Ruhul Alam

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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Don't you hate filling out long forms too? Not anymore! Here's a practical use of Generative AI that everyone can benefit from right now! Developed with ❤️ by VONQ's AI Labs.

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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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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 Forced top-down architectures could have been replaced by simple open-source frameworks.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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For the past couple of weeks, I've been revisiting the insightful book, “The Goal,” by Eliyahu Goldratt. This business novel on the “Theory of Constraints” was a recommended read during my MBA at UCLA a decade ago. The story follows Alex Rogo, a manager tasked with saving a failing manufacturing plant, who receives guidance from a physicist named Jonah through Socratic questioning. In this post, I’ll explore one concept from this book and share my thoughts on its relevance for a manager of managers. Read more at my Substack

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The feature we've been waiting for is finally here! No more endless scrolling to find timely news with Threads recent update. Learn all about it here! 👇 https://ow.ly/ZLM630sCUjZ

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"If you can't trust the [event] platform, the whole event is in jeopardy.” When you rely on events to engage, connect, and inspire your customers, like Upwork, you can’t risk a sub-par attendee experience. https://okt.to/s3olkT Cvent #VirtualEvents #AttendeeEngagement

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Is process actually valuable? It often feels like it gets in the way, especially when new process is first implemented, but smart processes should be empowering. They lay out the path to successful collaboration. Consider how chaotic an assembly line would be if each step operated without coordination with the others. Or how slow and dangerous driving would be if even one person decided the accepted "driving process" wasn't for them. While poorly thought out processes can indeed be a huge hindrance to productivity, I believe carefully considered and constantly re-evaluated and refined processes are crucial for long-term and large-scale success. I wrote more about my thoughts here: https://lnkd.in/gH_g6RRf

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We published an article about all the awesome product observability features of Segment we have today and upcoming in the near future. Bookmark this if you are a Segment user!

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Dan Walther has just released another insightful blog post on Endevor. In "Extending Element Options - Not all Elements are Alike," he highlights how CONPARMX and VALUECHK can help you save time and avoid creating too many processor groups. If you're interested in optimizing your process, this is definitely a must-read! Check it out now. #endevor #devops #mainframeappdev #buildautomation https://lnkd.in/egVxRPf6

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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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Building off of yesterday's fictional book announcement (https://lnkd.in/gBAU8nyZ) Here's the next book in the Nate Taylor Management Series.

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Who out there is playing with generative ai video tools? What tools are you using, and how do you like them? I have been playing with Runway and I am looking to compare it to other tools. Sora is not yet available to play with but looks amazing! What are you playing with?

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Ticket velocity isn't the *only* measure of an engineering team's effectiveness. At a high level, the best engineering organizations can predictably deliver against the priorities set forth by the business. That involves proper monitoring (through metrics) and coaching by effective managers. Here's a few non-velocity metrics I use to measure my organization: - Tenure of the team. - Platform uptime. - Number of critical issues in the last 8wks. - Automated code coverage percentages. - *Employee* vs customer detected bugs. - Number of requirements missed during project planning. Read more about my leadership style here: https://lnkd.in/geP2m7DJ #metrics #engineeringeffectiveness #team #leadership #software #tech #startup #management #dora #space #agile #projectmanagement #podcast

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If you want a lesson on inspiring loyalty in those who work for you, and garnering deep respect from the people who work with you, spend thirty minutes with Alexis Chitwood, MBA, PSM1, for whom phrases like "put people over process" go way beyond the words! "We follow the principle of people over process, putting people first the same way that we put our creators first. There’s a large sense of community and support at Raptive enforcing that idea that we can achieve more when we work together, and those lessons will stay with me forever." https://lnkd.in/eCYG_mw6

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Some takeaways for a Jeff Hinton interview with timeless insights. My POV as an app builder is that models are now being improved for better UX (faster response/multi-modal) and reasoning capability and cost continues to improve. The technology headroom remains strong, and the integration of LLM into all software experience will continue and expansion of the reach (invasion) of software + intelligence will also continue. - Next word prediction is not merely parroting of training data. To do next word prediction well, LLM needs and already exhibits strong reasoning behavior. This would continue to scale / improve with more data / training. - LLM is a compression of human knowledge. The compression mechanism is identifying similar concepts across domains and reduces duplications among them. This can also be a source of "creativity" as LLM "sees" correlations of seemingly unrelated ideas through this compression. LLM can be more creative as it sees similiarity / correlations across all domains beyond (human) convetional wisdom. - LLM can exceed the quality of its training data. Jeff gave an example of an experiment where a model trained on 50% intentionally incorrectly labeled data, can still perform at 90+% accuracy after training. - Multi-modal models can improve the overall world understanding and lead to better overall reasoning. It does not even need to generate multi-modal output. - Not ML specific, but an interesting life insights. Bad intuition comes from believing (or wanting to believe) everything you are told. It diminishes the ability to make a clear choice that would lead to differentiated success (or failure).

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Recently, Eric Ignasik and Michael Sols from The Software House asked me about my first steps as a VP of Technology at Habito. It's been a fun 8 months in a supportive environment with fantastic people! 😉 This interview discusses a few improvements ‌I made with a big emphasis on why servant leadership is important to me. Hope you’ll learn from it, ➡️ https://hubs.la/Q02Bw-Bv0 #softwaredevelopment #cto #softwaredelivery

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Markdown to... command line app - wat? That's the topic for today's unsolicited face time. A single Pipedown pipeline can be used in numerous contexts thanks to the way it consistently accepts inputs and produces outputs. I waffled about my love of pipelines a while back: https://lnkd.in/gERrAsvA Though it's much handier to see in action! https://lnkd.in/giR-KVDT Let me know your thoughts 🚀

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