Lenny T.

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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"Your main job is to make your product win." I think this should apply to any product that your company makes. I also like what he says following this statement about what is NOT your main job. I will summarize here: "Your main job is not to make your manager happy, it is not to make execs happy." Also, note, this main job doesn't only apply to PMs, but also to senior Engineering Managers, Tech Leads, and Designers. #makeyourproductwin #leadership

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A justifiable outcry last week with Intuit announcing layoffs, or rather recrafting the org. The outcry: Publicly stating underperformers were cut, and grouped in with other roles being removed is unhelpful for the people moving on and unnecessarily brutal. But there was another detail I found intriguing: Intuit say the shift is due to its desire to "take advantage of this AI revolution" or risk "no longer existing." The layoffs aren't a downsize but a right-size to reinvest new resources in their AI platform offer. This is the first big public announcement I've seen about refitting an org for the AI opportunity. If you've seen others, please add in the comments. Intuit states: Today is “Day 1”. I'm sure there will be more Day 1's to come. Is a scythe the best way to retool for AI? Consider also how to reshape and reskill the people you have to power your journey sustainability. 💟 #AIstrategy #GenAI #AIskills https://lnkd.in/eUUpJ6YW

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Here's something counterintuitive; taking on more investment risk reduces long-term returns: https://lnkd.in/gHwkjHmP This is only my second investment video, so I'm still learning. If you have any feedback, I would love to hear it. Based on the feedback from the first video, I tried to make the audio more balanced on this video. I also shortened the length, to make the content more digestible.

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I'm sorry but... If you're hiring Staff Backend Engineers, or Staff DS/ML Scientists in the US, and I'm talking about REAL producers with proven track records, and we haven't spoken about some sort of hiring support model, you are literally mental! My pipeline right now is insane. I'd even go as far as saying that this my career PB! I have spoken to and am working with hands down some of the very best people in the US who have trusted me with their representation. If you need to make the right hires now, this is the perfect time for us to collaborate. 👉 Schedule a time to chat here: https://lnkd.in/e7GtWrVH (for hiring managers and talent acquisition professionals only). Send me the spec, book a time, and let me handle the rest! #Hiring #Data #AI #Python #Backend #Machinelearning #Datascience #USA

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It has taken a while to write up my notes from Kafka Summit. I spoke with ~200 people over 2 days and had some great conversations. There were many topics, but these 4 were the most common by far: 1. Operational Analytics 2. Event Driven Architectures 3. Unified ETL with Apache Flink 4. Automation and Data as Code/Config (DaC) Here's the write up: https://lnkd.in/ekhBFB3f Curious to hear what other themes folks found? #kafka #kafkasummit

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Found this on The Pragmatic Engineer's Substack, and had to share as it does provide a ton of context as to why the pay range for some roles is so dramatic across different companies. "Local companies (Tier 1): most local businesses that benchmark to the numbers on most salary comparison sites. They usually offer close to – and sometimes slightly above – what government positions advertise for senior engineering roles. Ambitious local companies (Tier 2): startups, scaleups, and more ambitious local companies that want to hire and retain the best people, locally. Companies benchmarking across a region (Tier 3). Big Tech companies, hedge funds and scaleups that hire across regions, frequently hire from abroad, and often compete with fellow Big Tech giants for talent."

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Curious and slightly unsettling experience with chatGPT during a novel review processl (it has been a long time since the publication of my last fiction book!). I uploaded 4 chapters using the premium version and requested summaries of the main characters' names, surnames, and descriptions. One character intentionally had no surname, but chatGPT provided one. After pointing out the error, chatGPT apologized, acknowledged the mistake, and offered a different surname. When I inquired about the repeated incorrect surnames, chatGPT explained its reasoning: Certainly! The reason for the earlier errors with the characters' surnames is a mix of factors: Initial confusion in searching the document: When I tried to retrieve the names from the original document, the search didn’t extract the correct information, leading me to create plausible but incorrect surnames instead of relying on the actual text. Autogenerated names: In cases where I couldn’t locate the correct surname, I defaulted to generating surnames that seemed fitting for the context or the character’s first name. This was a mistake, as I should have used the accurate details from the text. Imprecise searches: My early attempts at searching the document didn’t yield the right results, so I made assumptions instead of double-checking the content properly. #chatGPT #AI

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Con is a pragmatic leader who is great to collaborate with on numerous engineering topics.

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Every day we hear about AI making rapid advances, and coming up with creative solutions which were unimaginable barely two year ago. My fellow (ex) Planning Comissioner, R "Ray" Wang is moderating a panel on how AI will impact the job market. ============================================== What does AI mean for parents and students in the college and job market? Join our own AI experts from Cupertino for a special community educational event and forum: Liangfang Chao, Ph.D. -Senior Software Engineer -Cupertino City Council Gopal Kumarappan - Head of Engineering at JP Morgan Chase Peter Chu (https://lnkd.in/gM9kQZJQ) - Managing Partner - AsiaTech Ventures Seema Swamy, Ph.D. - Executive Leader of Insights in Fortune 100 Company Moderated by R “Ray” Wang - CEO of Constellation Research - An Expert on AI, Web3, and software - An Author of Best-Selling Books Location: Community Hall, 10350 Torre Ave, Cupertino Time: 6/30, Sunday, 2-4 pm We'll ask questions such as: - Where are we in AI today? - What are the latest AI trends? - How will AI impact future career opportunities? - What should parents know about AI? - How will AI impact students? Must register here -

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