Dave Mao

When most people hear "solid-state," they think storage. But in Santa Clara, xMEMS Labs just gave that term new resonance, literally. The company just raised $21M in Series D funding led by Boardman Bay Capital Management, with Cloudview Capital, CDIB-TEN Capital, Harbinger Ventures, Susquehanna Asia Venture Capital, and other strategic investors tuning in. This isn't another round of capital, it's an #amplifier for a company turning silicon into a new language of #sound and #cooling for the AI era. Founded in 2018 by Joseph Jiang and Jemm Liang, xMEMS spun out of piezoelectric R&D at TSMC and turned what was once wafer-level theory into commercial hardware. Joseph Jiang, a veteran of Knowles, Fortemedia, and InvenSense, leads with precision and swagger, while Jemm Liang, who built Ultrachip from the ground up, drives the engineering with surgical focus. Co-Founder & CLO Wei-Fu Hsu, the former MediaTek exec and IP expert, ensures xMEMS' innovation is as protected as it is disruptive. Their play? A monolithic MEMS platform that transforms voltage into vibration, silicon into sound. Unlike legacy coil speakers and fans, xMEMS tech doesn't move parts, it moves markets. Over 500K MEMS speakers shipped in 1H'24, backed by >250 global patents. The Cypress & Sycamore lines redefine what "small" can do: 1mm-thin, full-range, and capable of sub-bass response once thought impossible in earbuds. Their XMC-2400 µCooling chip, 1mm-thin, fan-on-a-chip, inaudible, moves 39cc/sec of air at just 30mW. That's not cooling, that's choreography at a molecular level. Dr. Chester Hwang, named CTO on Oct 15, 2025, joins to push that curve further. Alongside veterans Mike Housholder (VP Marketing & Biz Dev), Jim Wargnier (VP N.A. & EU Sales), James Lee (VP & Korea GM), Steven P. Bentley (VP Sales), and Martin Lim (VP MEMS Tech), xMEMS is scaling at semiconductor speed. They're already in BleeqUp's AI sports glasses and teamed with Dongguan Rayking Electronics for TWS modules. When OEMs talk "next-gen," they're talking xMEMS-level integration. For investors like Boardman Bay's Will Graves, this isn't just sound engineering, it's signal intelligence. With AI pushing devices thinner, faster, and hotter, xMEMS' piezoMEMS platform sits at the intersection of #acoustics and #thermals, where every millimeter matters. Backing them is a bet that the next wave of #sensorytech won't just compute, it'll feel alive. So what's next? Scaling production, expanding capacity, and embedding xMEMS' silicon pulse into everything from earbuds to AI #datacenters. In a world chasing speed, silence, and fidelity, xMEMS isn't following the beat, they're building the frequency the future will hum to. #Startups #StartupFunding #VentureCapital #SeriesD #AI #Sound #SoundEngineering #Semiconductors #Data #DataDriven #Technology #Innovation #TechEcosystem #StartupEcosystem #Hiring #TechHiring If software engineering peace of mind is what you crave, Vention is your zen.

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People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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When people think about cloud marketplace success stories, they usually picture companies building AI infrastructure, ML tooling, or developer-first platforms. And for good reason—the demand is clear, and the alignment with hyperscaler ecosystems feels natural. That’s why UserTesting and its Alliance leader Sarah Jackson stand out. UserTesting isn’t an obvious cloud marketplace candidate. It’s a platform for gathering human insights—deeply valuable, but not a plug-and-play infrastructure product. And yet, Sarah has helped build a compelling, high-leverage marketplace motion. She didn’t try to wedge the offering into a technical box. Instead, she tied UserTesting’s mission—better customer experiences, faster product feedback, accelerated adoption—to a story that resonates deeply with hyperscalers: helping their customers win with digital transformation. She sold that vision internally, and more importantly, she operationalized it. The result? A multi-pronged marketplace motion that’s driving real impact. I’m excited for her to share that story live and get her interpretation of the findings from our State of Cloud Marketplace & Co-Sell 2025 report (co-created with Roman Kirsanov and Partner Insight). 📅 April 24 | 10–11 AM PT Join here -  https://hubs.ly/Q03fKNgk0 #cloudmarketplace #gtm #cosell #ecosystemleadership #userexperience #cloudsales #cosell #growththroughpartnerships

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As I'm leaving AWS re:Invent in Vegas, I’m reminded of a counterintuitive truth: competing with the public clouds is often really good business VCs love to ask, “Why wouldn’t the incumbent do this?” But in markets this large (and evolving this quickly) that’s rarely the right question. The real questions are: Who can assemble the best team, get to market fastest and with the most focus? And where are there opportunities to partner with the biggest players rather than compete? We’ve seen this pattern repeat across the ecosystem: -People assumed Vercel and AWS were destined to compete. Today, they’re strategic partners with CEO Guillermo Rauch in the keynote -Snowflake vs. Amazon Redshift. Now, AWS is one of Snowflake’s strongest distribution channels -In Datadog’s early pitches, the concern was, “If it’s just monitoring AWS, why not use CloudWatch?” Fast forward to now, and Datadog is now $50B+ public company We’re going to see the same dynamics play out with OpenAI and Anthropic. These are becoming foundational platforms. But that doesn’t preclude incredible companies from being built on, next to, and even around them. Some will look competitive. Many will end up as partners. And the biggest outcomes often start in those gray zones

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Another AWS re:Invent update for you: what happened in the infrastructure keynote yesterday? This keynote is often, to me, the most interesting: they share stories of the incredible design and engineering choices that it takes to build a successful global hyperscaler cloud. This year, of course, the lens was the growing demand for AI services. AWS' custom ARM hardware, Graviton, gets a new launch: M9g EC2 instances, available in preview, sport new Graviton 5 chips. This doubles the number of cores vs Graviton 4, and provides increased L3 cache size, to improve performance by reducing cache misses. We got an official Dave Brown definition of Serverless: it is "the absence of server management". We learned that Amazon Bedrock was originally built in a "traditional web service" type of architecture, with load balancers at the front and servers at the back. The learnings from that approach drove Project Mantle, which offers latency tiering, and fair scheduling, to reduce the impact of noisy neighbours on your own workloads. Mantle uses a request journal, so that failed requests can be tried again, making Bedrock more reliable. AI workloads need access to vector databases. AWS have invested in making vector search available across the platform. S3's Vector indexes offer great price/performance, and we learned how they can still achieve speedy access to these indexes even though they're stored on disc, not in memory. AWS' new Trainium 3 chip processes 5x the number of tokens per MW than Trainium 2. These packages have on-chip support for profiling workloads without impacting performance. AWS are making Neuron Explorer (a performance analysis tool) available, and as of Q1, you'll be able to use the features of Trainium natively from PyTorch, just by targeting 'neuron' instead of 'cuda' in that code, using NKI - the Neuron Kernel Interface, initially announced in September last year.

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Amazon Web Services (AWS) today announced a series of innovations that make it easier, faster, and more accessible for adopting and scaling AI and agentic systems. Of the many launches our CEO Matt Garman presented here at re:Invent 2025, I'd highlight three: → #FrontierAgents, a new class of fully autonomous, highly scalable AI agents that can work for hours or days without intervention. #Kiro serves as the virtual developer, AWS Security Agent acts as a security consultant, and AWS DevOps Agent operates as an on-call operations team. → #NovaForge, a solution that empowers companies of any size to build their own AI models by combining proprietary data with Amazon Nova-curated datasets. → #AmazonEC2Trainium3UltraServers, a new generation of processors delivering up to 4.4x more computational performance, 4x greater energy efficiency, and nearly 4x more memory bandwidth. And here's the kicker: we are already working on Trainium4. Today's announcements also include the largest expansion of new models to date in Amazon Bedrock's history, with the arrival of Google’s Gemma 3, MiniMax’s M2, NVIDIA’s Nemotron, OpenAI’s GPT OSS Safeguard, etc. Plus, we launched new Bedrock AgentCore capabilities, Nova 2 models, AWS AI Factories, AWS Security Hub, Database Savings Plans—a flexible pricing model that cuts costs by up to 35%—and more. These aren't just incremental improvements; they represent a phase shift in how we develop technology. It's the beginning of a new innovation cycle that will further accelerate business transformation and democratize access to cutting-edge AI for companies of all sizes around the world and, of course, in Latin America! Check out all the details here: https://lnkd.in/dbtF4EYP #AWS #AWSLATAM #reInvent #reInvent2025 #Cloud #AI #GenAI

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This week we announced more MongoDB product innovations that will help reshape what's possible in AI. Our new Voyage AI by MongoDB models deliver more accurate and cost-efficient results, while new partnerships for agentic AI workflows simplify scalability and reliability, making it easier than ever for businesses to build trustworthy AI solutions. Business and tech leaders across APAC are striving to unlock real-world impact from AI - whether it’s modernizing banking systems or building AI-first applications that scale globally. MongoDB’s ability to consolidate structured and unstructured data, power real-time decisions, and deliver integrated vector search has already demonstrated it's the ideal foundation for AI applications that demand accuracy, scalability, and adaptability in an ever-changing landscape. With these new advancements, I can’t wait to see how our customers push the boundaries of innovation. Read more here: https://lnkd.in/gADYwgyj #MongoDB #AI #VoyageAI #Innovation #AgenticAI

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Today on Training Data: Sanjit Biswas, founder & CEO of Samsara (NYSE:IOT) and former Sequoia Capital-backed founder of Cisco Meraki Sanjit shares the ups & downs of running neural nets on constrained compute and power footprints in the real world, ~2-10 watts Physical AI is hard 🫡 Listen to the full episode: Apple: https://seq.vc/mze Spotify: https://seq.vc/zq3 YouTube: https://seq.vc/185b91

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Lots of news out of Hetz Ventures portfolio today - Nimble announced their $47M Series B, led by Norwest with participation from Databricks Ventures. We've been proud to back Uriel Knorovich, Menachem Salinas and the entire Nimble team since early on, turning the live web into trusted, decision-grade data for AI agents and mission-critical workflows. Tonic Security launched the Tonic Mobilization Coordinator, the industry's first agentic remediation orchestrator. It automatically drives exposure remediation end-to-end, moving security teams from knowing they have risk to actually reducing it, continuously and at scale. Well done Sharon Isaaci, David Warshavski and Gregory Ainbinder. Anima launched their UX Design Agent on Product Hunt today: https://lnkd.in/demUMrUX. Designers and product teams can now go from prompt, Figma file, or live website to on-brand, production-ready frontend code in a single shot. Deepchecks released Know Your Agent (KYA), their answer to evaluation in the agentic era. Borrowing from the KYC framework in financial services, it gives teams a structured way to understand how their agents actually behave across complex multi-agent workflows. #proudseedinvestor https://lnkd.in/dyGTAHcg

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Two of AWS’ Sr. Principal Engineers, Nicholas Matsakis and Marc Bowes, take us inside the development of Aurora DSQL in a fascinating new blog. The post shows how the team scaled write operations without two-phase commit, overcame garbage collection hurdles, and ultimately embraced Rust for both data and control planes. What I find particularly interesting is how the team's journey with Rust evolved. They started cautiously, using it for a single component, and ended up rewriting almost everything in Rust. It's a reminder that sometimes the hard choices - like adopting a new language - can unlock tremendous value. The full post is well worth your time. It's not just about database internals, but also about team dynamics, a culture of learning (and questioning established approaches), and making difficult architectural decisions. Read it here: https://lnkd.in/eMhxBu9e Now, go build!

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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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If you thought AWS was just an AI infrastructure company, then the day 2 keynote at #AWSreInvent dispels that notion. Swami Sivasubramanian has been on message, picking up from where Matt Garman left off. AWS announced Nova Act, specific persistent Agents, Kiro, AgentForge updates and even brought Byron Cook to the stage to nerd out on how the company is focused on neurosymbolic reasoning. My takeaway is that AWS is positioning itself to be THE best place to run agents, while Nova is talked about as a frontier model, the clear focus this year is that AWS is positioning its platform as the destination for agentic workflows. We have all heard about the MIT study highlighting that 95% of AI projects fail to deliver ROI. The last 48 hours of announcements and my conversations with AWS executives all leave me with the opinion that AWS is focused, like it always has been, on the operational needs of its customers. For the first 19 years, that focus was on making Cloud easy to use and operate at scale. The next era: how do you operationalise agentic AI? AWS' response: run your agentic AI project on our cloud, we have your back.

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