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In the Latent Space podcast, Bret Taylor argued that strongly and statically-typed programming languages, such as Rust, could be especially well suited for AI coding, since the generated code can be validated by compilers for real-time feedback and reinforcement learning. However, unlike weakly or dynamically typed JavaScript or Python, there are few examples of Rust code in LLMs’ training corpora, and hence limiting the LLM's capability in generating Rust code.

In this talk, we will discuss the open-source Rust Coder project, which provides an integrated agentic framework based on the MCP protocol for generating complete and valid Rust projects. The Rust Coder framework enables the following functionalities for coding LLMs (e.g., Qwen Coder or Codestral).

• Provides Rust example code, explanations, and tutorials relevant to the user’s request within the LLM query context.
• Generates and parses generated code artifacts into Rust Cargo projects.
• Compiles and executes generated Rust Cargo projects.
• Executes the compiled project against test cases.
• Provides coding LLM feedback based on compiler and testing outputs.
• Runs continuously until all issues are fixed.

We will demonstrate how the Rust Coder project works, how to integrate it into your agents, and ways to contribute to the open-source effort. We will also discuss pilot results from a large Rust coding camp (1000+ college students) using the Rust Coder tool.

The Rust Coder is supported by two Linux Foundation Mentorship grants, as well as content provided by the Rust Foundation.

If Data Vault is a new term for you, it's a data modeling design pattern. We're joined by Brandon Taylor, a senior data architect at Guild, and Michael Olschimke, who is the CEO of Scalefree—the consulting firm whose co-founder Dan Lindstedt is credited as the designer of the data vault architecture.  In this conversation with Tristan and Julia, Michael and Brandon explore the Data Vault approach among data warehouse design methodologies. They discuss Data Vault's adoption in Europe, its alignment with data mesh architecture, and the ongoing debate over Data Vault vs. Kimball methods.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Highlights  “Scooter Braun's Ithaca Holdings Acquires Scott Borchetta's Big Machine Label Group” is what Sunday’s official press release reads, we’ll take a look at a sample of Swift’s data while on Big Machine and on Republic RecordsMission   Good morning, it’s Jason here at Chartmetric with your 3-minute Data Dump where we upload charts, artists and playlists into your brain so you can stay up on the latest in the music data world.DateThis is your Data Dump for Tuesday, July 2nd, 2019.Taylor Swift: Before and After Big MachineThe music business’ latest media frenzy revolves around a music mogul acquiring a top music star’s catalog.This is reminiscent of how in 1985, the late King of Pop, Michael Jackson, acquired the Beatles’ catalog of song copyrights, after receiving advice from Paul McCartney himself that music publishing was a great business to get into.Current music executive Scooter Braun and his Ithaca Holdings media company purchased Nashville-based Big Machine Label Group, Taylor Swift’s former label,  for lots of money. This was announced over the weekend.Big Machine’s assets include Swift’s catalog up through 2017’s “Reputation”.She signed to UMG’s Republic Records in 2018, and now owns her own future masters starting with the album “Lover”.While we don’t have data on the controversy, we can look at two tracks: one from Swift’s Big Machine era, and one from her Republic Records era.The former is “Look What You Made Me Do” from 2017’s “Reputation” album while the latter is the first single from the 2019 album “Lover,” “You Need to Calm Down”.Big Machine-owned “Look What You Made Me Do”...was released almost two years ago in August 2017.Currently at a 75 out of 100 Spotify Popularity Index (or SPI), it was at 91 SPI in Nov 2018.The track is on 1.6K Spotify playlists, 17 of them editorial including the This Is: Taylor Swift playlist.......while it also has a current spot on 77 Apple Music playlists and 27 Amazon playlists, all editorial for the latter case.In her Republic-era, “You Need to Calm Down”...was released just two weeks ago in June 2019.Currently at 92 SPI, it’s on less total Spotify playlists at 1.1K, but is on more editorial at 94, which makes sense since it’s relatively a brand new release.It’s on almost three times as many Apple Music playlists at 202, and 3.5x as many Amazon playlists at 98.So is it fair to say that the Republic era is “better”? Not necessarily- again, it’s just a newer track and her Big Machine track was in the middle of her 2014-2018 Spotify absence, limiting a big part of her data profile.But what this kind of side-by-side track comparison CAN do is help you evaluate how well tracks do under different promotional strategies, label teams or simply with different types of music.Hope it’s useful.Outro That’s it for your Daily Data Dump for Tuesday, July 2nd, 2019. This is Jason from Chartmetric.Free accounts are at chartmetric.comAnd article links and show notes are at: podcast.chartmetric.comHappy Tuesday, and we’ll see you tomorrow!