Brian L.

🚨🚨 🚨 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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Huge congratulations to the LanceDB team, Chang She and Lei Xu, on securing an $8M funding round from CRV! Their mission to build The Database for Multi-modal AI is an exciting one. It's amazing to see how far they've come since Chang's days as a star VP Eng at Tubi. Check out the TechCrunch article for more details on their journey. #AI #technews #fundinground

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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Since the YC deadline to hear back for W25 is coming up next week, here’s the story of how we went from 2 students coding in an MIT dorm room to being full-time YC founders building Delve in SF. For all you founders anxiously waiting to hear back, just keep building — you’ll be successful regardless of the decision. -- In Fall 2023, my co-founder Karun Kaushik and I were studying AI at MIT and spending all of our free time building an AI medical scribe to help doctors. We had vaguely heard about YC from a friend who had just gotten accepted but had no plans of applying whatsoever. However when the YC deadline rolled around, we spontaneously decided to put in an application on the very last day. We spent that whole day in Karun’s dorm room typing up the application and filming all the required videos. We finally hit submit the minute of the deadline! By the next day we had already moved on. We went back to the usual rhythm of coding in the dorm room, calling with doctors, attending conferences, etc. If anything, we were even more motivated now to get more traction and be successful regardless. Later that week, while we were eating in the dorm kitchen, I noticed I got an email saying we had a YC interview scheduled. My heart rate spiked. I jumped up out of my seat and immediately told Karun. I never seriously considered YC, but for the first time it hit me that it might actually be a possibility. When the day of the interview rolled around, we did mock interviews with former YC founders and focused mostly on calming our nerves. More important than the actual content of the interview, we wanted to show our excitement for building and hacking together as co-founders. Unfortunately the interview caught us by surprise. The group partners didn't seem to care as much about our actual AI medical scribe. Instead, they seemed more curious about a side idea we had listed in our application that we wrote up in a day. The idea was about streamlining HIPAA compliance (since we had to go through HIPAA for our AI medical scribe). By this point, we were already seriously considering pivoting into this and had made a lot of progress on the idea. The YC interview was our tipping point Not too long after, we got an ominous email titled “YC.” We were freaked out. I could not sleep the entire night. Then, in the middle of class the next day, I got a phone call from Karun with Surbhi on the other end of the line. My heart rate spiked. I ran outside of the classroom. That turned out to be our acceptance call:) -- For everyone out there still waiting to hear back, just keep grinding. Build as if you’ve been rejected and you might just be pleasantly surprised. I’m also always here to help with mock interviews/give advice! We’ve helped a handful of friends through the YC app process and are always here to support other founders!

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AI Innovation Taking Center Stage The recent news of /dev/agents raising a staggering $56M seed round at a $500M valuation is a testament to the transformative potential of AI-driven developer productivity tools. This massive funding round highlights: -The growing demand for AI agents that streamline workflows, enhance efficiency, and supercharge development teams. -How investors are betting big on technologies that redefine traditional software development processes. As someone passionate about leveraging AI for impact, this news reinforces the importance of continuous innovation. Tools like these aren’t just about productivity—they’re shaping how the world builds technology. Kudos to the /dev/agents team for this milestone! What are your thoughts on the future of AI in development? #AI #StartupFunding #Innovation #TechTrends https://lnkd.in/gXnd5DaB

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The biggest reason for churn off AI products is that they lack accuracy or reliability. You can fix that problem for RAG applications in one line of code with Pongo's semantic filter. On a benchmark of 100 questions from Airbnb's 2023 10-k, we improved top 3 accuracy by 40% over OpenAI embedding vector search. Try 500 queries free -> https://joinpongo.com/ Benchmark results -> https://lnkd.in/gaStUZgq

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Old school and probably not popular but I still believe products should be customer-satisfaction focused/led rather than technology-led. Use cases for AI should be byproducts of the product improvement process primarily. The thing though is that founders need to fundraise so they're likely to sprinkle some AI on their decks to even look serious. Not sure that's how the most enduring companies have been built. Of course, if sources of capital become AI-biased, founders have little choice. Happy to keep an open mind and dive in when a use-case that can 10x customer experience for Good Tenants customers and users shows up.

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YC's "request for startups" - Winter 2025 - Government + Public Safety 1. Build LLMs to automate specific government tasks like form filling, application reviews, and document summaries 2. Create computer vision systems for license plate detection and crime prevention 3. Build software that reduces police paperwork time from hours to minutes 4. Create tools for emergency response coordination and dispatch 5. Build platforms that help communities and law enforcement communicate effectively - Manufacturing 1. Build ML-powered robotics systems that reduce labor costs in US manufacturing 2. Create automation tools specifically for American factories competing with overseas production 3. Build specialized industrial robots for factory inspection and maintenance 4. Develop systems that help manufacturers operate efficiently in US industrial hubs - Chips + Engineering 1. Build LLM tools specifically for FPGA design and optimization 2. Create Al systems for ASIC design that reduce development costs 3. Build tools that optimize specialized computation like crypto mining or data compression 4. Develop Al-powered CAD/CAM software that makes engineering tools more accessible - Stablecoins 1. Build platforms for businesses to hold and manage stablecoins 2. Create tools that make it easy for developers to integrate stablecoin payments 3. Build systems for banks to issue their own stablecoins 4. Develop infrastructure for cross-border stablecoin payments and remittances - New Jobs 1. Create tools that help people run local service businesses 2. Build platforms enabling people to earn income providing services online 3. Create systems that help service providers work more efficiently with Al assistance 4. Develop tools that help small businesses operate at the same level as large corporations #YC

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If you are building with LLMs, you can get really far in terms of building a highly accurate and reliable product by optimizing your development cycles for fast iteration: ➡️ Pick an off the shelf model - Llama /OpenAI/Anthropic. ➡️ Religiously track the prompt-model generation pairs in production and patch each edge and corner case you identify through a combination of prompt iteration and retrieval optimization(for RAGs). Avoid getting stuck trying to research the current best model or wondering whether to finetune on your custom dataset. Focused iteration will get you to your ideal outcome faster than everything else.

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Introducing Superpipe Studio: a free and open-source tool to curate datasets, run evals, conduct experiments and optimize your LLM pipelines for accuracy, speed and cost. We've spent the last year building with LLMs and working closely with a number of companies and encountered the same problems repeatedly: - evaluating LLM software is hard, because... - collecting high-quality, representative, correcty-labeled data is hard... - which makes it hard to objectively compare different techniques, models and parameters... - which prevents continuous iteration and improvement of LLM software If you want to build a winning AI product - it all comes down to continuous evaluation and optimization. Unlike traditional software, you can't build it once, write some unit tests and sleep well at night. You need a virtuous cycle where product usage generates data that goes into your eval system, which is used to evaluate new models/techniques or fine-tune your own model. We tried existing tools in the market and found none of them really worked well to implement the virtuous cycle we needed, so we built a lightweight tool for internal use: Superpipe Studio. It helps you do 3 things: 1. curate and manage datasets 2. run experiments and compare them on accuracy/speed/cost 3. monitor your production pipelines (and expand your datasets with production data) We think everyone should have these abilities, so we're making Studio completely free and open-source with no restrictions. Studio is a work in progress and rough around the edges but still robust. We encourage you to try it and modify it to suit your needs. The gold-standard of AI software is Tesla/Waymo's full-self driving. Not every AI system needs 5 9's of accuracy, but the process for making your AI system better is the same that Tesla and Waymo followed. And it all starts with high-quality labeled data and a robust evaluation and optimization system. (Github and docs in comment)

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Hope you’re all well! I typically don’t share much publicly about @AptDeco. Most know me as a heads down kind of guy. But today I felt compelled to share some important and exciting developments. We launched a community investment round for AptDeco and I'm excited to share that we've raised over $3.5M from our customers, supporters and institutional investors. The overwhelming response from our community is truly humbling. Nothing is more validating than customers choosing to invest given their belief in the AptDeco team and our mission. Given this enthusiasm, our track record, and our plans for the future, I felt it was time to share this with all of you as well. When we started AptDeco, we set out to build a marketplace to make buying and selling furniture easy. But here’s what most people don’t know—under the hood, AptDeco is so much more than that. There’s a powerful tech engine driving everything we do, and that’s the real magic behind our success. With over $84M in furniture sold, partners like West Elm, Pottery Barn, Article, and many others + reaching profitability, we couldn’t be more excited about the years ahead. We’ve built a platform that has outgrown and outlasted our competitors - All who were much better funded. Now we no longer have competition. We’re the last man standing. Based on our current business trends, immediate plans, and a $58B market ripe for the taking, 2025 is poised to be AptDeco’s breakout year. With that we’re providing access to invest alongside the AptDeco community and our institutional investors. If you’re interested or just want to catch up feel free to message me (It would be great to hear from you :) or you can learn more about our progress and investing here:

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The birth of a $63B dollar idea. This is how DoorDash did their first public pitch ⤵ 1/ The Humble Beginnings ➠ A $63B Idea Started Small ➝ Tony Xu pitched DoorDash at YC demo day, August 2013. ➝ Back then, it wasn’t DoorDash; it was Palo Alto Delivery. ➝ Founders were the only Dashers. ↳ Office? School housing. ↳ Marketing? Dorm room flyers. 2/ The Leap Forward ➠ From Dorms to Domination ➝ Be ready to iterate. ➝ Listen to your customer. ➝ Be ready to pivot. ➝ Scrappy solutions can lead to scalable empires. ➝ Staying in the trenches sharpens your problem-solving skills. ➝ Investors care less about polish, more about potential. ↳ Even giants start scrappy. ↳ Relentless focus beats resources. 3/ Lessons for Fellow Founders ➠ Persistence Pays ➝ Stick with the grind—momentum compounds. ➝ Humble starts don’t limit massive outcomes. ➝ Small moves, smartly made, snowball into big wins. ↳ The only way out is through. What’s stopping you from launching that idea today? ▶ How much do beginnings really matter? ▶ Are we undervaluing grit over glamour? ▶ Could your “scrappy idea” be tomorrow’s billion-dollar breakthrough? // Repost this ⇄ // Source: via Kevin Jurovich 🙏 Follow me for daily posts on emerging tech and growth: https://lnkd.in/gqzS_9Tf

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TensorFuse (YC W24) is now one of the most popular deals for running serverless GPUs on YC! We're the trusted infra partner for many fast growing YC companies. Here’s what we do for our early users: 1) A private Slack channel for any MLOps related support. 2) Help with tasks ranging from downloading models from Huggingface or open-source repo, to writing inference code and deploying it on autoscaling infra. 3) Help with containerising your models (if you haven’t done it already) 4) Expedited approval of GPU quota limits (we have a dedicated support channel with cloud providers) If you want to deploy custom ML models on auto-scaling infra and don’t want the hassle to manage it, get started with our docs: https://lnkd.in/gS7KAMHN

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Who's got a Waymo experience to share? I just had my first truly bad one. First, I should say I love Waymo. It's not in many places yet, but it's poised to be everywhere soon. The app tells me I've done 26 rides covering 97 miles in SF and mostly it's been an exceptional experience. Apart from some quibbles like getting dropped off a block away up a big hill, it's a zen-like experience and a smooth better-than-human-driver experience on the way to wherever I'm going. But I think we're all bound to experience some hiccups as we journey to the future. In my case, I was taking a trip into the Financial District for an event and I had 6 minutes to spare given the ETA. Then all of the sudden it decides it wants to pull into the left lane, which is filled with parked cars. I sit at first amused at this decision. It pulls up right behind the parked car and waits. Then it goes through a dance of deciding it wants to get back in the original lane (everybody loves to snub a driverless car in traffic!) and pulling ever closer to the parked car ahead. Miraculously the driver of the parked car shows up, loads his trunk and departs with ease. The Waymo decides the next course of action is to pull directly behind the next parked car in the lane. My amusement fades. I call support via the touchscreen. My 6 minute buffer that had evaporated to 1 minute suddenly tells me I'll be 13 minutes late, a driver assistance team is dispatched and on their way. Yikes! Then magically (I think they manually took control), the Waymo behaves like a normal driver and inches back into the normal lane and I'm on my way, arriving 1 minute late to the meeting. So in the end, a bit of time stress for someone who really likes to be on time (I started a time company after all) and not really world-ending. In fact, despite this unfortunate experience, I'll be loyally riding again. And so I guess this ends as a parable about what Product Market Fit looks like. If you can mostly deliver superior experiences, there's room for rough edges. (pictured below, mom's first trip in a Waymo)

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Excited to welcome Lume (YC W23) to the KV portfolio! Lume uses AI to automate data mapping, empowering customer success & engineering teams to effortlessly understand, map, and validate data.

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Big news from StackAI! We closed a $3M Seed Round led by Google's AI fund Gradient Ventures. We now power the GenAI agenda of public companies and fast-growing startups, have over 40k registered users, and support more than 200 organizations. LLMs are the steam engines of our century. But it is only when we connect this engine to a set of wheels and a chassis that we can build truly powerful machines that take us beyond where we could go before. This is not "the bicycle for the mind," as Steve Jobs put it for the Macintosh; in comparison, this feels more like "the car for the mind"... Thanks to Stack AI, you don't need to reinvent the wheel or direct your best engineers to infuse AI throughout your organization. Our platform allows you to deploy custom AI assistants and AI-powered workflows with the click of a button. Both technical and non-technical users can leverage our end-to-end drag-and-drop visual interface and APIs. The next step in our journey is to accelerate the adoption of Artificial General Intelligence and become the Middle-Layer of AI. We are hiring the best engineers, makers, marketers, and operators to bring GenAI's power to as many people in the workforce as possible; reach out if you'd like to be part of this once-in-a-lifetime opportunity! A heartfelt thank you to all our existing investors and lead investors, Y Combinator, Gradient Ventures, Soma Capital, Epakon Capital, Beat Ventures; your support has been nothing short of exceptional. Thank you as well to our mentors Nicolas Dessaigne, Tom Blomfield, Richard Aberman, Michael Seibel, Garry Tan, Collin Wallace, Ashutosh Joshi, Colin Rogister, Dave Anderson, and Stephen Balaban. You can find our official announcement and blog post in the first comment.

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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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One interesting thing about Deepseek, according to its company CEO Liang Wenfeng, is that unlike many Chinese AI companies, they didn’t just copy Llama & tweak a few things, they innovated on a new architecture which allowed them to achieve these prices. Interview linked below.

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