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The GitHub release seems to have the final notes. It has at least the placeholder texts replaced:
> It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests
instead of:
> It is the result of X months of development since the last feature release by Y contributors
https://github.com/numpy/numpy/releases/tag/v2.0.0
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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No.
You may want to check out cupy
https://cupy.dev/
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StringZilla
Up to 10x faster strings for C, C++, Python, Rust, Swift & Go, leveraging NEON, AVX2, AVX-512, SVE, & SWAR to accelerate search, hashing, sort, edit distances, and memory ops 🦖
stringzilla[1] has 10x perf on some string operations - maybe they don't suck, but there's definitely room for improvement
[1] - https://github.com/ashvardanian/StringZilla?tab=readme-ov-fi...
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We had a developer meeting to discuss what should go into 2.0 in April 2023: https://github.com/numpy/archive/tree/main/2.0_developer_mee...
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einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
https://einops.rocks/#why-use-einops-notation
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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i think it’s fair to say that perl6 has been an “extremely rocky transition” ultimately it was renamed raku to reflect this and avoid camping on the perl5 version numbering
raku has good package compatibility via Inline::Perl5 and Inline::Python and FFI to languages like Rust and Zig
among the many downsides of the transition, one upside is that raku is a clean sheet of paper and has some interesting new work for example in LLM support
I have started work on a new raku module called Dan::Polars and would welcome contributions from Numpy/Pandas folks with a vision of how to improve the APIs and abstractions … it’s a good place to make a real contribution, help make something new and better and get to grips with some raku and some rust.
just connect via https://github.com/librasteve/raku-Dan-Polars if you are interested and would like to know more
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