Bryant Chou

ENOUGH WITH THE FRAMEWORKS! If you've worked with me, you've probably heard me say that. Product teams should be focused on driving outcome and impact, not falling in love with the process. And that's something Peter Yang from Roblox knows all too well. We had a great conversation on Next Gen Builders about how the PM role is evolving, how to use #AI to focus on more important work (like talking to customers), and more. Tune in to our conversation: https://lnkd.in/dY-AZh2s

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Gathering and prioritizing customer feedback - at scale! - is an incredible challenge. A decade ago, we standardized on portals where customers can contribute, sort, search, comment, and vote on ideas. Product Managers spent hours, days, and weeks triaging responses, responding to ideas, and generally becoming frustrated with not being able to keep up with the amount of incoming feedback! Today, Pendo launches Listen as part of the Pendo One Platform. It is such a massive step forward in how we can capture customer feedback from seemingly endless sources, collate, summarize, intelligently route, validate, and take action on it. It's really magically and scales beautifully. I think it focuses in on the difference between hearing the voice of your customer and truly listening. Hearing is about a single sense. Listening involves all of your senses. Pendo Listen deeply integrates our quantitative analytics, integrations, and replay to Listen to your customer and take action. Learn more: https://lnkd.in/eTPR4HiQ

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Some great insights from our VP of Product, Chris Allsopp on building a platform designed for a diverse set of users. "...We implemented a consistent design language across the platform while allowing for customizable views and access levels. We've created a fluid platform where actions on one side seamlessly influence another." This quote from Chris does a great job encapsulating one of our core values at Growthware of creating a fluid platform. Click the link in the comments to read the full article. #ui #ux #saas

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Polls can be a cheat code for earned media, and a great way to build authority. But there are levels to this. Here's how one of the best I've seen - Capital One - does it. (And what I'd do differently to increase earned coverage). Led by Shena Ashley, PhD, Capital One's Insights Center is a masterclass in leveraging polls to create unique insights that would do incredibly well via earned syndication. Here’s what they do great: ✅ Content that sticks: Crucial issues > fleeting trends. Focusing on topics like financial stability, mobility, and entrepreneurship, it's easier to rationalize investment on topics with a longer shelf life. Their insights + stories can drive coverage for months to come. ✅ Visual storytelling: Data doesn’t have to be dry, and Capital One presents original insights through clear and eye-pleasing visuals that are easy to understand. Publishers like charts, they love colorful charts, and they LOVE colorful charts that are easy to understand. Check, check, check. ✅ Strong methodologies and extensive polling numbers: Capital One frequently involves 7k+ participants in their studies - they are not half a**ing it. Well explained methodologies are table stakes to get your polls covered, and Capital One does a great job outlining their data collection process, sample size, and research methodologies.   ✅ Easy to steal: Each report has multiple easy to take stories/soundbites, and are also written as full pieces of content, which can be taken as whole. Finally --- if I could change one thing... Add a byline. Brand publishing NEEDS a byline - no news outlet wants to cover a story that can't be credited back to a human. All in all, polls can be incredibly valuable for driving earned syndication - check out Capital One's Insights Center in comments for killer examples.

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👩‍🚀 Fellow Tech People, we're exploring moving away from our custom API/websocket LLM interfacing at LEAGUES in favor of ELL. 🤔 The potential for streamlined API interactions, simplified prompt management, and reusable functions is really appealing. That said, I’m reaching out to the community: has anyone implemented ELL in production yet? I'm curious if ELL is the way to go for long-term scalability and maintenance reduction when interacting with LLMs. And what about Langchain—why choose one over the other? I'd love to hear your experiences and thoughts! #LEAGUES #ELL #Langchain #AI #TechInnovation

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Last week, Zach and I headed to Seattle for 2 days of our executive offsite. The goal? To set our vision for 2025. We focused on: → Solidifying our vision, → And allocating resources across our 3 product lines. We answered critical questions like → How can we get our customers to find even more value? → How can we be the rising tide that lifts all boats for our target industries - moving & insurance → How and when can we scale Yembo beyond our first two verticals I think this was our most productive offsite yet! Looking forward to executing on these priorities in 2025 - making ideas into code and into customer life improvements!

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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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Work at a natural pace. In Cal Newport's new book "Slow Productivity" he walks through 3 components to accomplishment without burnout. 1. Do fewer things 2. Work at a natural pace  3. Obsess over quality Working at a natural pace is an area I'm focused on as I transition to whatever is next in my career. Most of my career's natural pace was driven by anxiety, coffee, HIIT workouts and more caffeine - which I'm pretty sure isn't what he means. In my late 40's though, that just doesn't work anymore. Nor do I want it to. It's a tricky philosophy though because if you're in the startup world I don't know how you build something meaningful and work at a natural pace. I've never seen it happen. It appears this is a philosophy that needs to be applied to a specific time and place vs. everyone, everywhere. Anway, I don't have the answers, I'm just exploring different questions. What I do know is working at a natural place seems to be more appropriate for where I am today. 

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Where did all the product engineers go? Is there a new title in town? I'm talking about people who are skilled at traversing the entire stack (frontend/backend/api +) to build incredible product and ux. #engineering #recruiting #product

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This is a cool project, and another example of the emerging "replace stateful storage with S3" trend. I do wonder at what point someone will package this sort of thing up into a FUSE filesystem to make it generally usable across existing services. Or maybe every service has unique enough needs that it's better to handle it custom each time. https://lnkd.in/etcVBfVf

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a note on intuition and building products (via mike knoop, founder - zapier, go follow him on twttr) -- Wisdom for founding teams is "talk to users". Over the last 10 years this has become the playbook for doing 0 to 1 work. As the advice became dogma, some founders forgot why it exists, and a new failure mode emerged: "the blind walk to product-market fit". YC popularized "talk to users" in response to the classic mistake of coming up with some idea in isolation, building in secret for months or years, and launching just to learn no one cares. Talking to users forces faster contact with reality. Your initial idea and and intuition are likely wrong in material ways, so you want to shrink the time to learn this. At Zapier, I tell teams to try and model this behavior: ship in the AM, demo to users in the PM. If you do this, you get 5-7 hits on reality per week compared to a team on a 2-week release process (or worse). This all makes sense and many founders conclude their idea, intuition, or experience don't really matter. They model finding pmf as y=mx+b where cycle time is "m" and the initial idea is "b". In this model, founder intuition gives them an initial head start but impact is quickly overtaken by iteration speed. This leads them to heavily discount or even outright ignore their own experience and intuition -- many get stuck doing a fast cycle, but ultimately directionless, traversal through the pmf maze. The modeling mistake here is the "m" term is decomposed into (iteration speed) * (intuition). Every time you make contact with reality, you learn something new. And you get to decide what to do with that new information. Should you ignore it? Update but carry on? Change directions entirely? This is why intuition is important. Your idea and experience do not just establish initial position on the Y-axis. They are a multiplier on every decision or change you make. The faster your iteration speed, the more overall impact your experience can have. If you're ideas are right, they are right faster. If they are wrong, they are wrong more obviously. All other things equal, founders with experience in a domain outperform blank-slate founders doing the blind walk.

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I'm often asked about the difference between full-time and fractional product work. Here's one of the things I always tell folks -- fractional is all about impact, impact, impact 🔥 This means that the ability to develop context quickly, ramp up (and get educated) around new problem spaces & industries and sniff out potential challenges for a product and team are crucial...and also what makes the role so damn fun and dynamic. Side-note: This is not to say this isn’t the case with full-time work. The best full-time folks certainly adopt this mindset. But I know this to be *particularly* the case with fractional work and what you should expect when working with a stellar fractional professional.

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Talk to Buildstash and Robbie Cargill if you want to manage your game or app binaries.

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Last week I locked up Privy's Engineering and Design teams in a barn near Cape Cod. (Don't worry, they're free now). It was all in the name of our first In-person Hack Week—a week where we pause all regular work to focus on experimentation and learning with one rule: Learn something worth sharing every day. This time it was all about AI. So I got the coffee brewing and a full kegerator and they dove in. Our biggest learning? While AI makes hard things easy, it also makes basic tasks surprisingly difficult. If you've ever tried generating an image with specific text, you know what I mean. But you can't truly grasp these limitations until you've tested the tech yourself. Hence, hack week. When someone discovers something interesting, they can instantly grab 3 other team members, get around a laptop, and build it. Here's what else we've learned: → Daily demos were great for breaking down product visions into bite-sized projects. Details reveal themselves as you build. Typically, your assumptions from the morning are thrown out by lunchtime. → It’s hard to see how companies without an “AI Native” approach will survive, given the exponential productivity gains internally and for customers. Fittingly, we were just a mile from New Bedford—the 19th-century whaling capital, known as the “city that lit the world”—a reminder that nothing lasts forever. But this is less about new tech and more of a history lesson. Most companies talk about AI, but few have truly “burned the boats” and are going all-in. And I admire that courage—it reminds me of the courage of the whaling expedition sailors! It’s easier to go all-in on AI when there’s not a foundation to change. That’s why over 80% of the last Y Combinator batch described themselves as “AI companies.” But what if you’re more like New Bedford, built on a world where you were the only option? Enter kerosene. Invented in the 1850s, thanks to a perfect storm of events like the massive railroad expansion, it became the preferred choice for lighting cities and towns. What if kerosene followed the same path as AI Today? Kerosene was introduced and within 2 years, it was 98% cheaper and exponentially more powerful. That’s exactly what’s happening with AI. Taking a week off with your Engineering team might seem costly, but isn’t taking a week off to think, reflect, and tinker, the least expensive initiative on your roadmap today? Do you not think AI will play a 10x or 100x larger role in the coming years? And aren’t those “AI First” YC companies likely your competition soon? AI is having its Cambrian explosion moment. As with every hype cycle, there will be a peak of inflated expectations and a trough of disillusionment. But the last thing I want is to sit on the sidelines and miss the journey. I want to get deep in the details, because the better we understand both the power and limitations of AI, the more we can delight our merchants.

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Andrew Chen puts into words what many product people have realized in the trenches. There is more to product decision making than data. Having taste, sometimes rebranded as "product sense", is something I've been leaning into. There's usually not any meaningful data at the earliest stages anyways, it's about a developing a vision informed by your depth of experience, your conversations with customers, and your differentiated view of the world. https://t.co/ZR3HFdfDX9

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The #1 question I get asked is: “How do I hire a CEO?” (How to hire, incentivize, and manage a CEO) I created a guide with all my learnings from hiring ~40+ CEOs and leaders across Tiny (and many epic fuck ups along the way). Topics I cover include: Is hiring a CEO the right choice for you? When to hire a CEO? How to find the right CEO? Should you invest in a recruiter? What to look for in your CEO? How to diligence candidates? How to ensure a culture fit? How to negotiate & structure compensation? How to transition from founder to owner? How to relinquish control? How to determine CEO success? When to replace the CEO? Book Recommendations * A cheatsheet is included with key points from all sections. Get my guide for free → https://bit.ly/4bSTeUY Enjoy!

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Take it from me, there’s no better way to learn excellent and practical ways to overhaul your experimentation efficacy! Erin makes critical thinking fun and highly accessible in this workshop, June 11:

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Awesome to see how our Pinterest Engineering friends take advantage of an almost *700x performance difference* between Honeycomb and their Develocity (fka Gradle) dashboards to find outlier users, old machine types, or use of specific JDKs. 🔥 We love seeing our customers use Honeycomb in all parts of their development process, leveraging our support for custom metadata (monotonically increasing, unbounded build identifiers are a perfect stealth example of high-cardinality data) and 💥speed💥 to deliver better experiences for their users. https://lnkd.in/eGcKeUvY

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