Stephanie Bonk

Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Interesting comparison of human-based ride hailing services (UberX, Lyft) vs autonomous-driving one from Waymo. TLDR: Waymo is more expensive and a lot slower to get you to your destination. And this is in the city where Waymo vehicles and software are probably most optimized for. This isn't to bash on Waymo and other self-driving car platforms, which have made incredible progress. That being said, the problem is not solved and it will take many more billions and many more years to get autonomous driving services like this to be price and performance competitive to current ride hailing services. https://lnkd.in/eb4uZ2Um #autonomouscars #AI

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I asked two questions on LI this week, with wildly different outcomes: 1. What's the hardest part about scaling robotics? 2. What's the easiest part, or the one that you overestimated? Question 1 had plenty of insightful answers and great discussion. Question 2? Not a single reply! Not surprising: robotics is just hard. Every problem is a hair-on-fire problem. Scaling robotics is even harder. Your hair is on fire AND now you're responsible for keeping back the flames from your customers. This is exactly where Tangram Vision lives: helping the best robotics companies extinguish those hair-on-fire problems so that their customers stay happy. I'll keep posting questions about this in the coming weeks. We may not all have solutions, but understanding the problems is always the first step to success. Thanks Szymon Dzwończyk Krystian Gebis Nils Alstad Shaun Juncal Ivan Chernukha for your insight! #robotics #scaling

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This is f*cking hilarious: TuSimple's pivot from self-driving trucks to AI animation and gaming! 🤖🎮 Right now, everyone’s betting on humanoid and general-purpose robotics startups. But after 5 years in self-driving tech and building Kopernikus Auto, here’s the reality: most humanoid robotics companies will be dead. In the self-driving world, we’ve only seen a few real winners: Tesla, Waymo, and Comma.ai. Why?  - Humanoid robotics isn’t a hardware problem; it’s a software problem. - You either have infinite data, cash, talent, or a clear use case—or you won’t make it, no matter how much you raise. From over 100 companies, almost all in self-driving tech are dead or miles away from profitability. ✨ Don’t get distracted by the appeal of humanoids. Focus on real problems you can actually solve. What are you focusing on? 👇

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Benjamin Bolte from K-Scale Labs just posted his 'Robotics Predictions for 2025' containing some hot takes 🦾🔥 By the end of 2025.... 1. Humanoid hardware is commoditized - Hardware components will converge, but robot designs will remain different - Actuators no longer differentiators; industry moves towards open-source like MIT Cheetah - BOM for a humanoid will come down to a shocking <$8k! Question: What happens to incumbents like Telsa, Figure, 1X, Apptronik, etc., who have spent billions of $ in R&D to create robots with a $50 - $150k BOM? 2. No ChatGPT moment for robotics - A LOT of data (millions of hours) is needed to train a general-purpose robotics model - Language + multi-modal models will not be able to control a robot sufficiently - Progress will be incremental rather than spiky Question: If building a robot for a domain and bootstrapping to generality is a losing strategy, what's a better one? 3. More than half of humanoids will be sold to consumers - The 'killer apps' will be speech and vision - Traditional SLAs for business customers will force robotics teams away from building good generalist models - "Robotics companies will start looking more like SV tech companies, caring about user engagement and retention." Question: What are the 'killer' consumer use cases that will drive this adoption? What are the jobs to be done? Link to the full blog in the comments. What are your thoughts?

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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Waymo grew from 100K rides per week to 150K rides in two months. At 7.8M rides per year this is nothing compared to the 10B rides Uber provides. But here is the catch, Uber's costs are more or less fixed. Google's are going to come down exponentially. The cost for an AI team will go down very fast if expressed per ride. Given no drivers are needed and vehicle costs are coming down as well, it is just a matter of scaling up for Google to steal away clients from first Uber but afterwards also public transport, second car ownership and finally first car ownership. In 2025, Waymo can easily go to 1 or multiple millions of rides per month. 2026, 10-50M and in 2027, 1B per month. Giving that Tesla will want to launch in 2025 and scale in 2026, consumers will be the big winners. https://lnkd.in/e4rcAH6D

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I recently realized the most valuable aspect of #opensource software to me personally: It's that you can fix problems yourself. When I think back of the unsolved problems I've had over the years, they were all caused by proprietary software. Tech support is a time-consuming frustrating experience, and usually a dead end too. For open-source software I don't need tech support. The answers are all in the code. And I can save others some headache by contributing back. What do you think is the most valuable aspect of open-source software?

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Smart execution is the key to success in hardware startups Boom Supersonic's Blake Scholl discusses: - The importance of leveraging existing technologies - Refinement over reinvention - Prioritizing practical and user-centered design

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Many people in the robotics world reach out to me for career advice or ask about opportunities at Lucid Bots. One of the most common questions is: How do you know if a robotics company is pre- or post-product-market fit? A simple way to gauge this is by looking at their team structure (check out the LinkedIn "people" tab) and job openings. Are there roles in sales, marketing, and customer success? If so, how many? If most of the team and job postings are focused on engineering, it might be a sign they’re not yet actively selling or supporting a large customer base. Post-product-market fit companies focus on both technology AND growth—bringing in customers, keeping them happy, and expanding their reach. Also, search for their products on social media. Are customers posting pictures and videos using the product? It turns out that many brilliant robotics minds want more than just building cutting-edge tech—they want to see it make a real impact on customers, too!

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🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

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Humans have historically found the secret to some natural phenomena and put it on steroids. 💉 Consider the aerodynamic efficiency of a bird's wings – a concept we've amplified to create the marvels of airplanes and rockets. ✈️ 🚀 We're seeing something similar right with LLMs. The transformer approach to linking knowledge seems to produce an eerily similar result to what we do. I've seen LLMs hallucinate in a similar way to my 5 year old kid! 😵‍💫 We took the body of knowledge on the internet and created 100+ billion parameter models that hold information that any human mind can. 🧠 But there's more to the natural phenomena of intelligence. There's a few more unlocks that need to be discovered. Like the mechanisms behind novel problem solving. 🧮 It's only a matter of time – in my opinion – until we crack those, put those on steroids and then we're no longer in the pole position of the most intelligent entities. 🤖

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Excellent blog from the folks at Shield Capital about the infrastructure needed to accelerate autonomy! Miru (YC S24) is mentioned among a host of other awesome startups that build software tools for robotics startups. "Consider the world of web app development: developers have an extensive array of tools at their disposal, offering support for everything from coding assistance and test management to cloud infrastructure handling and beyond. This is not yet the case with autonomous systems, whose developer ecosystem remains more nascent and fragmented. Many autonomous systems developers describe how they rebuild common tooling like testing and simulation tooling across every autonomous systems development organization where they worked." This is why we built Miru. These teams deserve better off-the-shelf tooling. They shouldn't have to develop and maintain this themselves. If you're a developer struggling with CI/CD for your robot or edge device, reach out to me. Let's get this off your plate 🦾 https://lnkd.in/gk2fSHTq

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I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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I spent over half a decade working with driverless cars. I see massive potential for these vehicles to reshape NYC real estate. Waymo recently raised $5.6 billion in a Series C round led by Alphabet and supported by many other leading companies. Meaning the future of this technology feels closer than ever. These are the biggest Opportunities & Challenges Driverless cars present in the NYC market: 1. Reduced need for parking could free up valuable urban space, converting parking lots into housing, retail, or green areas. 2. Easier commutes may drive up outer borough property values, balancing Manhattan and other more densely populated areas of the City 3. NYC’s notorious traffic, pushback from unions on automation, unpredictable weather, and high foot traffic require advanced tech and regulatory adaptation. The next steps will be crucial as real estate, tech, and policy leaders navigate how driverless cars might reimagine our cities. How do you think driverless tech will impact real estate in NYC? P.S This is me in a driverless car back in 2017

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Optimize for 🚀 Bio Cores First, Silicon 🤖 Cores Second David Heinemeier Hansson makes a compelling case: prioritize human programmers (bio cores) over computer processing power (silicon cores). Here’s why it matters: 👉 Investing in programmer efficiency is far more valuable than solely optimizing for computer speed. 👉 Human labor costs far exceed computing costs. Boosting human productivity should be the main focus. 👉 In the age of AI and high-level programming languages, the bio cores drive your real gains. Bottom line: Make your programmers more productive, and watch your team (and tech) thrive. Read the full article here: https://lnkd.in/gK8HEGtk P.S. : The image below is brilliantly summarized using Napkin.ai for a quick visual breakdown.

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Open source has always been a major part of my career. I spent 13 years building MongoDB and its open source developer ecosystem, and at Viam all of the software that runs on a user's device, our APIs, and our SDKs are open source. Now, I am excited to share that Viam is now an official member of the Open Source Robotics Alliance. Let’s talk more about why open source is important to us:  Security - If machines are inside your house, your office, etc. security and privacy are paramount, and you'd want to know exactly what’s happening with that data. Open source not only makes companies responsible to write good software, but lets the community easily vet it. Innovation - I have said before, no one wants to contribute to a closed source ecosystem. We want developers to bring their ideas to life using Viam. To do so, open source is necessary, as it’s harder to contribute to closed source, and not nearly as rewarding. It keeps corporations in check - When betting your business on a technology, you are responsible for making sure there are some checks in place. By making everything on-machine open source, it means that if something happens, there are ways for users to move forward using open source without Viam. This is all just the tip of the iceberg. Open source has the power to democratize robotics, remove barriers to interoperability, and make automation much easier than it is today. Read more here: https://lnkd.in/eTc8p_Dk Open Robotics #opensource #openrobotics #viam #mongodb #robotics #opensourceroboticsalliance #OSRA

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A colleague referred me to this. Great use case in how using humans to perfect, train and scale an AI system. The difference between self driving mode of Tesla vs. Waymo. Waymo is way ahead because of using humans to train the models faster for edge cases. In driving, as we now there are too many edge cases. Similarly, a colleagued complained to me why Speechify does not work well to read legal briefs (for example the solution reads outs headers and footers along with text, it does not disregard table of contents). I told her the solution will become better. Right now we are in first or second grade. Key lesson: solutions will get better and faster for now with humans in the loop.

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