Miguel Rios-Berrios

This is more of a "talking to myself in public" thing than any kind of fully fleshed out hypothesis, so take this with the appropriate grain(s) of salt. But if I were interested in AI and were looking for an angle to explore that might yield something interesting, I'd start working on learning "Multiscale Mathematics" (also known as "Multiscale Modeling"). This is rooted in my personal belief that there has to be a "multi-scale" aspect to intelligence because there is such an aspect inherent in our underlying physical reality.

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RAG search is leaving money on the table. Six low-hanging fruits: synthetic data, date filters, better feedback, tracking metrics, full-text search, and question-like chunks. Most teams are overlooking these. Don't be one of them. https://lnkd.in/gHTnWxzZ

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Unpacking AI Reseach Product Manager role so you don't have to. 🥅 The goal of AI Research Product Manager is to make sure that researchers work on something that will ultimately drive business outcomes. 🥅 Researchers want to research, a PM wants to create impact. 📜 AI Research Product Manager should be comfortable to read and understand scientific papers and should have a good idea on what might come next. 🗿 The challenge of the role is often to find the intersection of user and customer needs and the things where the research space is pushing towards. 🧪 It is also extremely challenging for a PM to build credibility and interact effectively with scientists. After all, you are not a scientist. 👩‍🔬 To be efficient in this role you should be comfortable building prototypes by yourself. These notes are from my recent podcast with Guillaume (Synthesia / Google Search). Subscribe to my substack to get more product management insights: https://lnkd.in/eF76ZQqD

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McKinsey Says that Product Marketing Managers (PMMs) are the Quiet Force Behind Software Success A new McKinsey study (link in the comments, https://lnkd.in/gE9vyDxx) has put PMMs in the spotlight, emphasizing their growing importance in the software industry. Its findings reinforce the ideas I've shared in my previous posts on the evolving role of PMMs in the AI era (https://lnkd.in/ggjfSawh) and their pivotal role in driving personalization and efficient growth (https://lnkd.in/erV6Nipd). Here are some key insights from the study: ► Instrumental in Product Launches - PMMs are essential in launching successful products in today's competitive market. ► Revenue Growth - Companies in the top quartile in revenue growth have a 25-30% higher ratio of PMMs to PMs compared to bottom quartile companies, reinforcing the value of well-resourced PMM teams. ► Bridging Gaps - PMMs align product value with market needs, bridge gaps between teams, and orchestrate launch processes. As Goksu Nebol-Perlman, VP of Business Product Marketing at Meta, said, "Product marketing is the glue that keeps the product, the market, and the customer together." Why are PMMs critical more than ever? ► Market Differentiation - With changing market dynamics and increased competition, PMMs help products stand out and succeed. ► AI-Powered Analytics - As data and AI-powered analytics become essential for decision-making, PMMs use these tools to guide strategies and gain deep customer insights. ► Efficient Growth - AI helps PMMs with analytics to make smart decisions and target efficiently for sustained growth. ► Cross-Functional Collaboration - In a world where teamwork is key, PMMs are at the center of collaboration to create better customer experiences. By leveraging AI to understand customers deeply, make data-driven decisions, and deliver personalized experiences at scale, PMMs are driving efficient growth and success in the software industry. Visionary companies that recognize and invest in empowering their PMMs will be better positioned to thrive in the AI era. Your thoughts? ► How is the role of PMMs evolving in your organization? Are they being recognized as more than just the Masters of the One-Pager and Battle Card Wizards? ► Have you empowered your PMMs to drive strategic initiatives? ► How are they using AI to understand the market, analyze product-market fit, prioritize top segments, craft defensible positioning and messaging, develop personas and buyer's journeys, launch products, enable sales, and align cross-functional teams? Interested in collaborating and learning how to inspire PMMs with what's possible with AI? Please feel free to DM me. #ProductMarketing #PMM #StrategicMarketing #AIInMarketing #AI Michael Conn Product Marketing Alliance Rowan Noronha Product Marketing Community GrowthPath Partners

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~40% of Frec’s customers are engineers working in tech, many of whom expect frequent and large capital gains. They also prefer to self-manage their money, which makes Frec an ideal platform. Meet Evan: an engineering leader who upgraded from investing in ETFs to direct indexing. Direct indexing appealed to Evan because it offers comparable returns to his ETFs while saving him money on advisory and ETF fees. “ETFs are true and tested, and giving folks a different way of investing in the market while saving more money is really appealing.” He’s used many other robo-advisors and other fintech tools, but “Frec seems to maximize tax loss harvesting opportunities better than any other product that I’ve used.” He also likes Frec's transparent fee structure.  “I feel like with a lot of other companies, that’s muddled in with the cost of investments,” he told us, “and you don’t really know how much you’re paying or how much you’re not saving as a result. I think Frec does a really good job of laying all of that out and giving you a true understanding of the cost of these investment products.” Read more about his Frec journey here ⬇

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Predictions for the future of LLMOps are varied.   But what is clear from speaking to Clemens Rawert, Co-Founder at Langfuse;   Is that users have become a lot more thoughtful and systematic as LLMs have matured.   And building golden datasets are going to be even more important for quality benchmarks.   Just 2 points of importance at Langfuse (YC W23), Take a look at the video to find out what else.... 💡

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"Data democratization" (an iconic buzzphrase) has been around for a long time. Tons of companies have promised to deliver it... with mixed results. Lots of dashboards, heavy implementation, data siloes, the list goes on. But advancements in AI have made "simple data access for all" a reality. Check out Bessemer Venture Partners's Data Shift Right roadmap. Thanks for the shout out Lindsey Li, Yael Schiff, and Amit Karp.

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Listening to Julia Schottenstein who was at dbt Labs before joining LangChain as an early employee to “solve all of the problems that aren’t specifically in product and engineering” talk about AI application design is 45 minutes well spent. Given - “I think we have a million developers now who use LangChain, and our partner ecosystem is really important for making LangChain as special as it is.” - it’s especially interesting to consider these design insights. Top 3 takeaways from Julia’s interview on “The current state of the AI ecosystem” episode of The Analytics Engineering Podcast by Dan Poppy (dbtLabs). 1: Foundation is set, now it's about unleashing next level creativity. Question: “How much do you think the creativity being unlocked in the LangChain ecosystem is still modest relative to what will happen with the next major innovations in the underlying model architecture? Are we seeing the thing or are we just seeing a tiny little precursor to the thing?” Answer: “It has to be the latter. I've been in this space now for a year, and the types of applications people are building today versus last year are very different.” Question: “Because the models have gotten better or because people have gotten better at using it?” Answer: “Skillset mostly. People have figured it out. They've gotten more of the basics down. They've figured out how to push the technology further. Yes, the models are getting better as well and that helps. But as people learn best practices for prompting, for fine tuning, for testing, we can just go a lot further. 2: Accelerating the feedback loop to iterate, faster / better “And the other cool thing that we're, we're starting to experiment with is feedback from production. People liked your responses; can we use that to influence and improve your prompting strategy or improve the way that you actually can design your application.” 3: Jobs to be done, application logic + design “As you start to dissect and think about the different types of user experiences that are now possible, how do we design for them? It's totally new worlds that I took for granted. It is these small new ways to interact with your application and software.” “Your application logic has to reflect how people are going to use your app. And so if they're coming in with certain types of problems that they need to get solved, you're going to design your app in a different way.”

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In my experience of interviewing and working with many product leaders, a common trait among the great ones is treating their craft more as a science than an art. Delighting users, creating magical experiences, and ultimately developing software that users love to use is a science. An immediate red flag during interviews is when product leaders can't express details beyond "talking to customers" on how they build lovable products. While talking to customers is paramount, I listen for the structure and tools they use to become customer-centric and experts in the users they serve. Here’s what I look for: Methods to Listen For When Evaluating How Product Leaders Create Lovable Products: 🗺️ Journey Mapping: Understand what users do before and after they interact with your product or attempt to accomplish their job. 🔑 Key Moments Identification: Reveal the key moments that matter in the user journey. 😌 Emotional State Awareness: Understand the emotional states of users and how they change as they progress through the journey. 📊 Data Collection and Analysis: Collect and analyze data to understand funnels, drop-off rates, attrition, etc. I’m also on the lookout for specific techniques on how the product leader builds high-performing product teams. Here are some of the practices to listen for: 🏆 Recognition and Rewards: Establish clear KPIs that define success, create accountability, and drive alignment. Implement a system for recognizing and rewarding team achievements and individual contributions. 🤝 Consistent 1-on-1s: Conduct regular one-on-one meetings to mentor and guide the career and development of the product management team. 👥 Team Collaboration: Allow teams to craft their process and evolve it over time. My favorite team performance ceremony is the retrospective, where the team can gather in a safe place and collectively define how they will improve. 🔄 Cross-Functional Alignment: Create alignment between product, engineering, sales, marketing, and leadership. This involves regular cross-functional meetings, transparent communication of goals and progress, and a unified vision that ties the efforts of all departments together. Ensuring that all teams are moving in the same direction is crucial for driving consistent and sustainable growth. Overall, there are many important items across both themes that define effective product leadership. By listening for a product leader who employs these structured methods, companies can make a better hire to turn insights into actionable strategies, ensuring that the user experience is not left to chance but meticulously crafted and continuously improved, while building and managing high-performing teams that drive success.

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The best use case for overseas development I've heard: We have customers in the eastern hemisphere. To support those customers better, we're standing up product, dev, and support teams to help serve those customers needs. Two key ingredients in making that successful: commensurate pay and the same level of performance and accountability as US peers. #software #international #teammanagement #overseasdevelopment #startup #leadership #coaching

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Parsing tables from long pdfs is hard today, but getting easier everyday. Retrieval of mid sections of the document is hard today, but getting easier everyday. Taking a judgement call, getting everyone on the same page, decision making with imperfect information, communicating to inspire, asking the right questions, having an agenda is hard today, and will remain hard tomorrow. #Skills #AIAge #HumanEdge

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🚀 **Exciting News: Introducing Intrinsic Research** 🚀 Today marks the launch of Intrinsic Research, a brand-new Substack where we dive deep into the evolving world of data science, machine learning, and AI—while exploring their broader impact on society and culture. 🔍 What can you expect? Each week, we’ll break down complex data science concepts, share insightful stories from the cutting edge of AI, and explore how these technologies are shaping the future. Whether you're a seasoned tech expert or just curious about where AI is headed, Intrinsic Research is designed to keep you informed and inspired. 🎯 Why Subscribe? - Free insights into major trends and foundational concepts   - Exclusive, subscriber-only content and full archives for paid members   - Access to community discussions and special perks for founding members  Don’t miss out! Join us on this journey to unlock the mysteries of data, tech, and culture. #DataScience #AI #MachineLearning #TechCommunity #Substack #AIRevolution #IntrinsicResearch #TechInnovation #NewBeginnings

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I do a lot of AI automation work, much of it involving batch processing and reprocessing of vast amounts of text. The low cost and high quality of Claude 3 Haiku (25 cents per million tokens in and $1.25 per million tokens out) has been fantastic. But today, OpenAI released GPT-4o-mini for use in API calls, driving the cost of AI automations even lower: 15 cents per million tokens in and 60 cents per million tokens out! To give you a sense of the savings: I once accidentally ran my batch processor using the high-end GPT-4o model. With 12 million tokens submitted and 2.5 million tokens returned, it cost me $97.50. With GPT-4o-mini, today that cost is only $3.30. That’s over a 96% savings! 😲 I have reviewed the Llama 3 8b + 70b Meta open-source models for these automations. The cost is lower still (the model is free, but hardware time is not). However, in my tests, the quality just isn't the same. Plus, considering the additional effort to host an API to access the Llama 3 models, my engineering time and money have so far been better spent using the low-cost models from OpenAI and Anthropic. Anyone else finding similar results in savings + quality?

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🤔 Looking at YC's latest Request for Startups struck me: Every category—gov tech, manufacturing, fintech, space—has AI humming under the hood. 🔄 Maybe it's not about having AI (like it's not about "having electricity" ⚡). It's about reimagining whole industries with it. 🚀 Checkout the whole article: https://lnkd.in/gcQkKMja

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At Speakeasy we love working with devex leaders like Michael Heap to continuously raise the bar for what is "best in class" 🔥 We've been super lucky to work with him on what makes a great developer experience for iac and terraform. Come hear him and Thomas Rooney talk through how you can leverage OpenAPI and your API supply chain to build a provider and unlock the Terraform ecosystem. Link to the livestream in the comments 👇 #terraform #codegeneration #openapi

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🇪🇺 Be part of the conversation on July 11th at 3:00 PM CEST! Our #NonprofitPulse2024 webinar features a rockstar panel discussing the latest nonprofit trends across fundraising, data security, and the development of AI. Save your spot today! #Fundraising #AIforGood #NonprofitTrends

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