Jamil Dhanani

YC Demo Day Survival Guide: What VCs Say vs. What They Actually Mean 👇 What VCs say: "Not aligned with our strategy" > What they mean: "We have no idea what our strategy is, but it's definitely not this, mate." What VCs say: "That's a fascinating idea." > What they mean: "I think Juicero was a better startup idea than this." What VCs say: "We need to see more traction." > What they mean: "Your graphs are as flat as Kansas. I don't see any graphs with the line going up. Line must go up. Line must go up." What VCs say: "Please keep us in the loop." > What they mean: "Forget that you have my email. In fact, forget that email exists. If you message me again, I will call the Sopranos." What VCs say: "It's not you, it’s us" > What they mean: "It’s absolutely, unquestionably, definitively you. But if I tell you, you'll write an exposé, I’ll go viral on LinkedIn, and my partner will send me to a spa in Novosibirsk, Siberia for 6 months." What VCs say: "We’re really excited about your vision" > What they mean: "Your vision is wonderful. It’s your current business model that reminds me of a horror movie. Frankly, I may need therapy after seeing your revenue and churn." Any others? What am I missing?

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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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Today, I am super excited to announce our $17M Series A led by Notable Capital (formerly GGV Capital) with participation from Lightspeed and Datadog. 🚀 At Patronus AI, we are on a mission to boost enterprise confidence in generative AI. The sobering reality is that LLMs can fail. And it’s hard to predict when and where they will fail. Enter Patronus AI. Since we launched in September last year, we’ve moved faster than the speed of light ⚡Today, our product is used by numerous Fortune 500 enterprises and leading AI companies around the world. They have run millions of requests through our platform, and have caught hundreds of thousands of hallucinations and other mistakes in AI outputs, both in offline and online settings. Our AI research in evaluation model training, standardized benchmarks, and alignment techniques have done wonders. As part of the Series A, we’re thrilled that Glenn Solomon will be joining our board. Glenn has supported legendary companies over the years, like Hashicorp, Vercel, Square, Airbnb, Slack, Zendesk, and more. We couldn’t be more excited to be partnering with him and the whole Notable team. The round was also joined by other incredible luminaries like Gokul Rajaram, Jonathan Frankle, Jason Warner, Tristan Handy, Michael Callahan, Barr Moses, Factorial, Aparna Sinha, and Nadim Hossain! I’m so proud of us. Our team is the reason we’re here. Hwanghah, Varun, Jędrzej, Guillaume, Kornel, Selvan Sunitha, Maciej, Nino, Bartosz, Michał, Milder Edgar, and our research advisor Douwe Kiela. And of course, literally nothing would be possible without my amazing co-founder Rebecca. And guess what? WE’RE HIRING!!! https://lnkd.in/eTc7QsBu We’re just getting started. It’s still Day -1. The best is yet to come 🚀 Read our story: https://lnkd.in/eSm3kCwm Read our press release: https://lnkd.in/evBeXvet  Read our VentureBeat coverage: https://lnkd.in/exSZYUn7 Read our TechCrunch coverage: https://lnkd.in/eQniJnwx

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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