Haiyang Liu

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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Hey folks! Garrett Milster and I have only posted about Sacramento Labs once, so we figured it's time for an update 😊 **What is Sacramento Labs?** We’re a small team focused on launching software products that solve real-world problems. Think of us as a startup lab—constantly testing ideas, learning from feedback, and building tools that matter, and having an impact while doing it. **How do we decide what to build?** We like to build products that: -         Solve real-world problems in mostly ignored industries -         Are small, highly maintainable, and scalable codebases -         Make the world a better place directly, or by donating a percentage of profits to a relevant charity in that space You can learn more about our philosophy at sacramentolabs.com **What you can expect from us** We’ll be posting product ideas that are in various stages of our pipeline: Early Concept, Customer Development, In Progress, Launched, Scaling. We’d love to hear your feedback! **Our latest concept: Self-Hosted Security** Here’s the problem: - Businesses often use third-party URL shorteners to share internal tools, resources, or campaigns, but these services introduce serious risks. Enterprise companies like Google, Microsoft, and Stripe have built their own internal URL shorteners for this reason – but most businesses don’t have the resources to do that. - Phishing attacks are rampant, and links from external services can be spoofed or abused. - Branding and security controls are limited, leaving sensitive information exposed. - You can’t track link activity—who’s clicking what, when, or why. - When employees leave, their shared links still work, leaving potential vulnerabilities. Our proposed solution: Self-Hosted Security. It’s a plug-and-play solution for businesses to host their own branded URL shorteners with: - SSO for secure access tied to your company’s directory. - Link expirations to ensure old links don’t stay active indefinitely. - Audit logs to track activity and improve visibility into link usage. 📢 We need your input! Would this solve a problem for you? What features would make this indispensable? Check out the landing page and share your thoughts: selfhostedsecurity.com Want to chat? Email me at [email protected] 😊 💡 And here’s something we’re passionate about: a percentage of profits from Self-Hosted Security will go to the Electronic Frontier Foundation (EFF). Your feedback not only helps us create better solutions, but also contributes to making a positive impact. Thanks for being part of the journey!

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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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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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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I just realized: we should start saying "build memoization" rather than "build caching." We aren't really talking about least-recently-used or time-to-live algorithms, we're talking about "don't bother recompiling this piece if none of its inputs changed." (It has been puzzling me that some people are so deeply fearful of "build cache" as a concept, and honestly I think it's mostly because most build systems have non-hermetic state scattered all over the place. Maybe a slightly more precise term for the type of caching we're concerned with would clarify the semantics in question.)

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As of last year, almost 3% of Palantir alum were in a founder role. Maybe that number’s even higher now. Yesterday, we posted about some Palantir Technologies alums’ new ventures. But we were only able to capture some of the 100+ VC-backed companies founded by ex-Palantir employees. So here are some more cool companies they’re building: - Ashwin Sreenivas co-founded Decagon, a gen-AI platform for customer support. Decagon helps companies deliver better support faster, at a lower cost. (And they’re hiring on Paraform!) - Serguei Balanovich and Ka Ling Wu founded Upsolve AI (YC W24) after building a similar product at Palantir. It lets businesses offer customer-facing analytics easily and quickly, with AI. - Cobi B. founded Medicare navigation platform Chapter (also hiring on Paraform!) Chapter allows patients to get advice from licensed Medicare Advisors, providing customers with greater choice and clarity while helping them save money. - Andrew Bocskocsky is scaling Grata, a deal-sourcing platform for M&As. Grata’s B2B search engine is powered by ML and NLP technology to help investors source private companies. - Angela McNeal and Mayada Gonimah founded Thread AI after Palantir, to bring different AI systems together on one secure, observable robust platform. Congratulations on coming out of stealth mode and launching Lemma yesterday! - Eugene Marinelli is building Realm Alliance, a military-grade physical security platform for corporate security teams. Their product unifies existing surveillance systems and uses AI-enabled sensors to give teams complete real-time situational awareness. - David Benhaim co-founded Markforged. The public company is developing an industrial 3D printing platform that includes a full ecosystem of printers for different materials. In short, they’re inventing 3D printing processes for stronger parts. - Rebecca Egger is building Little Otter to reimagining children's mental health care. Little Otter’s online therapy platform offers a comprehensive, personalized approach designed for the whole family. - Former Palantir exec Alex Ince-Cushman co-founded Branch Energy to help businesses lower their energy bills and carbon footprint. - Faris Sbahi co-founded Normal Computing, building AI systems that natively reason about the real world. The company is using AI to tackle sensitive industrial and advanced manufacturing applications. - Matt Lynch and Raj Mehra founded Sage, the operating system for Geriatric care. Sage’s care platform uses real-time alerts and data insight to help caregivers effectively assist older adults, preventing injuries and injury-related deaths. - Trae Stephens also created Sol, the company behind the Sol Reader VR headset. It lets people read in a distraction-free environment.

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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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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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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With Waymo announcing the company is now conducting 50,000 paid rides per week in 3 cities, we decided to add some economic context to the announcement. Based on our analysis, Waymo is generating anywhere between $500,000 to just over a $1,000,000 a week in revenue depending on the average fare. Add in 2 more cities/expansions and Waymo is generating north of $100 million a year. How long do you think it will take Waymo to achieve $100 million a year in revenue? Let us know in the comments below. #RoadToAutonomy #AutonomyEconomy #Waymo #RoboTaxi #RideShare #AutonomousVehicle #Autonomy #Google

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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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11 years ago, I joined Falcon Social (Thanks Ulrik Bo Larsen now Brandwatch, and I was amazed by the growth opportunities with #SaaS. For the past 3 to 4 years, I’ve been immersed in the #AI field. Many possibilities I longed for over the last two decades are finally coming to life.  - I'm so 🔥 up what this new area will truly bring. Two things I see true in a few years: 1. There will be $100M+ vertical companies, but the catch is they will be built with 1/10 of the people we need to build $100 SAAS companies. 2. Many companies will struggle in each sector since it's now easier than ever to develop products quickly; speed no longer serves as a protective barrier. For you nerds, this is a fantastic source of content by Oliver Molander

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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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PSA: Stop using the original OpenAI CLIP weights. I see a lot of people using CLIP for multi-modal retrieval. What many people may not know is that there are 100’s of CLIP models now since OpenAI’s original release. These newer models offer significantly better performance across many benchmarks. For product search related tasks we recommend the following. + Fastest inference: ViT-B-32 [laion2b_s34b_b79k] + Best balanced: ViT-L-14 [laion2b_s32b_b82k] + Best-all round: xlm-roberta-large-ViT-H-14 [frozen_laion5b_s13b_b90k] You might notice these are not the ones that perform the best on the benchmarks. Why? Because the benchmark only test for very specific tasks which are often quite different from search and recommendations. These are ones we have found to be the best for product related search and retrieval tasks and generally very good first choices if you are unsure where to start (even for non-product related search). Here is a hugging face space with all the data: https://lnkd.in/gxu5UYkt Read more: https://lnkd.in/g5_D4ccs

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Seeing lots of great founders disrupting EDA with AI Most EDA tools don’t do real “engineering,” but rather take the rote tasks out of implementing engineering output from centuries of “experience” Can AI collapse the heuristics of the best engineers that ever lived to actually automate engineering? Hard to beat EDA at its own game by making it better with AI Generating the boundary conditions that engineers normally use to start the engineering process and better optimized at the end would be a great complement to existing EDA

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Waymo is probably both more expensive and longer waits on average than Uber or Lyft today, but they are close enough, and rapidly getting better. These markets will flip to fully autonomous when the tech is ready.

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Over the next 10 years, if not sooner, we will see 90% or more of vehicles on the road driving fully autonomously. Most of them will be robotaxis and part of the Tesla Robotaxi Network. I conducted an analysis of how much each Tesla owner could potentially earn by deploying their Tesla into the Tesla Network. At 50% utilization, they could generate $24,000 in net profit per year. This figure accounts for expenses such as FSD, charging, cleaning, insurance, vehicle maintenance, and financing costs. The future of Mobility-as-a-Service is poised to become a multi-trillion-dollar industry.

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