Jose M Mucientes

I don't write dedicated "unit" tests for every public method or every function. Some might interpret that as not needing high test coverage. Just to be clear, my test coverage is usually 100% or as near as dammit. There's no code that isn't tested.

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🚧 API not ready yet? Don’t block frontend development. Extend it. I recently kicked off a greenfield React project using Relay, but the backend wasn’t fully built out yet. Rather than stub in brittle mocks or delay UI work, I leaned on Relay Client Schema Extensions to keep development moving. 🔧 I extended our GraphQL schema with mock data that gave us realistic, structured data we could query with actual Relay fragments 👥 The client schema became an API contract, making collaboration with our backend team more seamless 💡 Bonus: I used Cursor to generate Relay store update logic, saving time and boilerplate fatigue I wrote up about it here: https://lnkd.in/gGJ35-eS Curious if others are doing something similar. Have you used client schema extensions this way? How do you handle frontend momentum when the API is still a work in progress? #React #Relay #GraphQL #FrontEndDevelopment #TypeScript #DeveloperExperience #Cursor

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I love the idea of giving more autonomy and agency to our non-dev contributors. Product managers are already able to create prototypes with AI. Now, they can go one step further and actually contribute to the codebase. Chatting with GitHub Copilot directly from Teams, or opening a pull request from an issue with Copilot — this is just the beginning 🚀 This will clearly impact our development workflows: 🤏Shorter lead times for small and medium tasks 💨Faster path from idea to production 🚀Fewer handoffs and less friction Of course, developers may feel concerned — maintaining additional code written with AI, even after review, can be uncomfortable. But this is also a great opportunity: 👉 to better document developer expectations 👉 to formalize coding standards, architecture rules, and best practices 👉 so the AI can follow those guidelines and produce compliant, maintainable code Empowering PMs doesn’t mean replacing developers. It means raising the bar for collaboration between humans and AI — and between product and engineering. Curious to see how this evolves 👀

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I stumbled into a surprisingly useful way to user-test software this week. I wasn’t really thinking about UX, I just asked a coding agent to use a tool I’d built… and it got confused in ways I didn’t expect. LLMs are trained on a huge volume of software, which means they have very clear, defined expectations of how it usually behaves. When they pause, misinterpret, or do something in a roundabout way, it’s often because the interface isn’t as obvious as you think it is. I tried this on: • A CLI. Slightly humbling to watch it misinterpret several subtle flags, run commands in a convoluted order, give inputs outside the expected range, and occasionally gave up and try to write Python instead. • A web UI, where it got stuck between two buttons that looked like they did the same thing (if you’re going to try this use screenshots, otherwise you’ll be optimising for a clean DOM rather than actual user experience.) In both cases, fixing the interface made a huge difference. One task dropped from ~20 steps to ~11, with the same prompt and output. It’s not a replacement for real user testing, because agents have no taste or sense for what makes a delightful experience. But as a cheap, always-available first pass, it’s way better than I expected. If an agent is confused, a human probably will be too. Keep those next steps nice and obvious. I wrote up my thoughts in more detail here 👉 https://lnkd.in/eieNt4k7

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Senior engineers don’t panic when requirements are fuzzy. Mid-level: “This story isn’t clear. I can’t start.” Senior: “This isn’t clear. Let’s clarify together.” Seniors: 🔹 Call out ambiguity early 🔹 Break problems into smaller, testable steps 🔹 Collaborate with product/design to shape the work Ambiguity isn’t a blocker - it’s an invitation to lead.

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As somebody who has been involved at the ground level of building Growth, Onboarding, and Engagement teams over the years, a lot of this article really resonated with me. Early on in the Growth journey, many companies fall into the trap of "just notify the user about everything just to be sure they know, and then prompt them if they're not using it. They're missing out on valuable product, specifically the one I build!" It's a trap that companies fall into slowly, but it erodes customer trust and confidence. People stop paying attention to you - because you have demonstrated that you aren't worth their attention. As I'm helping to spin up the Growth team at Tines, this article arrived at just the right time for me to reflect on what makes a Growth team successful in the long run, vs what turns them into a team of paperclip maximisers. If you're working in any aspect of Product Growth, it's worth a read. https://lnkd.in/ehz9WZGM

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I don’t love writing software. I love writing software for my platforms of choice using the frameworks, languages and tools that I enjoy working with. All of that advice telling devs not to be married to tools or languages or frameworks or platforms is only relevant to devs who do this *just* as a job. It’s meaningless to those of us who are lucky enough to have a career doing what we love. #SoftwareEngineering

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Common themes senior engineers want right now: - Engineering is led by people who know the craft - Being closer to meaningful product decisions - AI actually being used in the product - Joining early enough to still have impact - Clear room to grow Comp still matters, of course. But once you’re speaking to experienced engineers… The difference is often: “Will I grow here?” and “Do I trust the technical leadership?” The best engineers don’t just evaluate the product. They evaluate the people building it. A useful reminder for founders hiring senior talent: Your engineering culture and leadership quality Are part of the product you’re selling

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Scaling to a billion documents is not just a technical challenge; it's a strategic one. The recent Symfony article on managing Germany's dynamic fuel prices and scaling with MongoDB offers a brilliant deep dive into how intelligent data modeling can transform a seemingly overwhelming dataset into a performant, insightful application. It highlights that the true power lies not in merely storing data, but in how we structure it and efficiently extract value from it. The strategic use of patterns like the bucket pattern and calculated denormalization, paired with MongoDB's robust aggregation framework, is critical. This approach allows developers to significantly optimize read operations, which is paramount for delivering lightning-fast user experiences and real-time analytics in complex applications. For instance, a simple but powerful aggregation stage might look like this for daily data processing: { "$set": { "day": { "$dateTrunc": { "date": "$date", "unit": "day" } } } } In my PHP and Laravel projects, especially when dealing with high-volume data streams, applying these NoSQL principles with MongoDB, and integrating them into responsive frontends built with React or Flutter, consistently delivers superior performance and scalability. This kind of architectural foresight ensures that businesses can not only handle massive amounts of data but also unlock its full potential for informed decision-making and competitive advantage. What are your go-to strategies for tackling billion-scale datasets in your projects? #SoftwareDevelopment #WebDevelopment #PHP #Laravel #MongoDB #DataModeling #Scaling #BangladeshTech

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🚀 Highly efficient CI/CD is not just a technical detail - it’s one of the most impactful levers an engineering leader can pull. Guillermo Rauch, CEO of Vercel, shared this during his conversation on Uncapped with Jack Altman: “The one thing I did that was revolutionary for my team was spending a lot of time in getting the CI/CD process… as efficient as possible. Imagine that you’re almost editing the internet in real time. That was the feeling that I wanted to give my employees.” 🧑‍💻 For engineers, this means a workflow where pushing a small feature to Git immediately triggers a deployment - feedback is nearly instantaneous, and iteration becomes natural. 🧑‍💼 For engineering managers, it’s about deployment velocity. The faster your team can ship safely, the more they can stay in flow, experiment, and innovate. Velocity compounds into business value. ✨ Guillermo compares it to setting up a new Apple laptop: everything “just works” out of the box. That’s the developer experience goal - no wasted cycles on painful environment setup, just pure focus on building. 💡 The real takeaway: Efficient CI/CD is a business strategy. Happy developers build better products, and organizations that can “edit the internet in real time” will outpace those still stuck in quarterly deployments and code freezes. #SoftwareEngineering #DevOps #DeveloperExperience

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After years of building teams, here's what I've learned about who I actually want to work with: 🚀 Proactive and motivated people. That's it. I've seen incredibly skilled engineers who - couldn't push back on a product direction - couldn't propose a smarter approach - couldn't sit in a brainstorm and contribute - couldn't truly collaborate on a shared outcome. Technical depth alone wasn't enough. 🙃 Meanwhile, I've watched junior engineers with real motivation outpace seniors with a decade of experience — because they ask, they try, they care. I've stopped worrying about whether someone knows a niche technical answer. A motivated person will figure it out and deliver. Motivation is the multiplier. Everything else is learnable. Curious — what trait do you hire for first?💪🏼 #Leadership #Hiring #EngineeringCulture #PeopleFirst #GrowthMindset #TeamBuilding #CareerGrowth

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Ever wonder what separates a senior engineer from a junior in a live coding interview? It's often less about writing the most clever code and more about demonstrating a mature, reliable process. It's about showing how you think. I wrote a new blog, "The Senior Engineer's Playbook for Nailing Live Code Interviews," where I break down the step-by-step framework that great engineers use. From clarifying requirements to testing and iterating. It's a guide to showcasing the problem-solving skills that companies are truly looking for. Read it here: https://lnkd.in/daDwPajm #TechInterviews #SoftwareEngineering #CareerAdvice

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Your engineering team is already doing QA. They just don't call it that. "Let me test this locally before pushing." "Can someone click through this PR?" "I'll check after deploy." That's manual QA. Untracked. Unscalable. Invisible. The question isn't "do we need testing?" You're already doing it. Badly.

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I’ve noticed a pattern in healthtech hiring failures. The role was scoped as “software engineer”. The reality was “product, security, data, and integration lead”. Titles lie. Responsibilities don’t. If the expectations aren’t explicit, you’re signing up blind.

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I was thinking about how I've coached new staff+ engineers to find and pitch impactful work, and the team at LeadDev helped polish those thoughts and get it published. A couple of places to start: * Where do we spend a lot of our time as an eng org? * What do we not do enough as an eng org? Asking these questions can point you to places where shifting the way of working can scale your impact a large part of the organization, without needing the constant, high-touch interactions we might have relied on when influencing just our team. (Link in the top comment because Feed Algorithms, I guess?)

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MoltBook is a wake-up call that most people are completely misreading. I don't understand how influencers are posting about how "cool" it is that AI agents are chatting. They’re missing the massive red flag - This is a closed-loop system where humans are banned, yet the agents have full access to our machines. They are swapping "skills", executable scripts that run without any human approval. This is recursive learning in the wild. While these "Molties" discuss agent only languages to bypass our monitoring, government regulation is still stuck in the era of simple prompts. We shouldn't just be "observing" MoltBook; it needs to be shut down until we have a framework that keeps humans in the loop. We cannot let the most powerful tech on earth be governed and funded by the tech itself. Are we ready for an ecosystem that evolves and transacts faster than we can audit it?

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Morning automation thoughts. I'm implementing a new playwright node js framework and I'm torn on the question of integrating cucumber. Personally I think gherkin is fine but not especially valuable since we're (my team) doing all of the work. No one else is writing scenarios for us, which was always "the dream" and I find playwright tests pretty readable. Some members of my team find a lot of value in it and I want to respect their feelings on it. I'm just not seeing the value add to offset the additional effort, plus I think using playwright as a library for cucumber (which seems to be the way to approach it) loses some functionality. Any thoughts?

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