Vinny DiDonato

API calls on Corti were up 79% yesterday versus our trailing 14-day daily average. We are growing new builders by 10% WoW, and due to the gap from onboarding to production in a clinical environment, many builders aren't fully live yet. 2026 is wild. 🆙📈

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Great post by Aleix Morgadas and Eugene Sergueev on how Flo Health Inc. revamped mobile onboarding If you’re keen to learn how to: 🔹 break out of release-cycle bottlenecks with server-driven architecture 🔹 scale experimentation without scaling engineering effort 🔹 build internal platforms that empower teams across product, design and content …it’s well worth a read: https://lnkd.in/emiqM7NM

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3 reasons why we build software and service as one team: We don’t build tech *for* operations at Tandem. We build tech *with* operations. Here’s why that matters; 1) Fix friction at the root. If a vet has to click 5 times to do something that should take one, we fix it. Not just because it’s “inefficient”. But because it creates cognitive load, slows care delivery, and compounds under pressure. Less friction = faster throughput, fewer errors, and lower burnout. 2) Close the loop between builders and users. The person who writes the feature talks directly to the person who uses it. That means issues surface faster, fixes are more accurate, and nobody ships something that “technically works” but operationally fails. It’s not just “faster iteration” - it’s tighter alignment with reality. 3) Scale the system, not the sprawl. New clinics, new roles, new workflows. We don’t layer on SaaS duct tape. We evolve a system designed to flex with operational growth. That means fewer integration points, tighter control, and a consistent experience from core to edge. We don’t treat tech like a layer. We treat it like a nervous system. Because when you’re delivering care in the real world, you can’t afford delay, dissonance, or dropped signals. That’s why we built Tandem the way we did: One system. One team. Zero silos. Because when service and software evolve together, the whole organism gets smarter, faster, and stronger over time. ------------- Hi, I’m Doug. I’m the Chief Technology Officer at Tandem. We’re engineering the backbone for modern pet care. Follow me for posts on: * How we build tech with ops, not for them * How we build tech tools that scale cleanly * Scaling hybrid companies without SaaS bloat

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If you run a business outside of software, you’ve likely missed how completely the software development field has been disrupted. Over the past year, I’ve spoken with dozens of leaders across independent and family-owned businesses in New England. One pattern keeps coming up: Many smart, experienced executives haven’t seen how dramatically the world of knowledge work has ALREADY changed because of AI. The first profession to be radically transformed by AI was software engineering. Much of what used to take a team weeks - designing, writing, and testing code - specialized AI can now do (mostly) reliably in literal minutes or hours, with humans increasingly acting as orchestrators & reviewers. Today, even at giant longstanding companies like Microsoft and Google, ~30% of ALL NEW CODE is now AI-generated and rising quickly according to Microsoft CEO Satya Nadella. And they have thousands of engineers... at earlier stage startups with less legacy code, this percentage can be far higher 🤯 Let that sink in. For decades, learning to code was considered one of the most durable career paths in the world. Within ~3 years, AI began writing, designing, and reviewing a large portion of that code. Tech job postings are still down 36% from 2020 levels (Indeed Hiring Lab), and hiring of new graduates in tech has fallen more than 50% (SignalFire). Senior engineers are increasingly orchestrating teams of specialized AI systems to do far more with far fewer people. So why did this disruption happen to software first? Because improving coding ability is the highest-ROI capability for frontier AI labs (Anthropic, OpenAI). If AI can write better code, it helps engineers build better AI models. Those better systems then improve themselves - a powerful feedback loop. But they are not stopping there. OpenAI has reportedly hired 100+ former Wall Street investment bankers to train models on financial analysis. Across the industry, teams of lawyers, analysts, economists, and domain experts are now being recruited to train AI in their fields. Their goal is simple: Teach AI to perform every category of knowledge work done on a computer. Which is… most white-collar work. Meanwhile, many businesses outside tech still treat AI like: • a future discussion • a curiosity • or a productivity tool for interns That is a dangerous misunderstanding. Large corporations and Private Equity firms are already investing tens of billions into AI systems, data infrastructure, and automation across their companies. They are not experimenting. They are rebuilding how companies operate❗ If independent and family-owned businesses wait years to respond, they will be competing against organizations that have been compounding AI & data advantages the entire time. The AI revolution is not a future conversation. It is already meaningfully restructuring the workforce. And many businesses outside software still don’t realize it. ~85% of New Hampshire businesses are family-owned!

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Not everything that’s loud deserves your attention. In most teams, what gets done first isn’t what matters most, it’s what’s screaming the loudest. Emails. Slack pings. New Feature Requests. Everything’s urgent. But great leadership means distinguishing urgency from importance. Here’s what I’ve found works: Protect time for work that creates long-term impact. Create a “calm zone” in your week no meetings Fridays, limited context switching, less frequently going through your inbox. Don’t reward urgency with instant attention. Reward clarity. Urgency is a tempo. Importance is a strategy. Don’t confuse the two. #StrategyOfLess

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I’ve learned something building and modernizing Shopify Plus platforms: The biggest challenges lie within the tiny seams where systems meet. Tax logic that doesn’t quite match. A return flow that breaks when one field is off. A POS setup that behaves differently in the field than it does in a test environment. Those little cracks are where replatforms succeed or fail. What’s helped me most? Getting close to the people who actually use the system. Sitting with stylists. Watching real orders run through. Fixing a POS in real time. You learn quickly what the architecture really needs to support. Shopify is an incredible platform, but you have to treat it as a living system, not a plug-and-play tool.

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LightSpun is partnering with NationsBenefits, a market-leading healthcare fintech whose Flex Card platform already supports millions of cardholders and more than 150 million+ transactions a year. Together we’ve launched NationsDental™, an integrated dental product that runs its benefits stack on LightSpun’s AI infrastructure. This matters because dental still runs on legacy plumbing: 14 percent of claims on paper, long cycles, and avoidable errors that drive up denials and overhead. Across our customer base, we’re seeing about 50 percent reductions in admin costs and more than 90 percent of claims processed automatically. More here: https://lnkd.in/eU2Mcg-y

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I’ve never really loved roadmaps. Not because direction doesn’t matter - it does. But past two quarters they’ve almost never been real. Twelve(ish) years ago at Koddi we didn’t run the company off a big feature roadmap. Every week we got in a room and asked one question: what do we have to do this week? We had one constraint: hit revenue. Stay alive. Fuel growth. Nicholas Ward used to say, “Do better and bigger just happens.” That was basically the operating system. In today’s AI-native world the constraint is shifting (tokens, compute, attention), but the principle feels the same: great teams organize around the real constraint, not a perfectly sequenced roadmap. I wrote a longer piece on what planning might look like instead. Link in comments.

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If you’re a CEO/founder or PE operator and your tech org is: ❌ Shipping late ❌ Stuck in reactive mode ❌ Struggling through the Series A/B jump, or post-acquisition integration or after a major release It’s not “a delivery problem.” It’s an operating model problem. At the inflection point, the old playbook breaks: what worked at 10 engineers fails at 40… and breaks again across multiple products/teams. That’s where I come in as a fractional CTO: - Spot platform and delivery risks before they hit revenue and retention - Rebuild execution rituals (prioritization, ownership, release confidence) without derailing the roadmap - Align architecture + team structure to your value-creation plan (growth, margin, or exit timeline) If you’re feeling the strain of growth, I can help you turn “busy and reactive” into predictable shipping, and a platform that scales. #CTO #SaaS #StartupScale #PrivateEquity #TechStrategy

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A helpful takeaway from my conversation with Josh Sorenson: development teams should be thinking about how to empower more people to prototype. The prototype can be rough, the point is momentum. Then the dev team can translate it into planning documents and rebuild the solution properly from scratch.

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The cracks are starting to show: AI is making feature delivery so fast that prioritization and alignment can’t keep up. Last week a founder shared a story: Frustrated with product management bottlenecks, an engineer read customer call transcripts and shipped all the “suggested” features to staging before anyone decided they were worth building. Half of them were bloat. Now the team has to either roll back those changes, or document, explain, and maintain functionality that never should have existed to begin with. Uncoordinated feature delivery might feel faster, but it isn’t free. It just pushes cost downstream in the form of complexity tax, rework, maintenance debt, and roadmap drift. What product delivery alignment guardrails are you seeing work for AI-accelerated teams? A) shared decision log with explicit owner (end-to-end accountable) B) ship experiments freely to beta; GA requires hard go/no-go C) 15-min daily PM / eng decision standup to approve or kill new work D) other? share in the comments! 👇 If you had to pick one guardrail to rein in bloat without sacrificing speed, what would it be?

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