AI Product Accelerator

🚀 A Transformative Experience at the AI Agents Innovation Summit! 🚀 The AI Agents Innovation Summit was an extraordinary gathering of minds at the cutting edge of AI! From thought-provoking discussions to groundbreaking ideas, the energy was electric, and the insights shared will shape the future of AI. 🌟 A huge thank you to our incredible speakers who made this event unforgettable: Shashank Tiwari - CEO uno.ai Rakesh Jain - STSM and Researcher IBM Steve Wilson- Chief Product Officer Exabeam Jeremiah Owyang - Partner Blitzscaling Ventures Jenny Xiao - Partner Leonis Capital Lisa N. Cao - Product Datastrato Jessica Hammond - Founding Member Sidebar Priyanka Neelakrishnan, - Product Line Manager Palo Alto Networks Ines Cerdan - COS Vectice Kamala Srinivasan - Founder & CEO Upahara Jon Shalowitz - MD Union Square Advisors LLC Dat Daryl Ngo - AI Architect Arize AI Skip Everling - Developer Relations Kolena Ojus Save - Senior Developer Advocate Zoom Your expertise, passion, and insights are paving the way for AI innovation, and we’re incredibly grateful for your contributions. Special appreciation to Dhaval Bhatt, Founder of the AI Product Accelerator, for your visionary leadership and dedication in making this summit possible. Thank you, Microsoft Reactor and Roxana Teran, for all your help getting the community together! Workable Works - without you, this event wouldn't have been a 5 star experience it was. ⭐ To everyone who attended and engaged, thank you for being part of this inspiring journey! Let’s continue to drive AI innovation together. Stay tuned for more exciting updates! 🚀 #AIAgentsInnovationSummit #AICommunity #TechLeaders #AIInnovation #Gratitude

Have you ever been overwhelmed by so much information that you just don’t know where to start? Steve Wilson, an expert in corporate cybersecurity, faces that daily—but multiplied by petabytes of data. In the world of corporate cybersecurity, that’s what teams face every day. They’re not sifting through a few files; they’re trying to detect threats hidden in an endless flood of data, often after it’s already too late. 🚨 That’s where AI steps in—not just as a detector but as a co-pilot, helping analysts cut through the noise. Instead of spending hours answering 46 different questions for a single log file entry, they get the insights they need, faster. This is the future of cybersecurity: empowering experts to stop threats in their tracks, with AI leading the way. #Cybersecurity #AI #MachineLearning #DigitalTransformation #DataInnovation #Tech

AI model documentation might be the least-loved job in tech. As Ines Cerdan puts it, you’d almost rather see an error screen than update documentation, right? But in AI, this task isn’t just tedious—it’s essential. Static docs don’t cut it with AI models that evolve weekly. Instead, effective AI documentation should track the model's "DNA" in real-time: inputs, decisions, performance, limitations. This isn’t just a nice-to-have; it’s the backbone of scaling AI models that are understandable and improvable, version after version. Curious how other teams are tackling this challenge? #AI #MachineLearning #AIDocumentation #scalability #growth

If you’re ready to drive serious growth for your app, here’s how to make it happen: 1️⃣ Make it visual and memorable: Use Instagram Reels or YouTube Shorts to create snackable, engaging videos. Show off what your app can do with short clips of its most useful or quirky features—whether it’s guiding users through a 5-minute workout or providing language practice on the go. 🎥 Example: Share a 15-second video of your app’s AI leading a quick fitness routine. Add a call-to-action like “Give it a try!” and link to your app. 2️⃣ Community-building is your fuel: People don’t just want an app; they want a connection. Use Reddit and Instagram to start a community around your app, share tips, answer questions, and make your users feel part of something bigger. For more tactics on boosting app visibility, check out the full video. -Dhaval Bhatt Full Video: https://lnkd.in/gj8HGCBv

👀 Ever Noticed? Small Wins Don’t Get Much Applause – Shashank Tiwari You’ve probably seen it: companies rolling out “innovative” tech that’s 10% better. It might be cool—maybe it even is useful. But a 10% boost doesn’t exactly set the world on fire. It’s not even enough to change most people’s habits. So what happens? Usually, not much. Real change, the kind that makes the market sit up, starts at 5x, 10x, or even more. To get there, look for the overlooked frustrations: bureaucratic, tedious processes that everyone wishes were different. It’s the place where “why do we still do it this way?” meets “finally, a real solution.” That’s where the transformation gold lies.

You know how your favorite TV show has that one season that just goes completely off the rails? 2022 and 2023 felt like that in the tech world—plot twists we didn’t see coming and cliffhangers we couldn’t resolve. But just like a good show finds its groove again, so is the tech scene. 📈 Early-stage deals are climbing, and it’s not just about the numbers; the valuations are making a comeback across early, medium, and later stages. And AI? It’s the star of the show. Over 40% of today’s unicorn valuations are AI-driven—outpacing 2023’s high in half the time. Jon Shalowitz recently pointed out how this surge is more than just hype; it’s a fundamental shift in the industry. As we step into 2024, one thing’s clear: AI isn’t just leading; it’s setting the stage for everything in tech. ✨ Are you seeing this same momentum unfold in your sector? #AI #TechInvestments #StartupSuccess #2024Trends

Evaluating your LLM’s performance can feel like guessing the calories in your favorite dish—unreliable and frustrating. Luckily, a new wave of open-source tools is changing that. 1️⃣ DeepEval – A sharp tool for detecting hallucinations and checking relevance, giving your model’s results the reality test they need. 2️⃣ LLM as a Judge by Hugging Face – It scales human intuition into model evaluation, ensuring AI meets human standards. 3️⃣ Evidently – Think of it as your drift radar. It spots model shifts early, saving you from costly surprises. With these tools, you’re not just guessing; you’re getting solid, automated insights. Which one have you tried? - Dhaval Bhatt 🔗 This is just a small taste from the recent AI Agents Innovation Summit I hosted in SF. To get free access to the full recordings and explore how multi-agent systems are revolutionizing industries, click the link in my bio! #AI #MachineLearning #LLM #AIEvaluation #OpenSource #AIAutomation #DataScience #TechInnovation

What makes users pay for an AI app? 🤔 Not a premium subscription or a flashy feature, but something surprising: free stuff. Offer the basics for free, then give users the option to unlock the extras they can’t resist—like custom voices or more interaction time. It’s not just “freemium,” it’s keeping users invested without overwhelming them. Take a language app: free lessons get them started, but extra coaching or extended practice? That’s where the revenue comes in. This approach allows early AI startups to reach $10k/month, blending accessibility with irresistible add-ons. For more strategy tips, catch the full video. -Dhaval Bhatt Full Video: https://lnkd.in/gtTwxpYb #AIMonetization #TechEntrepreneur #AIStartup #FreemiumModel #Microtransactions #TechBusiness #AIInnovation

Ever thought about building an AI app but immediately hit pause because… coding? Let’s be real – not everyone’s diving into lines of code on a Saturday morning. But here’s the twist: you don’t have to code. With tools like Twilio Studio and Zapier, creating voice AI apps is as simple as plugging in an API key. In five minutes, you’re live with an AI saying, “Hello, how can I help you today?” – no coding required. Check out the full video for a step-by-step guide and see just how easy it is to get started. - Dhaval Bhatt Full Video: https://lnkd.in/gtTwxpYb #NoCode #VoiceAI #OpenAI #Twilio #Zapier #AIIntegration #Automation #FutureOfWork

How do you really know if an LLM product is working? Dat Daryl Ngo breaks it down simply: “In AI, it’s easy to get lost in tools and tactics, but four key signals help you know if you’re building something valuable.” 1️⃣ LLM as a Judge: Let your AI judge itself. Leading LLM teams, Dat explains, use their models as internal critics, providing self-evaluations for quality assurance. 2️⃣ Golden Datasets: Think of these as your product’s gold standard. Grounded in reliable engineering, they give your LLM something solid to measure against. 3️⃣ Coverage & Quality: Don’t just ask if it works—ask if it’s thorough. Coverage isn’t just about breadth but depth, something Dat emphasizes as a mark of true quality. 4️⃣ Problem-First Focus: Start with the problem, then bring in LLMs as one option in the toolbox—not the only tool. Dat warns against using tech for tech’s sake. These signals are the difference between LLM products that deliver real value and those that become side projects. Follow Dat’s advice, and build something that sticks. 🔗 This is just a small taste from the recent AI Agents Innovation Summit I hosted in SF. To get free access to the full recordings and explore how multi-agent systems are revolutionizing industries, click the link in my bio!