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I Built a Neural Network from Scratch in Rust — Then Compiled It to WebAssembly

A complete ML pipeline: engine, backprop, binary format, and a live browser demo. Zero dependencies. Under 200 KB total. If you have built machine-learning projects before, you have probably done it by importing PyTorch, TensorFlow, or scikit-learn and calling .fit() . Those are excellent libraries. This article is about what happens when you deliberately do not use them — when you build every piece of the pipeline yourself, in a language that compiles to WebAssembly, and the result runs live in the browser with no server, no Python, and no cloud bill. Here is the live demo: move four sliders, watch the predicted Iris species update in real time. The model is running entirely inside your browser tab, loaded from a 1.1 KB binary file, powered by ~100 KB of WebAssembly compiled from pure Rust. This is the story of how I built it and why the engineering choices made it work. Why Rust? Why WebAssembly? Why zero dependencies? Three constraints drove every design decision. WASM requires no_std or a carefully limited std . The wasm32-unknown-unknown target has no operating system, no file system, and no libc. A crate that links against rand , ndarray , or any library that makes OS calls will not compile to it without significant plumbing. An engine built from nothing but the Rust standard library compiles cleanly to every target, including WASM. A zero-dependency std -only crate is uniquely auditable. There are no transitive dependency trees to vet, no supply-chain risks, no version conflicts. Every line of code that runs in the user's browser lives in this repository. The deployment story becomes the technical story. A 100 KB WASM blob that runs locally in the browser is not just a cost optimisation — it is a privacy guarantee (user inputs never leave the machine) and a latency guarantee (inference is microseconds, not a round trip to a cloud API). That story is only possible because the engine has no external dependencies that would bloat the binary. The architecture: ei

2026-05-29 原文 →
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This Rewrite Isnt the Constraint: How a 300ms Tail Latency Hunt Led to a New Event Pipeline

We were burning 400ms in p99 tail latency on a core event-processing path in Veltrix. The upstream teams kept blaming the network, but the numbers didnt lie—64% of the time was spent inside the JVM, specifically in sun.misc.Unsafe.park during GC pauses. Every time we hit 80% heap pressure, the throughput collapsed and we lost 300k events per minute. That was the exact moment I stopped believing in the JVM as the runtime and started looking at the system boundary. The first attempt was aggressively tuned HotSpot with G1GC and pinning the critical threads to their own NUMA nodes. We set -XX:MaxGCPauseMillis=20 , -XX:+UseNUMA , and even migrated to Azul Zulu Prime because its handling of large heaps was supposedly better. The p99 dropped to 280ms, but the GC telemetry still showed a sawtooth pattern of 30–40ms spikes every 230ms on a 16GB heap. Profiling with JDK Flight Recorder told us 18% of CPU time was spent in card-table scanning. At that point I knew we were fighting the runtime, not the problem. The event pipeline was small—just JSON parsing, enrichment, and a single RocksDB write—but the JVMs generational collector couldnt stop moving objects. The architecture decision came during a four-day blackout window after a failed Blue-Green deploy. Three of us sat in a war room with a single Grafana dashboard showing 100% CPU steal time on the Kubernetes nodes. We had two choices: squeeze more life out of the JVM by manually balancing the heap or rewrite the critical hot path in Rust and give the compiler full control over memory layout. The Rust option meant losing the JVM ecosystem (no more async-profiler, no more one-liner heap dumps) but gave us stackless futures, zero-cost abstractions, and compile-time memory safety. We chose Rust. We forked the Cargo.toml wed used in a sidecar for metrics and started porting the event collector. The numbers after the rewrite told the story. We recompiled the same two endpoints— POST /events and GET /aggregates —and served them f

2026-05-29 原文 →
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