Echeyde Cubillo

🌉 San Francisco's Surprising Comeback: From 'Doom Loop' to Renaissance? Recent data suggests that San Francisco, once trapped in a pandemic-induced 'doom loop', is showing strong signs of recovery. Here's what's changing: 📈 Positive Trends: - Population Growth: Net gains over the past two years, with domestic in-migration positive for the first time since 2012. - Housing Market: Median home sale prices up 1.2% YoY to $1,427,500 in June. - Job Growth: The Bay Area added 7,400 jobs between January and June. Downtown Revival: Foot traffic beyond typical work hours has recovered to over 90% of pre-pandemic levels. - Crime Reduction: Property crime down 32% and violent crime down 14% in Q1 2024 compared to Q1 2023. - AI Boom: The city is positioning itself as a hub for the AI revolution. 🚧 Ongoing Challenges: - High office vacancy rates - Homelessness and drug crisis - Lagging tourism and business travel 🔑 Key Initiatives: - "Vacant to Vibrant" program offering free rent to businesses in empty storefronts - Increased police patrols and encampment sweeping - Crackdown on open-air drug dealing 💡 Expert Insight: "The rising AI empire is giving the city a new edge. Funding, talents, and culture related to the AI revolution will help the city turn the corner." - Lu Chen, Senior Economist at Moody's Analytics 🤔 What's Your Take? Are these positive trends sustainable, or is it too early to declare a comeback? How can other cities learn from San Francisco's recovery strategies? What role will AI play in reshaping urban economies, particularly in tech hubs? https://lnkd.in/gCnPCBRU

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Was chatting with another investor today on a call, and mentioned Tim Urban's Wait But Why Blog post, "The AI Revolution" and how prescient it was. Given that it was written in 2015, decided to flex AI's latest muscles to give it a new venue: a podcast. Podcast here: https://lnkd.in/gW8Dh89U Pretty easy to do: Step 1: Save the posts as PDFs. Step 2: Upload to NotebookLM, have it create a podcast. Step 3: Upload to Descript, give it captions. Step 4: Export, publish to Youtube. Total time to create the podcast: ~25 minutes. To generate this podcast and publish it even a few years ago would have taken >8 hours. Love the future (Original posts: https://lnkd.in/gW-PQXE7, https://lnkd.in/gg-nRvxz) #AIRevolution #GenerativeAI #WaitButWhy #TimUrban #NotebookLM #AIContentCreation #Podcasting #Descript #ArtificialIntelligence #ContentFlex #ProductivityHacks #FutureOfAI #AIinMinutes #TechEfficiency #AIandCreativity #YouTubePodcast #InnovationInContent #vibecap #vibecapital

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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 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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Investors beware: The LLM hype might be hitting a plateau. As this story suggests, LLMs are resource-heavy and lack true reasoning. So what’s next for AI? Instead of chasing the same "brilliantly stupid" models, it’s time to focus on more efficient, innovative approaches that can scale. But here’s the real question—have you sunk so much money into the old on-prem proprietary LLM infrastructure that you’re stuck, or will you pivot before the bubble bursts? #AI #Innovation #Investing #TechLeadership #Disruption #FutureTech https://lnkd.in/gYyibFSK

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inception llms: experimenting with a new LLM design pattern where I prompt Gemini to write code on my behalf which, in turn, uses Gemini to perform tasks - So meta, isn't it? One prompt for one task. in less than two hours created a full pipeline which would have taken days otherwise. Gemini wrote more than 97% of my code, discoverying libraries I was not aware of - overall, saving me tons of time as swe. Where do you think you can use this power in your product? try it yourself next time you develop a feature. I named it "inception llms"

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Building AI tools to enhance (and even replace) engineering workflows with code generation is one of the most exciting and worthy undertakings I see today. In this new post I take a close look at some key open questions we’re trying to answer to make AI coding tools a reality, alongside what we at Greylock are seeing in the current ecosystem of AI code startups. In most cases, startups are approaching this in one of three ways: (1) AI co-pilots to enhance engineering workflows (ex: Cursor (aka Anysphere), Codeium, CodiumAI) (2) AI agents to replace engineers (ex: Cognition, Factory) (3) Code-specific models (ex: poolside, Magic) There are quite a few open questions we have, but in this post I dive deeper into a few that keep recurring in our research: (1) How do we create more powerful context awareness? (2) How do we get AI agents to work better for E2E coding tasks? (3) Does owning a code-specific model lead to long-term differentiation? If you are a founder working on any of these concepts (or even just thinking about it), please get in touch with me. This post wouldn’t have been possible without the thought partnership of Ankit Mathur, Gwen Umbach, Itamar Friedman, Jamie Cuffe, Jerry Chen, Luis Legro Correa, Matan Grinberg, Michele Catasta, Prem Nair, Vaibhav Tulsyan.   Read the full post here: https://bit.ly/3UR4OJA

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Liquid AI, a one-year-old startup spun off from MIT wants to raise $300 million at a $3.7 billion. It would be 10x cap of its seed round of $37M in January. No product yet. They introduce a new class of time-continuous recurrent neural network models. "We construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers." https://lnkd.in/gbqXm8cZ

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We are quickly entering the "trough of disillusionment" in the hype cycle for generative AI. While GPTs excel in automating certain tasks (and can even automate away large portions of some jobs) they often fall short in others. Like all new technology, the issue lies in misapplying generative AI for tasks it wasn't designed for. This is a problem compounded by the GPTs themselves suggesting they can handle these tasks (often cheerfully!). This misalignment creates frustration and unmet expectations which we're starting to see more of these days. As the dust settles I firmly believe we'll recognize GPTs as a transformative technology across many industries. However it's crucial to understand their limitations and apply them appropriately. What are your thoughts on the current state of generative AI and its future applications?

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📈 AI Startup Sierra Expands HQ in San Francisco (4K to 41K SF) 🌁 Artificial intelligence firm Sierra is making a bold move by relocating to a larger office within San Francisco. The startup has signed a lease for a 41,104-square-foot space at 235 Second St., significantly upsizing from its current 4,000-square-foot office. 🏢 This move to San Francisco's South of Market neighborhood, where office vacancy rates have started to decline for the first time since 2020, is a positive sign for the city's hard-hit office market. The area's office vacancy rate, though still high at 28.8%, is slightly lower than six months ago. 📉 The deal contributes to the increase in leasing activity in San Francisco, which is up by about 1 million square feet compared to last year. This uptick is driven by the region's reputation as a leading national hub for AI companies. 💼 Sierra joins other AI firms expanding in the South of Market area, including Scale AI, which signed the city's largest office lease of 2024 with a 178,000-square-foot deal. So far this year, office tenants have signed leases totaling 750,000 square feet in South of Market, a significant increase from last year's 370,000 square feet. 🚀 Co-founded in 2023 by former Salesforce CEO Bret Taylor and former Google executive Clay Bavor, Sierra raised $85 million earlier this year to develop a customer support AI tool for businesses. The firm is set to relocate from its current space at 215 Second St. by next month. San Francisco's reputation as an AI hub, bolstered by its talent pool and six major AI research institutions, continues to attract companies. Recent significant deals include OpenAI’s sublease of over 400,000 square feet of Uber’s former headquarters and Anthropic’s takeover of Slack's former headquarters. These developments reflect a resilient and adaptive real estate market. As a commercial real estate professional, I’m excited to see how these trends shape the future of office space in our vibrant city.

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Big Week for Our Portfolio: New Heart Failure Candidate & “Synthetic Workers” Launch I’m excited to share two key updates from our Vibe Capital Fund I portfolio, each representing major steps forward in biotech and AI-driven automation: 1. Fauna Bio: First Development Candidate for Heart Failure Big News: Fauna Bio just announced Faun1083, a potential first-in-class therapy for Heart Failure with Preserved Ejection Fraction (HFpEF)—a significant and often underserved area. Learn More: Press Release: https://lnkd.in/gvxhUp7W Fauna’s LinkedIn Post: https://lnkd.in/g8mEDzWa Why It Matters: HFpEF makes up roughly half of all heart failure cases, impacting millions worldwide with few effective treatments. This announcement underscores how Fauna’s unique comparative genomics approach is yielding tangible breakthroughs. 2. Mission Control AI: “Synthetic Worker” Cohort Opening What’s New: Mission Control is inviting applications for Cohort 2 of its Design Partners Program, where enterprises can explore fully autonomous AI colleagues (a.k.a. “Synthetic Workers”) over 12–15 weeks. Learn More: Ramsay Brown’s LinkedIn Post: https://lnkd.in/g6TrwF97 Whitepaper: Available at https://lnkd.in/gczus98C Why It Matters: As AI usage explodes, Mission Control is helping organizations adopt and govern autonomous workflows in a responsible, real-world way. What This Means for Us At Vibe Capital, we prioritize startups tackling pressing challenges—whether it’s novel treatments for hard-to-treat conditions or AI infrastructure that redefines how we work. These milestones exemplify that ethos and highlight the incredible teams we back. If you’re a Founder: We’re always open to connecting with bold entrepreneurs in biotech, deep tech, AI, and more. Let’s chat. If you’re an Investor/LP: We welcome discussions about how we source and support companies pushing boundaries in healthcare, automation, manufacturing, and beyond. Congrats to both Fauna Bio and Mission Control on these big steps. We're proud to be part of their journey and can’t wait to see what’s next. #VentureCapital #Biotech #AI #HealthcareInnovation #Startup #SyntheticWorkers #FutureOfWork #HFpEF #DeepTech #Innovation #vibecapital #vibecap

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So - are LLMs Universal? This guy looked at a lot of proofs that LLMs are universal and concluded that they are all wrong or misleading. I didn't check all the proofs and I didn't check all the errors or misunderstandings that he mentions. But I would say that it is not confirmed that any realistic model of LLMs is universal. Specifically about https://lnkd.in/da4qHFgh I did not make up my mind if the model is realistic or not, but I think it makes improvement by 1) Getting the outer while loop into the LLM by emphasizing auto-regression 2) Getting the ability to skip parts of the input with the bounded context window, whether a realistic aspect of a theoretical model or not.

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Recently, Anthropic shared its perspective on California's proposed AI regulation bill, SB 1047, with the California Assembly. While recognizing the importance of safe AI development, Anthropic has raised substantial concerns about the bill's current form, which could potentially hinder innovation and harm the United States' competitive edge in AI. Anthropic's key points include shifting the focus from "pre-harm enforcement" to a more adaptable "outcome-based deterrence" model. They also advocate for regulations that focus more narrowly on frontier AI safety to avoid redundancy with federal requirements. Additionally, they suggest giving regulatory authority to the Government Operations Agency instead of creating a new state agency. Anthropic believes that these amendments will encourage innovation in risk reduction practices and incentivize companies to adopt measures that prevent catastrophic risks. In contrast, the AI Salon Policy & Civic Engagement Guild shares Anthropic's commitment to ethical AI and safe development but has broader concerns about the bills. We believe that overly burdensome regulations may discourage AI development and lead to job losses. Compliance requirements favor large companies, potentially driving smaller startups out of the market, and overregulation might cause California to fall behind other regions in AI development. Furthermore, compliance under penalty of perjury imposes significant personal risk on developers, and strict regulations could disadvantage California-based companies in the global AI market. On behalf of the over 1700+ members of the AI Salon, we urge our larger community to engage in this crucial dialogue about AI regulation. By advocating for well-balanced policies that protect public interests without stifling innovation, we can ensure California remains a leader in ethical and responsible AI development. Please sign the letters of support and join us in shaping a future where AI is developed and deployed with ethical integrity, social responsibility, and inclusive governance. Together, we can bridge the gap between AI technology and policymaking, fostering an environment where innovation thrives alongside robust safety measures. https://lnkd.in/e6MRBmcX #AIRegulation #EthicalAI #Innovation #PolicyEngagement #Anthropic #AISalon #PublicPolicy #TechPolicy Kyle Shannon Leah Fasten Brandon Tidd Stacie L. Matthew Karabinos, MAT Vikki Baptiste Jon Potter Jon Leland Cyndi Coon Daniele Hall

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Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

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All this proves is that we live in the part of the multiverse with the worst possible explanations.

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Insightful look into bioplatform strategy, and what it takes to raise a Series A from my partner, Omri Amirav-Drory: https://lnkd.in/dFv7GbF6

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LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

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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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