Kate Matsudaira

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Development teams face constant pressure to ship code faster while maintaining security and quality. Today we launched GitLab Duo with Amazon Q in GA to address these challenges head-on. By embedding Amazon Q's AI agents into GitLab's platform, we're accelerating developer workflows throughout the software development lifecycle directly from GitLab. Already seeing great feedback from customers like Volkswagen Digital Solutions and Availity. This is another step forward in our mission to make innovation a lot easier with GenAI. https://lnkd.in/emAth4vT

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Got to sit down with Vercel’s Founder and CEO Guillermo Rauch, to unpack how much change a single computer and a little curiosity can bring to the world. And how that early drive led to building Vercel, now powering $10M+ in developer productivity gains.

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I spoke with Tom Wilkie, CTO of Grafana Labs about what it’s like scaling open source projects while building a commercial company around them. Discussed the challenges, lessons, and what goes on behind the scenes of a tool so many of us use: https://lnkd.in/gD92CXuD

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I love the way Ozgun puts it: Ubicloud is an open-source alternative to AWS, at a fraction of the cost How do they dare take on these cloud giants? Özgun shares the backgrounds of the founding team (Daniel and Umur) and what it really takes to go head to head with big tech   More videos from e2vc Summit!

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Curious what folks think - CesiumJS includes a number of simple UI widgets to help jumpstart building apps: imagery picker, timeline, geocoder, etc. Would you be interested in additional open-source UI widgets such as trees, list views, 3D navigation, etc.? These would integrate seamlessly with CesiumJS, but not be required as part of the CesiumJS build. And these would be built on an existing UI tech stack in the most lightweight and decoupled way achievable; we don't want to reinvent the wheel, just add value for geospatial and AEC workflows. What do you think? What widgets would you want to see? And what tech stack would you suggest? Or not?

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How Vanta Uses AI w/ VP of Engineering Iccha Sethi! 10 min deep dive. We cover: - Hiring engineers for the AI era - AI unit tests for large code bases - Writing docs with AI - Vibe coding for eng leaders - Favorite AI tools

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A few seconds of silence can break the rhythm of an entire conversation. And with agents, timing defines reality — short delays feel human, long ones feel robotic. This post breaks down how we’ve worked to engineer latency out of Sierra’s voice stack: from detecting the end of a customer’s speech to streaming a response that feels instantaneous. https://lnkd.in/e4jreAyn

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Claude Code and Opus 4.5 have recently made meaningful strides in raising the bar on agentic development. However, I’ve discovered that when you treat the agents more as teammates and less as automatons they really excel. Let me explain. Claude Code’s GitHub integration finally lets me manage agents the way a great CTO manages developers: with clear objectives, structured workflows, nuanced guardrails, and most importantly, stable context. I start every project in Claude Code's planning mode now. The agents and I work together to draft comprehensive plans. I review. We iterate. Then, and this is the key, we instantiate those plans as GitHub epics. Each epic becomes a container for related issues. Each issue becomes a discrete task with clear objectives, architectural guidelines, and acceptance criteria. The agents work methodically through the issues and submit PRs when done. They even check each other’s work, just like a human team would. Because Claude Code maintains context through these epics, we’re no longer fighting context window exhaustion. A three-week project stays coherent from day one to deployment. The agents can see the overall arc of the project at any time, track progress, and recall the architectural decisions from week one as they implement features in week three. The results have been striking. Code quality has improved markedly. The agents are producing more consistent, better-structured code that actually follows our established patterns. Error rates have dropped to levels I’d expect from more senior developers, not the interns agents sometimes seem to channel. And if they stray, I just ask them to revisit the epic and validate whether they’ve delivered against the requirements. They usually course-correct. With a growing level of comfort, I’m now starting to let the agents monitor GitHub on their own and select their own epics to work on, updating the underlying GitHub issues as they make progress. They’re not just executing rote tasks anymore—they’re participating in overall project management. Great tech leaders don’t micromanage. They set clear objectives, establish workflows, remove blockers, and trust their teams to deliver. That’s precisely what I’m doing with my agent teammates now. I’m just applying decades of engineering management wisdom to a new kind of hybrid team. And it’s working. Carpe Agentem. 🎯 #AgenticDevelopment #GitHubIntegration #ClaudeCode #EngineeringManagement #AI #FutureOfWork

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For most people, money is still harder than it should be. If AI is going to matter in finance, it has to make financial products genuinely more helpful, secure, and responsive to real life. I’ve spent the last decade building large-scale data networks, and I’m convinced the next chapter of financial services won’t just be about access to data, it will be about understanding it. Open finance unlocked secure, permissioned connectivity. That foundation enabled a generation of innovation. Now AI is ushering in the next wave of innovation. Intelligent finance means systems that understand context, adapt in real time, and continuously improve. It means stopping fraud without adding unnecessary friction. It means making better decisions across payments, credit, and identity, with the accuracy and trust this industry demands. It also requires infrastructure that is purpose-built and understands how money moves. At Plaid, we’re building intelligence models that learn from patterns across our network and compounds with scale. As part of that work, we’ve developed our first transaction foundation model for finance to deepen how financial activity is understood across products. I believe this shift from open finance to intelligent finance will define the next decade. I wrote more about how we’re approaching it here: https://lnkd.in/gV35NPby 

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We signed a new customer yesterday that heavily uses Stripe for billing and has some complex multi-entity and multi-currency challenges. They tried to develop an integration with a couple other accounting solutions and while doing so, asked Claude Code what other systems they should consider. Claude suggested they explore SoftLedger and by the time we got on our first call, he already had a pretty good integration specification. So he got access to his account yesterday. This morning, this is the message received by our customer success team: "This is unbelievable. I would estimate I am 60-70% of the way through what I need to do. Brought in 23,000 journal entries overnight." Something has changed in the buying journey for companies seeking an integrated accounting solution and I love it. Accounting technology should be an API driven solution that conforms to a companies processes. Tools like Claude Code enable companies to build bespoke integrations for their internal processes like never before.

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99% success rate on a site we’d never seen before. Not bad for launch 🚀 Developers build on Deck to interact with websites that have no APIs, reading or writing data wherever users need it. What’s remarkable is the quality and consistency, even across new, unseen systems. Auth Agents are redefining what “integration” means. Check our guides if you're ready to start building: https://lnkd.in/eG4h6aMS

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In software everything becomes an abstraction layer of previous layers. Look at Micron or Broadcom. Not going anywhere. So SaaS could as well being the foundational layer behind the AI layer. Whether is APIs, inner data or workflows.

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