Sean Knapp

Today, we’re thrilled to publicly launch Phoebe and announce $17M in seed funding, in rounds led by GV and Cherry Ventures. We are building the immune system for software: An AI agent platform that continuously investigates live data, diagnoses emerging issues and generates preemptive fixes. As AI accelerates how software is built, reliability must keep pace. Phoebe started with faster troubleshooting, and our aim is to prevent the overwhelming majority of emergent problems before becoming customer-impacting failures. Huge thanks to the hundreds of early users at customers such as Trainline and PPRO. Thanks to our investors, including 20 CTOs and tech veterans joining as angels. And of course, thanks to the small and mighty Phoebe team! This is just the beginning! 🚀 Shoutout to Mark Kleinman at Sky News for breaking the news this morning (linked in the comments) Some of the many backers we're grateful for 🤗 🙏 GV (Google Ventures), Cherry Ventures, Anthony Danon, Carmen Alfonso Rico 🍫, William Gaybrick, Shola Akinlade, Yaron Shaer, Christian Dorffer, Tom Stafford, Thomas Wolf, Andrew Tan, Victor Riparbelli, Harsh Sinha, Jeanne DeWitt Grosser and more

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We made a big upgrade in our extraction product 💫 - view confidence scores in the UI! LLMs are cracked at document parsing, but sometimes certain pieces of the document are too hard for both LLMs (and sometimes humans) to understand. It is unrealistic to expect that agentic automation will be 100% accurate all the time. In order to bring this up to 100% while still saving a ton of time, you need confidence scores and HITL. You can set a confidence threshold and view low confidence items in yellow to quickly validate which results might be incorrect. Check it out: https://lnkd.in/gvXEEzqq

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🎧🍌 New podcast out today with Garrett Lord, Co-founder and CEO of Handshake Handshake came out of nowhere this year to be a serious player in the AI data labeling race. Garrett talks through how they built a Seal Team Six to help the frontier labs, plus the early days of building Handshake's $250m+ revenue business, driving across the US to land customers, sleeping in McDonald's parking lots, and crashing on couches for seven months raising their first round. Thanks to Ramp for supporting this episode, and to Jeff Richards, James Alcorn, Ilir Sela, and Ben Christensen for helping brainstorm topics for Garrett. Link to full episode in the comments!

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A nice summary here from Messari on what we're up to at VeChain. The TL;DR is: We're showing up other L1s when it comes to real, mass adoption. In Q2, two of our VeBetter applications, GreenCart and Mugshot, each surpassed one million users, offering real proof that web3 can support everyday sustainable behavior at scale. The Stella Pay Visa Card now allows users to spend their VeBetter rewards at more than 130 million vendors around the world. You do something good, you earn something real, and now you can use it all over the world. This is what Web3 should be about. Bringing positive change to millions of people. No crypto native knowledge required. Messari's full report: https://lnkd.in/gTmkg7dw 

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🚀🚀 AI is rewriting the rules of startup success. In our latest Crunchbase piece, Noah Lin and I dive into how we @ defy.vc have seen AI-first companies scale smarter with value model tactics—leveraging predictive power and network effects to reshape industries. From defying traditional metrics to building data moats, these strategies are a game-changer for founders. 💡 Stay tuned for a deeper follow-up where we’ll share actionable tactics for founders to scale their AI ventures! Full article 👇 #AI #Startups #Innovation #VentureCapital

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There's a real dilemma hitting the agentic integrations world. Fully synced data sets are outperforming live API integrations. Everyone’s excited about new ways to let AI call APIs directly... MCP, plugins, you name it. Cool tech, but here’s the truth: if you want real semantic search or intelligent querying, none of that matters unless you sync the data. Why? Because live API calls are slow, rate-limited, and stuck with whatever query methods the provider gives you. Semantic search requires preprocessing, embeddings, and indexing, which are all things you simply can’t do on-the-fly against someone else’s API. So while live connections are great for fresh lookups, the real unlock comes when you maintain a synced copy. That’s where you get instant results, flexible queries, and control over relevance. APIs let you access data. Syncing lets you use it intelligently.

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Today in “Scott Learns How Silicon Valley Works”: I’ve apparently been quoting ARR wrong this whole time. At Metronome we run a usage-based business. Customers sign up for committed contracts, find value, expand, and then negotiate new ones. It’s a virtuous cycle of NRR growth. To stay disciplined, we usually talk about cARR - the literal book value of our signed contracts. It’s conservative by design. But here’s the truth: most customers expand well beyond that number, often for multiple years. The confusion: I recently told an investor, “Our cARR is $X.” They assumed that meant a run-rate from last month’s revenue - and mentally discounted it by 50%. In reality, history says that $X in cARR is more likely to yield ~150% of X in actual revenue. A 3x swing, all in the framing. That’s just part of the fun (and pain) of building usage-based businesses: explaining why NRR, not ARR, is the real determining force. If you’re wrestling with usage-based monetization, let us know - we’ve had to learn this the hard way.

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INTRODUCING... ANOMALY DETECTION Customer reports are often your first sign of a widespread problem, but it can take time to recognize similar issues. Now, our AI will scan incoming issues in real-time to detect anomalous patterns like... → Active platform outages → UI bugs → Regressions You can then create an issue group to let all affected customers know once there's a fix. This makes it easier to spot large problems early and speed up your time to resolution. Enjoy!

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Here's how Lyft scaled from ML to GenAI Infrastructure without reinventing the wheel. Full video: https://lnkd.in/diNgXV6B In a standout talk, Konstantin Gizdarski gave a rare behind-the-scenes look at how Lyft expanded its mature ML platform to support LLMs and why that architectural choice mattered. 🎯 Key Insight: The breakthrough wasn’t starting from scratch; it was treating LLMs as a special case of ML models, and evolving the infrastructure accordingly. Here’s what stood out: 🔌 Unifying LLMs with Existing Serving Systems Rather than spinning up a separate stack, Lyft wrapped vendor APIs like OpenAI into their internal ML serving platform using custom proxies and client wrappers. This allowed them to: - Centralize observability, compute, and security - Remove client-side API keys - Gain full traffic attribution (who’s prompting what, where, and how) 🧪 Built-In Evaluation as a First-Class Citizen With LLM usage scaling rapidly, Lyft built its lightweight eval framework for: - Online input filtering (e.g. PII redaction) - Online output moderation (e.g. content safety) - Offline request–response scoring (LLMs evaluating LLMs) 🤖 Building Toward Assistants To support complex workflows, they’re developing an "Assistant" abstraction declarative interfaces that combine: - Core LLM functionality - Evaluations + proxies - Knowledge bases + tool use 🧠 Lesson: ML features → AI knowledge bases ML monitoring → AI evaluations By recognizing this conceptual symmetry, Lyft extended its platform rather than replacing it. 📌 Takeaway: Real GenAI adoption isn’t about picking the “right” model; it’s about platform architecture, user control, and infrastructure that scales with the org.

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The application layer the new security battleground, not the OS. Today, applications are ever-changing ecosystems made up of code packages, plugins, extensions, and increasingly, AI models. The software surface area of many organizations is 10x larger than it was before, yet companies have less visibility and control than ever. The insane part is that pretty much no one recognized the magnitude of this problem, except for the team at Koi Security. In just eight months, they’ve built a platform that delivers full visibility and control over this new application layer—detecting everything running on every endpoint and giving organizations the ability to actually enforce policies and remediate risk. And they're scaling incredibly fast. We are excited to co-lead their $48M Series A round. Here's what we saw in them: https://lnkd.in/dbrT4yRV Amit Assaraf 🎏

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New podcast episode up thanks to Kevin Werbach on AI + healthcare and much more. Here are a bunch of my lessons learned from building products + getting things done (that work) in gov https://lnkd.in/gswPbXwQ

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Two long years ago, at Movable Ink's Think Summit 2023, I shared how software developers have gained sizable leverage writing code over the past few decades. While there are only 1.6 million developers in the US, there are 160 million workers who haven't witnessed the same advances. But that was about to change! Fast forward to today. We're seeing AI transform companies in three fundamental ways: ⏰ We're nearing the end of humans writing software. Engineers, product managers, and designers are prompting AI to generate code, translate customer interviews into PRDs, and rapidly prototype new designs. 🛠️ We're capturing multi-step, manual, business processes and turning them into agentic workflows. Sales outreach copywriting, data transformation and sharing, prospect research, slide creation, and much more will be things of the past. 💻  And finally, the software that we build will be AI-first. Traditional UIs and user flows can break out of the constraints of human interaction. Think digital agents completing tasks or predictive recommendations eliminating entire steps. An individual contributor role will look more like an agent manager and those who learn how to harness these new tools will 10x their productivity relative to peers who don’t. Every business worker is about to experience leverage first-hand in a way that only developers have and skillfully managing agents will eclipse the ability to manage people. It's both a scary and thrilling time to be alive! 

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