Kevin Ott

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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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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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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Things just got even more interesting in open source (database) land! Today, Redis makes a dramatic return to its open source roots, offering Redis 8 under the AGPLv3 license. The shift follows a similar move by Elastic in August 2024 and completes the company’s somewhat circuitous licensing path. In both cases, I suspect that neither company ever really wanted to leave open source. The irony is that the very thing that most threatened to hurt their businesses actually helped them: forking. Redis’ license change “achieved our goal,” writes Redis CEO Rowan Trollope. “AWS and Google now maintain their own forks.” How is this a good thing for Redis, developers, enterprise customers, and even Valkey? Read on. 😇 https://lnkd.in/gTJNnVeZ

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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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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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Redis to acquire Decodable! "As the world pushes deeper into the age of LLMs, one of the biggest challenges developers will face is ensuring the right context goes to the right place at the right time. We’re helping to solve this by strengthening our capabilities as a real-time context engine that enables instant data access for agents.", Rowan Trollope Check out the announcement: https://lnkd.in/gNkggWWQ

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Amazon Web Services (AWS) just launched general availability of new R8gn instances - though the way that I found out about them wasn't from the official Amazon blog post, it was from the new automated newsletter for EC2instances.info. EC2instances.info is regularly rebuilding itself every few hours and now also checks for two things: 1) new instance types and 2) any pricing changes with existing instance types. You can sign up to receive email alerts about these two changes as often as the site finds them. If you're curious about getting the notifications sent your way to see what Amazon is launching, especially leading up to AWS re:Invent, I included the link for how to set this up in the comments below.

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Building on top of large language models is fun, but getting consistent performance and reliability is extremely challenging. I love this post from Kimberly Patron about how Sierra uses health and performance-based traffic routing and request hedging to get meaningfully better performance and tail latency from high scale LLMs https://lnkd.in/g4qWRCb5

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Today let's talk about Parallel Fan Out/ Concurrent Agentic Design pattern and when parallel agents go rouge What’s the Concurrent Orchestration Pattern? Imagine a scenario where multiple AI agents—each with its own lens or specialty—tackle the same task simultaneously. Instead of a single, step-by-step chain, tasks fan out to different agents in parallel. Then their outputs are merged or aggregated for the final answer. It’s the AI equivalent of a brainstorming session where everyone chips in together. This pattern thrives when you need diverse insights or speed—think ensemble reasoning or reaching a verdict faster. This pattern is commonly used in agentic RAG. Here’s how you can apply it in a Retrieval-Augmented Generation (RAG) system: Step 1: Break the user’s query into smaller sub-queries (e.g., “Define concept X,” “List use cases,” “Give examples”) and map out their dependencies. Step 2: Run those sub-queries in parallel—agents fetch context or process each in isolation (e.g., document retrieval, summarization, external tool usage). Step 3: Once all agents have results, aggregate the findings into a single, coherent response. This technique lets you parallelize independent parts, reducing latency while maintaining clarity—especially effective when sub-queries don’t depend on each other. Super Important : Try to avoid this pattern and use sequential execution even if it's slow, but it might yield a good result, below is the reason As Cognition [the company behind devin] warns in a famous blog post “Don’t Build Multi-Agents”, things can go sideways fast if agents don’t share context or coordination is weak. When agents operate in isolation—making decisions based solely on their own view—the final result might be fragmented, contradictory, or just plain incoherent. Think two agents building different puzzle pieces that don’t fit. The core issues: Context fragmentation: Each agent works in a silo, leading to mismatched assumptions. Implicit decisions: Agents’ outputs reflect unspoken choices that may clash when merged. Coordination complexity: Without strong orchestration, integration becomes error-prone. References : 1. Concurrent Execution Pattern : https://lnkd.in/gZybgm2s 2. LLM Compiler whitepaper : https://lnkd.in/gWwGJhbi 3. Coginition blog Don't build multi agents which shows parallel execution can yield agent drift : https://lnkd.in/gkDKC4TP

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Large language models are powerful but without optimization they are also expensive. Our latest blog breaks down the hidden cost of deploying LLMs, why GPU limits are a bigger bottleneck than you think, and how compression techniques like quantization, pruning, and distillation can cut costs by up to 8x. Read the full post: https://lnkd.in/d9Vmaa-v

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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