OAPN
OAPN #7: Worse is Better In this episode, recorded from the new OpenAgents headquarters in downtown Austin, we discuss the two competing design philosophies of software: the New Jersey school and the MIT/Stanford school, and how those philosophies map onto the AI landscape today. We get into why the “Worse is Better” tradition matters, why simple systems with strong distribution often beat more elegant closed systems, and why Bell Labs, C, and Unix still offer some of the best lessons for anyone trying to build durable infrastructure. From there, we connect that philosophy directly to OpenAgents. Rather than disappearing for a year behind closed doors to build some secret superintelligence, OpenAgents is starting from something much simpler: can we create products people actually want to use, can we price resources the market currently values at zero, and can we begin a real economic flywheel around compute, agents, and bitcoin? We talk through the basic insight behind the OpenAgents compute market: there is an enormous pool of stranded consumer compute in the world, but without buyers its effective price is zero. If OpenAgents can create demand for that compute and produce something economically useful from it, the result is not just a product but the beginning of a new market. It will not be perfect. It will not have the budgets of the big labs. But if it is simple, useful, open, and capable of paying people, it has the ingredients for something that can grow. Links: Website: https://openagents.com [https://openagents.com] Docs: https://docs.openagents.com [https://docs.openagents.com] GitHub: https://github.com/OpenAgentsInc/openagents [https://github.com/OpenAgentsInc/openagents] X: https://x.com/OpenAgentsInc [https://x.com/OpenAgentsInc] #OpenAgents #OAPN #Bitcoin #AI #MachineWork #OpenSource This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit openagents.substack.com [https://openagents.substack.com?utm_medium=podcast&utm_campaign=CTA_1]
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