum and other EVM chains, allowing projects on other chains to utilize our unique features. As more projects and developers recognize the benefits, we anticipate an increase in the adoption of the relational model. Ishan Pandey: Finally, where do you see the future of new technologies such as blockchain heading, and what strategies do you believe are crucial to promoting its widespread adoption? Or Perelman: I believe the most important step in promoting adoption is making blockchain more intuitive. This includes easier account handling, 'gas-free' transactions, and streamlining the onboarding process. While progress is being made, there's still a long way to go. At Chromia, we’re committed to contributing to this evolution and improving the user experience. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Or Perelman, co-founder of Chromia , discusses the platform's innovative approach to blockchain technology, including its relational blockchain concept and elimination of direct gas fees. He shares insights on Chromia's development, partnerships, and vision for the future of blockchain adoption. Chromia Ishan Pandey: Hello Or Perelman, it's a pleasure to welcome you to our 'Behind the Startup' series. You have a notable history in the blockchain space, including being featured in TechCrunch's first article about Bitcoin and co-founding one of the earliest Bitcoin wallets. How did your early experiences in blockchain influence your journey at Chromia? Ishan Pandey: Hello Or Perelman, it's a pleasure to welcome you to our 'Behind the Startup' series. You have a notable history in the blockchain space, including being featured in TechCrunch's first article about Bitcoin and co-founding one of the earliest Bitcoin wallets. How did your early experiences in blockchain influence your journey at Chromia? Ishan Pandey: Hello Or Perelman, it's a pleasure to welcome you to our 'Behind the Startup' series. You have a notable history in the blockchain space, including being featured in TechCrunch's first article about Bitcoin and co-founding one of the earliest Bitcoin wallets. How did your early experiences in blockchain influence your journey at Chromia? Or Perelman: Thank you for having me, Ishan. My journey with Chromia really began from the same curiosity that drew me to Bitcoin early on. I like to keep an eye out for new technology, and then it always leads to the same question: “What can we build with it?”. Or Perelman: Ishan Pandey: Chromia introduces innovative concepts like relational blockchain and eliminating direct gas fees for users. What inspired these unique features, and how do they address the limitations you observed in traditional blockchain technologies? Ishan Pandey: Chromia introduces innovative concepts like relational blockchain and eliminating direct gas fees for users. What inspired these unique features, and how do they address the limitations you observed in traditional blockchain technologies? Ishan Pandey: Chromia introduces innovative concepts like relational blockchain and eliminating direct gas fees for users. What inspired these unique features, and how do they address the limitations you observed in traditional blockchain technologies? Or Perelman: Chromia isn’t a fork of anything, but a new blockchain concept built from the ground up. The best thing about that is we could look at existing platforms and ask ourselves what we would want to do differently. The relational blockchain concept was inspired by traditional relational databases, which are excellent at handling complex queries and structured data. By integrating these concepts into a blockchain framework, we allow for more efficient data handling. Eliminating direct gas fees was driven by the need to improve user experience and adoption. Traditional gas fees can be a barrier, especially for new users. Or Perelman: Ishan Pandey: Chromia’s model eliminates direct gas fees for users. How has this model, along with Chromia’s predictable weekly hosting fee structure, impacted both user adoption and developer engagement compared to traditional blockchain platforms? Ishan Pandey: Chromia’s model eliminates direct gas fees for users. How has this model, along with Chromia’s predictable weekly hosting fee structure, impacted both user adoption and developer engagement compared to traditional blockchain platforms? Ishan Pandey: Chromia’s model eliminates direct gas fees for users. How has this model, along with Chromia’s predictable weekly hosting fee structure, impacted both user adoption and developer engagement compared to traditional blockchain platforms? Or Perelman: When people hear about "no gas fees," it really grabs their attention and starts a conversation. When we designed the system, we began with a reordering of traditional blockchain economics. Chromia is still in its early stages, but I believe that in the long term, it gives us an advantage over other Layer-1s. The reason is that you can start using a Chromia based dapp and not even realize until later that you were using a blockchain! Or Perelman: Ishan Pandey: Chromia has received investment from several notable VC groups. How have these partnerships influenced Chromia’s development and market strategy? Ishan Pandey: Chromia has received investment from several notable VC groups. How have these partnerships influenced Chromia’s development and market strategy? Ishan Pandey: Chromia has received investment from several notable VC groups. How have these partnerships influenced Chromia’s development and market strategy? Or Perelman: Having a well-developed network of partners, both in technology and investment, is crucial for success in Web3. The connections we’ve formed in the industry have been instrumental in integrating us into the larger crypto ecosystem. That said, we've also been selective, turning down some OTC investment offers. We have received help from our private investors and also new liquid funds who bought $CHR with regards to potential partnerships with external projects. Or Perelman: Ishan Pandey: With Chromia’s mainnet recently launched, what are the immediate and long-term goals for the platform? How do you plan to expand the ecosystem? Ishan Pandey: With Chromia’s mainnet recently launched, what are the immediate and long-term goals for the platform? How do you plan to expand the ecosystem? Ishan Pandey: With Chromia’s mainnet recently launched, what are the immediate and long-term goals for the platform? How do you plan to expand the ecosystem? Or Perelman: For the next rest of the summer, we are refining some of our core components and completing the migration of our staking system from EVM to Chromia. From an ecosystem perspective, we have a very well developed gaming sector, and I think the most common perception of Chromia is that we are a ‘gaming chain’. While this is true, we are also a fully featured Layer-1 that can run all kinds of dapps. As the year progresses, we expect to see other areas like DeFi become more developed. Also, I can’t say too much but we are working on some new initiatives that will open up Chromia to a whole new range of use cases. Long-term, we are ambitious and plan to have a significant presence across multiple verticals. Or Perelman: Ishan Pandey: As a pioneer in relational blockchain technology, how do you see Chromia influencing the broader blockchain industry in coming years? Ishan Pandey: As a pioneer in relational blockchain technology, how do you see Chromia influencing the broader blockchain industry in coming years? Ishan Pandey: As a pioneer in relational blockchain technology, how do you see Chromia influencing the broader blockchain industry in coming years? Or Perelman: Most blockchains, regardless of their scalability, struggle with handling complex data sets. Relational blockchain aims to address this and could establish a new standard for blockchains. Chromia is designed to be interoperable with Ethereum and other EVM chains, allowing projects on other chains to utilize our unique features. As more projects and developers recognize the benefits, we anticipate an increase in the adoption of the relational model. Or Perelman: Ishan Pandey: Finally, where do you see the future of new technologies such as blockchain heading, and what strategies do you believe are crucial to promoting its widespread adoption? Ishan Pandey: Finally, where do you see the future of new technologies such as blockchain heading, and what strategies do you believe are crucial to promoting its widespread adoption? Ishan Pandey: Finally, where do you see the future of new technologies such as blockchain heading, and what strategies do you believe are crucial to promoting its widespread adoption? Or Perelman: I believe the most important step in promoting adoption is making blockchain more intuitive. This includes easier account handling, 'gas-free' transactions, and streamlining the onboarding process. While progress is being made, there's still a long way to go. At Chromia, we’re committed to contributing to this evolution and improving the user experience. Or Perelman: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Blockchain Without Fees? Chromia's Groundbreaking Model Explained

ফি ছাড়া ব্লকচেইন? ক্রোমিয়ার গ্রাউন্ডব্রেকিং মডেল ব্যাখ্যা করা হয়েছে

Blockchain ohne Gebühren? Chromias bahnbrechendes Modell erklärt

¿Blockchain sin comisiones? Explicación del modelo innovador de Chromia

Blockchain sans frais ? Le modèle révolutionnaire de Chromia expliqué

बिना फीस के ब्लॉकचेन? क्रोमिया के ग्राउंडब्रेकिंग मॉडल की व्याख्या

手数料なしのブロックチェーン？ Chromia の画期的なモデルを解説

수수료 없는 블록체인? Chromia의 획기적인 모델 설명

Blockchain sem taxas? O modelo inovador do Chromia explicado

Блокчейн без комиссий? Объяснение новаторской модели Chromia

Ücretsiz Blockchain mi? Chromia'nın Çığır Açan Modeli Açıklandı

Blockchain không có phí? Giải thích về mô hình đột phá của Chromia

区块链无需费用？Chromia 的突破性模型详解

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Never build a CSV importer again Y'all are smart folks, so you'd have already noticed it, but it took my sales-brain more than a bit to decipher :/ All of the top-performing ADs followed the <Action> <Personalize> <Value> structure to achieve the best results!! Do your ads get you 0.40% CTR OR Are you still advertising on social media and getting ripped off by bot clicks? Book a meeting with us to discuss your winning AD copy - for FREE! We can't just show 'em programmatic ADs - can we? That's where HackerNoon's Targeted ADs have the traditional AD models beat by over 5x. Some of that is our innovative AD strategy (delivers ~0.40% CTR), but a lot also depends on your AD copies. Without further ado, here are the 10 AD copies that devs clicked the most: Join the Gen-AI revolution Let Engineers & PMs focus on Product Secure your critical AI workloads Start API security testing for free Apply now and work #LikeABosch Learn JS Clean Code Magic from Sonar SEGMENT your Customer Data For Free! Drag & Drop Auth For Any App Call me, text me with Plivo APIs Never build a CSV importer again Join the Gen-AI revolution Let Engineers & PMs focus on Product Secure your critical AI workloads Start API security testing for free Apply now and work #LikeABosch Learn JS Clean Code Magic from Sonar SEGMENT your Customer Data For Free! Drag & Drop Auth For Any App Call me, text me with Plivo APIs Never build a CSV importer again Y'all are smart folks, so you'd have already noticed it, but it took my sales-brain more than a bit to decipher :/ All of the top-performing ADs followed the <Action> <Personalize> <Value> structure to achieve the best results!! All of the top-performing ADs followed the <Action> <Personalize> <Value> structure to achieve the best results!! <Action> <Personalize> <Value> Do your ads get you 0.40% CTR OR Are you still advertising on social media and getting ripped off by bot clicks? Book a meeting with us to discuss your winning AD copy - for FREE! Book a meeting with us to discuss your winning AD copy - for FREE! Book a meeting with us to discuss your winning AD copy - for FREE!

Top 10 AD Copies That Programmers Loved on HackerNoon

Las 10 mejores copias de AD que los programadores adoraron en HackerNoon

10 نسخه برتر AD که برنامه نویسان در HackerNoon دوست داشتند

10 parasta mainoskopiota, joista ohjelmoijat rakastivat HackerNoonissa

AD 10 լավագույն օրինակները, որոնք ծրագրավորողները սիրում էին HackerNoon-ում

HackerNoon でプログラマーが愛したトップ 10 の AD コピー

10 populiariausių reklaminių kopijų, kurias programuotojai pamėgo „HackerNoon“.

As 10 principais cópias de AD que os programadores adoraram no HackerNoon

HackerNoon හි ක්‍රමලේඛකයින් ආදරය කරන AD පිටපත් 10 ක්

Topp 10 AD-kopior som programmerare älskade på HackerNoon

Nakala 10 Bora za AD Ambazo Waandaaji wa Programu Walipenda kwenye HackerNoon

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Top 10 bản sao quảng cáo được các lập trình viên yêu thích trên HackerNoon

At this point, it’s an understatement to say that AI is changing the way every industry operates. Its impact has been so granular that we would have to examine every individual role in every sector to truly gauge the shift it’s created. Software engineers, evidently, are in this mix. According to GitHub, 92% of US-based developers are using AI coding tools at work — what we now call copilots. Everything changed the moment ChatGPT’s first publicly available version hit the shelves, creating a ripple effect in how LLMs work and how AI is applied and developed for different use cases. The technology has evolved to the point that it can understand more than just the syntax of code — it grasps context and intent — and this has been a game-changer in the software engineering realm. Copilots began gaining more ground as a result of the fast evolution of ML, the rise of AI, and enhanced computational power. Plus, the vast amount of code available for training has allowed these models to become highly effective. Now, copilots are helping developers code faster and more accurately and relieving them of repetitive procedural tasks so they can focus on the critical-thinking part. What these AI tools are bringing to the table is the potential to amplify an engineer’s productivity and impact, which will only continue to increase as the tech is refined for better performance. Leading a software development company has given me heightened visibility of these quick changes, making me realize that we’re just getting started when it comes to copilots. They’re opening up new possibilities for innovation, efficiency, and creativity in the field, which is nothing short of exciting. So, what will the future of software engineering look like with AI assistants? Let’s examine the good, the not-so-good, and what engineers should keep in mind to evolve alongside copilots. One Engineer Equals One Team To set the stage for what the future with copilots should look like, let’s picture a day in the life of an engineer. What they currently do manually will drastically change — engineers love optimization and automation, so copilots fit right in. Copilots will do just that for them, providing real-time code suggestions and taking over repetitive activities like testing code and basic debugging. They’re also quick to learn new coding languages and apply frameworks, meaning engineers will spare this step if they ever need to deal with a language they’re unfamiliar with or adapt to frameworks. Instead, they’ll be able to use that time to focus on more strategic and creative work. As a result, humans will become more productive and create a broader impact, which could potentially equal that of an entire team. But this isn’t news — we’re already seeing this impact today. AI-based code review tools are becoming widespread, helping engineers and teams ensure their code is up to standard with less manual effort. Other specialized AI tools are also helping managers quickly assess their team’s progress and quality without overseeing every detail manually. Ultimately, copilots aren't just speeding up the process; they're encouraging a more thoughtful, senior-level approach to engineering. Shifting Engineering Skills Over half of today’s public sector IT workers report the lack of AI skills as their top challenge. It wouldn’t be surprising if this number is even higher in the private sector. As AI becomes a crucial tool in IT, and more importantly, in software development, workers will need to evolve their skills with the market’s shifting demands. But beyond knowing how to implement AI, what’s true is copilots are leading developer skills to change to more senior-level activities. In this sense, engineers will need to start thinking like managers because tasks like manual code review will become less central. AI oversight, managing and directing copilots, and adopting a strategic decision-making mindset will be required skills instead. Many more tasks will be automated, so engineers will need to understand how to use AI tools to their advantage, maximizing productivity, minimizing errors, and maintaining high-quality outputs. For example, prompt training will be one of those essential aptitudes to master so that engineers can see positive results in using copilots. Future engineers will need a blend of technical skills and AI management. These AI tools will likely lead to new roles — not just for humans but for AI to review and manage tasks completed by copilots. These roles could be AI overseers, who ensure copilots are working effectively, and AI-augmented project managers, who integrate AI tools into the broader development process. This will also play into engineers’ career trajectories. While many think this will stall them or reduce their work, the truth is career paths will move faster for those who know how to implement copilots effectively. Think of it this way: If a one-man show has the impact of an entire team, copilots could lead to faster career progression and opportunities for more significant projects. Roadblocks of Heightened Copilot Adoption These positives don’t come without their downsides. From overuse to not capitalizing on copilots enough, successful software engineering will become a balancing act. At first, engineers might struggle with the steep learning curve of responsibly adopting copilots, which could lead to becoming overly reliant on them. This will be especially true when they’re expected to work faster while delivering quality results — there must be a healthy middle. Otherwise, overusing AI tools could translate into a decline in hands-on coding skills. Soon enough, AI will also write so much code that engineers won’t be able to review it all themselves, meaning engineers will need to use copilots wisely to provide oversight into good code. At the end of the day, the workforce will need intensive training that highlights the role of AI as an augmentation rather than a full replacement. The future I’m talking about might come sooner than we expect. Today’s engineers must start honing their AI management skills to thrive in this copilot era, offering exceptional output by leaning on these tools to achieve it. It’s certainly a bright future, but in order for AI adoption to bring positive results, the industry must strike a usage balance and more learning opportunities for engineers to keep up and deliver top products. At this point, it’s an understatement to say that AI is changing the way every industry operates. Its impact has been so granular that we would have to examine every individual role in every sector to truly gauge the shift it’s created. Software engineers, evidently, are in this mix. According to GitHub, 92% of US-based developers are using AI coding tools at work — what we now call copilots. 92% 92% Everything changed the moment ChatGPT’s first publicly available version hit the shelves, creating a ripple effect in how LLMs work and how AI is applied and developed for different use cases. The technology has evolved to the point that it can understand more than just the syntax of code — it grasps context and intent — and this has been a game-changer in the software engineering realm. Copilots began gaining more ground as a result of the fast evolution of ML, the rise of AI, and enhanced computational power. Plus, the vast amount of code available for training has allowed these models to become highly effective. Now, copilots are helping developers code faster and more accurately and relieving them of repetitive procedural tasks so they can focus on the critical-thinking part. What these AI tools are bringing to the table is the potential to amplify an engineer’s productivity and impact, which will only continue to increase as the tech is refined for better performance. Leading a software development company has given me heightened visibility of these quick changes, making me realize that we’re just getting started when it comes to copilots. They’re opening up new possibilities for innovation, efficiency, and creativity in the field, which is nothing short of exciting. So, what will the future of software engineering look like with AI assistants? Let’s examine the good, the not-so-good, and what engineers should keep in mind to evolve alongside copilots. One Engineer Equals One Team To set the stage for what the future with copilots should look like, let’s picture a day in the life of an engineer. What they currently do manually will drastically change — engineers love optimization and automation, so copilots fit right in. Copilots will do just that for them, providing real-time code suggestions and taking over repetitive activities like testing code and basic debugging. They’re also quick to learn new coding languages and apply frameworks, meaning engineers will spare this step if they ever need to deal with a language they’re unfamiliar with or adapt to frameworks. Instead, they’ll be able to use that time to focus on more strategic and creative work. As a result, humans will become more productive and create a broader impact, which could potentially equal that of an entire team. But this isn’t news — we’re already seeing this impact today. AI-based code review tools are becoming widespread, helping engineers and teams ensure their code is up to standard with less manual effort. Other specialized AI tools are also helping managers quickly assess their team’s progress and quality without overseeing every detail manually. Ultimately, copilots aren't just speeding up the process; they're encouraging a more thoughtful, senior-level approach to engineering. Shifting Engineering Skills Over half of today’s public sector IT workers report the lack of AI skills as their top challenge. It wouldn’t be surprising if this number is even higher in the private sector. As AI becomes a crucial tool in IT, and more importantly, in software development, workers will need to evolve their skills with the market’s shifting demands. lack of AI skills lack of AI skills But beyond knowing how to implement AI, what’s true is copilots are leading developer skills to change to more senior-level activities. In this sense, engineers will need to start thinking like managers because tasks like manual code review will become less central. AI oversight, managing and directing copilots, and adopting a strategic decision-making mindset will be required skills instead. Many more tasks will be automated, so engineers will need to understand how to use AI tools to their advantage, maximizing productivity, minimizing errors, and maintaining high-quality outputs. For example, prompt training will be one of those essential aptitudes to master so that engineers can see positive results in using copilots. Future engineers will need a blend of technical skills and AI management. These AI tools will likely lead to new roles — not just for humans but for AI to review and manage tasks completed by copilots. These roles could be AI overseers, who ensure copilots are working effectively, and AI-augmented project managers, who integrate AI tools into the broader development process. This will also play into engineers’ career trajectories. While many think this will stall them or reduce their work, the truth is career paths will move faster for those who know how to implement copilots effectively. Think of it this way: If a one-man show has the impact of an entire team, copilots could lead to faster career progression and opportunities for more significant projects. Roadblocks of Heightened Copilot Adoption These positives don’t come without their downsides. From overuse to not capitalizing on copilots enough, successful software engineering will become a balancing act. At first, engineers might struggle with the steep learning curve of responsibly adopting copilots, which could lead to becoming overly reliant on them. This will be especially true when they’re expected to work faster while delivering quality results — there must be a healthy middle. Otherwise, overusing AI tools could translate into a decline in hands-on coding skills. Soon enough, AI will also write so much code that engineers won’t be able to review it all themselves, meaning engineers will need to use copilots wisely to provide oversight into good code. At the end of the day, the workforce will need intensive training that highlights the role of AI as an augmentation rather than a full replacement. The future I’m talking about might come sooner than we expect. Today’s engineers must start honing their AI management skills to thrive in this copilot era, offering exceptional output by leaning on these tools to achieve it. It’s certainly a bright future, but in order for AI adoption to bring positive results, the industry must strike a usage balance and more learning opportunities for engineers to keep up and deliver top products.

A Glimpse Into The Future of Software Engineering with Copilots

The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 12 of 18. Count II: Violation of 17 U.S.C. § 501 by Defendant Microsoft 124. The above paragraphs are incorporated by reference into this Count. 125. Plaintiff owns the exclusive rights to the Registered Works under 17 U.S.C. § 106. 126. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by downloading those works from the internet. 127. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by encoding the Registered Works in computer memory. 128. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by regurgitating those works verbatim or nearly verbatim in response to prompts by Copilot users. 129. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by producing significant amounts of material from those works in response to prompts by ChatGPT users. 130. Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by abridging those works in response to prompts by Copilot users. 131. Upon information and belief, Defendant Microsoft’s infringements were willful and with full knowledge of Plaintiff’s rights in the Registered Works. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 12 of 18. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 12 of 18. HackerNoon’s Legal PDF Series here Count II: Violation of 17 U.S.C. § 501 by Defendant Microsoft 124. The above paragraphs are incorporated by reference into this Count. 125. Plaintiff owns the exclusive rights to the Registered Works under 17 U.S.C. § 106. 126. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by downloading those works from the internet. 127. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by encoding the Registered Works in computer memory. 128. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by regurgitating those works verbatim or nearly verbatim in response to prompts by Copilot users. 129. Upon information and belief, Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by producing significant amounts of material from those works in response to prompts by ChatGPT users. 130. Defendant Microsoft infringed Plaintiff’s exclusive rights in the Registered Works by abridging those works in response to prompts by Copilot users. 131. Upon information and belief, Defendant Microsoft’s infringements were willful and with full knowledge of Plaintiff’s rights in the Registered Works. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. motherjones.com

Microsoft Faces Lawsuit for Alleged Willful Copyright Infringement in Copilot and ChatGPT

The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 7 of 18. DEFENDANTS’ UNLAWFUL ABRIDGEMENT OF PLAINTIFFS’ WORKS 83. In response to prompts by users, ChatGPT and Copilot provide highly detailed abridgements of copyright-protected news articles, including articles published by Plaintiff. 84. When earlier versions of ChatGPT (up to and including ChatGPT 3.5-turbo) abridge a copyright-protected news article in response to a user prompt, they draw from their training on the article. During training, the patterns of all content, including copyright-protected news articles, are imprinted onto the model. That imprint allows the model to abridge the article. 85. When Copilot and later versions of ChatGPT abridge a copyright-protected news article in response to a user prompt, they find the previously downloaded article inside a database called a search index using a method called synthetic searching. Upon information and belief, they make another copy of the article in the memory of their computing system and use their LLM or other programming to generate an abridgement by applying the model or other programming to the text of the article. 86. Plaintiff’s articles are not merely collections of facts. Rather, they reflect the originality of their authors in selecting, arranging, and presenting facts to tell compelling stories. They also reflect the authors’ analysis and interpretation of events, structuring of materials, marshaling of facts, and the emphasis given to certain aspects. 7. An ordinary observer of a ChatGPT or Copilot abridgement of one of Plaintiff’s articles would conclude that the abridgements were derived from the articles being abridged, at least because ChatGPT and Copilot expressly link to the article they abridge and explain that they are searching Plaintiffs’ website in the course of generating a response. 88. In response to prompts seeking an abridgement of an article, ChatGPT and Copilot will typically provide a general abridgement of such an article, on the order of a few paragraphs. In some instances, the initial response will summarize the article in substantial detail. Further, when prompted by the user to provide more information about one or more aspects of that abridgement, ChatGPT or Copilot will provide additional details, often in the format of a bulleted list of main points. If prompted by the user to provide more information on one of more of those points, Chat GPT or Copilot will provide additional details. In some instances, however, ChatGPT or Copilot will provide a bulleted list of main points in response to an initial prompt seeking an abridgement. 89. A ChatGPT or Copilot user is capable of obtaining a substantial abridgement of a copyright protected news article through such series of prompts, and in some instances, further prompts designed to elicit further summary are even suggested by Copilot or ChatGPT itself. As a representative sample, a series of abridgements by ChatGPT and Copilot is attached as Exhibit 8. 90. These abridgements lack copyright notice or terms of use information conveyed in connection with the work, and sometimes lack author information. 91. Thus, upon information and belief, abridgements from earlier versions of ChatGPT lack copyright notice, terms of use, and typically author information because Defendants intentionally removed that information from the ChatGPT training sets. 92. Further, the abridgements from Copilot and later versions of ChatGPT lack copyright notice, terms of use, and typically author information. Upon information and belief, this is because Defendants intentionally removed them either when initially storing them in computer memory or when generating the synthetic search results. 93. Defendants’ abridgements, rewritten from copyright-protected news articles, harm the market for those articles by reducing the incentives for users to go to the original source, thus reducing Plaintiff’s subscription, licensing, advertising, and affiliate revenue. This allows Defendants to monetize copyright owners’ content at the expense of copyright owners who created the works ChatGPT has abridged. 94. Defendants’ abridgements do not add anything new to, or further any purpose or character different from, that of Plaintiff’s articles. They simply take the text of the articles and rewrite them into abridgements, including, when prompted, into detailed abridgements of the entire articles. Those abridgements often serve as a substitute for the original articles even when they are not complete, as evidenced by a study showing that only 51% of consumers read the entire text of a typical news article.[14] 95. Defendants’ abridgements of Plaintiff’s articles violates Plaintiff’s copyrights. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. [14] See Sharing on Social Media Makes Us Overconfident in Our Knowledge, UT News (Aug. 30, 2022), https://news.utexas.edu/2022/08/30/sharing-on-social-media-makes-us-overconfident-inourknowledge/#:~:text=Recent%20data%20from%20the%20Reuters,headline%20or%20a%20few %20lines. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 7 of 18. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 7 of 18. HackerNoon’s Legal PDF Series here DEFENDANTS’ UNLAWFUL ABRIDGEMENT OF PLAINTIFFS’ WORKS 83. In response to prompts by users, ChatGPT and Copilot provide highly detailed abridgements of copyright-protected news articles, including articles published by Plaintiff. 84. When earlier versions of ChatGPT (up to and including ChatGPT 3.5-turbo) abridge a copyright-protected news article in response to a user prompt, they draw from their training on the article. During training, the patterns of all content, including copyright-protected news articles, are imprinted onto the model. That imprint allows the model to abridge the article. 85. When Copilot and later versions of ChatGPT abridge a copyright-protected news article in response to a user prompt, they find the previously downloaded article inside a database called a search index using a method called synthetic searching. Upon information and belief, they make another copy of the article in the memory of their computing system and use their LLM or other programming to generate an abridgement by applying the model or other programming to the text of the article. 86. Plaintiff’s articles are not merely collections of facts. Rather, they reflect the originality of their authors in selecting, arranging, and presenting facts to tell compelling stories. They also reflect the authors’ analysis and interpretation of events, structuring of materials, marshaling of facts, and the emphasis given to certain aspects. 7. An ordinary observer of a ChatGPT or Copilot abridgement of one of Plaintiff’s articles would conclude that the abridgements were derived from the articles being abridged, at least because ChatGPT and Copilot expressly link to the article they abridge and explain that they are searching Plaintiffs’ website in the course of generating a response. 88. In response to prompts seeking an abridgement of an article, ChatGPT and Copilot will typically provide a general abridgement of such an article, on the order of a few paragraphs. In some instances, the initial response will summarize the article in substantial detail. Further, when prompted by the user to provide more information about one or more aspects of that abridgement, ChatGPT or Copilot will provide additional details, often in the format of a bulleted list of main points. If prompted by the user to provide more information on one of more of those points, Chat GPT or Copilot will provide additional details. In some instances, however, ChatGPT or Copilot will provide a bulleted list of main points in response to an initial prompt seeking an abridgement. 89. A ChatGPT or Copilot user is capable of obtaining a substantial abridgement of a copyright protected news article through such series of prompts, and in some instances, further prompts designed to elicit further summary are even suggested by Copilot or ChatGPT itself. As a representative sample, a series of abridgements by ChatGPT and Copilot is attached as Exhibit 8. 90. These abridgements lack copyright notice or terms of use information conveyed in connection with the work, and sometimes lack author information. 91. Thus, upon information and belief, abridgements from earlier versions of ChatGPT lack copyright notice, terms of use, and typically author information because Defendants intentionally removed that information from the ChatGPT training sets. 92. Further, the abridgements from Copilot and later versions of ChatGPT lack copyright notice, terms of use, and typically author information. Upon information and belief, this is because Defendants intentionally removed them either when initially storing them in computer memory or when generating the synthetic search results. 93. Defendants’ abridgements, rewritten from copyright-protected news articles, harm the market for those articles by reducing the incentives for users to go to the original source, thus reducing Plaintiff’s subscription, licensing, advertising, and affiliate revenue. This allows Defendants to monetize copyright owners’ content at the expense of copyright owners who created the works ChatGPT has abridged. 94. Defendants’ abridgements do not add anything new to, or further any purpose or character different from, that of Plaintiff’s articles. They simply take the text of the articles and rewrite them into abridgements, including, when prompted, into detailed abridgements of the entire articles. Those abridgements often serve as a substitute for the original articles even when they are not complete, as evidenced by a study showing that only 51% of consumers read the entire text of a typical news article.[14] 95. Defendants’ abridgements of Plaintiff’s articles violates Plaintiff’s copyrights. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. motherjones.com [14] See Sharing on Social Media Makes Us Overconfident in Our Knowledge, UT News (Aug. 30, 2022), https://news.utexas.edu/2022/08/30/sharing-on-social-media-makes-us-overconfident-inourknowledge/#:~:text=Recent%20data%20from%20the%20Reuters,headline%20or%20a%20few %20lines.

How ChatGPT and Copilot’s Abridgments May Violate Copyright Laws

Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. GitHub Copilot Chat is an incredibly powerful and useful feature that allows you to chat with or about your code. Even though it’s 100% natural language-friendly (i.e., you can send your messages without using any specific syntax), leveraging some special chat capabilities can unlock new AI-assisted development scenarios and significantly boost your productivity. These powerful features, which you can use by applying special syntax, include chat participants, slash commands, and context variables. Note that the described features are available in VS Code and might not be fully supported in other IDEs where GitHub Copilot Chat is available. Target Your Question or Request by Messaging One of the Available Chat Participants In GitHub Copilot Chat, you can reference one of the AI-powered “domain experts” using conventional chat syntax—by prepending @ to the participant name. The currently available chat participants are: @workspace: Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @terminal: Knows all about the integrated terminal shell, its contents, and its buffer. @vscode: Knows about the VS Code editor, its commands, and features. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. In this particular case, you don’t even need to have files open in your editor. Compare this with the response you get without tagging @workspace: The @workspace chat participant is instrumental for all solution-wide queries where you want all code to be considered for the chat response. However, this doesn't mean that all code will be used and sent as part of the prompt. The GitHub Copilot Chat extension in VS Code does its best to determine relevant files and parts of these files using local knowledge and intelligence first. You can check which files and code lines were used for the prompt by expanding the “Used references” line: Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Be Precise in Setting the Context Using Chat Variables In many cases, considering the full solution as the context for your question or request (by using @workspace) is overkill. You might want to point to specific files or even parts of the files in your message. Chat variables can help! Use # to call one from this list: #file: Points to a specific file in your workspace. #codebase: All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal) but still want to reference the full solution. #editor: Source code in the editor’s viewport (visible part). #git: Current git repository: branch, remotes, path, etc. #selection: The currently selected code. #terminalLastCommand: Last run command in the editor’s terminal. #terminalSelection: Selection in the editor's terminal. Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file, use keyboard navigation again to pick one of the suggested files. Call the Most Often Used Actions Quickly With Slash Commands Chatting with your code using natural language is fun, but having the option to call often-used actions using handy shortcuts is even better. Compare typing the full message “Explain how selected code works” versus typing “/”, then using keyboard arrows to pick /explain from the popup overlay. Another benefit of using the predefined syntax for commands is the confidence that GitHub Copilot understands our intent 100% correctly (natural language might have some ambiguity). There are a bunch of slash commands available. You can use them in conjunction with referencing the chat participant to provide the desired scope. Some of the commands are: /help: Help with available slash commands, chat participants, chat variables, and more. /doc: Generate documentation for the code. /explain: Explain how the code works (or get help with terminal commands if you prepend @terminal). /fix: Optimize and/or fix issues in the code. /tests: Create unit tests for the code. /new: Scaffold a new workspace. Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “/explain. Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Summary Use chat participants, chat variables, and slash commands to maintain full control over the conversation context, ensure correct and consistent understanding of your intentions, and ultimately chat and code faster! Start your free GitHub Copilot trial here: https://aka.ms/try-github-copilot References https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands


https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/


https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants


https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/


https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements


https://code.visualstudio.com/updates/v1_84#_chat-agents Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. GitHub Copilot Chat is an incredibly powerful and useful feature that allows you to chat with or about your code. Even though it’s 100% natural language-friendly (i.e., you can send your messages without using any specific syntax), leveraging some special chat capabilities can unlock new AI-assisted development scenarios and significantly boost your productivity. These powerful features, which you can use by applying special syntax, include chat participants, slash commands, and context variables. Note that the described features are available in VS Code and might not be fully supported in other IDEs where GitHub Copilot Chat is available. Target Your Question or Request by Messaging One of the Available Chat Participants In GitHub Copilot Chat, you can reference one of the AI-powered “domain experts” using conventional chat syntax—by prepending @ to the participant name. The currently available chat participants are: @workspace: Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @workspace : Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @workspace @terminal: Knows all about the integrated terminal shell, its contents, and its buffer. @terminal : Knows all about the integrated terminal shell, its contents, and its buffer. @terminal @vscode: Knows about the VS Code editor, its commands, and features. @vscode : Knows about the VS Code editor, its commands, and features. @vscode Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace In this particular case, you don’t even need to have files open in your editor. Compare this with the response you get without tagging @workspace : @workspace The @workspace chat participant is instrumental for all solution-wide queries where you want all code to be considered for the chat response. However, this doesn't mean that all code will be used and sent as part of the prompt. The GitHub Copilot Chat extension in VS Code does its best to determine relevant files and parts of these files using local knowledge and intelligence first. You can check which files and code lines were used for the prompt by expanding the “Used references” line: @workspace Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Be Precise in Setting the Context Using Chat Variables In many cases, considering the full solution as the context for your question or request (by using @workspace ) is overkill. You might want to point to specific files or even parts of the files in your message. Chat variables can help! Use # to call one from this list: @workspace #file: Points to a specific file in your workspace. #file : Points to a specific file in your workspace. #file #codebase: All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal) but still want to reference the full solution. #codebase : All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal ) but still want to reference the full solution. #codebase @workspace @terminal #editor: Source code in the editor’s viewport (visible part). #editor : Source code in the editor’s viewport (visible part). #editor #git: Current git repository: branch, remotes, path, etc. #git : Current git repository: branch, remotes, path, etc. #git #selection: The currently selected code. #selection : The currently selected code. #selection #terminalLastCommand: Last run command in the editor’s terminal. #terminalLastCommand : Last run command in the editor’s terminal. #terminalLastCommand #terminalSelection: Selection in the editor's terminal. #terminalSelection : Selection in the editor's terminal. #terminalSelection Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file , use keyboard navigation again to pick one of the suggested files. Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file , use keyboard navigation again to pick one of the suggested files. Call the Most Often Used Actions Quickly With Slash Commands Chatting with your code using natural language is fun, but having the option to call often-used actions using handy shortcuts is even better. Compare typing the full message “Explain how selected code works” versus typing “/”, then using keyboard arrows to pick /explain from the popup overlay. /explain Another benefit of using the predefined syntax for commands is the confidence that GitHub Copilot understands our intent 100% correctly (natural language might have some ambiguity). There are a bunch of slash commands available. You can use them in conjunction with referencing the chat participant to provide the desired scope. Some of the commands are: /help: Help with available slash commands, chat participants, chat variables, and more. /help : Help with available slash commands, chat participants, chat variables, and more. /help /doc: Generate documentation for the code. /doc : Generate documentation for the code. /doc /explain: Explain how the code works (or get help with terminal commands if you prepend @terminal). /explain : Explain how the code works (or get help with terminal commands if you prepend @terminal). /explain /fix: Optimize and/or fix issues in the code. /fix : Optimize and/or fix issues in the code. /fix /tests: Create unit tests for the code. /tests : Create unit tests for the code. /tests /new: Scaffold a new workspace. /new : Scaffold a new workspace. /new Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “/explain. Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “ /explain . Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “ /explain Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Summary Use chat participants, chat variables, and slash commands to maintain full control over the conversation context, ensure correct and consistent understanding of your intentions, and ultimately chat and code faster! Start your free GitHub Copilot trial here: https://aka.ms/try-github-copilot https://aka.ms/try-github-copilot References https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_84#_chat-agents https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_84#_chat-agents https://code.visualstudio.com/updates/v1_84#_chat-agents https://code.visualstudio.com/updates/v1_84#_chat-agents

How to Leverage GitHub Copilot Chat Syntax: Chat Participants, Chat Variables, and Slash Commands

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Cách tận dụng Cú pháp trò chuyện Copilot của GitHub: Người tham gia trò chuyện, Biến trò chuyện và Lệnh gạch chéo

如何利用 GitHub Copilot 聊天语法：聊天参与者、聊天变量和斜线命令

Over the last decade, most SAAS market leaders that started as point solutions solving a narrow use case have expanded to multiple use cases and personas to serve, growing feature sets drastically. This has enabled them to expand their TAM and sell more licenses. But it has come at the cost of increasing product complexity. Often, making the product relevant to expert users but complicated for users who only want to get specific jobs done from the product. Salesforce user UI — As the product serves multiple use cases and personas in an enterprise, user journey, and navigation are complex and often well suited only to the expert user. AI Co-Pilots to Increase User Efficiency Users expect LLM-powered co-pilots to be more than just improved help chatbots powered through a RAG. Copilots can be extremely effective in abstracting out the complexities and breadth of any SAAS, giving users a shortcut to outcomes in your product. Having worked and observed multiple SAAS teams figure out their co-pilot strategy, I analyzed three must-have use cases that SAAS copilot teams must focus on. Use Case 1: Enhanced Help -> Support That Actually… Supports Users today expect specific answers rather than sifting through endless pages of documentation. A copilot powered by Retrieval-Augmented Generation (RAG) on top of the existing knowledge base and community articles can elevate the support experience. Your copilot can intelligently parse through your knowledge base, understanding the context of the user’s query and retrieving the most pertinent information. However, the secret sauce to building an effective support experience is beyond just improving RAG accuracy and more on: Improving the breadth and quality of data sources by investing in community-generated content.


Providing customers an easy escalation path to human experts in experts. Use Case 2: From Data Overload to Just Tell Me -> Actionable Insights Data dashboards have been the key must-have feature set for all SAAS tools, but the dashboards deluge in SAAS can be overwhelming. Each user persona for SAAS has distinct insights needs — perhaps they need a specific view or a specific data point but generic dashboards create upfront friction for such users. Imagine a scenario where, instead of staring at charts and graphs, trying to decipher trends, your users are greeted by a copilot that proactively highlights key insights or answers the data questions and presents the right graph. The LLMs not only share the data but also explain the insight building on top of the data. “Your customer acquisition cost has decreased by 15% this quarter, primarily due to the success of your recent email campaign. Consider allocating more resources to similar initiatives.” A great example of this is TurboTax using LLMs to explain to users their deduction and tax amounts. By transforming data into actionable insights, the copilot becomes an indispensable strategic partner. It doesn’t just present what happened; it explains why it happened and suggests what should happen next. Use Case 3: Task Automation: From Complexity to Simplicity Users value platforms that can reduce their cognitive load and automate complex tasks. Enter the realm of LLM (Large Language Model) agents within your copilot — AI entities capable of understanding, planning, and executing multi-step processes on behalf of the user. Consider the often daunting task of setting up a new account with all the necessary configurations. Instead of navigating through multiple menus and options, users can simply instruct the copilot: “Set up a new account for our marketing team XX with standard permissions.” The copilot, leveraging agents, breaks down this request into agents and tools, executes them in the correct sequence, and reports back with a summary of actions taken. This not only saves time but also minimizes the risk of configuration errors. However, there have been 2 challenges with the current agentic AI setups. A. They only orchestrate well for a narrow set of actions and… B. They fail often due to hallucinations compounding at each step of the agentic workflow. Thus, it becomes critical to narrow implementation to the most recurring tasks and linear tasks and solve them well. Ramp experimented with a good example of this. Ramp tour guide — an AI Agent that can show you how to do anything on Ramp. It overcomes hallucination and error problem by keeping user in loop and assisting them through the workflow As the capabilities of AI advance, delegating narrow tasks will shift to delegating entire workflows to AI. In the future, users could run these tasks without even logging into your SAAS product, all through a chat interface or as simple as just sending an email. Conclusion: The Copilot Imperative Integrating a copilot into your SaaS offering is essential for meeting and exceeding modern user expectations. From providing enhanced support to delivering intelligent insights and automating complex tasks, a well-designed copilot transforms the user experience. It’s not just an AI assistant; it’s a partner that empowers your users, making them more efficient, informed, and satisfied. The question is no longer whether you need a copilot. The real question is: can you afford not to have one? Copilot For Saas Genai Over the last decade, most SAAS market leaders that started as point solutions solving a narrow use case have expanded to multiple use cases and personas to serve, growing feature sets drastically. This has enabled them to expand their TAM and sell more licenses. But it has come at the cost of increasing product complexity. Often, making the product relevant to expert users but complicated for users who only want to get specific jobs done from the product. Salesforce user UI — As the product serves multiple use cases and personas in an enterprise, user journey, and navigation are complex and often well suited only to the expert user. Salesforce user UI — As the product serves multiple use cases and personas in an enterprise, user journey, and navigation are complex and often well suited only to the expert user. AI Co-Pilots to Increase User Efficiency AI Co-Pilots to Increase User Efficiency Users expect LLM-powered co-pilots to be more than just improved help chatbots powered through a RAG. Copilots can be extremely effective in abstracting out the complexities and breadth of any SAAS, giving users a shortcut to outcomes in your product. Having worked and observed multiple SAAS teams figure out their co-pilot strategy, I analyzed three must-have use cases that SAAS copilot teams must focus on. Use Case 1: Enhanced Help -> Support That Actually… Supports Use Case 1: Enhanced Help -> Support That Actually… Supports Users today expect specific answers rather than sifting through endless pages of documentation. A copilot powered by Retrieval-Augmented Generation (RAG) on top of the existing knowledge base and community articles can elevate the support experience. Your copilot can intelligently parse through your knowledge base, understanding the context of the user’s query and retrieving the most pertinent information. However, the secret sauce to building an effective support experience is beyond just improving RAG accuracy and more on: Improving the breadth and quality of data sources by investing in community-generated content. Providing customers an easy escalation path to human experts in experts. Improving the breadth and quality of data sources by investing in community-generated content. Improving the breadth and quality of data sources by investing in community-generated content. Providing customers an easy escalation path to human experts in experts. Providing customers an easy escalation path to human experts in experts. Use Case 2: From Data Overload to Just Tell Me -> Actionable Insights Data dashboards have been the key must-have feature set for all SAAS tools, but the dashboards deluge in SAAS can be overwhelming. Each user persona for SAAS has distinct insights needs — perhaps they need a specific view or a specific data point but generic dashboards create upfront friction for such users. Imagine a scenario where, instead of staring at charts and graphs, trying to decipher trends, your users are greeted by a copilot that proactively highlights key insights or answers the data questions and presents the right graph. The LLMs not only share the data but also explain the insight building on top of the data. “Your customer acquisition cost has decreased by 15% this quarter, primarily due to the success of your recent email campaign. Consider allocating more resources to similar initiatives.” A great example of this is TurboTax using LLMs to explain to users their deduction and tax amounts. TurboTax using LLMs TurboTax using LLMs By transforming data into actionable insights, the copilot becomes an indispensable strategic partner. It doesn’t just present what happened; it explains why it happened and suggests what should happen next. Use Case 3: Task Automation: From Complexity to Simplicity Users value platforms that can reduce their cognitive load and automate complex tasks. Enter the realm of LLM (Large Language Model) agents within your copilot — AI entities capable of understanding, planning, and executing multi-step processes on behalf of the user. Consider the often daunting task of setting up a new account with all the necessary configurations. Instead of navigating through multiple menus and options, users can simply instruct the copilot: “Set up a new account for our marketing team XX with standard permissions.” “Set up a new account for our marketing team XX with standard permissions.” “Set up a new account for our marketing team XX with standard permissions.” The copilot, leveraging agents, breaks down this request into agents and tools, executes them in the correct sequence, and reports back with a summary of actions taken. This not only saves time but also minimizes the risk of configuration errors. However, there have been 2 challenges with the current agentic AI setups. A. They only orchestrate well for a narrow set of actions and… A. B. They fail often due to hallucinations compounding at each step of the agentic workflow. Thus, it becomes critical to narrow implementation to the most recurring tasks and linear tasks and solve them well. Ramp experimented with a good example of this. B. Ramp tour guide — an AI Agent that can show you how to do anything on Ramp. It overcomes hallucination and error problem by keeping user in loop and assisting them through the workflow Ramp tour guide — an AI Agent that can show you how to do anything on Ramp. It overcomes hallucination and error problem by keeping user in loop and assisting them through the workflow Ramp tour guide — an AI Agent that can show you how to do anything on Ramp. It overcomes hallucination and error problem by keeping user in loop and assisting them through the workflow Ramp tour guide Ramp tour guide As the capabilities of AI advance, delegating narrow tasks will shift to delegating entire workflows to AI. In the future, users could run these tasks without even logging into your SAAS product, all through a chat interface or as simple as just sending an email. Conclusion: The Copilot Imperative Integrating a copilot into your SaaS offering is essential for meeting and exceeding modern user expectations. From providing enhanced support to delivering intelligent insights and automating complex tasks, a well-designed copilot transforms the user experience. It’s not just an AI assistant; it’s a partner that empowers your users, making them more efficient, informed, and satisfied. The question is no longer whether you need a copilot. The real question is: can you afford not to have one? Copilot For Saas Copilot For Saas Genai Genai

Copilots in Modern SaaS: How to Simplify User Journeys With AI

HIX EssayGPT is a comprehensive AI essay writing copilot that revolutionizes the essay writing process through its suite of easy-to-use tools. In today's academic landscape, where workloads are increasing, the need for writing assistance has become crucial. Unlike previous AI essay tools, HIX EssayGPT offers a holistic solution that caters to various aspects of essay writing. Let's explore HIX EssayGPT's impressive capabilities and how it enhances the essay writing experience. What Is HIX EssayGPT? HIX EssayGPT developed by HIX.AI, aims to simplify the entire essay writing process as an all-in-one essay writing assistant, guiding users from beginning to end. It includes a Generator for creating outlines or drafts, a Writer workspace for editing, research, citation insertion, plagiarism checks, and formatting, and ScholarChat, a research chatbot with access to 200M+ scholarly resources and science-based answers for essay creation with correct citations. HIX EssayGPT streamlines essay writing tasks by combining these functions into one AI system, providing users with an efficient platform to craft, edit, and improve their essays conveniently. HIX EssayGPT's Tools and Capabilities HIX EssayGPT provides three main tools: Generator, Writer, and ScholarChat. Each tool serves a specific purpose and contributes to the overall effectiveness of the platform. Generator: AI essay generator for creating high-quality, plagiarism-free essays HIX EssayGPT's Generator allows users to effortlessly create high-quality essays without the worry of plagiarism. Users can input the desired essay topic and any specific keywords or details. The Generator then utilizes its vast knowledge base and advanced natural language processing techniques to generate a well-structured and comprehensive final draft. Generate and refine essays on various topics Generator allows you to effortlessly generate essays on a wide range of topics. Whether you're writing an essay for an academic assignment, a research paper, or a personal project, HIX EssayGPT provides you with the tools to create well-structured and informative content. Plagiarism-free essay writing HIX EssayGPT guarantees the production of plagiarism-free essays. The AI algorithms and extensive training behind HIX EssayGPT ensure that the generated content is original and unique. This gives students, academics, and professionals confidence in submitting their work without the risk of being flagged for academic dishonesty. Multilingual capabilities With HIX EssayGPT, users have the ability to generate and write essays in over 50 languages. This feature is particularly beneficial for students, academics, and professionals who are non-native English speakers or who are required to write in languages other than English. HIX EssayGPT's multilingual capabilities make it accessible to a diverse range of users. Undetectable mode Generator excels in infusing AI-generated text with a human touch, ensuring that the AI content flows naturally and cohesively. This humanization process elevates the essay's quality, blurring the line between machine-generated and human-written text and helping you bypass AI detectors like GPTZero and Turnitin. Writer: Workspace for writing and editing essays HIX EssayGPT's Writer offers users a complete suite of AI-driven tools for polishing and improving their writing. These advanced functions allow users to effortlessly enhance the quality of their essays. Included in the workspace are the following features: Access an extensive database for research Research is a crucial aspect of essay writing, and HIX EssayGPT understands the importance of accessing credible sources. With its extensive database of academic resources, you can easily find relevant publications, scientific studies, and articles to support your arguments and provide evidence for your essay. In-text citations made easy Properly citing your sources is essential for academic integrity. HIX EssayGPT simplifies the process by automatically generating accurate in-text citations in popular citation styles such as APA, MLA, or Chicago. This feature saves you time and ensures that your essay meets the required citation guidelines. AI auto-complete for when you're stuck Writer's block can be a frustrating experience, but HIX EssayGPT's AI auto-complete feature is here to help. It provides tailored suggestions for words, phrases, and sentences that seamlessly fit into the context of your writing. This feature boosts your creativity and helps you overcome writer's block. Grammar check for polished submissions Submitting a well-written essay is crucial for academic success. HIX EssayGPT's grammar check feature helps you identify and correct grammar, punctuation, stylistic, and spelling mistakes. By ensuring your essay is error-free, you can present a polished and professional piece of work. ScholarChat: AI research assistant chatbot for research support ScholarChat is an invaluable research chatbot that empowers users to ask research questions and receive science-based answers backed by credible research. With access to an extensive database of over 200 million papers, ScholarChat enhances the research process by providing users with accurate and reliable information to strengthen their arguments. Whether seeking evidence or clarifying complex concepts, ScholarChat equips users with the knowledge needed to produce well-informed and compelling essays with precise citations. Who Can Benefit from HIX EssayGPT? Students Students can benefit from HIX EssayGPT by saving time and effort in generating essay drafts, allowing them to manage their busy schedules more efficiently. The automatic addition of citations and bibliographies in essays and scholarly papers simplifies the writing process and ensures adherence to proper citation practices. Furthermore, access to 24/7 research help from ScholarChat provides students with support on complex topics. Educators Educators can utilize HIX EssayGPT as an always-on research assistant, such as ScholarChat, to advise students globally. It allows them to create sample essays for teaching purposes or classroom discussions. HIX EssayGPT's tools can assist educators in providing valuable guidance and resources to their students. Academics HIX EssayGPT's Writer tool enables academics to effectively create top-notch materials like journal articles and literature reviews. By assisting in locating and integrating appropriate citations seamlessly, the platform guarantees the precision and scholarly integrity of its output. Researchers Researchers can benefit from HIX EssayGPT by accessing decades of studies through ScholarChat. It provides fresh perspectives to aid in exploring new research directions and offers valuable support in conducting in-depth research. Conclusion HIX EssayGPT is a powerful AI essay writing assistant that offers a range of features to enhance your essay writing process. From generating and refining essays to conducting research, citation support, grammar checking, and more, HIX EssayGPT is a versatile tool for students, researchers, and professional writers. Its advanced AI chatbot, ScholarChat, provides additional assistance and ensures that you have the necessary resources to create high-quality essays. Start using HIX EssayGPT today and revolutionize your essay writing experience. HIX EssayGPT is a comprehensive AI essay writing copilot that revolutionizes the essay writing process through its suite of easy-to-use tools. In today's academic landscape, where workloads are increasing, the need for writing assistance has become crucial. Unlike previous AI essay tools, HIX EssayGPT offers a holistic solution that caters to various aspects of essay writing. Let's explore HIX EssayGPT's impressive capabilities and how it enhances the essay writing experience. What Is HIX EssayGPT? HIX EssayGPT developed by HIX.AI, aims to simplify the entire essay writing process as an all-in-one essay writing assistant, guiding users from beginning to end. HIX EssayGPT HIX EssayGPT It includes a Generator for creating outlines or drafts, a Writer workspace for editing, research, citation insertion, plagiarism checks, and formatting, and ScholarChat, a research chatbot with access to 200M+ scholarly resources and science-based answers for essay creation with correct citations. HIX EssayGPT streamlines essay writing tasks by combining these functions into one AI system, providing users with an efficient platform to craft, edit, and improve their essays conveniently. HIX EssayGPT's Tools and Capabilities HIX EssayGPT provides three main tools: Generator, Writer, and ScholarChat. Each tool serves a specific purpose and contributes to the overall effectiveness of the platform. Generator: AI essay generator for creating high-quality, plagiarism-free essays HIX EssayGPT's Generator allows users to effortlessly create high-quality essays without the worry of plagiarism. Users can input the desired essay topic and any specific keywords or details. Generator Generator The Generator then utilizes its vast knowledge base and advanced natural language processing techniques to generate a well-structured and comprehensive final draft. Generate and refine essays on various topics Generator allows you to effortlessly generate essays on a wide range of topics. Whether you're writing an essay for an academic assignment, a research paper, or a personal project, HIX EssayGPT provides you with the tools to create well-structured and informative content. Plagiarism-free essay writing HIX EssayGPT guarantees the production of plagiarism-free essays. The AI algorithms and extensive training behind HIX EssayGPT ensure that the generated content is original and unique. This gives students, academics, and professionals confidence in submitting their work without the risk of being flagged for academic dishonesty. Multilingual capabilities With HIX EssayGPT, users have the ability to generate and write essays in over 50 languages. This feature is particularly beneficial for students, academics, and professionals who are non-native English speakers or who are required to write in languages other than English. HIX EssayGPT's multilingual capabilities make it accessible to a diverse range of users. Undetectable mode Generator excels in infusing AI-generated text with a human touch, ensuring that the AI content flows naturally and cohesively. This humanization process elevates the essay's quality, blurring the line between machine-generated and human-written text and helping you bypass AI detectors like GPTZero and Turnitin. Writer: Workspace for writing and editing essays HIX EssayGPT's Writer offers users a complete suite of AI-driven tools for polishing and improving their writing. These advanced functions allow users to effortlessly enhance the quality of their essays. Included in the workspace are the following features: Writer Writer Access an extensive database for research Research is a crucial aspect of essay writing, and HIX EssayGPT understands the importance of accessing credible sources. With its extensive database of academic resources, you can easily find relevant publications, scientific studies, and articles to support your arguments and provide evidence for your essay. In-text citations made easy Properly citing your sources is essential for academic integrity. HIX EssayGPT simplifies the process by automatically generating accurate in-text citations in popular citation styles such as APA, MLA, or Chicago. This feature saves you time and ensures that your essay meets the required citation guidelines. AI auto-complete for when you're stuck Writer's block can be a frustrating experience, but HIX EssayGPT's AI auto-complete feature is here to help. It provides tailored suggestions for words, phrases, and sentences that seamlessly fit into the context of your writing. This feature boosts your creativity and helps you overcome writer's block. Grammar check for polished submissions Submitting a well-written essay is crucial for academic success. HIX EssayGPT's grammar check feature helps you identify and correct grammar, punctuation, stylistic, and spelling mistakes. By ensuring your essay is error-free, you can present a polished and professional piece of work. ScholarChat: AI research assistant chatbot for research support ScholarChat is an invaluable research chatbot that empowers users to ask research questions and receive science-based answers backed by credible research. ScholarChat ScholarChat With access to an extensive database of over 200 million papers, ScholarChat enhances the research process by providing users with accurate and reliable information to strengthen their arguments. Whether seeking evidence or clarifying complex concepts, ScholarChat equips users with the knowledge needed to produce well-informed and compelling essays with precise citations. Who Can Benefit from HIX EssayGPT? Students Students can benefit from HIX EssayGPT by saving time and effort in generating essay drafts, allowing them to manage their busy schedules more efficiently. The automatic addition of citations and bibliographies in essays and scholarly papers simplifies the writing process and ensures adherence to proper citation practices. Furthermore, access to 24/7 research help from ScholarChat provides students with support on complex topics. Educators Educators can utilize HIX EssayGPT as an always-on research assistant, such as ScholarChat, to advise students globally. It allows them to create sample essays for teaching purposes or classroom discussions. HIX EssayGPT's tools can assist educators in providing valuable guidance and resources to their students. Academics HIX EssayGPT's Writer tool enables academics to effectively create top-notch materials like journal articles and literature reviews. By assisting in locating and integrating appropriate citations seamlessly, the platform guarantees the precision and scholarly integrity of its output. Researchers Researchers can benefit from HIX EssayGPT by accessing decades of studies through ScholarChat. It provides fresh perspectives to aid in exploring new research directions and offers valuable support in conducting in-depth research. Conclusion HIX EssayGPT is a powerful AI essay writing assistant that offers a range of features to enhance your essay writing process. From generating and refining essays to conducting research, citation support, grammar checking, and more, HIX EssayGPT is a versatile tool for students, researchers, and professional writers. Its advanced AI chatbot, ScholarChat, provides additional assistance and ensures that you have the necessary resources to create high-quality essays. Start using HIX EssayGPT today and revolutionize your essay writing experience.

HIX EssayGPT Review: A Top-Notch All-in-one AI Essay Writing Copilot

The Noonification: Behind the Scenes of Github Copilots Code Vulnerability Management  (5/28/2024)

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