RustAssistant: Using LLMs to Fix Compilation Errors in Rust Code

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  1. glicol

    Graph-oriented live coding language and music/audio DSP library written in Rust

    I am creator and maintainer of several Rust projects:

    https://github.com/chaosprint/glicol

    https://github.com/chaosprint/asak

    For LLM, even the latest Gemini 2.5 Pro and Claude 3.7 Thinking, it is difficult to give a code that can be compiled at once.

    I think the main challenges are:

    1. Their training material is relatively lagging. Most Rust projects are not 1.0, and the API is constantly changing, which is also the source of most compilation errors.

    2. Trying to do too much at one time increases the probability of errors.

    3. The agent does not follow people's work habits very well, go to docs.rs to read the latest documents and look at examples. After making mistakes, search for network resources such as GitHub.

    Maybe this is where cursor rules and mcp can work hard. But at present, it is far behind.

  2. 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.

    InfluxDB logo
  3. asak

    A cross-platform audio recording/playback CLI tool with TUI, written in Rust.

    I am creator and maintainer of several Rust projects:

    https://github.com/chaosprint/glicol

    https://github.com/chaosprint/asak

    For LLM, even the latest Gemini 2.5 Pro and Claude 3.7 Thinking, it is difficult to give a code that can be compiled at once.

    I think the main challenges are:

    1. Their training material is relatively lagging. Most Rust projects are not 1.0, and the API is constantly changing, which is also the source of most compilation errors.

    2. Trying to do too much at one time increases the probability of errors.

    3. The agent does not follow people's work habits very well, go to docs.rs to read the latest documents and look at examples. After making mistakes, search for network resources such as GitHub.

    Maybe this is where cursor rules and mcp can work hard. But at present, it is far behind.

  4. kcores-llm-arena

    LLM Arena by KCORES team

    Gemini 2.5 pro is far ahead of even Claude

    Chart:

    https://raw.githubusercontent.com/KCORES/kcores-llm-arena/re...

    Description of the challenges:

    https://github.com/KCORES/kcores-llm-arena

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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