Jackson Coelho

𝗛𝗼𝘄 𝘁𝗼 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝘆𝗼𝘂𝗿 𝗰𝗵𝗮𝗻𝗰𝗲𝘀 𝗼𝗳 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗮 𝗽𝗼𝘀𝗶𝘁𝗶𝘃𝗲 𝗿𝗲𝘀𝘂𝗹𝘁 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗮 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻? I often receive messages like this: "Hi Mariana, I’m a Senior Kotlin Developer with 10+ years of experience. Do you know of any open positions?" There is absolutely nothing wrong with reaching out. I appreciate when people take the initiative, and I try to reply to everyone. But let’s be honest. What are the odds that I am currently looking for someone with that exact profile? It is not impossible, but you would need a bit of luck. Here is my advice: Start by finding a position that matches your profile (in a formal published job or in a post shared on LinkedIn). Then contact the recruiter or talent team who posted it. The chances are much higher that the person you message is directly involved in that vacancy or knows who is. #JobSearchTips #RecruitmentAdvice #LinkedInTips

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Alright, #WeAreDevelopers Berlin. On Thursday, July 10, I will be doing *TWO* talks -------------- 12:10-12:40, MICROSOFT Stage 3 (limited space, come early) Introducing JSON Structure - A Better Schema Mostly everyone uses JSON Schema in some way and mostly everyone had also stepped on their own toes using it. Why? Because we all want a data definition language to define data structure and JSON Schema isn't one. In this talk, you'll learn about JSON Structure, a data definition language that looks quite familiar, but provides better guardrails and fewer pitfalls -------------- 14:10-14:40, Stage 7 Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata Event streams are central to delivering actionable insights for analytics and AI. However, without rich metadata to contextualize these streams, data consumers often struggle with inconsistencies, poor quality, and a lack of interpretability. By leveraging metadata as a cornerstone of event stream architecture, organizations can unlock clarity in their streaming data, bridging the gap between raw event streams and actionable insights. This clarity not only enhances the quality and usability of event streams but also ensures alignment between developers, analysts, and AI systems, enabling them to extract maximum value from data in motion. This session explores how rich metadata transforms event streams into a reliable foundation for advanced analytics and AI workflows. We will examine the role of schema definitions, semantic annotations, and cataloging practices in improving data quality and context. #wad #berlin #conference

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Next week at SREcon in Dublin, several of my colleagues will present talks and lead discussions on critical aspects of reliability engineering. Pierre Theo Klein, Garrett Holthaus, and Rubén Barroso will present “STPA for Software Systems–Illuminate the Unknown Unknowns”: SREs know about some of the flaws and vulnerabilities in their systems. They might also have intuition on where to look for additional issues–"known unknowns." But what about the "unknown unknowns"–outages waiting to happen that nobody is even looking for? What if we could understand our systems enough to be able to methodically explore these complex interactions and build a comprehensive list of possible outage scenarios? In STPA, we model systems based on control-feedback loops, creating a hierarchical control structure, or HCS. In this session, we'll use a real Google system to show how an HCS can help you gain a new perspective and understanding of your system. We'll note similarities to common patterns in software design so you can start thinking about similar vulnerabilities in your own systems. Tomasz Koczorowski will present “the Bitter and the Sweet of Running a Planet-Scale Build & CI Stack at Google”, about managing Google's planet-scale Build & CI stack from an SRE perspective. He’ll explore the complexities of supporting over 100,000 monthly users and massive workloads, maintaining a 98% cache hit rate across diverse computing environments. Discover resource management strategies for doubling year-over-year growth, the application of the Pareto principle, and critical caching layers. You will gain insights into optimizing the build stack and maintaining a 24/7 service with a small SRE team through strategic planning, monitoring, and collaboration. He will present availability risks, standardization, and the pros and cons of a monolithic Build & CI stack Jennifer Petoff will lead a discussion on “Oops to Ops: Establishing SRE Practices”: Establishing SRE practices, gaining buy-in, and building momentum around reliability can be a significant hurdle for many organizations. This interactive discussion session, designed for both SRE newcomers and seasoned practitioners, will explore common challenges and effective strategies for implementing SRE principles based on our collective experience. We'll delve into practical approaches for demonstrating the value of SRE, fostering a culture of shared ownership, and overcoming resistance to change. Participants are encouraged to share their experiences, ask questions, and contribute to a collaborative exchange of ideas on how to successfully integrate SRE into their organizations and build a strong foundation for operational excellence. (continued in comments)

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Did you know you can use mittwald's hosted AI models (currently in beta) for coding assistance in both VS Code and JetBrains IDEs? Just pushed a bit of documentation (read more at https://lnkd.in/gPKSUHVV) that explains how to configure both JetBrains AI Assistant as well as Continue (for VS Code, or JetBrains IDEs) and Cline (also VS Code) to use open-source models like Qwen3-Coder-30B-Instruct via the mittwald API. All models are hosted on german servers, and no user data is stored (let alone used for training purposes).

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I've found that the Superpowers framework (https://lnkd.in/dMd5sHz9) is very effective when working with coding agents. I think most engineers working with coding agents realise how important the planning phase is. However, I've never been *completely* convinced by the native planning mode that you get with tools like Claude Code or Cursor. Over the last couple of weeks, however, I've gotten pretty consistent planning documents from Claude via the Superpowers plugin. I also like how each plan is broken down into two separate documents: one for design, and one for implementation. The actual implementation is then carried out via a new subagent per task, with additonal spec compliance and code quality reviews in between. This does obviously result in higher token usage, but I've found that the results are generally consistent (and I don't have to constantly keep going back and forth). Definitely worth taking a look at!

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Accidental quality. That's what I call the hidden trap in traditional example-based tests. In my recent talk at Techcamp Hamburg, I spoke about how enumerating test cases creates a dangerous illusion of safety, while the real bugs hide in the blindspots we never think to check. Property-based testing flips the narrative: it searches for bugs instead of just testing for them. If you have 1000 users and you are writing 50 handpicked test cases, are you sure you are not missing any particular use-case? https://lnkd.in/eTyjrPPi

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