Stephen Gross

🌟 Attention Engineering Managers and Technical Leaders! 🌟 In just two weeks, on June 28th at 10 AM PDT, I’m excited to host an interactive roundtable on Feature Flags and Delivery in conjunction with ELC! 🚀 Why Attend? This is a collaborative discussion where your expertise and curiosity are key! Whether you're experienced or eager to learn, your contributions are invaluable. 🔍 Discussion Points: - Decoupling Processes - Boosting Team Efficiency - Optimizing Feature Delivery - Tracking Platform Usage Expect rich conversations on best practices, unique implementations, methodologies, and the latest tools enhancing feature flagging and delivery. Who Should Join? - Engineering Managers - Technical Leads - DevOps Engineers - Software Developers This roundtable is your chance to: - Share your experiences - Gain new insights - Network with peers - Discover practical recommendations RSVP Now and mark your calendars for June 28th at 10 AM PDT! Let’s collaborate and elevate our skills together! #EngineeringManagement #TechLeaders #FeatureFlags #DevOps #SoftwareDevelopment #TeamEfficiency #TechRoundtable

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Supercharging your engineering teams doesn’t mean you have to sacrifice quality.  When teams are moving fast, it's common to see an uptick in quality issues making their way to production. The knee-jerk reaction might be to slow things down or intensify TDD/BDD unit tests. While helpful, these measures often don't tackle the root causes.  Consider structural changes to the motions your teams go through with their deployment strategies. Implementing a 3-tiered or 4-tiered deployment model—comprising dev, test, staging, and production environments, all integrated within a robust CI/CD pipeline.  This keeps your teams moving fast without compromising quality.  Here’s a brief rundown of each environment's role and tips to maximize effectiveness: 🛠️ DEV: The playground for developers, either locally or in the cloud. This space allows for unimpeded experimentation and innovation without affecting other environments. 🔍 TEST: Mirroring the production layout, this is where all changes—including non-code adjustments like IaC/terraform, database migrations, etc.—are first pushed. It is the first environment where initial functional testing and  automated test suites help find problems.  🎭 STAGING: Your production's dress rehearsal. This near-replica of your production environment is where the final tests occur, ensuring all changes are polished and stable. It also offers a sneak peek to internal stakeholders and select customers, demonstrating reliability and readiness. 🌐 PROD: The final frontier where thoroughly tested changes are deployed, ensuring minimal disruption or downtime. Pro tips: Sync Your Environments: Keep all environments as close to production as possible. Regularly update your staging environment with recent data to expose your team to real-life edge cases, using data scrambling or mocking for privacy. Automate Everything: Commit to automating your deployment processes across code, infrastructure, and data changes. The investment pays off in faster, higher-quality releases. Embed QA in Your Culture: Integrate quality assurance throughout your process, from ideation to functional testing. Focus on comprehensive, holistic tests that simulate real user interactions for the greatest impact. Expose Scaling Issues:  Operate lower-tier environments with reduced resources to reveal potential scaling issues early. This helps identify and rectify less optimal approaches before they escalate in production environments, ensuring smoother scalability under real-world loads. By rethinking our approach to deployment, we not only enhance our ability to scale but also solidify the quality that our customers expect and deserve.  Unlock your team’s potential and make the investments.  The dividends are compounding! #EngineeringExcellence #QualityAssurance #DevOps #DevSecOps #Agile #Scrum

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Whew, fellow engineering managers, do we need to talk? We had more yeses to these three questions than either front-line engineers OR execs: 📈 I do not feel the potential for advancement in my current role 🚀 Leadership at my company is out of the loop when it comes to engineering challenges 💥 I do not feel secure in my current role. Pair that with the 69% of EMs who’ve experienced burnout in the past year and it’s enough to make even the best of us look at the engineer/manager pendulum with renewed interest. For me, the saving grace is the big improvements we as an engineering industry have made on informing and driving the business strategy over the decades I’ve been in tech, and wanting to stay in the room where it happens. Check out the 2024 State of Engineering Management to think about where you and your team falls on the trends we're all experiencing firsthand. (And for funsies, you won’t believe which group thinks AI is a gimmick. ✨)

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Transitioning from IC to EM is incredibly difficult for many software engineers. I'm no exception to this - becoming an engineering manager after being an individual contributor was hard. This is especially true for EMs that still are expected to be delivering code. Finding the right balance is hard. You're used to your individual impact being what showcases your abilities. But your impact is now represented by your ability to empower your team on their deliverables. 👇Check the comments for the full video!👇 ---- 🎬 Check out Dev Leader on YouTube for multiple full-length videos weekly! 🗣️ Share with your network! #CareerChange #SoftwareEngineer #EngineeringManager

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This morning I've got a guest post on Google's Open Source Programs Office blog: what to do when your open source contribution stalls out. There are posts and posts about how to improve contributor experiences from the maintainer side, but I couldn't find much about how to push through a rough contribution process from the contributor side. Turns out you, as a contributor, actually have quite a lot of power here! I wrote down some of the strategies that have worked for me and my team as we contribute to the Git project; they apply for open source work, but also for any kind of decision stalling out. https://lnkd.in/g3eD6W4N

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I will add one more thing. The code review should be the last line of defense, not the first. The design should be correct and reviewed (if need be) and the code should be correct as much as the ability and knowledge of the author allows.

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I've been toying with this idea for the past few weeks: there's a limited number of production lines of code a team can have per engineer before quality starts to suffer through unexpected bugs or reliability issues. Let's call it team's carrying capacity :-) Teams can increase their carrying capacity by investing in testing and integration practices, reducing model complexity, and as their engineers learn more about the domain and the tech stack. Carrying capacity probably drops temporarily as you add more people to the team or add more external dependencies to the project. This framing is interesting because it opens up interesting trade-off discussions as: * is adding engineers to the team worth it? our carrying capacity will drop for the next 6mos so we won't do new stuff or will start seeing bugs in existing stuff if we were already at capacity * not deprecating a project will either decrease our quality or prevent us from building new features * is it worth adding a dependency to the project instead of doing it ourselves? It feels intuitive, but obviously needs more digging. I'd be curious to see if tools like Sourcegraph or Swarmia could be used to start measuring the ratio of production lines of code / engineer and see if there's something useful there. Has anyone seen attempts at measuring something similar?

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Before I get too much side-eye or glares for suggesting that technical leadership doesn’t happen from the top down, let me be clear: Yep, there are always the big-picture folks, and that includes making the new tiger team, x-factor squad, one-with-the-force co-op, etc., to build the next-gen new hotness, crypto-AI-magic thing. That stuff is important. No one wants to get caught with their pants down while the rest of the market’s off chasing the new takes-half-the-time-for-twice-the-value framework of definitely-not-hyped tech (it's always hyped, but the hype isn’t always wrong). I don’t want to devalue that stuff—it’s just not what I’m here to talk about, at the moment. I want to talk about the importance of engineering software that never stops evolving. The software that changes under your fingertips, the kind that needs upgrades, maintenance, and a whole lot of patience to deal with a constant onslaught of new features that may or may not play nice with everything else. Building software isn’t like airplane maintenance, though we sometimes treat it that way—like you can just bolt on a new engine or install a bigger cargo door, and everything will keep running smooth. It’s not that simple. It's not that simple for planes either. It’s more like a garden. It needs continuous care and attention. You’ve got to notice when a new fruit vine starts choking the azaleas, or when that little oak sapling suddenly shoots up and blocks the sunlight from the flowers below. The point is that reliable, steady software—the kind people rely on every day—has to change over time, but it can’t be allowed to decay. If you let things slide, it turns into a tangled mess of bugs and instability that can’t take a new feature without everything else breaking down. We can’t let it become insecure or unusable. The trick is helping it evolve carefully and thoughtfully—supporting the new, upgrading the old, and making sure everything still plays well together. Doing that well requires folks in the weeds—people who care about testing, security, accessibility, and the side effects that ripple through the whole system. It’s not a game of follow-the-leader. It’s about fostering a culture of care and respect, where everyone understands that the small stuff matters and no one’s role is too small to make a difference. It’s more like resource management in a cockpit. We’re all responsible for making sure that the evolution happens with care and expertise, and that we each know that we have a role to play—and that those roles, too, will evolve. Hey you in the weeds—you’ve gotta care about that invasive bush creeping across the garden bed. If you don’t, those carefully spaced tulip bulbs you planted are toast. And you over there, keeping track of which libraries and dependencies are on life support—your voice matters. Your teammate's voice matters. Pay attention and don't forget about what's going on in the garden while everyone is excited about the new hotness.

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The Art of Interviewing: Are We Failing Great Candidates? As a seasoned Lead Solutions Architect in QA/QE w/20+ years of industry experience, I’ve been part of countless technical interviews in my last Organization, both as a panelist and a mentor. Over the past 6-7 months, I’ve coached over 25 professionals (all in good faith, no money involved), conducted mock interviews, and helped them prepare for real-life scenarios. But despite all the preparation, a glaring issue keeps surfacing-a fundamental flaw in how interview evaluations are conducted. Here’s what I’ve observed: 👉 For Junior to Mid-Senior roles, about 50-65% of R1 interviews are wrapped up in just 10-12 minutes—a time barely sufficient to scratch the surface of a candidate’s technical depth or problem-solving approach.   The candidate is bombarded Q's on code syntax and asked to write 2-3 lines of code. Okay, that’s absolutely fine. But simply if the candidate does not answer say 2 or 3 out of 10-12 questions **YOU CHOSE** to ask him back-to-back in first 10-12 minutes, does not prove that He/She is not hands-on. They can learn the fundamentals behind those 2-3 Q's. Give them a Chance!     👉 For some of peers, with whom I brainstormed for Senior positions, the opposite happens. Round 2 Interviews stretch beyond 30 minutes, often hitting the 60-80 minute mark. While this might sound like thorough evaluation, it often veers into unstructured territory, leading to unfavorable outcomes. At first, if R2 panelist does not find the candidate suitable in the initial 20-30 minutes, then what inspired them to extend the discussion so long?   "Tell me 1 or 2 most challenging situation that you have encountered in your career. How did you solve those?" A lot of Interview panelists are fascinated with asking this particular question. But the evaluation gets digressed and looses it worth, when the Panelist starts expecting that the candidate response should resonate very well with the Hiring Company's Challenges. How can we judge any candidate, be it junior / mid-senior / even senior management, based on their responses with a bias towards only what you want to hear or what you have solved. No two Projects / Lob’s will have ditto same challenge situation. Likewise, not every candidate might have encountered exactly same situation that you are expecting to them to explain. Lastly even, if the problems are similar, there is no such thumb-rule to address and solve a problem in only one way. Because everything is subjective, situation-specific, and Individual Approach. As far as problem was resolved, that demonstrates Leadership and Problem Solving Skills.   So, where’s the problem? Let us park this for now; we will diagnose that as well.   I would love to hear from you on above. What has your experience been with technical interviews—either as a candidate or a panelist? How can we make the process better? Share your thoughts below! #TechLeadership #InterviewInsights #QA #QE #HiringExcellence

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A question to my friends, colleagues, and Linked-In associations that are full TDD developers. Folks who consider themselves proficient or have mastered TDD. 1. How long did it take you to to become very proficient (e.g. to start thinking fully test first)? 2. Did you have a mentor or some aid that helped you along the way? 3. What kept you going down the proficiency/mastery path? I'll start: 1. It took me about 3 years to make it a habit and for me to truly embrace TDD and find that it could replace "test after" development. 2. I did not have a mentor. I did not have YouTube or trainings. I had constraints about creating automated tests. I had teammates that were similarly inclined (constrained). 3. I immediately saw the value of automated tests. I struggled with test first getting in the way of me feeling productive (because I was not proficient and in the learning/gaining proficiency phase) with test first. I'd start every day test first and quickly transition to test after, because it was getting in the way of my "productivity and programming...damn it I just want to write the implementation!" At the end of the day I'd reflect and most days realize that writing tests after was so much slower. Slowly, my time spent test first increased. At some point I transitioned to pseudo coding my intended implementation (to satiate my need to "just write the damn code and not be bothered with tests"). More time passed and I realized that my pseudo code was an inaccurate crutch, I ditched that too and would start with a test. There was a period of time where I'd start with a test and get lost in implementing more than just making the test pass (cycles of sunk cost fallacy here, until I learned it's ok to delete and write it later - faster, better, stronger). There were periods of time where I'd forget to run the test first to see it red before making it green. Too many years messing around with mocking frameworks before learning better design. Struggles with testing internal implementation details instead of external behavior. A whole bunch of stuff I can't think of now and am probably blocking out. I stuck with it though and yikes, does it pay off. I can't imagine not doing TDD now. Coding w/o it is like running through a vat of molasses. So much time wasted in the debugger and writing things that my evil programmer monkey brain distracts me with that YAGNI. I'm writing and asking about this because last week I attended a conference where I talked with a fellow TDDer who'd heard me mention that it took me 3 years to become fully TDD proficient and he shared a similar story even though he was much younger than myself (I attribute my slow adoption due to lack of a mentor/coach and training aids like Pluralsite, YouTube, Jon Reid live coding sessions). I'm curious about others mileage on this. I'll add that I am currently 100% on board with TDD. I am also 100% always on the lookout for a new and better way to replace it. That's off topic though.

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There is a disturbing trend in tech job listings lately, companies are asking for much much more for much less salary. When your list of responsibilities basically reads like a posting for a QE, SDET and DevOps role you’re not looking for one person you’re looking for 3. I understand that the current perception is that this is an employers market, but if you’re not getting the candidates you want perhaps look critically at your job posting. People are desperate for work but you CANNOT retain good talent if you expect them do the work of three people for less than market for one. Sometimes I feel like employers are setting themselves up for The Great Resignation part 2.

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Having a ton of fun writing an ORM for V2 of Case Kit. Typescript has such an incredible type system. I have autocompletion for column names, the ability to include relations, relations of relations, etc etc etc... all returning properly typed data (no classes in sight). It's been really satisfying seeing it coming together, and it's been great getting my hands dirty with some (relatively) complex generics. And to head off the obvious question… why aren’t I just using Prisma? Well there’s a couple of things I’m doing differently that I think are marginally better: there’s no code generation involved here, it’s just types (inspired by Drizzle); there’s no Rust binary you need to deploy, and it hews much closer to Postgres behaviour and syntax (the aim here is not to abstract away which database you’re using). But the real blocker to me using Prisma was that its extensions don’t support modifying queries of nested relations - a hard requirement for me due to the way I have multitenancy and soft deletes set up. It’s not shown in the video but the Case Kit ORM lets you write simple middleware that will be applied to every level of a nested query. Perfect for setting timestamps or adding clauses on tenant id or things like that.

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I don’t post a lot on LinkedIn. I find a lot of the posts are obvious statements of well known ideas or successes. I’ve decided to start posting on LinkedIn, to share, to vent, to find a little group therapy so to speak. I’ve been managing software engineers for over 10 years now. Coming from an IC background, making the transition from IC to a first line manager hasn’t been easy, but it has been rewarding. I don’t read a lot of books, in fact, just a few, and not really related to software engineering or management or the like. Most of my experience has been self learned, through failures, through mistakes; hard knox so to speak. I like leading a team, but don’t feel like a leader. I position myself as a team member on a team that has joint responsibility for success and failure. Leading by example when I can, and being led by others at times. Of course I get to make some of the decisions, but I rather see the team lead itself. It is more rewarding that way. What I find most intriguing is that one size does not fit all. The hardest part of managing is the people. Each person is unique with different experiences, different motivations, different ethnicity, different backgrounds, different home lives. You aren’t given a prebuilt team that is optimal for success. You are given the raw materials, some that are a good match for the job and others that need to be coached, developed, and nurtured. None of my team members ever took a compatibility test to determine how well they’d work with me or each other. Some are meticulous in the way they operate, some are “cowboys” (sorry for the gender team). Some can deal with ambiguity, the technical complexity, the problem solving, the scale, the quality, etc. Some need to be shown how to unblock themselves, how to find a way forward with little experience or the lack of confidence. Some flourish in an environment full of chaos, others struggle. What makes a puzzle interesting is that it takes different shapes and sizes and time to solve. Apparently there is a character limit on posts. This is my first post, it is raw and unfiltered (no help from ChatGPT). It probably lacks context and clarity, but might resonate with some. I’ll post a continuation in the coming days to help tie these thoughts together with other thoughts still yet to come.

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I rarely post on LinkedIn, but I'm curious to find some folks to chat with about some ideas for an Enterprise Product Management product. My thesis is that the current suite of tools do not do a sufficient job providing features for managing sprawling, complex suites of products + product lines that mature tech organizations maintain. Most PM products are geared toward startups and smaller companies with simpler products, but few tools help PMs and other leaders manage the sprawling complexity of a situation like 30 products over 6 product lines, 14 industry solutions, and components from a dozen acquisitions. Let me know if you'd be down to chat for 20 minutes one day about it!

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Geoffrey Hinton's contributions is significant enough that I won't quibble with the category for his prize. (Nobel committee could create a special category just for him for all I care.) Widely (and rightfully) recognized as a leading expert in deep learning, his research touches everything from * Foundational concepts such as backprop https://lnkd.in/gMunbBzA * Foundational model architectures such as AlexNet https://lnkd.in/gq2JPJ6h * Optimization algorithms such as Momentum and RMSProp * Visualization techniques such as t-SNE https://lnkd.in/gjuw3vhA I don't think it's possible to overstate how profound the impact of machine learning and artificial intelligence will have on society, promising to unlock discoveries, improving productivity. Even ideas about what mastery of language or reasoning means, will need to be re-thought. Professor Hinton deserves all the credit for all of this.

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I'm going to post a few challenges I have had as a software QA. I should be relaxing today but those that know me, know my brain is always in thirty places at once. To developers: Trust your QA. Y'all don't know how many times I've heard, "Well, it works on my local". I don't care. In the test environment, it breaks. I do my due diligence to investigate why, present evidence in logs and from the database, present recordings, and meticulously document steps of reproduction and results. And even after, I still get, "It works on my local". Get off the dang local. NO ONE knows this software, app, platform, etc. better than the QA because we live it more than anyone else does. To business analysts: I know you all are overwhelmed. But please stop copying and pasting Acceptance Criteria for similar features. It makes a mess for developers and QA because the AC doesn't match or conflicts with not only the developed feature but integration as well. Which brings me to my next point... What QA is: Everyone thinks of us as just testers. I am sick of that. I spend half my time working with the product owner, BAs, and Devs, just making sure the AC reflects the solution needing to be resolved. I can't tell you how many times I have had to address with the BAs conflicts in a story over AC, nonsensical AC, WTF verbiage, etc. So then the developers get confused and we are in long meetings addressing what is messed up. We are here to help you ask the questions you didn't think to ask. We don't just test..... we look at future issues too. We like process improvements. Automation: Stop asking me to automate a one-off feature test that hasn't even gone through UAT yet. It's time wasted. Too many companies do this. Might as well give me the day off for the time it takes to do that because the automation is useless after UAT. Learn the purpose of automation. Product Owner: When I tell you before a demo not to demo certain things because there are still issues, don't shred me after the demo because you decided to demo them anyway and they break. I TOLD YOU not to do that, and why. Data: QA worst nightmare. My last position I would get two test records to test against 20 AC when I first started. I said "Helz no". I built up a catalog of test records I could use, positive and negative, as I progressed because I want multiple records against every AC. Which I often shared with the developers for their unit tests who loved it. QA needs data, all. You give me two records, don't yell at me that coverage isn't thorough. Probably sounded more a rant. But I'm opinionated. Where many QA are not. And these are common issues. Want to improve QA, improve the above.

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Isn’t it funny that 60% of engineering managers see a shortage of qualified candidates while 56% plan to increase headcount? The math is not mathin’. 🧮 Maybe it’s time to rethink what makes a “good engineer”. Skills are evolving with AI and machine learning, and our criteria needs to evolve too. The “passion engineer” coding all night is rare now, and that’s okay — maybe even better — because we can introduce more diverse skill sets, experiences, and problem-solving. Let’s evaluate our talent definitions, invest in training, and embrace diverse paths of excellence. ✨ For more insights like these, check out Jellyfish's 2024 State of Engineering Management report: https://lnkd.in/eKAJ5jZy

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"The core art of Operations Management is observing the messy, human chaos of business and applying order to it." The Operator's handbook is a great guide to the various operations disciplines like #businessoperations or #bizops, #revenueoperations or #revops, #financialops or #finops,#legalops, #marketingops, #peopleops and #productoperations / #productops that have emerged as a result of shift from hardware to software-based value creation (see my previous post too --> https://lnkd.in/gwNWwkNh?). Great introduction, though I find it lacking as it did not include factory operations, supply chain, store operations that rely far more on offline-online interfaces. For any operator out there, process, analytics, metrics, cadence, solutioning and execution are the key concepts to apply.

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I've been thinking a lot lately about how dogmatic software engineering is/has-become/always-was(?). I see so many so-called "best-practices" applied on projects that turn into utter disasters. I think I've got a way of reframing software engineering so we can actually have productive dialogs about what actually matters and what is actually harming our projects. If you are an experienced software engineer or project manager, take a look. I'd really like your thoughts and an open discussion on this topic.

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