Wei Xu

Huge congratulations to the LanceDB team, Chang She and Lei Xu, on securing an $8M funding round from CRV! Their mission to build The Database for Multi-modal AI is an exciting one. It's amazing to see how far they've come since Chang's days as a star VP Eng at Tubi. Check out the TechCrunch article for more details on their journey. #AI #technews #fundinground

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🤯 Imagine the CEO of the world’s hottest company says the next trillion-dollar opportunity is something your startup has already been delivering results with for over a year in stealth! #JensenHuang and #MarkBenioff have the world buzzing about #AIAgents. 🐝  But at #CalvinBallTech, we've been quietly delivering game-changing results with them for over a year!  🤫 Don't get caught up in the hype 🙅‍♂️ Stay tuned. CalvinBall Tech is about to come out of stealth, and we're bringing proven AI Agents that have already solved critical problems for real businesses. 💪 Here’s a quick couple of questions for anyone showing you fancy roadmaps and dazzling demos: Do they have proven results already? Can they start delivering tomorrow? Gurnoor Dhillon Ratu Nadiah KhairunnisaSahal ZainEva ZuliyaFaris -Yoga Wigardo Dharnesh GordhonAntoine de CarbonnelFrançois Walewski #AICanSolveThat #AISuperAgents #AIRevolution #FutureOfWork #EnterpriseAI #CalvinBallTech #StartupJourney  #CalvinballerCode

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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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AI & SaaS: A Tale of Two Futures What if Excel and other SaaS apps don’t vanish but evolve, blending seamlessly with AI? In a recent Interview (https://reurl.cc/eGz06j), Satya Nadella, Microsoft’s CEO, suggests the “agent era” will replace traditional applications. That’s Nadella’s vision: AI as a co-star, enhancing our favorite tools—think Excel with Python instead of Excel replaced by something else. But here’s the twist: 🔸 I see AI’s disruptive power surpassing any need for standalone software. 🔸 Why open an app when a smart agent can directly pull, manipulate, and display data on demand? Nadella believes AI + SaaS = Harmony. He sees Copilot orchestrating tasks, quietly boosting productivity. Developers still matter, refining user experiences to work with AI rather than against it. I foresee a more dramatic shift. If AI can generate everything from sales forecasts to text summaries instantly, maybe the spreadsheets and CRMs we rely on will fade away. Where do you stand? 📌 Collaboration or Disruption? 📌 AI enhancing SaaS or AI replacing it? No matter which path emerges, one thing’s certain: AI is rewriting the rules—and the next chapter of SaaS will be far from business as usual. Your thoughts? Let’s discuss. #AI #SaaS #FutureOfWork #Microsoft 

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One interesting thing about Deepseek, according to its company CEO Liang Wenfeng, is that unlike many Chinese AI companies, they didn’t just copy Llama & tweak a few things, they innovated on a new architecture which allowed them to achieve these prices. Interview linked below.

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Language models enable your phone to better understand the world and look out for you. Great to start wider testing of Scam Call Detection on Pixel using AIcore and Gemini Nano https://lnkd.in/emAKSPz5

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How you can make your LLM applications better!

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Wow - I was wrong. Would have expected most revenue coming from their API or Enterprise businesses. Predictions-- I expect this to flip in the next 12 months: 1/ Consumers will get more and more for free and cancel their ChatGPT+ subscriptions. OpenAI themselves just made GPT4o available to their free users, so they're undercutting themselves. 2/ Enterprises will start paying more and more for AI - MSFT and Google are way better positioned for this than oAI. 3/ Shocked API is only 15% - that means Reprompt is > .01% of total OpenAI API usage. That's kind of crazy given we're still quite small. We are still in the first innings.

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🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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Releasing quantized Llama models. Faster, lower memory footprint, designed to run on small devices. Details here: https://lnkd.in/gdmjfByi

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Want to tease something my team has been working on over at Buffer! The attached image isn't a mockup, but a screenshot of a live prototype of RSS feeds directly in Buffer! It's only available internally and still pretty rough at this point, but we'll have it available for user testing soon. With this enhancement, we hope to help Buffer users easily find new and engaging content to share with their audiences. What do you think? Will RSS feeds in Buffer help you create more content?

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Come join us tomorrow in SF to learn more about the latest updates to run AI workloads on Google Cloud!

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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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Daniel Wilson and Reza Rassool Thanks for the opportunity to join the conversation, and more importantly to contribute to the critical work of Kwaai Personal AI -- turning AI into a meaningful benefit versus a threat in people's lives. (My comments start about 30 min in, after Reza's introduction to Kwaai))

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At Next LX, we're building a simulation-based training program that prepares candidates in an AI-powered workplace. We call it a "flight simulator for office work." But we sometimes get asked "Why build a simulator?" Simulations are the best way to learn. Period. Give me 5 minutes to try to convince you. # The Principle of Simulation-Based Training We firmly believe that simulation-based training is the most effective way to acquire *any* skill. The fundamental principle is simple: the best way to learn to do something is to actually do the real thing. For instance, if you wanted to teach someone how to hit a baseball, you wouldn't merely lecture them on the physics of baseballs or have them watch instructional videos. You'd take them to a field and have them hit real baseballs. This hands-on approach is the core of simulation-based training. # What is Simulation-Based Training? Simulation-based training involves performing the skill in an environment that mimics the real-world context as closely as possible. It allows learners to practice the actual skill they're trying to acquire, but in a controlled, safe environment. In fact, simulation can be even more effective than real-world practice for several reasons: 1. Structured Repetition: Simulations allow for repeated practice without real-world consequences. You can retry scenarios multiple times, refining your skills with each attempt. 2. Enhanced Assessment: Simulations can incorporate more comprehensive assessment tools than are possible in real-world settings. For example, a baseball simulation might use high-speed cameras to analyze a player's form in minute detail. 3. Optimal Sequencing: In a simulation, learning experiences can be carefully sequenced to keep learners in the "zone of proximal development" - challenging enough to promote growth, but not so difficult as to cause frustration. 4. Low-Stakes Practice of High-Risk Scenarios: Simulations allow for the safe practice of rare but critical situations that would be too risky or expensive to replicate in the real world. # Current Job Training Methods While there are several popular methods for job training, each has significant limitations compared to simulation-based training: 1. YouTube: "But you can learn for free on the internet!" While an incredible resource, watching videos alone doesn't provide the hands-on experience necessary for skill mastery. 2. Online Courses: "There's a course for everything!" These offer good sequencing and repetition but often lack real-world context and application. 3. Project-Based Learning: "Just build stuff!" While valuable, standalone projects may be disconnected from the real work environment and miss crucial contextual learning. 4. College Education: Traditional higher education often fails to fully prepare graduates for specific job requirements, necessitating additional on-the-job training. Convinced? Thoughts?

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After speaking to so many user researchers, designers and product managers, the team at Raijin.ai - AI Analysis Assistant realized that everyone had their own unique way of synthesizing their user research data. And so, we are releasing our "Ask AI" function to help you to guide the AI to extract the precise information you want. How does this differ from ChatGPT AI or Perplexity you may ask? Let me share: ✅ Focused and relevant context for every query - An average user interview of 1 hour has about 5,000 spoken words. ChatGPT's outputs starts to get more generic and shallow as you exceed the 1,500-2,000 words mark. Sometimes it even stops generating. We have built automated checkers and tests to go beyond that limit, enabling you to still get highly relevant outputs based on your questions. ✅ Do all your work in one workspace - You want all your prompts, outputs and documents in one single place: no more annoying copy and pasting prompts, inputs and outputs from one place to another (a MAJOR win IMHO) ✅ Easily search and retrieve results - if you have tried searching for past information across multiple chats in ChatGPT, you will understand this huge pain point If you are an over-worked researcher/designer/PM and want to accelerate your transcript analysis and data synthesis process, come join our Beta Tester Program now 😁 Sign up via our website (link in comments) or just drop me a text on LinkedIn! #userresearch #customerresearch #customerintelligence #productmanagement #productdesign #generativeAI #techstartup #UIUX #UIUXdesign #validation

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