AxonIQ’s Post

Before there was OpenAI, there was OpenAPI. One of my favorite projects at Marstone, Inc. was working with the #OpenAPI spec (aka Swagger) and tools like Prism (https://lnkd.in/esQK5_fZ) to create a contract between the frontend client and backend APIs, so that we could start developing the frontend against a functioning API before the code was actually written. What Prism allows, in their own words: > Maximize efficiency by enabling frontend and backend teams to work simultaneously. With Prism's mock server, frontend developers can start building against the API without waiting for the backend to be completed. This parallel development approach significantly accelerates API development and streamlines team collaboration.

Call all of my Kubernetes and cloud-native application builders and Platform Engineers out here. A SIMPLE BUT IMPORTANT FAVOR TO ASK OF YOU 👇 🙏 Please reshare if you can, because this is big. Here's the quick story and a small ask: I've been lucky enough to meet amazing people in my career. One of the best things of being close to so many innovative teams is that I am adjacent, or directly contributing to a constant stream of knowledge and ideas. This is why helping people and their companies has become such a valuable differentiator to just pushing out content with a sole purpose of satisfying some algorithm. One of my amazing GTM Delta clients is Causely. I'm a fan. I was a fan before they were a client so it is like a perfect pairing to be able to directly help grow their reach as well. If you're operating Kubernetes and want to be an early access partner to develop the future of application reliability with a super cool platform that automates root cause analysis and remediation...do these quick 2 things: 1. Check out the platform sizzle reel we collaborated and let me know what you think: https://lnkd.in/gnrXzuDR 2. Get into the development program by requesting a demo We are also looking for folks who want to try things out and share their thoughts with community blogs. It's super cool, and I'm so proud of the team that are making this platform a reality. #PlatformEngineering #DevOps #Kubernetes #K8s #CloudNative #Observability #ApplicationReliability

"Are applications built on no-code considered IP?" This is a question I keep hearing. The maturity of products like Retool, Bubble, and many others makes this a recurring issue. These products are great for getting MVP solutions that can be used to drive revenue, but will they stand up to investor due diligence? My approach to this question is that most of these solutions understand that code often signifies IP, so they offer the option to back up the no-code and low-code workflow, UIs, and applications to github repos. They integrate into CI/CD processes and allow an application to be trackable via change management systems. So while not truly 'code' equal, they are getting to feel and act more and more like traditional technology stacks. StackAI offers AI workflows trackable by github commits, similar to the infrastructure as code model of HashiCorp and Pulumi. Has anyone run into this challenge?

Are you in the early stages of starting a company? Read this guide to better understand APIs and learn what is meant when developers reference them.

Ever spent hours trying to figure out if an error affected one user or thousands? At Dapr Day 2024, Harry Kimpel showed us how OpenTelemetry is changing the game for distributed systems observability. Through live demos of weather services and order processing systems, he revealed: 🔍 How to track requests across complex service meshes 📊 Ways to add business context to your traces (because user_id > random_id) 🎯 Techniques for pinpointing issues faster with custom attributes 🚀 Why OpenTelemetry is becoming the industry standard (with first-class support in @Daprdev and .NET Aspire) The best part? No vendor lock-in. Just pure, standardized observability goodness. Ready to give your debugging superpowers an upgrade? Watch the full talk: https://diagrid.ws/3BYKoIH #OpenTelemetry #Observability #Dapr #CloudNative #DaprDay

🚯 Surveying your developers is 𝐚 𝐰𝐚𝐬𝐭𝐞 if you don’t take action on the feedback to make improvements. But it's easier said than done. In this The New Stack article, Sooraj Shah dives into actionable strategies to turn insights into impact. Featuring expert advice from DX's Abi Noda and practical questions (for eg. in the picture below) used by Port's own customers, this is a must-read for anyone committed to enhancing their developer experience. This is the second part of this series - link to part 1 and 2 in the comments!

#kubenet Update: Expanding and Enhancing Network Automation using #kubernetes principles. As we move beyond the initial phase of proving that the #kubernetes framework can effectively handle network automation, #kubenet is now transitioning to a more open and user-friendly platform. Here’s what we’ve accomplished and where we’re heading: • Enhanced Business Logic Capabilities: #kubenet now supports #python, Jinja templates, and Go templates, simplifying the way users can write and implement your business logic. • Decoupled Architecture: We’ve successfully decoupled #kubenet from the dependency on a #kubernetes cluster facilitated by #choreo (https://lnkd.in/evBKMtHw), enhancing flexibility. • Refined Data Model: The abstract data model has been split and decomposed, making it easier for users to extend and modify it to fit their specific needs. (https://lnkd.in/eGQ8ARJU) • Version Control Integration: To foster collaboration and streamline workflows, we’ve integrated a version control backend. This integration supports both development and production environments, ensuring that changes are tracked and managed effectively. • Advanced Configuration Validation: #kubenet has integrated #sdc (https://docs.sdcio.dev) with offline YANG config validation, improving the reliability and correctness of network configurations. • Python Prototyping for CRDs: We are currently prototyping the use of Pydantic to enable writing of Custom Resource Definitions (CRDs) in Python, aiming to make the development process more accessible and powerful. (https://lnkd.in/edSJ5qkA) We will explore this in #autocon workshop this week.

Supercharge Software Delivery With Event-Driven RAG Enterprise applications are composed of many software containers, each involving a complex set of dependencies. To triage a container for vulnerabilities, hundreds of pieces of information need to be retrieved, understood, and integrated—a tedious process that can take days. This demo looks at how we use generative AI to shorten the process to mere seconds. This is AgentMorpheus, an event-driven, retrieval-augmented generation application built using NVIDIA NIM inference microservices, NeMo Retriever, and the Morpheus SDK. Key Points: Enterprise software is very complex Ensuring software security is time-consuming and repetitive Using NVIDIA-based RAG agents enables faster, more secure software vulnerability analysis, moving from days to seconds. https://lnkd.in/dABzs2HV

AI-generated code can behave in unexpected ways if developers don't thoroughly review it! Dynatrace CTO Bernd Greifender explains six key reasons why AI-generated code could make it more difficult to maintain the reliability and security of software over the long term

AI-generated code can behave in unexpected ways if developers don't thoroughly review it! Dynatrace CTO Bernd Greifender explains six key reasons why AI-generated code could make it more difficult to maintain the reliability and security of software over the long term