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Comment by DylanDmitri | Hacker News Reader

I think PL is progressing just fine. If we look at major advancements in the past decade: LLVM, WASM, async/await, Typescript, Golang, Rust

The more interesting question is "what's happened to IDEs?" -- they're basically unchanged over the same time period. I think they feel "unglamorous" relative to PL and thus attract less passion projects.

I want to see an IDE focused on hybrid code/data systems (say Postgres/Python). Keep your schema, your data, and you glue language in one spot, edit it all together. Set up unit tests by specifying an initial "mocked" Postgres state by writing test data in columns, see it percolate through your code, etc.

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> major advancements in the past decade: LLVM, WASM, async/await, Typescript, Golang, Rust

Are these really major research achievements? What research contributions are there in WASM, async/await, Typescript, or Golang? I can see in LLVM and Rust though.

async/await came out of Microsoft research; each year they build and test dozens of concepts. Struck gold with async/await and within a couple years that pattern got retrofitted into most major languages (JS, Python, Rust, C++, C#)

I was wrong about Golang and Typescript, I did some reading and found most of their signature features in older languages.

WASM is some sort of political consortium building triumph, but I think has more potential than everything else on the list. The performance is shockingly good (see Google Blob Opera, Mozilla Pyiodide, etc). Over the next decade everything will become a fast Electron app.

> async/await came out of Microsoft research

I don't think this is the case - most people trace it back to Peng Li's paper in 2007.

These are all good engineering, but they aren't research.

A genuine IDE for data science would be very useful, in my opinion. Something to unify code, data and models would be really interesting. An ideal tool would allow frictionless experimentation, but also maintain continual reproducibility.

I'd love the same. I often think that a good data science IDE will end up looking like RStudio with mlflow abilities. I barely use R at all anymore (or IDEs really), but RStudio is still the best attempt at a data science IDE that I've come across.

knitr/Sweave integration for literate programming, dedicated panes for plots and printing `help` documentation, and not having to mix code and output like notebooks do. It gets a lot of things right (for me).

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