Jarren Reid

What a week this has been for innovative companies in Seattle (first Smartsheet and now OctoAI). Today NVIDIA made public the news that they have acquired Madrona portfolio company OctoAI. Partnering with the founders since Day 1 (ok Day -1) has been one of the greatest experiences of my life. Luis Ceze, Jared Roesch, Tianqi Chen, Jason Knight, Thierry Moreau and Carlos Guestrin were all involved in the research and insights that helped create the TVM open source technology at the University of Washington computer science school and then the creation of OctoAI back in 2019 to drive commercial impact with this technology. From sorting out the terms of the original "pre-seed" round over a bottle of Caymus Vineyards wine to helping Amazon Web Services (AWS) understand the dynamics around the open source movement in Applied AI to learning our way in to product-market fit with the BEST inference optimization service available (OctoAI can help customers find their optimal fit of performance, customization, and cost), OctoAI has been the best! The company has helped set the table for AI-driven innovation for the years ahead and they will be a perfect compliment to the many capabilities at Nvidia. Luis, his leadership team, the board (including Carlos Guestrin Lip-Bu Tan and Mike Dauber) and so many "friends of OctoAI" know what a motivated, talented and authentic group of people are associated with this company. It is inspiring to see an acquisition at a total deal value of over $250 million in this highly dynamic and fast-moving phase of AI disruption. It is even more exciting to anticipate what will be possible with innovations powered by AI models and related technologies in the years ahead. Madrona has been investing in Applied AI for over a decade and we truly believe that we are just getting started! One of the hardest judgement calls as an investor and board member is to assess the benefits and risks of selling a company at a key inflection point in the journey. Nvidia made a compelling case that the impact for the OctoAI team and technology would be maximized within the market leader for AI infrastructure. I will be rooting every day that they all maximize this impact in the months and years ahead!

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For those of you still doubting Artificial Intelligence....wake the f$%& up!!! Go walk around downtown San Francisco and maybe all of Google's Waymo driverless cars on the streets may be a wake up call for you. If you need more proof take a look at Y Combinator's current cohort of companies as well as all the AI and AI-Enabled companies in the MedTech Innovator current cohort of companies or the ones pitching at LSI and LSI Europe!

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The VC funding model when everything works out fine... In real life: Only a few startups make it! So if you are considering going the VC route: Have a backup plan!

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UIUC/Georgia State create LlamaF, an architecture with FPGA-based accelerationdesigned to improve the inference performance of large language models (LLMs) such as TinyLlama 1.1B on embedded systems. As LLMs transform natural language processing across multiple sectors, their implementation on resource-constrained devices presents challenges due to significant memory and computational requirements.   LlamaF addresses these challenges through post-training quantization and an architecture specifically optimized for embedded FPGAs. By employing a group-wise quantization strategy, it reduces the model size from 4.4GB to 1.1GB, which lowers off-chip memory bandwidth needs while maintaining predictive performance. The architecture includes a fully pipelined accelerator for group-wise quantized matrix-vector multiplication (GQMV), allowing for asynchronous computation during weight transfers and resulting in notable performance enhancements.   Testing on the Xilinx ZCU102 platform highlights LlamaF's capabilities, achieving a speedup in inference speed ranging from 14.3 to 15.8 times and a 6.1 times improvement in power efficiency compared to executing the model solely on the ZCU102 processing system. This performance enhancement is largely attributed to the efficient execution of matrix operations, which are a significant portion of the runtime during inference.   The design of LlamaF integrates advanced software techniques, including task-level scheduling that overlaps off-chip parameter transfers with kernel execution, thereby improving throughput and reducing latency. The hardware design is organized into three primary stages: pre-processing, dot-product, and accumulation, which together facilitate efficient data flow and computation for LLM inference.   LlamaF signifies a notable advancement in the acceleration of LLMs on embedded FPGAs, enabling more effective deployment of these sophisticated models in environments with limited resources. Arxiv: https://lnkd.in/exhriYyj

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During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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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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Amazing turnout & insightful discussion for our AI x Blockchain event!! More to come

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Hot take... YC S24 was the best YC batch i've seen. I break it down why here: https://lnkd.in/enNcbfRK YC is evolving, from in person demos days to new group partners... I cover how this shift is influencing investment strategies, the rise of sectors like batteries and space tech, and why traction—not hype—continues to be the ultimate indicator of value. Catch the full breakdown below.

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👀 Looking at our recent investments at Draper Associates, here’s what standout founders nailed, making our decision to invest much easier (and faster): 💥 Visionary Tech with a Bold Vision – These founders are not here for incremental wins; they’re reimagining what’s possible in across tech - biotech, robotics, aerospace, and beyond. Their vision is big and audacious, tackling complex problems that demand breakthrough solutions, others have not been willing to try. 👊 Clear Passion & Purpose – The drive in these founders is undeniable. They’re committed to their mission with a depth of purpose that shapes every choice. One founder, for example, took two years completely off to learn and meet everyone in longevity biotech. This level of grit and dedication is rare but essential for the most impactful founders. 🌟 Assembling All-Star Teams – They bring together top-tier talent from diverse disciplines: experienced business leaders with successful exits or industry experience at top companies, technical co-founders with deep expertise, often PhDs or industry pioneers, and even published authors. It’s a complementary team, unified by a shared vision. They make the: "Why this team?" obvious - at a glance. 💻 Data-Driven to the Core – These founders present data insights that show why their approach is unique, especially against a competitive field of companies. This data is delivered with clarity and they make the complex simple. Many are published in top journals, adding depth and credibility to their approach, and reinforcing that they know their tech inside out. ⚡️ Memorable, Clear Vision – They don’t just pitch; they make the future feel tangible, with a vision that’s both compelling and concise. If I can relay their pitch in under 30 seconds to my team, they’ve killed it. 📣 Transparent & Proactive Communication – The best founders don’t wait for questions. They come prepared, often with a comprehensive FAQ doc, give timely updates to our questions, and are candid about challenges. This level of openness builds trust and strengthens our partnership. 💸 Milestone-Driven Funding Strategy – Each funding stage aligns clearly with already achieved or upcoming achievable milestones. They’ve mapped out a roadmap that minimizes risk at every step, giving us confidence in their journey. 💰 Revenue Milestones Along the Way – Even if it'll take a decade to hit revenue on their big vision, these founders find revenue generation stops along the way to profitability. They share a trajectory that connects early wins to revenue, showing how every step is a building block toward their bigger vision. Overall, the best founders are incredibly thoughtful, prepared, and fantastic verbal / written communicators. #founders #startups #fundraising #VC

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New Tesla Patent Reveals Clues to an Exciting Future of Humanoid Robots and AI Integration In a interesting discussion, Herbert Ong and Scott Walter dive deep into Tesla's recently published patent application WO2024072984A1 (link in comments). Entitled "Actuator and actuator design methodology," the patent primarily focuses on controlling robot movement through strategic actuator placement. However, the document also unveils important clues about the potential for end-to-end neural net training of humanoid robots, reminiscent of Tesla's approach to training its vehicles with FSD. Ong and Walter explore the concept of personalized humanoid skills. Imagine purchasing a bot with built-in capabilities, then having the option to augment and customize those skills through a "Skills Store" akin to Apple's App Store. This opens up a world of possibilities for tailoring robots to specific tasks and preferences. The discussion also touches on the potential for various robot form factors, each designed to excel at particular skills. Picture a tennis-playing robot that comes pre-trained with basic volleying instructions, but requires a "Skills Upgrade" to perform at a master level. This raises questions about the need to manufacture and train a diverse range of robot body types and sizes, mirroring how humans possess ideal physiological characteristics for different activities. Later, Ong and Walter discuss a scenario in which Tesla builds the hardware – including automobiles, humanoids, and other bot form factors – while xAI develops the brains. This arrangement opens up opportunities for licensing agreements between the two companies, that may help the companies navigate future antitrust concerns as these technologies are deployed at scale. The convergence of advanced robotics and artificial intelligence holds the potential to catalyze a profound societal transformation. As Tesla and xAI continue to push the boundaries of what's possible, I can envision a future where intelligent, adaptable robots become integral to various aspects of our lives – from manufacturing and transportation to healthcare, recreation, and beyond. The seamless integration of cutting-edge hardware and software will revolutionize the way we live and work, ushering in an era of unprecedented efficiency, productivity, and innovation. While there are still challenges to overcome and ethical considerations to address, the prospect of these combined technologies driving positive change on a global scale is awe-inspiring. #FrontierFrontier #Humanoids #AI #Robots

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Sharing a sanitized version of the mu ventures Q2 q'ly LP update. Included musings: 👉 SEOs shrinking impact, and the rise of LLMO (large language model optimization) 👉 examples of the Commerce AI application layer coming to life 👉 a modern fund strategy that accepts companies require less capital, time, and people to trigger the power law 👉 Mu's guide to investing in AI 👉 all 30 companies in the Mu-niverse (and several others we admire) Founders, VCs, LPs - if you like how we think and suspect we can partner, hit us up! https://lnkd.in/dTJ2zjav

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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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It is great to see DARPA engaging broadly with startups and the private sector (as it always has) to discuss what technologies could be most critical to future national security. In such a conversation today ppl mentioned quantum computing, encryption and sensing, semiconductor design and manufacturing, new materials and chemistries, AI Safety and risk of AI aided bioterrorism and biological warfare, zero knowledge proofs, fully homomorphic encryption, US weapons and broader defense manufacturing capacity, hydrogen production technologies, and autonomous swarms.

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YC S24 Summary I think a lot of you have been waiting for this one! YC S24 in numbers: - 256 companies were announced, 243 presented at demo days. - 193 companies in Machine Learning / AI. - 160 companies from the US. - 37 women-founded companies! We started working with the batch well before demo day. We selected 30 companies (which turned out to be the top of the batch), and managed to have calls with 22. In the end, we invested in just 1 company! We wanted to invest in 2, but didn’t have space in the round of the second one. They offered us a spot at a valuation $5M higher than yesterday, so we declined. The alignment between companies' performance and valuations is pure madness. Here are some real examples (revenue - round size and valuation): 1. $870k ARR - $3M at $30M 2. $250k ARR - $1.5M at $25M 3. No revenue - $2M at $25M 4. $700k ARR - $5M at $40M 5. $18k ARR - $2.5M at $25M Honestly, I couldn’t believe it. Most of the calls were before demo day, and in all these cases, 70-80% of the round was already committed! So, this isn’t just the founders' imagination; many investors had already agreed, signed, and transferred money. What do you think of these terms? Share your thoughts in the comments 👇

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Super helpful article and accompanying doc with a #Startup #Equity Calculator to determine the equity for early hires, thanks to Pear VC head of talent Matt Birnbaum! Thanks for sharing Pejman Nozad! 🙏🏼 You can read more here How to structure startup equity for early hires: https://lnkd.in/ggmpT5-Y Google Doc: https://lnkd.in/gjsvths6

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So many burnt out founders. But also: So many surplus elites. And also: A new paradigm for low-risk idea-stage innovation in founder/startup/community/vc formation. That's Slyk IO We level up together: https://lnkd.in/grEmV-R6

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Friends, Q2 was a quarter of progress and learning for us at HawkTower, where we invest in early-stage startups applying breakthrough computing technologies to maximize California's natural resource markets. Our focus is on technology-enabled interventions (e.g., AI-enabled geospatial radars for lithium-ion batteries in the waste stream) for critical, market-level imperatives (e.g., disaster prevention) that can drive rapid commercial adoption (e.g., placed on every recycling truck in the world). Our current investment priorities are: - Agricultural productivity and efficiency - Biomanufacturing - Labor and mobility automation - Waste stream optimization and decarbonization - Disaster prevention and management Here’s a snapshot of what we’ve been up to: Milestones and Investments: - In late April, we officially launched our fund, filing with the SEC. Yay. - By June, we completed our first fund close ahead of schedule, with backing from family offices, foundations, high-net-worth individuals, and organizations aligned with California's future. - Our portfolio now includes three early-stage investments (Farm-ng, Agriful Software, and Nexstera Tech). Strategic Partnerships: - We believe that startups operating closer to their customers and resources, especially in agriculture and biomanufacturing, have a strategic advantage. For instance, the Salinas Valley, a major agricultural hub, is less than an hour from Silicon Valley and serves as a center of thought leadership in the convergence of agriculture and AI (from Western Growers, Taylor Farms, and others). - Similarly, the North San Joaquin Valley is emerging as a biotech and agtech innovation hub, and we are proud to support BEAM Circular, a startup hub for the circular bioeconomy. Key Relationships: - Karen Warner, CEO of BEAM Circular, joined HawkTower as a strategic advisor. - Jackie Cruz, Ed. D. Cruz, Hartnell College’s VP of Advancement, advises on Salinas Valley and workforce development. - Ben Palone from Western Growers, with his technical background in agriculture, became a trusted advisor. - Matthew Hoffman from Driscoll's Global R&D team provides insights on global agriculture, product positioning, and investment strategy. - We collaborated with Monterey Bay DART for workforce development and automation in agtech. - Dominic Milano and the Milano Technical Group from Merced collaborated on key initiatives and advised us on investments. - HawkTower is participating in Santa Cruz Works's accelerator cohort as a mentor and advisor. Thank you Doug Erickson. - We published an overview of entrepreneurship across the UC system with input from across the University of California system. Moving into the fall, we remain deeply committed to supporting founders shaping California's future natural resource markets. Stay tuned and be in touch! : ) Db

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