🤖 Gemini 3.0 Spotted in Early A/B Testing
A fascinating leak-style glimpse into Google’s upcoming AI model and how developers creatively benchmark multimodal systems. It highlights the growing enthusiasm for reverse-engineering public AI tools.
An early look at Gemini 3.0, Google’s next-generation AI model, discovered through A/B testing in Google AI Studio. The author reports better SVG generation quality, longer outputs, and slower token times than Gemini 2.5 Pro—hinting at major architectural upgrades and multiple test variants.
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📊 Your Data Model Defines Your Future
A sharp and thought-provoking essay urging founders to see data architecture as strategy. Essential reading in an age where structure, not speed, defines defensibility.
Matt Brown argues that a startup’s success is largely determined by its data model—the way it structures and prioritizes information. Drawing from Slack, Notion, and Figma, he shows how early architectural choices built enduring competitive advantages. As AI levels the coding field, deeply aligned data models become the strongest source of differentiation.
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🧠 Automerge: Collaborative Systems with Formal Rigor
A benchmark example of how academically grounded engineering can drive sustainable open source infrastructure. Automerge exemplifies durability and precision in collaborative software design.
Automerge is a long-running open source project developing robust, high-performance tools for real-time collaboration. Backed by contributors from industry and academia, it applies formal verification and database-style optimizations to ensure consistency in distributed systems.
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⚙️ Python as a Declarative Configuration Language with Starlark
A thoughtful contribution to the ongoing conversation about declarative versus programmable infrastructure, emphasizing developer ergonomics and configuration safety.
This article explains how OpenRun leverages Starlark—a Python-inspired configuration language—for defining deployments declaratively. It shows how Starlark simplifies complex setups, enabling deterministic, typed, and logic-aware configurations while integrating seamlessly with formats like TOML and JSON.
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