impl Rust: Avro IDL tool in Rust via LLM

Jon Gjengset live-codes a Rust implementation of an Apache Avro IDL parser and compiler using LLM assistance.

Some of you may be familiar with Avro, the Apache take on Google's Protocol Buffers. Where Protobuf has just one file format, `.proto`, Avro has *three*. Two of these are JSON-based. The first, .avsc files, are used for "schemas", which are like Protobuf message types. The second, .avpr files, are for "protocols", which are like gRPC service declarations. The third file format, .avdl, uses an Interface Description Language, or IDL ( avro.apache.org/docs/1.12.0/idl-language/ ), which is intended for humans (as opposed to machines) to read and write and looks more like a .proto file. Avro comes with a tool that converts those IDLs into JSONs. That tool is written in Java and maintained by the Avro folks, but also seems to have stagnated somewhat. In particular, it produces *really* unhelpful errors, to the point where I've heard of people spending an hour chasing down a misplaced comma. My sense is that this is probably not *too* hard to fix in the Java version, but after digging a little I discovered that the parser behind this tool uses the ANTLR parser generator ( antlr.org/ ). ANTLR supports code generation to many languages, *including Rust*! And you know what that means: let's try to port it to Rust using something like miette ( docs.rs/miette/ ) for errors, and see how good we can make it! Since we a) have access to the existing Java code and b) there's an infinite supply of tests (the same IDL passed to the Java tool should produce the same JSON), this is also a perfect candidate for powercoding (LLM + review the code), so we decided to see if we could get a complete substitute up and running in just four hours 😅 The resulting repository can be found at github.com/jonhoo/avdl Live version with chat: youtube.com/live/NqV_KhDsMIs 00:00:00 Introduction 00:03:36 What is Apache Avro? 00:13:44 Setup and trying out the Java tool 00:23:25 ANTLR parser generation 00:46:07 Planning the implementation with Claude Code 01:46:10 Reviewing AI-generated Rust code 02:33:30 Making the LLM iterate 02:44:38 Setting up for a multi-agent workflow 02:57:56 Seeding a decent CLAUDE.md 03:03:08 Parallel issue identification and fixing 03:38:26 Making worktrees work 03:46:22 Brainrot, FOMO, and LLM use 04:16:37 Designing a self-correcting agent loop 04:53:14 Wrap-up & next steps