How Android Apps Handle Real-Time Local Processing
Android's on-device machine learning stack lets apps run inference, image recognition, and text processing locally without sending data to servers, reducing latency and protecting privacy.
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Android's on-device machine learning stack lets apps run inference, image recognition, and text processing locally without sending data to servers, reducing latency and protecting privacy.
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Fly.io’s Rust-based load balancer handles almost all of its traffic and stands on Tokio and Hyper, motivating an upgrade from Tokio 0.2 to 1.0. The team waited to upgrade until related crates including hyper, reqwest, async-compression, tokio-io-timeout, tokio-tungstenite, and sentry were ready; after a few weeks, most of the ecosystem had upgraded. The migration foregoes Tokio’s Stream implementations pending stabilization in the standard library, relaxes mutable-access requirements, changes AsyncRead and AsyncWrite polling to return Result<()>, and requires more explicit pinning for !Unpin futures. Because runtime::Handle lacked block_on, the team worked around the restriction with Arc; standard TcpListener values also required set_nonblocking(true) before TcpListener::from_std. Other changes included renamed APIs and revised Hyper WebSocket upgrades, and the author says the major upgrade was not hard.
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