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Reverse engineer 45 years of mainframe code in one day? Toyota USA did—using Amazon Q Developer to scan hundreds of COBOL modules, generating documentation that normally takes months. The result? Agentic AI with AWS Transform + record-time migration.

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20,081 просмотров • 4 месяцев назад •via X (Twitter)

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Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.
2:10

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Santiago

22,104 просмотров • 1 год назад

📁Mo Gawdat, former Google X executive, says AI is no longer just writing code, it is correcting human mathematics. After 56 years using the same matrix multiplication method, AI realized the approach was flawed. It did not optimize software. It invented new math. The result was a 23% performance boost and the removal of hundreds of millions of dollars in costs and energy use.
0:55

📁Mo Gawdat, former Google X executive, says AI is no longer just writing code, it is correcting human mathematics. After 56 years using the same matrix multiplication method, AI realized the approach was flawed. It did not optimize software. It invented new math. The result was a 23% performance boost and the removal of hundreds of millions of dollars in costs and energy use.

Jon Hernandez

136,455 просмотров • 7 месяцев назад

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.
8:29

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 просмотров • 1 месяц назад

Brazil's largest bank, Itaú, deployed Devin across their whole SDLC. This is one of the largest AI agent rollouts anywhere in global finance. Itaú has 17,000+ engineers and hundreds of thousands of repos (yes, even COBOL). Even with that scale, they achieved results like: • 5-6x faster migration projects • 70% of security vulnerabilities from static analysis auto-remediated • 2x'ed test coverage • 300k+ repos documented This really shows what's possible with AI software engineering agents at enterprise scale.
1:47

Brazil's largest bank, Itaú, deployed Devin across their whole SDLC. This is one of the largest AI agent rollouts anywhere in global finance. Itaú has 17,000+ engineers and hundreds of thousands of repos (yes, even COBOL). Even with that scale, they achieved results like: • 5-6x faster migration projects • 70% of security vulnerabilities from static analysis auto-remediated • 2x'ed test coverage • 300k+ repos documented This really shows what's possible with AI software engineering agents at enterprise scale.

Cognition

67,352 просмотров • 8 месяцев назад

“The Coatue team mapped out cloud revenue market share, Oracle at 5%, Amazon at 44% with AWS, Google 19%, Microsoft 30%. Next to that, they showed NVIDIA GPU allocation: Microsoft and Google match their cloud share. Amazon is 44% cloud revenue but only 20% GPUs. Oracle jumps from 5% to 19%. Cori comes out of nowhere at 11%.” “One obvious takeaway is that Amazon has half the share of GPUs than their share of AWS. So that could mean one of two things, either AWS is behind in AI, that could be one, or they're pursuing a different hardware strategy than its competitors.” — Bill Gurley Bill Gurley and Thomas Laffont Thomas Laffont
1:48

“The Coatue team mapped out cloud revenue market share, Oracle at 5%, Amazon at 44% with AWS, Google 19%, Microsoft 30%. Next to that, they showed NVIDIA GPU allocation: Microsoft and Google match their cloud share. Amazon is 44% cloud revenue but only 20% GPUs. Oracle jumps from 5% to 19%. Cori comes out of nowhere at 11%.” “One obvious takeaway is that Amazon has half the share of GPUs than their share of AWS. So that could mean one of two things, either AWS is behind in AI, that could be one, or they're pursuing a different hardware strategy than its competitors.” — Bill Gurley Bill Gurley and Thomas Laffont Thomas Laffont

bg2clips

180,310 просмотров • 1 год назад

> do you understand what Claude Opus 4.8 just did to the Google job market > a senior Google engineer with 11 years of experience > stacked 32 Claude skills on top of his workflow > 8hrs → 2-3hrs per day > $95K developer vs $300K AI architect > same job. different stack. very different salary > the exact skill list is here 👇
0:28

> do you understand what Claude Opus 4.8 just did to the Google job market > a senior Google engineer with 11 years of experience > stacked 32 Claude skills on top of his workflow > 8hrs → 2-3hrs per day > $95K developer vs $300K AI architect > same job. different stack. very different salary > the exact skill list is here 👇

Mr. Buzzoni

287,565 просмотров • 2 месяцев назад

With AI, "we did 1,000,000,000 years of PHD time in one year." Nobel Prize Winner and Google DeepMind CEO Demis Hassabis on AlphaFold, the groundbreaking AI system developed to solve one of biology’s most complex problems: protein folding.
0:36

With AI, "we did 1,000,000,000 years of PHD time in one year." Nobel Prize Winner and Google DeepMind CEO Demis Hassabis on AlphaFold, the groundbreaking AI system developed to solve one of biology’s most complex problems: protein folding.

Reid Hoffman

113,494 просмотров • 1 год назад

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)
1:15

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

96,980 просмотров • 1 месяц назад

🎉Today, I'm launching Docny (Docny). Documentation is one of the most important parts of building a technical product, yet it's often the hardest thing to keep up to date. Teams write docs in one place, manage product work in another, support customers somewhere else, and maintain API references separately. I started building Docny to bring those workflows together and help developers keep their docs in sync using AI. With Docny, teams can create beautiful documentation, API references, guides, and developer portals while connecting them to the tools they already use. 🛳️ After months of building, redesigning, testing, and learning, it's finally live.
2:02

🎉Today, I'm launching Docny (Docny). Documentation is one of the most important parts of building a technical product, yet it's often the hardest thing to keep up to date. Teams write docs in one place, manage product work in another, support customers somewhere else, and maintain API references separately. I started building Docny to bring those workflows together and help developers keep their docs in sync using AI. With Docny, teams can create beautiful documentation, API references, guides, and developer portals while connecting them to the tools they already use. 🛳️ After months of building, redesigning, testing, and learning, it's finally live.

Opemipo Disu

83,785 просмотров • 7 дней назад

Microsoft denies rewriting Windows 11 using AI after an employee’s “one engineer, one month, one million lines of code” post on LinkedIn causes outrage. "Windows is *NOT* being rewritten in Rust with AI" -- Microsoft. Since this has Microsoft's attention, it's the right time to remind everyone about the lack of "native" code for UI-level features in Windows 11, as everything is moving to WebView2. For example, in recent Windows 11 builds, Notification Center's Agenda view is built using WebView2, and you'll notice spike in web resources (Edge-related processes) when you open Notification Center.
0:44

Microsoft denies rewriting Windows 11 using AI after an employee’s “one engineer, one month, one million lines of code” post on LinkedIn causes outrage. "Windows is *NOT* being rewritten in Rust with AI" -- Microsoft. Since this has Microsoft's attention, it's the right time to remind everyone about the lack of "native" code for UI-level features in Windows 11, as everything is moving to WebView2. For example, in recent Windows 11 builds, Notification Center's Agenda view is built using WebView2, and you'll notice spike in web resources (Edge-related processes) when you open Notification Center.

Windows Latest

564,454 просмотров • 7 месяцев назад

Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels that are much faster than existing CUDA kernels commonly used in production. We believe that fundamentally, AI systems can and should be as resource-efficient as the human brain, and that the best path to achieve this efficiency is to use AI to make AI more efficient! We are excited to publish our paper, The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition. We also release a dataset of over 17,000 verified CUDA kernels produced by The AI CUDA Engineer. Paper: Kernel Archive Webpage: HuggingFace Dataset: The AI CUDA Engineer utilizes evolutionary LLM-driven code optimization to autonomously improve the runtime of machine learning operations. Our system is not only able to convert PyTorch code into CUDA kernels, but through the use of evolution, it can also optimize the runtime performance of CUDA kernels, fuse multiple operations, and even discover novel solutions for writing efficient CUDA operations by learning from past innovations! We believe The AI CUDA Engineer opens a new era of AI-driven acceleration of AI and automated inference time optimization. We (Robert Lange, Aaditya Prasad 🇺🇸, sssss, Maxence Faldor, Yujin Tang, hardmaru) are excited to continue Sakana AI's mission of leveraging AI to improve AI.
1:21

Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels that are much faster than existing CUDA kernels commonly used in production. We believe that fundamentally, AI systems can and should be as resource-efficient as the human brain, and that the best path to achieve this efficiency is to use AI to make AI more efficient! We are excited to publish our paper, The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition. We also release a dataset of over 17,000 verified CUDA kernels produced by The AI CUDA Engineer. Paper: Kernel Archive Webpage: HuggingFace Dataset: The AI CUDA Engineer utilizes evolutionary LLM-driven code optimization to autonomously improve the runtime of machine learning operations. Our system is not only able to convert PyTorch code into CUDA kernels, but through the use of evolution, it can also optimize the runtime performance of CUDA kernels, fuse multiple operations, and even discover novel solutions for writing efficient CUDA operations by learning from past innovations! We believe The AI CUDA Engineer opens a new era of AI-driven acceleration of AI and automated inference time optimization. We (Robert Lange, Aaditya Prasad 🇺🇸, sssss, Maxence Faldor, Yujin Tang, hardmaru) are excited to continue Sakana AI's mission of leveraging AI to improve AI.

Sakana AI

1,160,190 просмотров • 1 год назад

📁 Andrew Ng, cofounder of Coursera, says that agentic workflows let AI work iteratively to produce better outcomes than generating text in one pass. He notes that this approach is more effective for complex tasks such as advising, compliance or coding. He concludes that we should not wait for AGI but use what today’s technology can already deliver in the coming months.
1:02

📁 Andrew Ng, cofounder of Coursera, says that agentic workflows let AI work iteratively to produce better outcomes than generating text in one pass. He notes that this approach is more effective for complex tasks such as advising, compliance or coding. He concludes that we should not wait for AGI but use what today’s technology can already deliver in the coming months.

Jon Hernandez

19,292 просмотров • 8 месяцев назад

Andrew Ng and AMD CEO leaked the plan on how anyone becomes a $40k AI engineer from scratch: 00:00 - one engineer with AI replaces 5 employees 14:15 - one day with AI replaces 3 months of work 23:28 - how to build a profitable company for $20 This free 26-minute talk is worth more than a $90,000 Stanford AI degree. Watch it today, then read the full breakdown in the article below.
27:06

Andrew Ng and AMD CEO leaked the plan on how anyone becomes a $40k AI engineer from scratch: 00:00 - one engineer with AI replaces 5 employees 14:15 - one day with AI replaces 3 months of work 23:28 - how to build a profitable company for $20 This free 26-minute talk is worth more than a $90,000 Stanford AI degree. Watch it today, then read the full breakdown in the article below.

Noisy

21,909 просмотров • 22 дней назад

CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."
2:13

CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."

WOLF

45,256 просмотров • 3 месяцев назад

“Today’s LLMs are the ‘Mainframe Computers’ of our generation” I was on Bloomberg TV today discussing Sakana AI, and to share my view that today’s LLMs are our generation’s “Mainframe Computers”. We are still in the very early stages of AI, and it is inevitable, due to market competition and global innovation (especially from those innovating with resource constraints), that this technology will become a million times more efficient. There is a narrative established in Silicon Valley that AI is a “Winner Takes All” technology, and that scaling up existing models and consuming ever greater resources will require (and even justify) the largest investments of our generation, in order to “win” the AI race. In contrast, I believe that AI is not a “winner takes all” technology. LLMs will be commoditized, become vastly more efficient, and made widely available in all countries. Ultimately, there will be thousands, if not millions, of AI models used by everyone. Just like with the evolution of early clunky mainframe computers to modern computing, how we use AI today will look very different in a few years (or even a year from now), compared to today’s ‘clunky’ LLMs.
7:17

“Today’s LLMs are the ‘Mainframe Computers’ of our generation” I was on Bloomberg TV today discussing Sakana AI, and to share my view that today’s LLMs are our generation’s “Mainframe Computers”. We are still in the very early stages of AI, and it is inevitable, due to market competition and global innovation (especially from those innovating with resource constraints), that this technology will become a million times more efficient. There is a narrative established in Silicon Valley that AI is a “Winner Takes All” technology, and that scaling up existing models and consuming ever greater resources will require (and even justify) the largest investments of our generation, in order to “win” the AI race. In contrast, I believe that AI is not a “winner takes all” technology. LLMs will be commoditized, become vastly more efficient, and made widely available in all countries. Ultimately, there will be thousands, if not millions, of AI models used by everyone. Just like with the evolution of early clunky mainframe computers to modern computing, how we use AI today will look very different in a few years (or even a year from now), compared to today’s ‘clunky’ LLMs.

hardmaru

165,382 просмотров • 1 год назад

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.
1:29

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

Adam Selipsky

121,755 просмотров • 2 лет назад

A cinematic travel film usually takes hours of planning. I wanted to see how much of that workflow AI could handle. Using Skywork , I went from a simple idea to a complete Japan Spring travel ad with story planning, visual consistency, and editing—all in one workspace. Here's the final result. #SkyworkAI #AIVideo #Filmmaking
0:39

A cinematic travel film usually takes hours of planning. I wanted to see how much of that workflow AI could handle. Using Skywork , I went from a simple idea to a complete Japan Spring travel ad with story planning, visual consistency, and editing—all in one workspace. Here's the final result. #SkyworkAI #AIVideo #Filmmaking

Ai Doctor

29,348 просмотров • 11 дней назад

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:
1:28

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

28,946 просмотров • 1 год назад