Jerene Yang

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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This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders of magnitude in the past decade. This introduces massive implications in a world of AI Agents. For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. Yet the same has not been possible for essentially any non-digital services. Historically, anytime you want to solve non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend to only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. This also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve. Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. Just as Moore’s law steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents present the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.

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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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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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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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7 RECOMMENDATIONS FOR HARDWARE STARTUPS It’s hard to understate the difficulty of the v1 launch of a consumer hardware company. I see a ton of new HW startups coming out. Here are some counterintuitive recommendations… 1) Target a small market for v1, not a large market. You need a small number of people to love your product and this will earn you the right to build a v2. You have to understand your v1 will not make you a successful company. Your v1 needs to prove that you deserve to exist in the first place. In the case of WHOOP, we started with pro athletes who were willing to put up with our v1’s short battery life and flaky Bluetooth connections because they so desperately needed Sleep and Recovery data. A broad consumer base would have rejected it. 2) Be great at no more than 1-3 things. If you try to be great at more than that, you will build a hardware that is mediocre at a wide range of things. You won’t think it’s going to be mediocre, but it will be. 3) You want a small launch. Not because you want small sales but because you want to have a steady ramp. It’s much better to do 100k of sales steadily over 12 months than in 2 months with a huge drop off. Keep in mind you need to manage your manufacturing partner and that there’s nothing better or more efficient than steady and predictable. 4) CAC is your enemy. Referrals are gold. The magic of referrals is that they signal high NPS and they help you grow steadily. Again everything that helps you have a steady HW manufacturing plan is good. Building a referral program for v1 launch is a good use of your time. 5) Be excellent at Software. Turns out being good at both Hardware and Software is a hard thing and something that your BIG COMPANY competitor is actually not great at (excluding Apple here). For some reason, there’s such a magnifying glass on the Hardware development, that Software is then overlooked by consumer startups. Don’t make that mistake. You can be great at both. 6) You should spend an unreasonable amount of time and money on ONE aspect of your Hardware. Could be the design or a technical breakthrough. But something needs to be SPECIAL. In our case, we designed a modular battery pack that allowed Whoop to be charged without taking it off your body. This made our development timeline much longer, but it allowed us to achieve something that no other wearable has achieved since: 24/7 health data. 7) Finally, have an unrealistic belief in yourself and your team. You are not supposed to succeed and the odds are stacked against you. Building hardware takes confidence and you’re up against big players who will rip you off. We went so far as to leave a message for our competitors on our circuit board (pictured below). In the case of Amazon and others, the prophecy came true. If you’re building consumer hardware, I wish you nothing but success. It will be super hard, but super rewarding. Good luck 👊🏼 #consumer #hardware #wearable #startup #whoop

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Alphabet committed another $5 billion recently to Waymo's self-driving cars. A renewed vow of confidence. Per my post yesterday, fast growing additional compute and better AI architectures are changing self driving cars rapidly. Notably, some traditional car manufacturers pulled out investments in full self-driving cars. It didn't think the tech was going to be ready for decades to come, if ever. I think it may be a mistake given AI and compute grow exponentially, not linearly. As an example, it may take 20-40 years to achieve 1% of progress, then just 5-10 years for the remaining 99% (instead of 1,000s of years per our intuition) link in comments #AI #Transformers #waymo

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"So getting back to NVIDIA, I don’t see any force stopping the juggernaut in the next few years, other than a huge decline in the need for AI compute, and that seems unlikely. Any startup competitor could easily be acquired by NVIDIA for tens of billions of dollars and it would barely be material given NVIDIAs market cap. And while the other major chip companies are mounting a challenge, particularly AMD, the demand for GPUs is so great, and will be for the next few years, there is room for massive growth for multiple players. When NVIDIA eventually slows down, I suspect it will come from one of two areas. The first is a surprising shift in market use cases. Given the long development cycles of computer chips, it’s hard to be as responsive to the market demands as software companies can be. And product designers have to choose some level of predictability and reliability in their components and their partners, which is why they favor proven tech over innovative new designs most of the time. But the way NVIDIA benefited because their chips for graphics cards happened to be a good match for new AI workloads - I think something similar could happen to slow them down. Some new popular AI workload will be a better fit for something other than a GPU, but a chip that is already established in the market in other ways. Or secondly, it comes from a company that is building their own chips and decides to get into the broader semiconductor business. This means Apple, Amazon, Facebook, or most likely - Google. But all those companies, even if they can compete technically with NVIDIA, lack the infrastructure to sell and support chips and those who develop them. Building those capabilities will be slow." https://lnkd.in/dFpj5hqv

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There is a recent wave of criticism against 1:1s that goes pretty much like: 1) feedback is learning so it's best when shared all together, so everybody learns 2) when somebody has an issue, they should discuss it immediately and not wait for the 1:1 You will notice this take almost always comes from CEOs (e.g. most recently Jensen Huang and Brian Chesky) Strong, hands-on CEOs tend to optimize things around themselves. So, for the use of their time, a single meeting where to share all the feedback in e.g. two hours is more efficient than separate, individual meetings with all the leadership. But is it for everybody else, too? In that single meeting, the CEO cares about 100% of the agenda, and each other probably about ~10%. So we are trading everybody's time for the CEO's time. Also, how much room do people have in that very meeting to discuss thoughtful feedback and personal stuff? Not to say how comfortable they are in front of everybody else. We should be careful at cargo-culting stuff from—even successful—examples. cc spotted this post thanks to my friend Aviv's comment

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If you invested $10k in NVIDIA's Seed Round of venture funding in '93 and kept it through the '99 IPO til today, it would be worth ~$100,000,000 🚀. Let's look at the early funding/valuation rounds of NVIDIA through today: Founded: Apr. 5th, '93 by Jensen Huang, Chris Malachowsky & Curtis Priem Seed: Jun. '93, Raised $2M at a ~$10M Valuation, Led by Sequoia Capital Series A: Dec. '93, Raised $4M at a ~$20M Valuation, Led by Sequoia Capital Series B: May '94, Raised $10M at ~$40M Valuation, Led by Sequoia Capital Series C: '97, Raised $25M at ~$100M Valuation, Led by Kleiner Perkins Series D: '98. Raised $100M at ~$500M Valuation, Led by Microsoft IPO: January 22nd 1999, at a $626 Million Valuation 2004 Market Cap: ~$3.5 Billion 2009 Market Cap: ~$6 Billion 2014 Market Cap: ~$14 Billion 2019 Market Cap: ~$135 Billion 2020 Market Cap: ~$314 Billion 2021 Market Cap: ~$753 Billion 2022 Market Cap: ~$600 Billion 2023 Market Cap: ~$1.1 Trillion Current Market Cap: ~$3.5 Trillion Angeles Investors CodeGPT #ExponentialGrowth #investing #ValueCreation #Venture #Startups #AI

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Puff pieces like this really annoy me. In it, there's the implication that NVIDIA doesn't put much stock into technical interview performance. This is nonsense. You only get to reference checks if you pass your interviews. According to insiders, you're unlikely to pass if you do poorly in just one onsite round (see our guide to NVIDIA's process linked in the comments): https://lnkd.in/g97kPGJV

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Some takeaways and opportunities for new AI product tooling from my time at KubeCon last week: 🖥️ CPU Inference and On-Prem Deployments: While we're still in the early innings of GPU Inference optimization, there are opportunities worth building towards in the CPU inference space as well. Optimizing CPU inference on-premises is critical for reducing cloud reliance, improving data privacy, and cutting costs. Companies aim to streamline local compute efficiency by enabling faster, more resilient operations with reduced network dependency. 🎮 Texture Generation and Modeling Behavior in AI Systems: Advanced AI techniques, such as texture and mixed-generative models, are proving useful in applications requiring nuanced behavioral analysis and are particularly valuable in industries like gaming and simulation, where creating realistic environments and interactions is crucial. However, training these AI models often requires vast datasets and considerable processing power. Infrastructure teams must ensure that these models can operate at scale, often requiring hybrid solutions that span both on-prem and cloud environments. 👀 Observability and Real-Time Monitoring on multi-format data: AI-powered observability tools can aggregate and analyze systems across data from kernel, applications, and infrastructure layers and do so in real-time, improving troubleshooting and lowering MTTR (mean time to response). However, balancing insights with rising data storage costs remains a key challenge for enterprises. 💻 Modernizing Codebases with AI Transformation Tools: AI tools are helping modernize legacy codebases, reducing technical debt and easing maintenance. While adoption challenges persist, these tools are evolving to make code updates faster and more efficient. Solutions like Moderne, and Codemod are enabling engineers to translate outdated code into modern, efficient formats with minimal manual intervention. 💡 AI-Driven Cost Management and Efficiency: Infrastructure costs are continuing to skyrocket as companies scale up, particularly in terms of data storage, compute, and network usage. AI offers promising opportunities to optimize these expenses through predictive cost management, dynamic resource allocation, and automated scaling. By implementing tools that forecast usage trends and recommend cost-saving measures, such as with CloudZero organizations can gain a clearer picture of their resource consumption. This proactive stance on cost management allows for more efficient resource utilization, ensuring that infrastructure spending aligns with actual demand. AI is no longer a nice-to-have in the infrastructure software space; it’s quickly becoming a competitive necessity. If you're a founder building in any of these areas or themes, I'd love to chat.

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Tom Hollingsworth and I had a great briefing with Anyscale this week - a company I became aware of due to their connection with Ray, the widely-used open source AI platform. Anyscale is focused on enabling AI development on the software side by commercializing the open-source Ray platform. They're not focused only on training large generative AI models, they're building a versatile platform that supports multiple AI models, allowing businesses to scale across various workloads, including training, online inferencing, and offline batch processing. Customers typically use hardware in the cloud, often seeking to maximize their committed spend with major hyperscalers like AWS and GCP. While most of their deployments are on NVIDIA GPUs, Anyscale maintains a hardware-agnostic approach, supporting a broad array of compute resources. Their platform is primarily adopted by AI platform teams within organizations, who are responsible for delivering AI capabilities to internal users such as data scientists. These teams prioritize performance, scalability, and cost-efficiency, while also focusing on accelerating developer velocity through the creation of internal AI tooling and platforms. Anyscale’s differentiation lies in the deep integration and optimization of Ray, which serves as the backbone of their platform. They have invested significantly in enhancing Ray's performance, reliability, and scalability, making it the go-to standard for scaling AI workloads. Anyscale also offers purpose-built developer tools such as distributed debugging and log viewing, as well as integrated workspaces and notebooks to streamline the development process. Their managed services further add value by providing enterprise-level security and governance features. Additionally, Anyscale has developed extensive integrations with the existing tech stacks of their customers, including popular tools like Weights & Biases. As generative AI continues to gain traction, Anyscale is positioning itself to empower a broader range of businesses, beyond those training the largest models, to leverage AI through a scalable and efficient platform. It's incredible to learn from people like Robert Nishihara in briefings like this for The Futurum Group, and I thank Matthew Connor and the team for setting this up!

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Prompting for scale will soon be obsolete... i think...i hope. Prompting is an error-prone, discovery-based process, unsuitable for scalable AI. Many folks rely on black-box LLMs behind API walls (e.g., Claude, GPT-4), lacking direct control over LLM model weights. Programmatically custom-pruned LLMs will be the future of controllable, production AI, not prompting. hint--> #steeringvectors

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Wow, this is a great interview! DHH is such a great philosopher—even better than he is as a programmer. I've critiqued Rails in the past, and while it's not my main focus anymore as I'm shifting towards my own framework, Radaptor, it's still so clear to me that Rails has significant architectural issues. He keeps citing Shopify as an example of Rails handling tremendous amounts of requests, but they're using a heavily tweaked version that's basically not Rails anymore—probably running a full-fledged CMS on top and using a modular approach, not the default, put-everything-into-a-folder method. It seems DHH is currently in a state where he can't accept critique about Rails' architecture, which may ultimately hurt those who rely on Rails heavily. That said, ActiveRecord is fantastic, and Stimulus is also very, very good. He has some really good ideas, and some of his gems are truly gems. My problem with his approaches is about the architectural decisions that really hurt maintainability and performance, so they hurt developer happiness, which he advocates for—just like me. I think in a couple of years, he'll realize how Rails could be further simplified to be two orders of magnitude better. I truly recommend watching the entire 2-hour interview when you have the time.

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July 29 2024 – 5 Notable Articles To Read Article: NVIDIA Partner SMC Raising $950M to Tap AI Server Boom Takeaway: SMC - Sustainable Metal Cloud provides data center capacity to clients, offering them access to Nvidia AI chips and advanced cooling technology. Source: Bloomberg Link: https://lnkd.in/dFXr43uT Article: OpenAI Is Launching Search Engine, Taking Direct Aim at Google Takeaway: Developed with input from publishers, SearchGPT will provide real-time summaries of website information. Source: The Wall Street Journal Link: https://lnkd.in/dSDj5fpA Article: Royal Caribbean Group Raises Its Outlook. Cruise Demand Is Strong. Takeaway: “Exceptional demand for our vacation experiences has accelerated our performance by generating significant yield growth over the past several years,” Chief Executive Jason Liberty said in the earnings release. Source: Barron's Link: https://lnkd.in/dpznkWbS Article: Alibaba Group-backed Baichuan raises $691M as China seeks to catch up on generative AI Takeaway: The business is valued at 20B yuan with the funding round included existing backer Alibaba, along with internet giant Tencent and smartphone maker Xiaomi Technology. Source: PitchBook Link: https://lnkd.in/dmWTKFan Article: J.P. Morgan pitches in-house chatbot as AI-based research analyst Takeaway: Approximately 50,000 employees are using a large language model developed by the bank to enhance productivity. Source: Financial Times Link: https://lnkd.in/dx6Z2sU4 #news #ai #research #business

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