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CodeRabbit Adds Support for VS Code to AI Code Review Platform

CodeRabbit today added support for the open-source Visual Studio Code editor to its platform that employs generative artificial intelligence (GenAI) to review code.

Company CEO Harjot Gill said adding support for VS Code will significantly expand the reach of a platform that is already integrated with AI coding tools such as Cursor and Windsurf.

CodeRabbit is primarily used within any Git-based repository to review code as commits are being made. That approach ensures that all code destined for a production environment has been reviewed by an AI platform that is accessed via a natural language chat interface.

Alternatively, application developers can also embed CodeRabbit to review code in real time within the tooling they are using to write code.

The overall goal is to surface routine mistakes in a way that developers can either catch themselves or enable a human reviewer of code to focus on more complex issues, said Gill.

That capability is going to be especially critical in an era where the volume of code being generated using AI tools will far exceed the ability of anyone to manually review that code, he added.

CodeRabbit traverses each code repository in the Git platform, prior to pull requests and related Jira/Linear issues, using a code graph analysis capability that identifies code dependencies across files, and custom instructions using Abstract Syntax Tree (AST) patterns. Used by nearly 5,000 customers to cut the time spent reviewing code in half, CodeRabbit can also pull dynamic data from external sources such as a large language model (LLM) as needed, noted Gill.

Additionally, CodeRabbit will generate crisp summaries of code changes, whether they affect a single line or add an entire new feature.

In contrast, existing code review tools are much more limited, he added. Modern code review cycles require developers to understand not just the quality and behavior of new code that has been generated, but also adhere to coding practices at the organization level and within the syntax of individual programming languages in addition to understanding file dependencies that impact other parts of the code and any conformity to security policies that might be required.

It’s not clear how widely AI is being applied to DevOps workflows, but a Futurum Research survey finds 41% of respondents now expect generative AI tools and platforms will be used to generate, review and test code.

While much of the initial focus on using AI to automate tasks has been on writing code, AI tools that review code will become just as widely adopted, if for no other reason,n code reviews are a task that not many developers enjoy, said Gill. Many organizations have historically set up two-person teams to review each other’s code. Now, more of that time can be allocated to either writing code or fine-tuning the code that might have been generated by an AI tool.

It’s still relatively early as far as the adoption of AI in DevOps workflow, but as more tedious tasks continue to be automated, it’s becoming clear that much of the drudgery that often conspires to slow down the pace of application development will steadily be reduced if not outright eliminated.

May 14, 2025
Survey: Software Quality Continues to Be Sacrificed in the Name of Speed

A global survey of 2,750 IT, DevOps leaders and software developers finds nearly half of respondents (45%) admit their organization prioritizes delivery speed over software quality, with 63% acknowledging their organization deploys code without fully testing it. Only 13% of respondents said their organization is focused on improving software quality.

Conducted by Censuswide on behalf of Tricentis, a provider of an application testing automation platform, more than one-third (34%) of respondents say ongoing maintenance and unresolved technical debt are the largest obstacles to delivering high-quality software. Other issues include poor communication between software development and quality assurance teams (33%) and a disconnect between leadership and development teams (29%).

The primary reasons cited for not testing code are the need to expedite release cycles (46%) and accidental slips (40%). Just under a quarter (24%) also admitted they are not confident in their test coverage.

More troubling still, 81% said software quality issues cost their business between $500,000 and $5 million every year, with nearly 40% estimating losses of more than $1 million. Two-thirds (66%) of respondents also conceded their organization is likely to suffer an outage or major disruption in the next year, with nearly a quarter (23%) deeming their organization to be at extreme risk.

Many respondents noted there is a lack of clear quality metrics provided by the organization (29%), a disconnect between leadership and development teams over what defines quality (28%), and budget constraints (25%). The top five benefits they seek from better software quality include improved team efficiency (17%), stronger customer satisfaction and retention (15%), reduced security and compliance risk (14%), increased revenue (14%), and faster delivery (14%).

Lee McClendon, chief digital and technology officer for Tricentis, said when it comes to software engineering there continues to be too much emphasis on speed over quality. The irony is that addressing quality issues in the latter stages of a DevOps workflow only serves to slow organizations down further when work needs to be redone, he added.

There is simply too much focus on metrics that track how fast software is developed versus how well, noted McClendon.

On the plus side, a total of 80% of respondents said they believe that advances in artificial intelligence (AI) will enable them to improve software quality without adversely impacting the rate at which software is being developed. Nearly 90% also said they can effectively measure the return on investment on generative AI tools and platforms. However, two-thirds (66%) noted it takes investments in software development and delivery at least three years to pay off, with over one-third (34%) anticipating AI technology investments will take even longer.

The majority (82%) also said they are excited about AI agents taking on repetitive tasks in the software development lifecycle, while 84% believe AI will help teams deliver under increasingly tight deadlines. A full 89% said they trust AI agents to make decisions that impact software delivery. At the same time, bias remains a key concern for 38% of respondents, and the same percentage are wary of over-reliance on AI tools.

Finally, nearly all respondents expect autonomous testing will be useful for quality assurance (QA) testing, with the biggest impact being improving software performance (28%), overall quality (28%), analyzing test results (25%) and test case maintenance (23%).

Hopefully, the quality of applications will improve in the age of AI, but the one thing that is certain is that when it comes to achieving that goal there is still a lot of room for improvement.

May 13, 2025
Moderne Allies with Diffblue and Azul to Streamline App Modernization

Moderne today revealed a pair of alliances with Diffblue and Azul Systems that promise to accelerate application modernization initiatives.

As a provider of a platform that leverages an open-source OpenRewrite Lossless Semantic Trees (LSTs) project to create structured representations of complex codebases developed by Moderne, the alliances with Diffblue and Azul provide deeper integrations with unit testing tools and a Java code analytics platform, respectively.

Moderne CEO Jonathan Schneider said, for example, DevOps teams will be able to more easily test refactored code using Diffblue Cover testing tools that employ machine learning algorithms and reinforcement learning to automatically generate unit tests.

In a similar fashion, Azul, a provider of a Java virtual machine (JVM) platform, has developed Code Inventory, a module within the Azul Intelligence Cloud platform that surfaces insights into runtime environments. That integration will make it simpler for DevOps teams to identify, for example, dead code to reduce the overall footprint of a Java application, said Schneider.

Moderne uses the OpenRewrite LST platform to provide DevOps teams with a set of extensible recipes for refactoring legacy applications that can be used to programmatically modernize code bases. Those recipes are now being extended to include offerings from third-party vendors such as Diffblue and Azul to streamline application modernization workflows, said Schneider.

Additionally, Moderne has developed an artificial intelligence (AI) agent, dubbed Moddy, that automates tasks such as determining where payment processing is occurring within an application spanning multiple code bases. That capability is critical because it enables DevOps teams to leverage recipes to update code spanning multiple applications, rather than having to manually address each instance of that code one application at a time, noted Scheider.

It’s not clear how many application modernization initiatives organizations might either have already launched or are being considered, but during uncertain economic times, there is typically much greater sensitivity to the total cost of IT. One of the primary goals of such initiatives is to enable organizations to either move an application to a platform that is less expensive to run or, less ambitiously, simply make it consume fewer infrastructure resources.

Historically, the challenge has been that the level of manual effort required to achieve those goals can take months, sometimes even years, to complete. The OpenRewrite LST platform provides a more programmatic alternative that can be integrated with various generative AI tools and platforms to reduce the overall amount of manual effort that would otherwise be required.

The level of complexity involved with modernizing applications will, of course, vary widely. Moving an application from one virtual machine to another or updating it to run the latest version of Java is not nearly as complex an endeavor as trying to convert COBOL code into another programming language so that an application can run on another platform. Regardless of the scope of the project, however, as the amount of manual effort required continues to decline, the number of these initiatives that organizations might be willing to undertake should correspondingly increase.

May 13, 2025
Kong Adds Event Gateway to API Management Platform

Kong Inc. today added a gateway to its API management platform that is specifically designed to integrate event-driven applications using application programming interfaces (APIs).

Company CTO Marco Palladino said Kong Event Gateway enables software engineering teams to now apply the same fundamental approach used to integrate other classes of applications across event-driven applications. Kong Event Gateway is based on Kong Konnect, the same foundation that Kong uses to enable DevOps teams to invoke REST APIs alongside APIs for accessing large language models (LLMs).

In general, event-driven applications are both more complex to build and integrate than almost any other type of application. However, as more modern applications that process and analyze data in near real-time, based on, for example, messaging platforms such as Kafka, are built, the number of event-driven applications being built and deployed in recent years has steadily increased.

Kong Event Gateway makes it possible to invoke Kafka via HTTP APIs or as native Kafka services that communicate over the Kafka protocol. That latter native Kafka proxy option is currently in early access.

The overall goal is to simultaneously make it easier to reuse Kafka clusters in a way that can also be centrally governed more easily, said Palladino.

Additionally, traffic moving between EDA workflows and endpoints can be encrypted to ensure application security, he added.

In general, application management has become more challenging as the number and types of applications being deployed have become more diverse. DevOps teams today are typically managing a range of monolithic and microservices-based applications. Most of those applications are batch-oriented, but as digital business transformation initiatives have increased, there is now a higher percentage of applications that need to process data in near real-time using an event-driven architecture.

The Kong Konnect platform provides DevOps teams with a series of options for managing APIs using a combination of a service mesh, proxy software, ingress controllers and API gateways. Depending on the level of complexity and the number of APIs that need to be managed, Kong Konnect is designed to enable DevOps teams to choose whatever method they prefer to integrate applications. Regardless of approach, application connectivity is maintained by DevOps teams at Layer 7 rather than by a networking team relying on a lower Layer 4 of the networking stack.

Regardless of how application connectivity is achieved and maintained, it’s apparent that application environments are becoming more complex. The challenge is finding a way to streamline the management of application connectivity when new dependencies between APIs are being created almost daily. In fact, one of the reasons many organizations are embracing platform engineering as a methodology for managing DevOps at scale is to streamline the management of application connectivity.

Hopefully, as part of that transition, DevOps and network operations teams will become more integrated than they typically are today. In the meantime, however, it’s clear DevOps teams now at the application level have access to a higher level of abstraction that provides them with more programmatic control than ever over how applications are integrated.

May 13, 2025
How Benchmarking Can Help Software Development Teams Achieve CISA’s “Secure by Design”

In April 2023, the Cybersecurity and Infrastructure Security Agency (CISA) launched its Secure by Design initiative, directing technology companies to ‘prioritize the security of customers as a core business requirement, rather than merely treating it as a technical feature’.

Since then, more than 300 of the world’s top software manufacturers have signed CISA’s Secure by Design pledge, committing to ‘built-in’ protection that incorporates the seven goals of the pledge: Increasing multifactor authentication (MFA); better enabling customers to do their own patching; reducing default passwords; decreasing vulnerabilities; publishing a vulnerability disclosure policy (VDP); expanding vulnerability transparency reporting in every common vulnerabilities and exposures (CVE) record for products; allowing customers to collect evidence of intrusions affecting products.

It is very encouraging to see such widespread support. Technology providers are committed to integrating defense as a fundamental component of the entire software development life cycle (SDLC), ensuring robust protection from the start, while also complying with regulatory guidelines.

The support arrives at a time when the software development industry faces considerable challenges, as developers too often rely on poor coding patterns and vulnerable components to meet deadlines. Nearly two-thirds of developers say they find it difficult to write code that is free from vulnerabilities, according to our research. About half admit to leaving vulnerabilities in their code. As artificial intelligence (AI) tools have become de facto coding assistants, the potential for security compromises has only increased, with the majority of software and security team members conceding that insecure AI suggestions are common.

To be clear, developers should not solely bear responsibility here. They often work under significant pressure to produce more code within increasingly tight deadlines. Moreover, there is no standardized method for them to assess their security proficiency or compare their practices against peers.

That is why pursuing industry-wide benchmarking is essential as part of a commitment to Secure by Design. It helps set standards for success that lead to a perfectly balanced state of protection and productivity. Ideally, such benchmarking will foster a ‘security-first’ mindset among developers, making security an inherent part of their approach to coding. This will result in impactful risk mitigation and the continuous quantification of their ability to safeguard the products they produce.

Currently, development teams participate in various training sessions and mandated compliance programs to boost these skills. However, these approaches are fragmented and not comprehensive. As a result, they cannot provide a standardized roadmap for the industry-wide defense of software products, which benchmarking could achieve.

The measurement of benchmarks — across learner distribution (how individual learners are distributed in terms of skill level) and skill attrition (a reflection of knowledge retention) — provides critical metrics that enable organizations to evaluate their internal practices against industry peers and the state of the industry overall. As a starting point, organizations should assess their developers’ security skill sets in areas such as language proficiency, contributions to secure code reviews, identification and remediation of vulnerabilities (i.e., structured query language [SQL] injection, cross-site scripting) and implementation of secure coding practices (e.g., proper authentication mechanisms).

Organizations can more readily achieve CISA’s Secure by Design through benchmarking by implementing the following best practices:

Aligning Benchmarking With Industry Standards and Measuring Progress

Establishing a standardized approach to evaluate and improve security practices against proven benchmarks is essential. Teams should incorporate recommendations from organizations such as the National Institute of Standards and Technology (NIST) and the Open Worldwide Application Security Project (OWASP), while maintaining the flexibility to customize practices to meet their specific organizational and industry needs. These teams must then assess their progress based on specific criteria to measure the effectiveness of their secure coding and encryption throughout the entire SDLC.

Enhancing Protection Across the Board
When effectively executed, benchmarking initiatives elevate teams in multiple ways. They help identify and address vulnerabilities, thereby reducing overall risk. They encourage organizations to regularly assess and continuously upskill their developers’ security capabilities, ensuring that teams stay informed about evolving threats and how to mitigate them. Moreover, they foster a culture where security is viewed as an integral part of daily efforts toward continuous improvement, not an afterthought.

Staying Ahead of Regulatory Requirements
Benchmarking supports organizations as they work to satisfy a wide range of regulatory conditions by providing structured documentation of security practices and skill levels. It also demonstrates a commitment to reducing or eliminating risks, aiming for a state of optimal defense.

When you think about the phrase ‘secure by design’, it means that defense comes first and is always a given. It is an innate, foundational part of the product, not something that is merely ‘tagged on’ and/or perceived as a bothersome ‘checklist item’.

By expanding developer team members’ knowledge of threats and measuring their ability to defend against them, benchmarking elevates organizations to new levels of ideal protection, regulatory compliance and continuous improvement. This not only fulfills CISA’s pledge but also builds greater trust with customers.

May 12, 2025
Sumo Logic Previews AI Tool to Analyze Telemetry Data

Sumo Logic has created a prototype of a generative artificial intelligence (AI) tool that creates text-based summaries from large volumes of log and telemetry data collected by its log analytics platform.

Additionally, Sumo Logic is now also making it possible to deploy detection rules as code, which makes it simpler to incorporate into a DevSecOps workflow.

Sumo Logic is adding an ability to create baselines for user and entity behavior to reduce the number of false positives that are created when relying solely on static thresholds.

Finally, the company is adding support for multiple intelligence feeds by adding support for Structured Threat Information eXpression (STIX) syntax and the Trusted Automated eXchange of Intelligence Information (TAXII) data format.

Chas Clawson, Field CTO for security at Sumo Logic, said collectively these capabilities will help DevSecOps teams keep pace with the increased velocity at which code is now being created using various AI tools. Rather than having to search log data for anomalies, it is now becoming possible to employ AI to create a natural language prompt that generates a summary of actionable insights that surfaces key patterns, extracts relevant context, and highlights likely root causes, he added.

That capability should also help bridge the device that often exists between DevSecOps teams and cybersecurity professionals that, while capable of detecting a vulnerability, often lack the context needed to accurately assess its level of severity, noted Clawson.

It’s not clear how widely AI is being applied to DevSecOps workflows, but a Futurum Research survey finds 41% of respondents now expect generative AI tools and platforms will be used to generate, review and test code. As those tools are incorporated into DevOps workflows, it’s apparent that many of the tasks that conspire to make securing software supply chains a set of tedious tasks that few DevOps teams enjoy. However, if it becomes simpler to not only identify pressing issues but also instantly create and test a patch, the overall state of application security should steadily improve.

Better yet, the number of vulnerabilities being created might eventually decline as application developers take advantage of AI-infused tools that identify issues as they are writing code.

Unfortunately, many developers are now generating code using general-purpose large language models (LLMs) that were trained using examples of flawed code that were randomly collected from across the web, which in the short term means there might be more vulnerabilities finding their way into production environments. However, it’s just as equally possible that many developers who lack security training are now creating higher-quality code thanks to an LLM.

Each organization will need to determine to what degree to rely on AI technologies to automate various tasks and processes, but at the very least, they should be creating a list of tasks that are likely to be automated using AI technologies to better understand where to focus the efforts of their human software engineering resources. It’s not likely AI will replace the need for software engineers, but it should make being one a lot more enjoyable, as more tedious tasks, such as patching applications, are increasingly automated.

May 8, 2025
Pulumi Previews IDP to Manage Reusable Software Building Blocks

Pulumi today previewed an internal developer platform (IDP) through which DevOps and platform engineering teams will be able to access reusable building blocks that can be used to create templates and blueprints to build applications.

Scheduled to be generally available later this year for self-hosted environments or via a software-as-service (SaaS) application, Pulumi IDP will be made available at no extra cost to the company’s existing base of more than 3,500 customers.

The overall goal is to make it simpler for DevOps teams of any skill level to set up golden paths for building applications using an IDP that provides drift and policy detection and remediation tools, auditing of outdated components and templates, and change management when rolling out updated versions.

Additionally, there are approval workflows to enable teams to delegate and maintain guardrails, a visual import tool that helps teams bring existing unmanaged cloud infrastructure under the management of Pulumi with just a few clicks, and an integrated identity access management (IAM) tool to ensure least privilege controls are maintained.

Pulumi CEO Joe Duffy said DevOps teams will also be able to employ a mix of graphical, lowcode, REST application programming interfaces (APIs), programming tools and DevOps pipelines as they best see fit to deploy and update the Pulumi IDP.

Eventually, Pulumi will also extend its existing generative artificial intelligence (AI) tooling to create AI agents that will automate many existing IDP management tasks, noted Duffy.

While IDPs have been gaining traction as a means to apply guardrails to self-service workflows that developers employ to access software components and infrastructure-as-code, they typically require a lot of software engineering expertise. Pulumi is pursuing an alternative approach that provides built-in search, semantic versioning, documentation and usage tracking tools to make it easy to discover and share patterns.

Compatible with TypeScript, Python, Go, C#, Java or YAML software components, the registry that is at the code of the Pulumi IDP will make it simpler to enforce security, compliance, cost controls and other operational rules, said Duffy.

Many DevOps and platform engineering teams have, of course, already built and deployed an IDP. However, managing, updating and securing those platforms requires a lot of undifferentiated effort that doesn’t necessarily contribute to the bottom line of an organization, noted Duffy.

Pulumi is making a case for an extensible alternative IDP that is easier to integrate with multiple existing DevOps tools and platforms, including the open source Backstage IDP originally developed by Spotify, he added. Pulumi is also willing to meet customers wherever they are on the road to adopting best platform engineering practices, said Duffy.

IDPs, of course, are not a new concept, but as more organizations realize how much productivity there is to be gained by ensuring developers have access to a set of secure reusable components that they can directly download, there is now a greater appreciation for their need. The challenge is that many DevOps teams have found IDPs to be more complex to deploy and maintain than anticipated.

Hopefully, there will come a day when IDPs can be taken more for granted. In the meantime, however, DevOps teams may need to revisit their decision to build versus buy an IDP when there are so many other pressing concerns to address.

May 6, 2025
Five Great DevOps Job Opportunities

DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

LinkedIn

Adobe
New York City
Senior DevOps Engineer
$153,600 to $286,600

Indeed.com

American Express Business Travel
Scottsdale, AZ
Senior DevOps Engineer
$151,757 to $170,000

Dice.com

Esri
Redlands, CA
Principal DevOps Cloud Engineer
$145,600 to $241,280

The Job Network

Raft Company
Tampa, FL
DevOps Engineer
$140,000 to $190,000

SimplyHired.com

Forward Slope
San Diego, CA
DevOps Engineer III
$125,000 to $160,000

May 5, 2025
AWS Adds MCP Support to Amazon Q Developer Platform

Amazon Web Services (AWS) this week added support for the emerging Model Context Protocol (MCP) to Amazon Q Developer, a suite of artificial intelligence (AI) agents for application developers that can now be more easily integrated with other AI tools and data sources.

Adnan Ijaz, director of product management for developer agents and experiences at AWS, said support for MCP in the command line interface (CLI) that AWS makes available to developers to access any MCP server. That capability will also soon be extended to the integrated development environment (IDE) that AWS provides for Amazon Q Developer.

Originally developed by Anthropic, MCP enables two-way connections between data sources and AI tools. Cybersecurity and IT teams can both expose data through MCP servers or build AI applications, also known as MCP clients, that connect to these servers. That approach makes it possible to query internal systems without blindly scraping data or exposing backend systems. In effect, an MCP server acts as an intelligent gateway, translating natural language prompts into authorized, structured queries.

For example, developers can via MCP in addition to describing AWS, also describe database schemas to build an application without having to directly invoke a specific variant of SQL or write any Java code that might be required.

The ultimate goal is to reduce the current level of bespoke connectors that would otherwise be needed to achieve the same level of integration, said Ijaz.

Exactly how much AI agents are now being used to build software is unclear, but a Futurum Research survey finds 41% of respondents now expect generative AI tools and platforms will be used to generate, review and test code. The one certain thing is that the volume of code being created is going to continue to increase exponentially in the months and years ahead as AI agents are integrated into software engineering workflows.

Each organization will, as a result, need to determine for itself to what degree to rely on AI agents to build and deploy applications. The quality of the code being generated by AI tools can vary widely, and in many instances, organizations are hesitant to deploy code in production environments unless a human developer thoroughly understands how it was constructed.

Nevertheless, as AI coding continues to evolve, the productivity gains are too much to ignore. Application development teams, at the very least, should by now be experimenting with various approaches, especially as it becomes simpler to combine multiple tools to build the next generation of AI-infused applications.

In the meantime, the pace of AI innovation is only going to accelerate. Before too long, the code surfaced by AI tools will steadily improve. DevOps teams will soon find themselves building, deploying and updating a wide range of applications at levels of scale that a few short years ago might have seemed unimaginable.

The issue now will be determining to what degree the pipelines currently used to drive the workflows relied on to manage DevOps processes might need to be revamped to accommodate all that code.

May 2, 2025
AI Native Dev: Shaping the Future of AI-First Software Development
In an era where artificial intelligence is rapidly transforming the software development landscape, a new community has emerged to lead this evolution: AI Native Dev. The community is centered around the website, https://AINativeDev.io/, The site serves as a platform for a raft of different types of content and information.

This platform is not just a repository of tools and insights though; it’s a vibrant hub for developers dedicated to reimagining software creation through an AI-first lens.

I recently had a chance to sit down with my friend Patrick Debois, of DevOps fame who is a prominent member of this budding community. You can listen to my DevOps Chats podcast with Patrick for more color and details.

What is AI Native Dev?

AI Native Dev is a collaborative initiative that brings together developers, technologists and thought leaders to explore and define the principles of AI-native software development. Unlike traditional AI-assisted development, which integrates AI tools into existing workflows, AI Native Dev advocates for a foundational shift. It emphasizes building software with AI as a core component from the ground up, fundamentally altering how applications are conceived, designed, and maintained.

The community is spearheaded by industry veterans, including Guy Podjarny, founder of Tessl and former CEO of Snyk, and Simon Maple, a seasoned developer advocate. Their combined expertise in security, development, and AI positions AI Native Dev as a leading voice in the AI-driven development movement.

Beyond GuyPo and Simon Maple, other members of the community include Patrick Debois, Olivier Pomel, Macey Baker and Amanda Brock.

Core Principles of AI Native Development

At the heart of AI Native Dev are four guiding principles that encapsulate the ethos of AI-first software engineering:
1. Specification-Driven Development: Emphasizing clear, structured specifications that serve as the foundation for AI-generated code. This approach ensures that AI tools can produce accurate and contextually relevant codebases.
2. Context-Aware Development: Leveraging AI’s ability to understand and integrate comprehensive project contexts, leading to more informed decision-making and cohesive software solutions.
3. AI Agents as Developers: Transitioning AI from a supportive role to an active participant in software creation, where AI agents can autonomously develop and operate components of software systems.
4. Human Oversight by Design: Ensuring that AI-driven workflows are designed with clear human oversight mechanisms, maintaining alignment with human intent and ethical standards.
These principles are not static; AI Native Dev encourages community contributions to refine and evolve these guidelines, fostering a dynamic and inclusive development philosophy.

Engaging with the Community

AI Native Dev offers multiple avenues for engagement, catering to a diverse range of interests and expertise:
- Discord Community: A space for real-time discussions, knowledge sharing, and networking with like-minded developers passionate about AI-native development.
- AI Native Dev Podcast: Hosted by Guy Podjarny and Simon Maple, the podcast delves into the nuances of AI-native development, featuring conversations with industry leaders and innovators.
- AI Native DevCon: An annual virtual conference that showcases practical applications of AI in software development, offering insights into current trends and future directions.
- AI Dev Tooling Landscape: A comprehensive, community-curated repository of AI tools and technologies spanning the entire software delivery lifecycle, from prototyping to deployment.
These platforms collectively serve as a nexus for learning, collaboration, and innovation, empowering developers to stay at the forefront of AI-native methodologies.

The Vision Ahead

AI Native Dev is more than a community; it’s a movement aimed at redefining the paradigms of software development in the age of AI. By fostering a culture of openness, collaboration and continuous learning, AI Native Dev aspires to equip developers with the tools, knowledge and mindset necessary to harness AI’s full potential.

As AI continues to evolve, so too will the practices and principles of software development. AI Native Dev stands as a beacon for those ready to embrace this transformation, offering a roadmap for building software that is not only enhanced by AI but fundamentally rooted in it.

Join the AI Native Dev Community

Embark on your journey into AI-native software development by exploring the resources and connecting with the community at AINativeDev.io. Whether you’re a seasoned developer or new to the field, AI Native Dev welcomes all who are eager to shape the future of software development through the lens of artificial intelligence.
May 1, 2025