Tonmoy Navare

💡 The Sean Ellis test is a simple yet effective method to gauge product market fit. Just ask your users, "How would you feel if you could no longer use this product?" and if 40% or more respond "very disappointed," then you have a leading indicator that you achieved product market fit. Sean Ellis highlights on Lenny Rachitsky's podcast that you can further ask your users why they feel this way to understand what resonates with them the most. In the past, I've run this test at the end of the product journey, however, some companies like Nubank go one step further and optimize for product-market fit down to the feature level. #ProductManagement #ProductDiscovery

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𝑳𝑳𝑴-𝒂𝒔-𝑱𝒖𝒅𝒈𝒆 🧑‍⚖️ 𝘓𝘓𝘔 𝘌𝘷𝘢𝘭𝘴 & 𝘵𝘩𝘦 𝘪𝘯𝘴𝘪𝘥𝘦 𝘴𝘵𝘰𝘳𝘺 𝘰𝘧 𝘩𝘰𝘸 Twilio Segment 𝘣𝘶𝘪𝘭𝘵 𝘊𝘋𝘗 𝘊𝘰𝘱𝘪𝘭𝘰𝘵 𝘵𝘰 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦 𝘮𝘢𝘳𝘬𝘦𝘵er 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 3𝘹 These days I'm spending an irrational amount of time thinking about evals. It started with a conversation months ago with Jaimal Soni, when he was showing me an absolutely 🔥🔥 demo of what he's been building in stealth. I couldn't believe what I'd just seen. "What's one of the hardest problem you had to solve?" I asked. "Evals." Evals are the way to assess (ahem evaluate) LLMs and systems built with LLMs. In software engineering, you'll hear a lot about test coverage and unit tests — little tests to verify that your code is doing what you expect. But generative AI & LLMs are a little different: unlike your deterministic code, models are stochastic, meaning you never really know what the output will be until it's generated!Ask chatGPT the *same* question and you will get *different* answers. This is where LLMs get their magic, but also creates problems when you're asking an LLM to solve discrete tasks. As we've been building out CustomerAI & Generative Audiences, we needed to think about this problem from the start. Generative Audiences allows marketers to write natural language to generate new audiences, without having to navigate a sophisticated audience-building UI. We’ve been blown away by the results of – customers are experiencing a 3x improvement in median time-to-audience creation, and world-class feature retention rate IF the audience generation works on the first attempt. So evals are 🔑 Ankit Awasthi, Salman Ahmed, Alfredo Láinez Rodrigo, & James Zhu came up with a really clever solution to ensure that CDP Copilot is generating audiences that are accurate & represent the intent of the prompt. But it wasn't easy...and today we're sharing how we used synthetic data & LLM-as-Judge architecture to accelerate marketer workflows 3x.

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🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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As some of you may know, I run a book club at Gracenote a few times annually where we read books focusing on product management, strategy, and general business. For those interested, I've compiled a list of the best books we've covered and published it on Medium. You can find the full list here: https://lnkd.in/gfk3FgtS #BookClub #ProductManagement #BusinessStrategy #KnowledgeSharing

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The 80/20 rule is one of the most important principles for PMs, yet many can struggle to apply it in practice. One of the best applications is when scoping a new product / epic / feature. Start by identifying the 20% of potential functionality that can provide 80% of the value. Here's how: When evaluating proposed functionality, ask yourself: Is this absolutely needed in order for the user to accomplish the task? Or, would this only marginally enhance the speed / ability in which the task is performed? Add the list of enhancements to the backlog, then use a tight feedback loop with early adopters to determine what to build next. #productmanagement #pareto #principles #software #userexperience

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