Charles Huang

Better technology won't drive the next AI wave of innovation. Your indomitable spirit will. Last week hit different. Our internal AI agent - our flagship innovation project - was showing a steady decline in weekly active users. Double digit drop since Q1. The metrics we'd celebrated at launch were now keeping me up at night. My typical playbook would scream "push harder, iterate faster." But sometimes, an indomitable spirit isn't about relentless pushing - it's about the courage to pause and face uncomfortable truths. Let me be vulnerable: After two decades leading tech teams, my biggest blind spot has been mistaking motion for progress. Especially with AI products, where the temptation to continuously iterate can mask a deeper truth: we're not solving the right problem. The hardest moment? Standing before our leadership team and admitting: "We're not ready." We need to pause, rethink our approach to building, and become better listeners. Today's market rewards speed, but it reveres wisdom. Your indomitable spirit isn't measured by how hard you can push - it's measured by your courage to pause when that's what your customers need. Thank you to the many who are helping me grow in this journey. 🙏 Special shoutout to Daina Souza Oenning, Timucin Uygungil, Phil McHugh, Matt Kiernan, Miguel Rodriguez, Sowmya S., Anne-Marie Costigan, Laurie Aquilante Faiola, Elaine Bermingham, Tony Abiera, Kelsey Balimtas, and Michael Renahan.

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Everyone's talking about “founder mode,” but as it gains traction, founders will start using it as an excuse for poor management. Founder mode isn’t a license to run a company at 5,000 the same way you ran it at 5. Jensen Huang, CEO of NVIDIA, exemplifies what I like to call “scaled founder mode”. Scaled founder mode is being highly involved in the most important things, that only you can do to drive the company forward. Nvidia is one of the most successful tech companies in the world, with a $3 trillion market cap. And Huang, with a 31+ year tenure, is the longest-serving CEO in the industry. He’s a highly effective manager. And he’s still very much in founder mode. When interviewed by Stripe's Patrick Collison, Huang said “CEOs are pinch hitters. We should be working on the things that nobody else can or nobody else is.” Nvidia’s leadership team of 60 reports directly to Huang. But he doesn’t do 1:1s. This way, the team of 60 is relatively flat, with no silos and no privileged access to information. Not saying that killing 1:1s is the answer to speedy execution. But Nvidia found that this way, everyone contributes to solving problems with equal access to important info. And because Huang sees his primary role as being a custodian of the culture, he eats in the cafeteria with employees every single day. At almost 30k employees, you can’t get much more “in the details” than that.

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Meta's latest release of Llama 3 is yet another incredible moment in the state of AI. It's not only best-in-class for open source models but exceeds most other proprietary models as well in performance. This has stunning implications for the ecosystem -- developers, investors, and customers -- when you can have an open source model that performs as well or better than closed approaches. But at a more macro level, what's amazing is that we have now essentially all major tech companies spending in aggregate $100s of billions of dollars on advanced AI R&D and infrastructure, that eventually accrues as value to all developers out there. Given the battle for the major tech platforms to lead in AI, we're going to only continue to see the rate of advancements and leap-frogging continue to accelerate. The implications of this rate of change is to ensure that you have an architecture that lets you eventually take advantage of these improvements over time, and one that anticipates major breakthroughs in the future. Work backwards from a world where models could be 10X higher performance or cheaper than they are today, and build for that world. And ultimately, there's really never been a better time to be in tech than right now.

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Here's my new post about why Apple is best positioned to win the personal AI race. One big reason - privacy. Put simply, people don’t trust other AI players with their data: • Google and Meta rely on ads • OpenAI has seen alot of drama • Microsoft is dealing with the fallout from its AI Recall feature People’s trust in Apple to protect their personal data is a huge strategic advantage. In my full post, I share more about: • Why personal context is the holy grail of AI • 3 reasons why Apple is best positioned to win • How LLMs could become a commodity 📌 Read my full post for free here: https://lnkd.in/g6ydZMJd

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It's an incredibly exciting day for us here at Katanemo - as we launch 🚀 Arch. An open source gateway engineered with (fast) LLMs to handle the muck of prompts. Build fast, robust and personalized GenAI apps with Arch. https://lnkd.in/gdXpVWEu. Arch is built with the belief that prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic. Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling "backend" APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way. ⭐ Core Features: 🏗️ Built on Envoy: Arch runs alongside application servers, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs. cc Adil Hafeez was a contributor to Envoy Lyft 🤖 Function Calling: For fast agentic and RAG apps. Engineered with SOTA.LLMs to handle fast, cost-effective, and accurate prompt-based tasks like function calling, and parameter extraction from prompts. Our modes can run under <200 ms!! 🛡️ Prompt Guard: Arch centralizes prompt guards to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code. 🚦 Traffic Management: Arch manages LLM calls, offering smart retries, automatic cut over, and resilient upstream connections for continuous availability between LLMs or a single LLM provider with multiple versions 👀 Standards-based Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with exiting observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance. - Visit our Github page and star the project! : https://lnkd.in/gdXpVWEu - To learn more about Arch our docs: https://docs.archgw.com/ A big thanks 🙏 to my incredibly talented team who helped us to our first milestone as we re:invent infrastructure primitives for Generative AI. #llms #generativeAI #genAI #prompt #networking #security # cc Taimur Rashid Ammar Hanafi Arjun Chopra Adil Hafeez Shuguang Chen Quoc T Hajar Mousannif Raja Iqbal Arslan Ahmad Jafar Shameem Usman Shakeel Usman Anwer Swami Sivasubramanian George Mathew George Elissaios William Smith Maria Paracha Kamlesh Talreja Farheen Omar Muazma Zahid Reto Kramer Merrin Kurian Anurag Gupta Vishal Shukla Ram Balasubramanian

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Ever wonder how teams at Airbnb & directors like Spielberg turn big ideas into unforgettable experiences? ✨ It all starts with storyboarding - it’s like sketching out your project with a comic strip to visualize each step before you begin. We’re launching a "Multimodal Storyboarding Starter Kit" for developers, enabling you to unlock GenAI storyboarding in your apps. Check out StoryFlow, a demo app built on VideoDB: 1. Enter your app description. 2. List user steps. 3. Hit Generate & watch your user journey come to life! Want early access? Reply or like this post! :)

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Every startup CEO should be using this. Meet your new AI SDR.

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📊 New insights from Tubular Labs show an emerging trend that every podcaster should take note of: full episodes and longer clips are driving serious engagement on YouTube. 🎙 Tubular’s analysis shows that podcasters like Kill Tony (No. 1 on the list) earned a massive 933.5 million minutes watched across YouTube and Facebook - up 238% YoY by posting full episodes. 🎞 Shannon Sharpe’s Club Shay Shay, Joe Rogan, and Theo Von are seeing similar success with long-form video strategies. And this means one thing. 💰 A huge increase in ad Revenue Potential We know audience attention aren’t shrinking and extended sessions on YouTube mean more ad opportunities against each upload. These full video episodes and longer clips are a monetisation goldmine. P.S. If you're looking to turn your YouTube channel into a revenue driving machine which engages with younger audiences, DM me to find out how we can help.

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Today, the journey of Hadrius in NYC seems almost surreal. Reflecting back, I realize just how much of our early days were spent in a whirlwind of ideas and possibilities. ◉ The beginning of our YC journey Initially, our focus was scattered. ↳ Hadrius and Quantbase were operational, yet our attention was divided. We endlessly debated potential paths, engaging with mentors and peers to gather insights. ↳ I recall one of our earliest notions—embedding advertisements in Airbnbs. It sounded promising but turned out to be a flawed concept. I learned this the hard way after discussing it with a founder who had pursued this idea, only to find it ineffective despite significant investment. ◉ The end of YC's batch with the demo day ↳ Armed with a clearer vision, our participation in the W23 batch of Y Combinator was transformative. The interactions, the shared struggles, and insights from fellow entrepreneurs refined our perspective and strategy. ↳ Subsequently, our $2 million seed funding round marked a significant milestone. Investors like Y Combinator and Lynett Capital saw potential in our revised mission, empowering us to scale Hadrius dramatically. ↳ Today, Hadrius manages compliance for over $200 billion in assets under management (AUM). Our journey from scattered ideas to a focused powerhouse reflects our resilience and adaptability—an ongoing saga of innovation and growth to become the ultimate 'Financial Compliance Copilot'. It's just the beginning. 🚀 ------- Ready to take your firm’s compliance up a notch? Send me a DM on LinkedIn & activate the 🔔 for more content like this!

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Reddit, Inc.’s Q3 Report for 2024 is out, and there are some standout changes that make Reddit a serious contender in the digital ad world. Here’s what’s driving the momentum: Revenue Surge: Reddit pulled in a record $348.4 million this quarter—up a massive 68% from Q2, with ad revenue alone climbing 56%. More brands are recognizing the value of Reddit’s unique audience, and it’s making a big impact. Audience Growth & Engagement: Daily Active Users reached 97.2 million, marking a 47% increase year-over-year. New tools and better personalization are keeping users engaged longer, positioning Reddit as a rich environment for brand interactions. Product Enhancements: Reddit has introduced new ad formats and machine learning improvements to keep content relevant and personalized. Expanded machine translation tools are also helping non-English users connect, which makes Reddit more globally accessible for marketers. Enhanced Advertising Solutions: Reddit’s “full-funnel solutions” are now reaching mid-market and small-to-medium businesses, with click-through rates up over 40% and click volume doubling. If you haven’t explored Reddit’s ad options, now may be the time to take a closer look. Financial Health: For the first time, Reddit achieved GAAP profitability with $29.9 million in net income. Solid cash flow and a 90.1% gross margin make Reddit a stable platform, ready for long-term ad strategies. Reddit isn’t just for discussions anymore—it’s quickly evolving into a powerful marketing channel with serious potential.

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What I learned from Jerry Yang & Larry Page TL;DR: Technology companies launching disruptive products should religiously focus on end-users above everything else, including partners. Solve end-user problems and everything else follows– adoption, revenue, and partners. Yahoo! & Jerry Yang (2006) In 2006, phone calls were expensive and based on time & distance pricing. Skype changed all that with VoIP, which bypassed telco networks and made super cheap calls using the Internet. They already amassed 34 million users and were growing at insane rates. At the same time, Yahoo Messenger had over +110 million users worldwide. We could leverage this userbase and create a VOIP product to challenge Skype. Yahoo had all the advantages on paper: the brand, the users, and the technology. So, with the leadership of Brad Garlinghouse, we built a competing product with a very disruptive pricing plan – 1 cent/min; Skype's pricing ranged from 2-3 cents/min. However, a week before the launch, Jerry Yang paused the project at a partner’s request. ATT-SBC was concerned our pricing would cannibalize its own voice business. Yang did not want to upset that relationship. Ultimately, Yahoo missed its opportunity, and Skype was off to dominate the VoIP market. Google & Larry Page (2008) Before the iPhone, companies had to negotiate with mobile carriers to get their apps on phones. Google was struggling to secure deals with Verizon for Google Maps and a new phone concept, Android. Around the same time, the FCC planned to auction 3G spectrum. Google pushed for open application and open device requirements on the carriers who won the auction. This infuriated the carriers. They argued that such conditions would decrease the spectrum's value below the $4.8 billion auction floor set by the US government. The FCC was leaning towards the carriers. The agency did not want to risk the auction not meeting the government's financial goals. Google's only option was to participate in the auction to ensure the minimum bid. Inside Google, the decision to bid was intensely debated. Android founder Andy Rubin argued the carriers would hate us and never partner with Android; Chris Sacca argued that bidding was the only way to guarantee Google's future in mobile. Page sided with Sacca. Page’s rationale: “. . . the carriers already hate us. If we don’t bid, they would hate us and not respect us.”  We bid, the openness provisions were triggered, and we secured a path for Google apps on 3G. Sixteen months later, Andy got his deal Verizon –  Verizon Droid! Page was willing to sacrifice mobile carrier deals to ensure Google’s products were universally accessible to its users. He played spectrum poker with $4.8 billion to achieve that result. (cont. below)

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Today, we're launching something that gets us closer to our vision of Helping AI teams confidently build better AI applications - Athina IDE 🔥 🧪 Athina IDE is a collaborative editor for AI teams to prototype, experiment, and evaluate LLM-powered products 🎯 Core Philosophies 1. Great AI products are a team effort: PMs, Data Scientists, SWEs, MLEs, QA teams, etc., all have to come together to build something great ...so we made sure that non-technical users can experiment, prototype, and run tests on Athina just as easily as technical users. 2. Great AI products are built around great data ... which is why the entire editor revolves around your data and gives you powerful controls like a spreadsheet. 🤔 So what can AI teams do in this editor? 1. Work with Data Like a Spreadsheet: Filter, search, sort, edit, annotate, tag, and delete data with ease. 2. Prototype complex pipelines: Add dynamic columns to run LLM prompts, execute code, make API calls, retrieve data, run transformations and more. 3. Measure AI quality: Run evaluations within the editor itself - there are 50+ preset evaluation metrics or you can create your own custom evals (no-code). 4. Run Experiments: Re-generate datasets with different prompts, models, or retrieval parameters in just a few clicks. Technical users can run more complex experiments programmatically using our SDK. 5. Compare everything side-by-side: Athina IDE has a compare mode where you can compare any number of datasets side-by-side and run evaluations on all of them simultaneously. ⚡️ How Early Users Are Using Athina IDE ⚡️ 1. Testing Transcription Models: One of our early users is using Athina IDE to compare multiple speech-to-text models side-by-side using dynamic columns. 2. Creating Multi-Step Evaluations: Some teams have been creating multi-step evaluations that involve extracting structured data from LLM responses and then running JSON validation rules on it. 3. Comparing Multiple Models: Many teams are running experiments with 5-10 different models, or prompts, or retrievers + a suite of evals at once to determine the best fit for their specific use cases. We’re super excited about this - if you're an AI product builder, we'd love to know what you think! See the link in the comments - our website has some demo videos. It took months of effort from our entire team to ship this! Huge shoutout to Akhil Bisht Vivek Aditya Akshat Gupta Shiv Sakhuja 🙌

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There’s no single UX approach for using LLMs today. I see companies trying 3 main approaches: 1) Chatbots, 2) Integrating with existing interfaces, and 3) Agent-driven workflows. Let's explore. 1.) Chatbots Imagine you have tons of data and simply add a chatbot. I see many founders wanting to go this route: they’ve got a successful legacy business and now want users to query a chatbot powered by LLMs plus their own data. It’s usually the first thing people try, but it’s unlikely to be the long-term answer for most cases. Do we all want endless chatbots? Probably not. It makes users think too much before they get clear value from the product itself—they’re left wondering, “What am I supposed to prompt this thing with?” 2.) Integrating into Existing Interfaces This approach is closer to how we use products today. Here, you upload data and receive a tailored analysis. It takes more planning to decide what those outputs should be, but it’s in line with existing experiences. Think of LinkedIn’s “Get an InMail drafted” feature—GenAI integrated directly into familiar workflows. It’s new yet feels familiar, and the product does the heavy lifting in the background, creating the right prompts to make things seamless for the user. 3.) Agent-Driven Workflows Then there are agents that run workflows on behalf of users. I haven’t yet incorporated an agentic workflow into my daily routine, but this seems to be where we’re headed. It’ll likely take some time before this becomes a standard way of interacting with these tools, but the potential is there. MindStudio, Cassidy, and Zapier are doing a great job in powering these use cases. The best products will likely blend these methods; for example: - If you’re onboarding someone in India, maybe lean into voice features, knowing voice notes are widely used there. -For someone over 50, keep it super simple—they don’t need to know it’s AI working behind the scenes. -Or, if you’ve got a tech nerd, let them explore agentic workflows. Our team at Casper Studios is spending a lot of time thinking critically about these with customers. What else are you thinking about from a UX x AI perspective?

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Stanford University's latest Foundation Model Transparency Index (FMTI) includes Writer LLMs, and the results are impressive! This was the first time Stanford Institute for Human-Centered Artificial Intelligence (HAI) evaluated Writer’s Palmyra-X model in FMTI, reflecting our growing impact to building customer trust through transparency, especially in enterprise applications where security, accuracy, and data protection are paramount. Palmyra-X achieved some of the highest marks for enterprise-grade models based on a set of 100 indicators. But in so many ways, our commitment goes far beyond FTMI expectations. At Writer, we understand that enterprises need top-notch performance paired with solid assurances about data management and protection. Our strategy isn't just to meet industry standards — it’s to surpass them. 🔍 Here are just some of the additional ways we're setting higher standards with our customers: Zero data retention: We never store data longer than is needed to provide our service, and give customers the right to delete data at any time. No training on customer data:
We don’t use the data you share with us to create, modify, or train our models. Zero model distilling & quantization: We do not distill or quantize our models, so customers can achieve peak reliability of accuracy, quality, and performance at all times. Toxic filter transparency: We share the mechanisms of our toxic content filters, ensuring our customers understand the safeguards we have in place to prevent harmful content generation. Bias mitigation: We’re transparent with our customers about our ongoing efforts and methodologies to identify and mitigate bias in our models. We’re providing our customers with technology that's not only powerful but also secure, and empowering them with the knowledge to make informed decisions. Our participation in this research is just the first step. We’re on a journey to raise the bar on transparency and redefine industry standards. Stay tuned for more to come! Link: https://lnkd.in/g8sMU2Qx

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Ami Vora is the Chief Product Officer of Faire. Prior to Faire, Ami spent over 15 years at Meta where she was VP of Product and Design for WhatsApp (2B+ users), VP of Product for Facebook’s ads system (now a $130B business), and director of product at Instagram where she created Instagram's first ads platform. In our conversation, Ami shares: 🔸 Why execution eats strategy for breakfast 🔸 Using metaphor to rally teams around a shared goal 🔸 “Dinosaur brain,” “Toddler soccer,” and “hill climbing” 🔸 A pro tip for handling disagreement 🔸 Tips for working well with product-minded founders as a product leader 🔸 The story of Ami’s incredible 15-year journey from temp to VP 🔸 Much more Listen now 👇 - YouTube: https://lnkd.in/gQasbCbp - Spotify: https://lnkd.in/gQCjrhwn - Apple: https://lnkd.in/ghYTapTG Some key takeaways: 1. Execution eats strategy for breakfast: While strategy is undeniably valuable for guiding direction and setting goals, it’s the execution—the tangible implementation of those plans—that truly drives success. Without strong execution, even the best strategies can’t succeed. This means being willing to get your hands dirty, to tackle the mundane and sometimes tedious aspects of the work that lead to real results. Successful execution allows you to learn from both your triumphs and your setbacks, enabling you to refine and improve your strategy over time. 2. When handling high-stakes product reviews with executives (who are so inundated with information that they can't go deep on all of it, aka "dinosaur brain"), focus on distilling complex information into concise, actionable recommendations. By doing so, you not only clarify your recommendation but also empower execs to offer useful context, patterns, or insights at the right altitude. 3. When having a disagreement, use phrases like “Fascinating! You have to tell me more about why you think that” to show a genuine interest in learning. While everyone loves being right, delivering the best outcome is what truly matters. Put your ego aside, embrace curiosity, and collaborate to broaden your perspective and information set. 4. Use the “hill climb” metaphor to remind your team that moving from a local to a global optimum is like traversing hills and valleys. Imagine your product roadmap like climbing a mountain range, with your current solution being a local peak. Reaching the global optimum or the summit requires leaving your comfortable vantage on an arduous journey to explore other hills and valleys. Trying to play it safe by staying put means forfeiting your shot at reaching the global maximum. Use this analogy to remind your team that achieving the best outcome demands risk-taking and perseverance through challenges. Embrace these hurdles as crucial steps toward success, knowing each stride brings you closer to the summit.

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Founder <> founder convos are the biggest unlock for company building. I spent the last 2+ weeks in SF. Besides meeting with customers, I spent a ton of time with founders / early stage builders that I deeply respect (shoutout to Abhinav Asthana, Christina Cacioppo, Kevin Van Gundy, Akshay Kothari, Yuhki Yamashita, Tara Viswanathan, Celine Halioua, & Michelle Valentine). I’m extremely grateful to have the chance to spend time with these folks. Some reflections from these brilliant people. 1/ People > everything. A company is a collection of people. Company success comes down to hiring the best people. 2/ Startup problems aren’t *that* unique. Startups are HARD and even if you think your challenges are specific to your company, they’re likely not. Someone else has faced the same thing, and you likely don’t need to reinvent the wheel. 3/ Let some fires burn. Focus > trying to do everything. As a startup, you can’t fix everything at once with limited resources (even if you want to). You need to focus on the stuff that will move the needle the most. Pick the top 1 or 2 problems, fix them, then move onto the next. It’s okay if some fires need to burn for a little in the meantime. 4/ Company operating cadences are key. We’re redoing our cadences now, so it was super interesting to learn how others run their board meetings, leadership meetings, all hands, etc. A consistent theme for board & leadership meetings is to have everyone complete a prompt in written doc, share with each other, then use the time to have meatier discussions. 5/ Be competitor aware, customer obsessed. Competitors cause unnecessary noise. Build products and make decisions based on customer / market need and founder intuition, never based on competition. Are these learnings helpful? If so, can share more founder learning posts in the future!

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Last night I had the fortune of representing LaunchDarkly at Sourcegraph's CodeAI Summit NYC. An important theme I took from last night's event? Context. Models like specifics. Computers can't read our brains or know what we want to achieve. Technology is kinda useless without the right context. As we move forward in this increasingly AI-generated world (for better and for worse) let us not forget the importance of adding context to the equation. Whether that's providing context to a dataset through how it was collected, sharing context of your repo to better aid a code assist tool, or simply providing context behind the why of our decisions. It's an interesting (and important) place to keep in our minds. #AI #MachineLearning #DeveloperAdvocacy #DeveloperEvents

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This week on the Mobile Dev Memo podcast: I speak with Mike Taylor about his new book, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs, as well as the skill gap in performance marketing. Find the episode by searching for "Mobile Dev Memo" wherever you consume podcasts.

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Generative AI monetization: an interview with Michael Mansard Generative AI application monetization is still in early days. Michael Mansard from Zuora has done some hard thinking about this. He shares his insights in this Ibbaka interview. https://lnkd.in/gSJ7-qSe #AI #GenerativeAI #GenAI #monetization #pricing #innovation

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