Michael Lysaght

OK so the reality of some conferences is pretty grim - very surface-level, vendor-driven content. I'm NOT a fan of 20-minute sessions... I can barely understand the problem being discussed in that amount of time. It just does not work for me. That's where UberConf makes a huge difference. 90 min in-depth sessions, on relevant topics by industry experts and practitioners. Conference size is limited to maintain a very comfortable environment without the need to walk 15 min to your next session, or it gets filled up. ooh... food is also great, fully catered for breakfast, lunch, and dinner (guaranteed great food)! https://uberconf.com/ I'm excited to join the amazing speaker line-up for a third year in a row. The opportunity to engage, share, and learn with the best (both speakers and attendees) is second to none. I've got a couple of new sessions planned, but I am most excited for: Crafting Consistent APIs at Scale: Embracing Linting and Reusable Models. Turns out that creating resources and building APIs in a distributed model is hard (who knew right?)! As teams expand and projects multiply, maintaining a unified API design can pose significant challenges. This presentation delves into the powerful combination of linting and reusable models as tools to navigate these challenges and ensure consistency across large-scale API ecosystems. Come check it out! Discount Code: uber24-travis4

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One of my favorite acronyms in #SoftwareEngineering is WTF - Where to Focus. When you have a small team you should focus as much energy as possible on the pieces that make your business competitive and buy everything else. I guarantee your cost estimate to justify building a tangential capability over buying it is missing a bunch of hidden cost factors.

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There's an oft-cited stat from Pendo that 80% of software features are rarely, if ever, used. While it's impossible to know the accuracy of that, it's not hard to believe. I've been part of multiple feature factories, where emphasis was put on new features to 'claim' rather than taking time to ensure we've actually solved the desired problem before moving onto the next. If you're a Product Manager/Leader in the Indy area, join us on August 14 for a conversation around best practices for measuring product success. We'll have food, drinks, and hopefully some great learnings! (Registration link in the comments)

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Cut Down Your Platform Engineering Org By 50%. Your Business Will Be OK! If you are a large enough tech company, you have a bloated platform engineering team! A couple of decades ago, Joel Spolsky coined the term 'Architectural Astronauts'—engineers who create grandiose, abstract designs that often fail to address real-world problems. Unfortunately, this issue has only worsened, especially in platform engineering. One of my biggest career regrets? Not fighting hard enough against these platform architects. Forced top-down architectures could have been replaced by simple open-source frameworks. I remember it taking over a year to build a self-service API because of a top-down, inversion-of-control framework forced upon all engineering orgs. Read about what killed popular products like Digg, Orkut, NetScape etc.  You can trace it back to bad engineering thought leadership. The amount of platform engineering waste is staggering. Most large tech companies are a graveyard of abandoned platforms and frameworks. Most in-production frameworks have one tenant and are on the deprecation path. The engineering 'thought leaders' who build these systems got promoted and will still rationalize their decisions by passionately talking about edge cases that will never happen. Almost every tech stack begins with off-the-shelf standard frameworks and platforms, mostly open source. But no two businesses are the same. And companies often start building their own platform pieces. As these platform teams grow, they become out of touch with the actual needs of the business. Led by powerful, disconnected engineering leaders, these teams wield more power than they should. Their solution to an inadequate platform? Build a new one, rather than improving the existing one. Unfortunately most of your IP & patents are from these platform teams. Most of your top engineering brain power is working on these platform teams. You will be warned how you will lose the top talent when you question these investments. If you have a 100+ people platform engineering org, cut it down by 50%. Your business will be OK.

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Appreciate seeing that App Store review time has roughly halved since the summer (down to about 4hrs compared to 9hrs). I can't help but wonder why starting a review for Testflight external beta is still at the 8hr mark (though I certainly understand that those should be lower priority compared to production builds). I +1'd the Google Play IN_REVIEW status issue and recommend others do the same! On the green goblin [Android] side of things, I've personally experienced a lot of variance lately, resulting in some unexpectedly [days long] reviews. I also see that Google recommends waiting for production reviews while alpha reviews are in-progress, citing longer review times. My experience has been the opposite, where the synchronous reviews results in a MUCH longer review cycle. The numbers point to improvement, but there's still further progress to be made, both for timing and consistency of policy enforcement.

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You know there's some momentum happening if Marty Cagan decides to write about a product topic you've been at work trying to enable for years. Love to see it, and BIG shout out to Stacey Langer for providing her expertise in this article! 🧘‍♀️ Here are a few other pros that came to mind for the product model: 1. The government product model makes product (public) value and risk management possible. Product risks—such as value, desirability, viability, and feasibility—are not risks that traditional project management models can effectively manage. Government agencies interact with the public through the services they offer, but government digital experiences are not always inherently valuable. It’s what these digital services do for users that create individual and public value. Building tech alone ≠ value. 2. Evidence-driven decision-making uses discovery, iterative delivery, and testing with actual end users, where you continuously learn. This enables many things, mainly building with your users while validating the solution direction. It can also enable an incremental funding option specifically for products/services (traditionally called ‘projects’) that are especially complex, high priority, and/or contain many unknowns. 3. Driving technology from a place of vision and strategy measured in outcomes that can align and even influence policy changes. 🤸‍♀️ Some realities in trying to adopt a product model in gov: 1. The word ‘product’ often can be confusing, especially when it doesn’t fully represent what is being built and managed. A lot, if not most, of the technology work happening in government often involves solving service design challenges. So, in gov products, you focus on solving for the end-to-end experience, which can mean processes and non-tech things. 2. Conway’s Law and government systems replicate the organizational structures in agencies, which are often siloed and isolated. This is often represented as 'legacy tech.' Much of the work in 'product' in government is modernizing legacy systems, which requires the same principles as the product model but often a different emphasis on its varied approaches. Not all products or digital services start from the same place; many are not greenfield. 3. Traditional ATO structures can stop the product operating model in its tracks. To overcome this, it is important to integrate the ATO into the product model, utilizing a ‘continuous ATO’ operating model. But see point 2 above. At the end of the day, the product model isn’t a process you learn to implement and then just implement. It’s not just ‘Agile’ or Scrum or DevOps. Those methods can (or sometimes can’t) enable the product model. It's a mentality and discipline for continuously using new inputs to deliver value throughout a product's LIFE. It's a cultural change as much as an operating change, and there is no 'single way' to do it. However, operating from product thinking principles can help us start from the right place.

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Someone was wrong in the interwebs! 😲 In a recent discussion, someone argued that because software development always involves novel challenges and requirement changes along the way, estimating effort and schedules is inherently inaccurate to the extent of being completely wasteful and pointless. I disagree! I consider there are three main things needed for successful long-term work: 1️⃣      Define your objectives. Understand what you’re trying to accomplish. What you build is a means to an end, not the end itself. 2️⃣      Work incrementally, validating and adjusting as you go. Having to take a step back to fix things is a sign of progress, not failure. 3️⃣     Improve your ways of working. Regularly evaluate and reflect on how you work and adjust as needed. The smaller the scale of the work, the easier this tends to be. But the larger the scale, the more important it becomes. So, estimating and waste? If you don’t track and measure your work and progress, how can you improve your ways of working? Learning requires feedback. If you measure what you need for improvement, you will have everything you need for efficient estimates with zero waste in time or effort. What about the point? When you start something, the first and most important step is to understand your objective: What is the objective? What is the value of the outcome? Ie. the #1 on the list above? A ballpark figure is enough. And often, the value of the objective is relative: which is more valuable, A or B? You might not have the real exact values, but you probably don’t need them either. Usually, the values are equally off at this stage, so you can compare. Then you need a rough estimate of the effort. A “well, I’d say it’s X months, or Y if in Strömsö” from the team is just fine.  Is the effort more or less than the value of the work? If less, go for it! Thinking if something is worth trying or not is anything but pointless. Once you begin, work incrementally, validating as you go, and continue estimating and projecting. The earlier you get projections, the earlier you get answers to the critical questions: -         Should you keep trying, or pull the plug? -         Can you reach the goals with your current approach, or should you adjust (scale the team, cut the scope etc)? For fixing anything, these two answers are the tools you have, and both are time sensitive. Scaling a team or making drastic changes in the scope too late is unhelpful, and shutting down a project late is costly and demoralizing. To get the answers, pretty much the only thing you need is a simple burn-up chart. I think it’s a fairly good investment. So, wasteful and pointless? I really, really disagree. Remember! Estimation or projection is not, nor do they create the deadline or budget. Never fall for that. Deadlines come from business needs, ie. from your objectives (“Our Olympics result app should be ready for the Olympics!”). Projections reveal if you’re on the right track.

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Anthropic released another paper on their ongoing work towards interpretability of large foundational models like their Claude 3 family, built on previous work by many. Their work continues to be enlightening and inspiring. The findings in this work are generally similar to previous findings on smaller models but scaled up to Claude 3 Sonnet: 💡 they find large numbers of "features" corresponding to combinations of neuron activations and these features map to concepts that we recognize/understand: for example, features for transit infrastructure, particular people, concepts (like bias and "objects lacking sentience"). This might sound familiar from classical machine learning, but the interesting thing is that there is no single thing/neuron encoding each of these concepts, it's a particular combination of neuron activations that embeds these concepts in "superposition" in the network. 💡they observe a sort of "distance" of these feature activations that arrange themselves "near" semantically similar features. When they use their sparse autoencoder approach to learn more and more features they observe that the level of abstraction in the detected features becomes more granular, in ways that are interpretable and meaningful. 💡when they artificially fire these activation patterns during inference, it affects the output in ways that relate to the "feature". For example, asking Claude "what's the most interesting science" normally it responds Physics, but if you clamp the feature activations for the feature "Brain Science" to 10x their max values then Claude responds: Neuroscience. Ways to interpret _and_ influence the model. Great readable work [1] along with an interactive explorer of some of the "features" themselves [2] 🎂 There's an easter egg too: if you scroll in the feature explorer tool and try and select one of the "Randomly selected features from 1M" you get a message "if you want to see the rest of the features, we're hiring!" with a link to their recruiting page :) [1] https://lnkd.in/gZbparwp [2] https://lnkd.in/gC9eus53

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I often take screenshots of applications that make me cranky and jot down why. I do this mostly for myself. It is a great practice. I wasn't aware anyone actually gave it a term - "Friction log" makes sense. It’s a technique I recommend to aspiring product managers lacking hands-on experience. By choosing a product you regularly use, identifying its flaws, and suggesting improvements, you not only enhance your analytical and problem-solving skills but also build a portfolio that showcases your ability to enhance user experience. Great article!

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