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[return to "Anthropic acquires Bun"]
1. dts+0D[view] [source] 2025-12-02 20:52:05
>>ryanvo+(OP)
A lot of people seem confused about this acquisition because they think of Bun as a node.js compatible bundler / runtime and just compare it to Deno / npm. But I think its a really smart move if you think of where Bun has been pushing into lately which is a kind of cloud-native self contained runtime (S3 API, SQL, streaming, etc). For an agent like Claude Code this trajectory is really interesting as you are creating a runtime where your agent can work inside of cloud services as fluently as it currently does with a local filesystem. Claude will be able to leverage these capabilities to extend its reach across the cloud and add more value in enterprise use cases
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2. hoppp+hP[view] [source] 2025-12-02 21:55:18
>>dts+0D
It's fine but why is Js a good language for agents? I mean sure its faster than python but wouldn't something that compiles to native be much better?
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3. chatma+2Q[view] [source] 2025-12-02 21:58:21
>>hoppp+hP
JS has the fastest, most robust and widely deployed sandboxing engines (V8, followed closely by JavaScriptCore which is what Bun uses). It also has TypeScript which pairs well with agentic coding loops, and compiles to the aforementioned JavaScript which can run pretty much anywhere.
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4. mrcsha+5U[view] [source] 2025-12-02 22:21:12
>>chatma+2Q
> It also has TypeScript which pairs well with agentic coding loops

The language syntax has nothing to do with it pairing well with agentic coding loops.

Considering how close Typescript and C# are syntactically, and C#'s speed advantage over JS among many other things would make C# the main language for building Agents. It is not and that's because the early SDKs were JS and Python.

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5. chamom+zX[view] [source] 2025-12-02 22:42:01
>>mrcsha+5U
Typescript is probably generally a good LLM language because - static types - tons and tons of training data

Kind of tangent but I used to think static types were a must-have for LLM generated code. But the most magical and impressively awesome thing I’ve seen for LLM code generation is “calva backseat driver”, a vscode extension that lets copilot evaluate clojure expressions and generally do REPL stuff.

It can write MUCH cleaner and more capable code, using all sorts of libraries that it’s unfamiliar with, because it can mess around and try stuff just like a human would. It’s mind blowingly cool!!

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