Adrien Lacquemant

Super excited to announce that Federato has raised a $100M Series D, led by Goldman Sachs! 🚀 Insurers are ready to move past the Old Core and embrace AI-native, end-to-end policy lifecycle platforms that change how insurance business gets done. Read more about it here: https://lnkd.in/g4r58CiB

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In just five days, Boston will become the hub for startup leaders, investors, and operators at TechCrunch All Stage 2025. What stands out about this event is its relentless focus on execution—no fluff, just practical sessions designed to help founders navigate today's tough fundraising and scaling environment. With breakout talks on AI-driven product development, cap table strategy, and market readiness, attendees get real-world tools rather than buzzwords. One insight that really caught my attention is how pivotal operator-led startups are becoming, a shift that Jon McNeill from DVx Ventures highlights as essential for the next wave of disruptors. This goes beyond having a great idea—it's about founders who deeply understand the operational challenges from day one. Combine that with interactive roundtables and live pitch feedback, and the event promises to be a rare opportunity for founders to sharpen their storytelling and market approach while building meaningful connections. As funding landscapes tighten and competition grows, how are you ensuring your leadership and operational strategies adapt to maintain momentum ahead of your next scale-up? Would love to hear how others are balancing visionary thinking with day-to-day execution in today’s startup climate. #TechCrunchAllStage #StartupGrowth #FounderLeadership #ScaleUpStrategies

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What Moving to NYC Last August Taught Me About Engineering Ambition I moved to New York last August. One thing became obvious quickly. The ambition here is intense. At tech meetups, coffee shops, and coworking spaces, everyone is building something. AI products. Marketplaces. SaaS tools. Infrastructure layers. The energy is addictive. I like it. I have spent six years in startups. I enjoy speed. Shipping fast. Iterating weekly. Solving problems without perfect information. But being in NYC sharpened something for me. There is a difference between velocity and durability. A lot of teams move fast. Few think about what breaks at scale. I have seen microservices introduced before the core workflow was even stable. I have seen background jobs written without idempotency. I have seen schema decisions rushed that later required painful migrations. Everything works at 1x. Scale is where fundamentals collect their tax. When retries stack. When background jobs duplicate records. When observability is not good enough to debug production. When a shortcut becomes a three month refactor. Moving here did not make me want to slow down. It made me want to build with more intention. In a city obsessed with momentum, fundamentals become leverage. Data modeling. Ownership boundaries. Operational clarity. Thinking in terms of 10x, not just next sprint. The engineers who stand out are not the loudest in the room. They are the ones asking better questions. What fails first? Can this be deleted later? Are we solving the right constraint? Ambition is powerful. But durable systems win.

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What's up with the rise in #fractional? I see it in all the circles I follow - HR, Tech, Product. Y'all think this is a fundamental / permanent market structure shift, or just an artifact of the current/recent macro env?

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Proud of our team and how hard we have been working to create an API that enables AI agents to make purchases. We are getting closer to making universal checkout a reality, focusing on security, performance, and scale. If you are curious about how we are tackling this problem, you can read our white paper here 👇🏾 rye.com/whitepaper

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DeepSeek’s rise has been wild to watch. From hedge fund roots to topping app store charts, all while running models cheaper and faster than the big names? That’s no small feat. Their focus on reasoning models is especially interesting and definitely shaking things up in the AI space. #AI #DeepLearning #Innovation #TechTrends #MachineLearning #Startups https://lnkd.in/dDfmM99D

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I added a stricter mypy rule to tighten our codebase - partly to make our Claude code agents more effective. Then… mypy --strict lit up the terminal like a Christmas tree. Thousands of legacy violations. So you’re stuck: - Fix years of history (and freeze delivery), or - “Only lint the diff” (and pray you don’t break an untouched caller) The problem: diff-only checks give false confidence for semantic issues (types, API changes, unused vars). Type safety isn’t local. What you really want is a quality ratchet: scan the whole repo, snapshot today’s debt, and block only new violations in CI. If this is your exact pain, check out a great post by Ofek Ben-Yaish and Genway AI SnapLint tool to solve it. (I’ll drop the Medium link + the open-source repo in the first comment.) How are you rolling out stricter standards without grinding shipping to a halt?

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