Kate Matsudaira

When a CEO publicly spits out their own product, they're revealing something far more damning: that they never saw the product as the point. To the MBA-trained executive, the product is just a vehicle. A means to margins, multiples, and an exit. The thing itself — what it does, who it serves, the craft behind it — is almost beside the point.

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Stardew Valley meets Enterprise RAG? 🚜 🤖 One of the hardest challenges in moving Generative AI from prototype to production is authorization. How do you ensure the LLM only retrieves data the specific user is allowed to see? Our friends Sohan Maheshwar at AuthZed and Rohit Ghumare at motia just dropped a fantastic reference architecture solving exactly this. Using a Stardew Valley "harvest logbook" as the use case, they demonstrate how to build a complete, multi-tenant RAG pipeline. The Stack: ⚡ Orchestration: motia (Event-driven workflows) 🧠 Memory: Pinecone (Vector search & storage) 🔒 Security: #SpiceDB (Fine-grained authorization) This is a masterclass in building secure, context-aware AI applications. Check out the full tutorial below. 👇 #GenerativeAI #RAG #VectorDatabase #Pinecone #Authorization #Engineering #KnowledgeableAI

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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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The fastest way to build scalable systems isn’t planning. It’s proximity. In my first practice, I didn’t have a private office. My “office” was a desk in a hallway. Not ideal...but it forced me to see everything break in real time. Every bottleneck. Every workaround. Every question that kept getting asked. That created an immediate feedback loop. Most owners do the opposite. They build the office they dreamed of, close the door, and disappear into it. Over time, small issues stop getting escalated. Problems don’t go away, they just get normalized. You can’t fix what you don’t see. Real systems are built by being in it: -Answering the same question five times. -Fixing the same mistake until it stops happening. -Watching where the process actually breaks (not where you think it does). Only after the process holds do you step back. Presence comes before scale.

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Always fun to host engineering dinners with Sumeet Vaidya and Crafting. Few takeaways for me: 1. We are living through one of the most dynamic periods in software engineering. Everyone by now knows to heavily leverage “Plan” mode upfront when coding with AI - but now we are hearing there’s even more time/tokens/turns spent on “Ask” mode before writing the Plan. And PRs of plans! We are literally flying up the abstractions curve. 2. Documentation used to be a luxury, but it is now cheap and easy. Documentation is increasingly agent-first, and markdowns in the repo seem to work a lot better than Confluence/Notion. 3. Historically the magic of GitHub was how it captured the collaboration context - PRs, discussions, reviews, commit history. Today so much of that context sits in prompt/chat history which does not go in GitHub or anywhere else! Feels like this needs to be solved. Openclaw’s memory system and Granola are both inspirations.

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I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.

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