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This doctrine exists to govern the creation, operation, maintenance, and retirement of reliable software systems that may live for decades, outlast teams, grow by orders of magnitude, and operate in hostile production environments.
Running Playwright projects from GitHub inside a browser-extension runtime
Running Playwright projects from GitHub inside a browser-extension runtime
Browser automation usually assumes a controller outside the browser: a local process, a CI worker, or a driver that can launch and own browser instances. 100xbot started from a different constraint.
The original 100xbot question was:
Can we do browser automation from inside the browser at all, given browser-extension CSP rules and the fact that we cannot just fetch JavaScript over the network and execute it?
The extension manifest keeps extension pages on script-src 'self' 'wasm-unsafe-eval'. Code from GitHub cannot be treated as a normal remote script include. The automation runtime also cannot depend on arbitrary network-loaded JavaScript running with extension privileges.
For a long stretch, this had nothing to do with Playwright. The first answer was 100xbot's own browser-native automation stack. It had background capabilities and content scripts. It had DOM and tab tools. It had workflow execution and file storage. It ha
The Anatomy of Small Neural Networks: Parameter Space Structure, Architecture Selection, and the Depth-Width Tradeoff
The Anatomy of Small Neural Networks: Parameter Space Structure, Architecture Selection, and the Depth-Width Tradeoff
Abstract
We present a systematic empirical investigation of feedforward ReLU networks at minimal scale (1-16 hidden neurons), revealing structural properties of parameter space, training dynamics, and architecture selection that are obscured at practical scale. Through 11 controlled experiments totaling over 10,000 training runs, we establish several novel findings: (1) the Hessian eigenspectrum of a trained network decomposes into exactly four tiers — steep, moderate, weak, and zero — whose counts are predictable from architecture alone, with the steep tier recovering the target function's degrees of freedom; (2) this structure emerges during training via a sharp phase transition (27 steep eigenvalues collapse to 4 between steps 50-100 for a 16-neuron network), after which 97% of training time is spent exploring the solution manifold rather than improving the function; (3) exhau
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