Zhile Zou

A new study from Carnegie Mellon, “Does AI-Assisted Coding Deliver? A Difference-in-Differences Study of Cursor’s Impact on Software Projects” that looks at a more recent period, tracking code quality in projects using GenAI tools up to mid-2025, finds that AI briefly accelerates code generation, but the underlying code quality trends continue to move in the wrong direction. https://lnkd.in/gm6dxb8W

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I made an explorable esoteric research environment with a Custodian named MERIDIAN that can launch its own agent swarms to solve problems. Does isopsephic arithmetic and lookup for any Neoplatonists :-) https://lnkd.in/gYzqVn7m

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Indeed. A small number of big tech companies have grossly overinvested in LLMs and overextended themselves -- even laying off tens of thousands of critically needed human employees and enshittifying their primary products and services to reallocate investment into it -- shooting Nvidia's profits and shares to the moon -- all in an insane attempt to "be first" for something that has no forseeable compelling ROI. It's a collective delusion and hype bubble. This is markedly different from the strategic "operate in the red to build marketshare" strategy of the few survivors of the dot-com bust, because at least those survivors were able to articulate (and realize) a logical ROI after reaching a tipping point of critical mass. This is more like the 98% of dot-com startups that burned bright fueled by VC right until they burned themselves out because they never actually had any believable plan for monetizing anything they were building. The lack of ROI will come rudely crashing down on these companies over the next several quarters as they run out of tricks to cook the books and hide the losses. Shareholders will get spooked and back out, share prices will crash, and big tech companies that have bet the farm on LLMs will suddenly find themselves without the necessary employees, capital, quality products/services, or reputational sway needed to save themselves. And when they collapse, Nvidia will collapse with them. And then maybe, finally, we can get back to the business of making things of actual value to humans.

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From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

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Apache Spark’s new "Real-time" Mode in Structured Streaming delivers millisecond-level latency for ultra-fast, continuous stream processing. It will be interesting to see how it will perform compared to Flink!

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Proud to see our work with Nimbus Therapeutics featured in the AWS re:Invent keynote! Our team developed drug-discovery aware LLMs using Nova Forge, combining Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) to create custom model tailored for Nimbus. Thanks Andy Lapastora Krishnateja Killamsetty Elyse (Jiali) Zhang and Clement PERROT for the exceptional work. Also thanks to the wonderful AGI and Nimbus team for making this collaboration a success. cc: Hannah Marlowe, PhD Sri Elaprolu Taimur Rashid Sharlina Keshava Caleb Wilkinson Alexandra Fedorova

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