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    We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…

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  8. 8WA

    Hi HN, I’m building Waycore, an open-source project exploring what a flexible, offline-first field computer should look like for outdoor, survival, and off-grid scenarios. The core goals are adaptability and resilience: modular hardware (external sensor/tool modules) extensible OS with support for external apps (guidelines in progress) no required internet connection — maps, models, and knowledge work offline optional LTE/Wi-Fi when available and explicitly enabled A major focus is on-device agentic AI, not just chat or image recognition. The AI is intended to: read live sensor…

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    Tom from Tensil here - happy to answer questions! We developed Tensil to bring custom ML accelerators to people who don't have the resources of companies like Google, Facebook and Tesla. Currently, we're focused on supporting convolutional neural network inference on edge FPGA (field programmable gate array) platforms, but we aim to support all model architectures on a wide variety of fabrics for both training and inference. Tensil is different from other ML accelerators in that it is open source and really easy to use. For example, you can generate a custom accelerator with one command: $…

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