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Re:How HN: Ironkernel – Python expressions, Rust parallel on Wednesday March 18, 2026 @01:22PM (#47428545)
I built this after watching 7/8 CPU cores idle during a Monte Carlo sim. multiprocessing added 189ms serialization overhead to a 9ms computation.

ironkernel lets you write element-wise expressions with a Python decorator, compiles them to a Rust expression tree at definition time, and executes via rayon on all cores. ~2k lines of Rust, ~500 lines of Python.

The win is expression fusion: NumPy evaluates `where(x > 0, sqrt(abs(x)) + sin(x), 0)` as 5 passes with 4 temporaries. ironkernel fuses into 1 pass, zero temporaries, and skips dead branches (no NaN from sqrt of negatives). 2.25x NumPy on compound expressions at 10M elements. For BLAS ops like SAXPY, NumPy is faster — ironkernel doesn't call BLAS.

Early stage: f64 only, 1-D only, expression subset only (intentional — parallel safety guarantee). Numba warm is 3.2x faster (LLVM JIT vs interpreter).

Show HN: Lazygotest – Terminal UI for running Go tests interactively

https://github.com/YuminosukeSato/lazygotest

Hi HN! I'm the creator of lazygotest. I built this because I love lazygit's UX and wanted the same smooth experience for Go testing. Instead of switching between terminal and editor, or scrolling through verbose test output, you can now: Browse packages and tests with vim-style navigation (j/k, gg/G) Run tests with Enter and watch real-time results in split panes Track test...

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Show HN: Pyproc – Call Python from Go Without CGO or Microservices

https://github.com/YuminosukeSato/pyproc

Hi HN!I built *pyproc* to let Go services call Python like a local function — *no CGO and no separate microservice*. It runs a pool of Python worker processes and talks over *Unix Domain Sockets* on the same host/pod, so you get low overhead, process isolation, and parallelism beyond the GIL.*Why this exists** Keep your Go service, reuse Python/NumPy/pandas/PyTorch/scikit-learn. * Avoid network...

( Read More... | 9 comments | on HN )