How to Find Success with Code Reviews

The New Stack Podcast

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<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/state-secrets-inside-the-making-of-the-electric-state">State Secrets: Inside The Making Of The Electric State</a></span>

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State Secrets: Inside The Making Of The Electric State

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Step inside the world of The Electric State! Join host Francesca Amiker as she takes you behind the scenes of Anthony and Joe Russo’s epic new Netflix adventure. Over six in-depth episodes, explore how the filmmakers transformed Simon Stålenhag’s stunning graphic novel into a cinematic experience like no other. Exclusive interviews from the cast and crew—including Millie Bobby Brown, Chris Pratt, Stanley Tucci, and the Russo Brothers—will help break down the film’s jaw-dropping visuals, emotional core, groundbreaking technology AND the creative secrets that make The Electric State. Join us in the countdown to The Electric State premiering exclusively on Netflix on March 14th. State Secrets: Inside The Making of The Electric State coming March 7th.

לפני שנה 34:16

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Code reviews can be highly beneficial but tricky to execute well due to the human factors involved, says Adrienne Braganza Tacke, author of *Looks Good to Me: Actionable Advice for Constructive Code Review.* In a recent conversation with *The New Stack*, Tacke identified three challenges teams must address for successful code reviews: ambiguity, subjectivity, and ego.

Ambiguity arises when the goals or expectations for the code are unclear, leading to miscommunication and rework. Tacke emphasizes the need for clarity and explicit communication throughout the review process. Subjectivity, the second challenge, can derail reviews when personal preferences overshadow objective evaluation. Reviewers should justify their suggestions based on technical merit rather than opinion. Finally, ego can get in the way, with developers feeling attached to their code. Both reviewers and submitters must check their egos to foster a constructive dialogue.

Tacke encourages programmers to first review their own work, as self-checks can enhance the quality of the code before it reaches the reviewer. Ultimately, code reviews can improve code quality, mentor developers, and strengthen team knowledge.

Learn more from The New Stack about code reviews:

The Anatomy of Slow Code Reviews

One Company Rethinks Diff to Cut Code Review Times

How Good Is Your Code Review Process?

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#DevOps #Software Development #Tech #News #Tech News #Technology #Thenewstack #New Stack #Development #Joab Jackson #Alex Williams #Software Engineering #Heather Joslyn #Cloud Native #Kubernetes #Open Source #Darry Taft

How to Find Success with Code Reviews

The New Stack Podcast

521 subscribers

published לפני שנה

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MP3•בית הפרקים

Learn more from The New Stack about code reviews:

The Anatomy of Slow Code Reviews

One Company Rethinks Diff to Cut Code Review Times

How Good Is Your Code Review Process?

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

891 פרקים

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The New Stack Podcast

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How Heroku Is ‘Re-Platforming’ Its Platform 18:01

לפני 23 hours18:01

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Heroku has been undergoing a major transformation, re-platforming its entire Platform as a Service (PaaS) offering over the past year and a half. This ambitious effort, dubbed “Fir,” will soon reach general availability. According to Betty Junod, CMO and SVP at Heroku (owned by Salesforce), the overhaul includes a shift to Kubernetes and OCI standards, reinforcing Heroku’s commitment to open source. The platform now features Heroku Cloud Native Buildpacks, which let developers create container images without Dockerfiles. Originally built on Ruby on Rails and predating Docker and AWS, Heroku now supports eight programming languages. The company has also deepened its open source engagement by becoming a platinum member of the Cloud Native Computing Foundation (CNCF), contributing to projects like OpenTelemetry. Additionally, Heroku has open sourced its Twelve-Factor Apps methodology, inviting the community to help modernize it to address evolving needs such as secrets management and workload identity. This signals a broader effort to align Heroku’s future with the cloud native ecosystem. Learn more from The New Stack about Heroku's approach to Platform-as-a-Service: Return to PaaS: Building the Platform of Our Dreams Heroku Moved Twelve-Factor Apps to Open Source. What’s Next? How Heroku Is Positioned To Help Ops Engineers in the GenAI Era Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Container Security and AI: A Talk with Chainguard's Founder 20:51

לפני 3 ימים20:51

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In this episode of The New Stack Makers , recorded at KubeCon + CloudNativeCon Europe, Alex Williams speaks with Ville Aikas, Chainguard founder and early Kubernetes contributor. They reflect on the evolution of container security, particularly how early assumptions—like trusting that users would validate container images—proved problematic. Aikas recalls the lack of secure defaults, such as allowing containers to run as root, stemming from the team’s internal Google perspective, which led to unrealistic expectations about external security practices. The Kubernetes community has since made strides with governance policies, secure defaults, and standard practices like avoiding long-lived credentials and supporting federated authentication. Aikas founded Chainguard to address the need for trusted, minimal, and verifiable container images—offering zero-CVE images, transparent toolchains, and full SBOMs. This security-first philosophy now extends to virtual machines and Java dependencies via Chainguard Libraries. The discussion also highlights the rising concerns around AI/ML security in Kubernetes, including complex model dependencies, GPU integrations, and potential attack vectors—prompting Chainguard’s move toward locked-down AI images. Learn more from The New Stack about Container Security and AI Chainguard Takes Aim At Vulnerable Java Libraries Clean Container Images: A Supply Chain Security Revolution Revolutionizing Offensive Security: A New Era With Agentic AI Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Kelsey Hightower, AWS's Eswar Bala on Open Source's Evolution 37:52

לפני 8 ימים37:52

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The Kro Project: Giving Kubernetes Users What They Want 21:51

לפני 10 ימים21:51

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OpenSearch: What’s Next for the Search and Analytics Suite? 20:10

לפני 15 ימים20:10

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Kong’s AI Gateway Aims to Make Building with AI Easier 21:05

לפני 22 ימים21:05

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What’s the Future of Platform Engineering? 26:44

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AI Agents are Dumb Robots, Calling LLMs 28:31

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AI agents are set to transform software development, but software itself isn’t going anywhere—despite the dramatic predictions. On this episode of The New Stack Makers, Mark Hinkle, CEO and Founder of Peripety Labs, discusses how AI agents relate to serverless technologies, infrastructure-as-code (IaC), and configuration management. Hinkle envisions AI agents as “dumb robots” handling tasks like querying APIs and exchanging data, while the real intelligence remains in large language models (LLMs). These agents, likely implemented as serverless functions in Python or JavaScript, will automate software development processes dynamically. LLMs, leveraging vast amounts of open-source code, will enable AI agents to generate bespoke, task-specific tools on the fly—unlike traditional cloud tools from HashiCorp or configuration management tools like Chef and Puppet. As AI-generated tooling becomes more prevalent, managing and optimizing these agents will require strong observability and evaluation practices. According to Hinkle, this shift marks the future of software, where AI agents dynamically create, call, and manage tools for CI/CD, monitoring, and beyond. Check out the full episode for more insights. Learn more from The New Stack about emerging trends in AI agents: Lessons From Kubernetes and the Cloud Should Steer the AI Revolution AI Agents: Why Workflows Are the LLM Use Case to Watch Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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Goodbye SaaS, Hello AI Agents 30:02

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The transition from SaaS to Services as Software with AI agents is underway, necessitating new orchestration methods similar to Kubernetes for containers. AI agents will require resource allocation, workflow management, and scalable infrastructure as they evolve. Two key trends are driving this shift: Data Evolution – From spreadsheets to AI agents, data has progressed through relational databases, big data, predictive analytics, and generative AI. Computing Evolution – Starting from mainframes, the journey has moved through desktops, client servers, web/mobile, SaaS, and now agentic workflows. Janakiram MSV, an analyst, notes on this episode of The New Stack Makers that SaaS depends on data—without it, platforms like Salesforce and SAP lack value. As data becomes more actionable and compute more agentic, a new paradigm emerges: Services as Software. AI agents will automate tasks previously requiring human intervention, like emails and sales follow-ups. However, orchestrating them will be complex, akin to Kubernetes managing containers. Unlike deterministic containers, AI agents depend on dynamic, trained data, posing new enterprise challenges in memory management and infrastructure. Learn more from The New Stack about evolution to AI agents: How AI Agents Are Starting To Automate the Enterprise Can You Trust AI To Be Your Data Analyst? Agentic AI is the New Web App, and Your AI Strategy Must Evolve Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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How Generative AI Is Reshaping the SDLC 21:42

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Amazon Q Developer is streamlining the software development lifecycle by integrating AI-powered tools into AWS. In an interview at AWS in Seattle, Srini Iragavarapu, director of generative AI Applications and Developer Experiences at AWS, discussed how Amazon Q Developer enhances the developer experience. Initially focused on inline code completions, Amazon Q Developer evolved by incorporating generative AI models like Amazon Nova and Anthropic models, improving recommendations and accelerating development. British Telecom reported a 37% acceptance rate for AI-generated code. Beyond code completion, Amazon Q Developer enables developers to interact with Q for code reviews, test generation, and migrations. AWS also developed agentic frameworks to automate undifferentiated tasks, such as upgrading Java versions. Iragavarapu noted that internally, AWS used Q Developer to migrate 30,000 production applications, saving $260 million annually. The platform offers code generation, testing suites, RAG capabilities, and access to AWS custom chips, further flattening the SDLC by automating routine work. Listen to The New Stack Makers for the full discussion. Learn more from The New Stack about Amazon Q Developer: Amazon Q Developer Now Handles Your Entire Code Pipeline Amazon Q Apps: AI-Powered Development for All Amazon Revamps Developer AI With Code Conversion, Security Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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OAuth Works for AI Agents but Scaling is Another Question 25:36

לפני 8 weeks25:36

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Maya Kaczorowski noticed that AI identity and AI agent identity concerns were emerging from outside the security industry, rather than from CISOs and security leaders. She concluded that OAuth, the open standard for authentication, already serves the purpose of granting access without exposing passwords. Kaczorowski, a respected technologist and founder of Oblique, a startup focused on self-serve access controls, recently wrote about OAuth and AI agents and shared her insights on this episode of The New Stack Makers. She noted that developers see AI agents as extensions of themselves, granting them limited access to data and capabilities—precisely what OAuth is designed to handle. The challenges with AI agent identity are vast, involving different approaches to authentication, such as those explored by companies like AuthZed. While existing authorization models like RBAC or ABAC may still apply, the real challenge lies in scale. The exponential growth of AI-related entities—from users to LLMs—could mean even small organizations manage hundreds of thousands of agents. Future solutions must accommodate this massive scale efficiently. For the full discussion, check out The New Stack Makers interview with Kaczorowski. Learn more from The New Stack about OAuth requirements for AI Agents: OAuth 2.0: A Standard in Name Only? AI Agents Are Redefining the Future of Identity and Access Management Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

The New Stack Podcast

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LLMs and AI Agents Evolving Like Programming Languages 28:08

לפני 9 weeks28:08

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The rise of the World Wide Web enabled developers to build tools and platforms on top of it. Similarly, the advent of large language models (LLMs) allows for creating new AI-driven tools, such as autonomous agents that interact with LLMs, execute tasks, and make decisions. However, verifying these decisions is crucial, and critical reasoning may be a solution, according to Yam Marcovitz, tech lead at Parlant.io and CEO of emcie.co. Marcovitz likens LLM development to the evolution of programming languages, from punch cards to modern languages like Python. Early LLMs started with small transformer models, leading to systems like BERT and GPT-3. Now, instead of mere text auto-completion, models are evolving to enable better reasoning and complex instructions. Parlant uses "attentive reasoning queries (ARQs)" to maintain consistency in AI responses, ensuring near-perfect accuracy. Their approach balances structure and flexibility, preventing models from operating entirely autonomously. Ultimately, Marcovitz argues that subjectivity in human interpretation extends to LLMs, making perfect objectivity unrealistic. Learn more from The New Stack about the evolution of LLMs: AI Alignment in Practice: What It Means and How to Get It Agentic AI: The Next Frontier of AI Power Make the Most of AI Agents: Tips and Tricks for Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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Writing Code About Your Infrastructure? That's a Losing Race 31:21

לפני 10 weeks31:21

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Adam Jacob, CEO of System Initiative, discusses a shift in infrastructure automation—moving from writing code to building models that enable rapid simulations and collaboration. In The New Stack Makers, he compares this approach to Formula One racing, where teams use high-fidelity models to simulate race conditions, optimizing performance before hitting the track. System Initiative applies this concept to enterprise automation, creating a model that understands how infrastructure components interact. This enables fast, multiplayer feedback loops, simplifying complex tasks while enhancing collaboration. Engineers can extend the system by writing small, reactive JavaScript functions that automate processes, such as transforming existing architectures into new ones. The platform visually represents these transformations, making automation more intuitive and efficient. By leveraging models instead of traditional code-based infrastructure management, System Initiative enhances agility, reduces complexity, and improves DevOps collaboration. To explore how this ties into the concept of the digital twin, listen to the full New Stack Makers episode. Learn more from The New Stack about System Initiative: Beyond Infrastructure as Code: System Initiative Goes Live How System Initiative Treats AWS Components as Digital Twins System Initiative Code Now Open Source Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

The New Stack Podcast

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OpenTelemetry: What’s New with the 2nd Biggest CNCF Project? 30:14

לפני 11 weeks30:14

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Morgan McLean, co-founder of OpenTelemetry and senior director of product management at Splunk, has long tackled the challenges of observability in large-scale systems. In a conversation with Alex Williams on The New Stack Makers , McLean reflected on his early frustrations debugging high-scale services and the need for better observability tools. OpenTelemetry, formed in 2019 from OpenTracing and OpenCensus, has since become a key part of modern observability strategies. As a Cloud Native Computing Foundation (CNCF) incubating project, it’s the second most active open source project after Kubernetes, with over 1,200 developers contributing monthly. McLean highlighted OpenTelemetry’s role in solving scaling challenges, particularly in Kubernetes environments, by standardizing distributed tracing, application metrics, and data extraction. Looking ahead, profiling is set to become the fourth major observability signal alongside logs, tracing, and metrics, with general availability expected in 2025. McLean emphasized ongoing improvements, including automation and ease of adoption, predicting even faster OpenTelemetry adoption as friction points are resolved. Learn more from The New Stack about the latest trends in Open Telemetry: What Is OpenTelemetry? The Ultimate Guide Observability in 2025: OpenTelemetry and AI to Fill In Gaps Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

The New Stack Podcast

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What’s Driving the Rising Cost of Observability? 24:55

לפני 12 weeks24:55

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Observability is expensive because traditional tools weren’t designed for the complexity and scale of modern cloud-native systems, explains Christine Yen, CEO of Honeycomb.io. Logging tools, while flexible, were optimized for manual, human-scale data reading. This approach struggles with the massive scale of today’s software, making logging slow and resource-intensive. Monitoring tools, with their dashboards and metrics, prioritized speed over flexibility, which doesn’t align with the dynamic nature of containerized microservices. Similarly, traditional APM tools relied on “magical” setups tailored for consistent application environments like Rails, but they falter in modern polyglot infrastructures with diverse frameworks. Additionally, observability costs are rising due to evolving demands from DevOps, platform engineering, and site reliability engineering (SRE). Practices like service-level objectives (SLOs) emphasize end-user experience, pushing teams to track meaningful metrics. However, outdated observability tools often hinder this, forcing teams to cut back on crucial data. Yen highlights the potential of AI and innovations like OpenTelemetry to address these challenges. Learn more from The New Stack about the latest trends in observability: Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth Observability in 2025: OpenTelemetry and AI to Fill In Gaps Observability and AI: New Connections at KubeCon Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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